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[ "<title>Background</title>", "<p>Clinical vertebral and non-vertebral fractures are the most frequent fractures in patients presenting to the emergency ward of the hospital with a fracture [##REF##18245581##1##]. After such fracture, patients are at increased risk for subsequent fracture and guidelines on osteoporosis advocate to evaluate patients presenting with a fracture in order to consider treament to reduce the risk of subsequent fractures [##REF##16987116##2##].</p>", "<p>One aspect of care identified within the management of fracture patients is the existence of contributors to secondary causes of bone loss [##REF##12364413##3##]. Effective therapy requires that these contributors be recognised and when present managed appropriately [##REF##12364413##3##,##REF##15293701##4##]. If these conditions, however, are not recognized, treatment may be suboptimal or ineffective [##REF##12004995##5##,##REF##18180974##6##].</p>", "<p>Apart from bone mineral density (BMD)-osteoporosis (T-score less than or equal to -2.5) [##REF##15293701##4##,##REF##11440324##7##,##REF##7941614##8##] many risk factors are related to fracture risk, independently of BMD, such as clinical risk factors [##REF##1790407##9##], fall risks [##UREF##0##10##], prevalent morphometric vertebral fractures (MVF) [##REF##17245546##11##] and secondary osteoporosis [##UREF##1##12##]. There is increasing evidence that secondary osteoporosis is more prevalent than initially thought, not only in males, but also in females [##UREF##2##13##], but the true prevalence of contributors to secondary osteoporosis is unknown and no consensus regarding its evaluation is available [##REF##12364412##14##].</p>", "<p>Published data from referral centres for evaluation of osteoporosis indicate that 32 to 37% of women with low BMD have a history of other diseases or medications known to contribute to osteoporosis [##REF##12364413##3##,##REF##12528756##15##]. From 20% up to 64% of patients had previously unknown secondary causes of osteoporosis that were only identified by laboratory testing [##REF##12004995##5##]. In a study of patients with a clinical fracture, a high prevalence of contributors to secondary osteoporosis (77%) was reported, but the study included only a limited number of patients with measured low BMD [##REF##16283067##16##]. In a study of patients with a hip fracture, 80% had secondary causes of bone loss, mainly related to disturbed calcium and vitamin D homeostasis [##REF##18180974##6##]. To date, we lack studies on the prevalence of contributors to secondary osteoporosis in other fracture populations.</p>", "<p>The purpose of this study was to determine the prevalence of contributors to secondary osteoporosis, in the context of other bone- and fall-related fracture risks in patients presenting with a clinical vertebral or non-vertebral fracture and with a low BMD. Identifying and correcting contributors will enhance treatment effect aimed at reducing the risk of subsequent fractures.</p>" ]
[ "<title>Methods</title>", "<p>In this prospective observational study, 100 consecutive and consenting patients older than 50 years, who presented between April 2005 and April 2006 with a clinical fracture at Maastricht University Hospital in the Netherlands, were included. After receiving medical treatment for the fracture, patients had a consultation with the fracture nurse. The fracture nurse provided information about the study and invited the patients to the Fracture and Osteoporosis Outpatient Clinic. Patients already on osteoporosis treatment (44/1246, 4% of all) or with pathological fractures due to malignancy or Paget's disease of bone were excluded from the analysis. Patients who agreed to participate were further referred to the program. Patients with osteoporosis according to World Health Organization (WHO) criteria for BMD [##REF##15293701##4##] and in whom all laboratory data were available were included in the present study (Figure ##FIG##0##1##). This group was part of the evaluation of all consecutive patients presenting with a clinical fracture, of whom 35% had osteoporosis and 44% had osteopenia [##REF##18245581##1##]. The medical ethical committee of the University Hospital Maastricht approved the study (MEC 03-194-5).</p>", "<p>BMD in the left or right hip and the lumbar spine was determined using dual X-ray absorptiometry (DXA) with Hologic QDR 4500 Elite. Diagnosis of osteoporosis was based on the WHO criteria for BMD [##REF##15293701##4##], as provided by the manufacturer for women and men. Patients were classified according to the lowest value of T-score in either total hip or spine.</p>", "<p>All patients were interviewed for bone-related risk factors for fracture (previous non-vertebral and vertebral fractures, mother with fracture, body weight &lt;60 kg, severe immobility, use of glucocorticoids) and fall-risk factors (falls in the past 12 months, use of assistive devices, sedative medication, activities of daily living, mobility, impaired vision, articular complaints, urine incontinency), according to the Dutch guidelines on osteoporosis [##REF##16987116##2##] and fall prevention [##UREF##0##10##]. Additionally, data about vitamin D status (regular sun exposure, dietary intake and supplements), calcium intake [##REF##17106282##17##,##REF##16036066##18##], height, history of height loss [##REF##10450408##19##] and a description of the circumstances leading to the fracture (with specification of fall from a standing height or other trauma) were recorded.</p>", "<p>Patients with T-scores ≤-2.5 were given a pre-planned set of laboratory tests that included erythrocyte sedimentation rate (ESR), haemoglobin, leucocytes and serum levels of creatinine, calcium, albumin, alkaline phosphatase, 25-OHD<sub>3 </sub>and TSH. Calcium and creatinine were measured in a 24-hour urine collection. All laboratory analyses were performed in the same laboratory. Patients with osteoporosis and having the full set of evaluation were sent for a consultation with either a rheumatologist or an endocrinologist. The specialist decided further investigation and treatment. When clinically appropriate, additional tests were performed. The diagnosis of contributors to secondary osteoporosis was based on all data from the medical files. Renal insufficiency was defined with the cut-off value of creatinine clearance ≤40 using the Cockroft Gault formula [##REF##17492567##20##]. Vitamin D status was defined as severely deficient when values were ≤30 nmol/L, deficient when between 30 and ≤50 nmol/L, insufficient when values were between 50 and ≤75 nmol/L [##REF##15776217##21##,##REF##15797954##22##] and abnormally high when above 220 nmol/L [##REF##12499343##23##]. Exploration for hypogonadism in men was considered when a morning serum testosterone level was below 12 nmol/L [##REF##15910536##24##] and for thyroid disorders when TSH were outside the reference ranges (0.4–3.5 mU/L). Hyperparathyroidism was diagnosed when serum parathyroid hormone (PTH) levels were above 5.5 pmol/l. Further exploration for hypercalciuria was considered when the total urinary calcium in a 24 hours collection exceeded 7 mmol/d and creatinuria indicated an appropriate collection (between 4.5 – 13.3 mmol/hour) [##REF##16305288##25##]. According to clinical judgement, patients suspected to have lactose intolerance had a lactose tolerance test [##REF##10192605##26##].</p>", "<p>Vertebral fracture assessment (VFA) [##REF##16940447##27##,##REF##11090235##28##] by single X-ray absorptiometry on the lateral spine images was performed to identify the presence of morphometric vertebral deformities (MVD). Images were saved in a digital format. Physician Viewer software (Hologic, USA) provided the tools necessary to perform quantitative vertebral morphometry. Visual assessment and measurements of the anterior, posterior and mid heights from T4 to L4 were performed twice, by a trained rheumatologist (BD). These assessments were inputted into a database. The observer was blinded from the results of the first measurements. The intra-observer coefficient of variation (ICC) at vertebral level for heights was 0.917 (95% confidence interval (CI): 0.905–0.930, Cronbach's alpha 0.959). The arithmetic mean heights of the two measurements were used for calculation. The anterior-posterior ratio, the middle-posterior ratio, the posterior-posterior ratio were calculated. Prevalent morphometric vertebral deformities (MVD) were defined according to the Genant grading [##REF##8237484##29##]. Vertebral deformities were classified into three types (wedge, biconcavity, crush) and three grades (mild (any ratio &lt;20%), moderate (any ratio between 25–40%), and severe (any ratio &gt;40%)).</p>", "<p>The WHO Fracture risk assessment tool (FRAX) was used to calculate the absolute 10-year risk for major and for hip fractures in women and men [##UREF##1##12##].</p>", "<p>Statistical analyses were performed using SPSS version 12.01. Categorical variables and proportions were analyzed using chi-square statistic. Odds ratio (OR) with 95% confidence intervals (95% CI) were calculated based on the chi squares. One way Anova and chi-square statistics were used to analyze differences in continuous variables between subgroups. Observations were considered significant if two-sided p-values were &lt; 0.05.</p>" ]
[ "<title>Results</title>", "<p>Of the 100 patients, 73 were women and were 27 men. Mean age was 68 years (standard deviation: 10 years, range: 50 to 90 years). Demographic data are summarized in Table ##TAB##0##1##. The majority of patients were Caucasian (97%). Fractures were found at the following locations: clinical vertebral fractures (n = 4), clavicle (n = 3), pelvis (n = 2), humerus (n = 10), radius and/or ulna (n = 24), hand (n = 6), hip (n = 17), tibia/fibula/patella (n = 8) and foot (n = 21). Five patients had multiple simultaneous fractures and 80 patients had fractures after a fall from standing height.</p>", "<p>A total of 86 contributors to secondary osteoporosis were diagnosed in 57 patients (Table ##TAB##0##1## and ##TAB##1##2##). Contributors consisted of known medical conditions (34 in 27 patients) or newly diagnosed (52 in 50 patients). Seven patients had only known contributors, 20 had known plus a newly diagnosed contributor and 30 had only newly diagnosed contributors. One contributor was found in 32 (of whom 24 were vitamin D deficient), more than one contributor in 25 and 43 had none.</p>", "<p>Based on serum levels of 25-OHD<sub>3</sub>, 11 patients had severe deficiency, 31 were deficient and 31 had insufficient serum values. All were newly diagnosed. Serum levels of 25-OHD<sub>3 </sub>could not be predicted by any of questions on vitamin D or by the sum of those questions. Calcium intake below 1200 mg was reported in 86 patients. Only three patients had both a calcium intake above 1200 mg and a serum 25-OHD<sub>3 </sub>level above 75 nmol/L (Figure ##FIG##1##2##).</p>", "<p>Five patients had secondary hyperparathyroidism, of which four were newly diagnosed (Table ##TAB##1##2##). Hyperparathyroidism was secondary to renal insufficiency in three cases and to low calcium intake in two cases. We found 14 patients with renal insufficiency, of which 6 were newly diagnosed. Three patients were known with hyperthyroidism, 1 new case of exogenous hyperthyroidism and 1 new case of hypothyroidism was detected. One new case of lactose intolerance was diagnosed. Further contributors included anorexia nervosa in 2 women, documented hypogonadism in one men, pulmonary diseases in 5 patients (chronic obstructive lung disease and asthma), alcohol abuse in 4 men, inflammatory rheumatic diseases in 3 patients (2 with rheumatoid arthritis and 1 with giant cell arteritis) and 3 with severe immobility. Most of these patients did not receive preventive measures for osteoporosis prior to the fracture and were thus not recognized as having a contributor to secondary osteoporosis before the fracture occurred.</p>", "<p>Other laboratory abnormalities that required further exploration were found in 14 patients (18 abnormalities in total), including exogenous hypervitaminosis D (n = 1), hypercalciuria (n = 3), TSH outside normal ranges (n = 13) and low serum testosterone in one men (Table ##TAB##2##3##). Among the 9 patients being treated for hypothyroidism, one was over-treated while three were under-treated based on abnormal serum TSH levels. Among the 3 patients being treated for hyperthyroidism, two were under-treated while one was over-treated.</p>", "<p>According to the Dutch guideline for osteoporosis 54 patients had clinical bone-related risk factors for fractures in addition to their current fracture (Table ##TAB##3##4##). A history of an additional clinical fracture after the age of 50 was present in 31 patients (two with a previous clinical spine fracture). Additionally, 12 had a mother that had suffered one or more fractures, 3 were severely immobilised, and 23 had a low body weight (60 ≤kg). One bone-related risk factor was found in 41 patients, 2 in 12 and 3 in one patient. According to the Dutch guideline for fall prevention, we found fall related risk factors in 79 of the patients: 22 patients had one risk factor, 21 had two risk factors, and 36 had more than two risk factors. An overlap between clinical bone related risk factors and fall related risk factors was present in 45 patients. The prevalence of clinical bone-related and fall-related risk factors was similar between patients that had documented contributors secondary osteoporosis and those who did not (50% versus 59% for clinical bone related risk factors and 79% versus 84% for fall-related risk factors for fractures).</p>", "<p>VFA could be performed for 93 patients. Lateral spine images were not available in 7 cases due to severe scoliosis or other technical difficulties such as positioning patients with humerus fracture on the DXA table. On VFA, 57% of patients had a MVD, 31% had more than one MVD and 31% had moderate and severe MVD.</p>", "<p>The 57 patients with contributors to secondary osteoporosis were older (71 versus 64 yrs, p &lt; 0.01) (Table ##TAB##0##1##). They had more of some fall risks (multi-medication use (13 versus 3, p &lt; 0.05), restricted activities of daily living (34 versus 15, p &lt; 0.05) and disturbed vision (13 versus 3, p &lt; 0.05) (Table ##TAB##3##4##)). They had lower calcium intake (744 versus 993 mg, p &lt; 0.05) (Table ##TAB##0##1##) and more MVD (67% versus 44%, p &lt; 0.05, OR: = 2.6, 95% CI: = 1.1–6.0) (Figure ##FIG##2##3##).</p>", "<p>In contrast, the proportions of women (70 versus 77%) and of patients with fragility fractures (79 versus 81%) were similar between patients with and without contributors. There were also no differences in the prevalence of bone-related clinical risks (59 versus 50%).</p>", "<p>Based on the FRAX tool, patients with contributors had a higher calculated absolute 10-year risk for major (16.5 vs. 9.9%, p &lt; 0.01) and for hip fractures (6.9 vs. 2.4%, p &lt; 0.01).</p>", "<p>Compared to patients with a high-energy trauma, patients with fragility fractures were older (69 versus 63 years), had better activities of daily living (43 versus 16 patients), and more osteoarthritis (44 versus 4 patients) (Table ##TAB##0##1## and ##TAB##2##3##).</p>" ]
[ "<title>Discussion</title>", "<p>In the 100 patients older than 50 years presenting with a recent clinical fracture and osteoporosis and referred by the surgeons to the rheumatologists in collaboration with the endocrinologists the prevalence of contributors to secondary osteoporosis was high: almost two out of three patients had one or more contributors most of which were correctable.</p>", "<p>Our results show that many patients (27%) had known contributors to secondary osteoporosis, a percentage similar to that of Tannenbaum et al. in women with postmenopausal osteoporosis who were seen in an osteoporosis referral centre (32%) [##REF##12364413##3##]. The categories of known contributors to secondary osteoporosis were globally similar as reported by Tannennbaum [##REF##12364413##3##] (endocrine, gastrointestinal and inflammatory rheumatic and pulmonary diseases, severe immobility, alcohol abuse). One exception was glucocorticoid users who are presumably frequently referred to an osteoporosis clinic, but were not represented in our group of patients. In contrast to Tannenbaum, we performed the laboratory test set also in patients with already known contributors and 20 additional contributors were diagnosed in 20 patients (mainly low 25-OHD3, n = 14). Presumably none of the patients with known contributors had received attention in the context of osteoporosis, as none had osteoporosis treatment or calcium and vitamin D supplements.</p>", "<p>In the other, presumably healthy patients without known contributors, laboratory testing identified newly diagnosed contributors to secondary osteoporosis in 30 more patients, mostly vitamin D deficiency and renal disorders. The number of patients with newly diagnosed contributors (50%) was higher than reported by Tannenbaum et al. (33%) and concerned mainly vitamin D deficiency, secondary hyperparathyroidism (to renal insufficiency and to low calcium intake), malabsorption and exogenous hyperthyroidism [##REF##12364413##3##]. In contrast to Tannenbaum, we performed TSH not only in patients with a history of thyroid diseases, but in all patients, and were able to diagnose one new case of hypothyroidism.</p>", "<p>Vitamin D status could not be identified by history, despite including four specific questions regarding vitamin D intake. It has been shown that there is only a modest relation between reported vitamin D intake from an extensive dietary questionnaire and serum levels of 25-OHD3 [##REF##8702085##30##]. In our study a wide spectrum of levels of serum 25-OHD3 were found, from severely deficient to normal. There is still no consensus about how much vitamin D supplements are required to normalise serum levels. Some propose a unique dose of 800 IU/day together with 1000–1200 mg calcium/day to achieve 50 nmol/L [##REF##16622587##31##,##REF##16704554##32##]. Others state that a unique dose of 800–1600 IU/day would normalize serum levels to &gt;75 nmol/L [##UREF##3##33##,##REF##15776217##21##]. As patients with low serum levels of 25-OHD<sub>3 </sub>require, at least temporarily, high doses of vitamin D supplements while those with normal levels require less or none [##REF##16026981##34##], measuring serum 25-OHD3 levels is helpful in patients with osteoporosis in order to decide about appropriate vitamin D supplementation [##REF##12364413##3##].</p>", "<p>The calcium homeostasis was further compromised by the low calcium intake (&lt;1200 mg/day) in most patients, resulting in secondary hyperparathyroidism in 2, and only 3% of the patients had both adequate calcium intake and vitamin D status. Correcting these combined deficiencies has been demonstrated to reduce fracture risk, at least in institutionalized elderly women [##REF##8772587##35##] and to reduce the risk of falls [##REF##12568412##36##]. Calcium and vitamin D supplementation are thus needed in most patients presenting with a fracture and osteoporosis. However, supplementation with calcium and vitamin D alone is an insufficient measure in patients with osteoporosis, as drug therapy for osteoporosis has been shown to reduce the risk of fractures on top of correcting such deficiencies. Our data, together with those of Edwards et al. [##REF##18180974##6##] indicate that calcium and vitamin D deficiency is frequently present in patients presenting with a fracture, and that these deficiencies need to be identified and corrected.</p>", "<p>Interestingly, the presence of contributors was similar between women and men, and between patients with fractures associated with low or high-energy trauma, suggesting that evaluation for secondary contributors is indicated in women and men and after low or high-energetic trauma.</p>", "<p>An additional 14 patients had laboratory abnormalities that required further investigation, mainly hypercalciuria, uncontrolled treatment of thyroid disorders and low testosterone (in one man). Hyperthyroidism, whether endogenous or exogenous, can increases bone turnover and contributes to secondary osteoporosis [##REF##12803168##37##,##REF##12930603##38##]. Hypothyroidism on the other hand increases the risk of fractures through low bone turnover if untreated or high bone turnover if over treated [##REF##16151671##39##]. Thus fine-tuning thyroid treatment is indicated. The same is probably true for patients with hypercalciuria in whom thiazides are indicated [##REF##16515769##40##], and for hypogonadism in men that can be treated with testosterone supplementation [##REF##16474020##41##], although no fracture prevention data are available in these conditions.</p>", "<p>Therefore, measuring serum 25-OHD<sub>3</sub>, calcium in 24 hours urine, serum creatinine, TSH, PTH as proposed by Tannenbaum et al. is indicated in patients with osteoporosis and a recent clinical fracture, and enabled us to identify 47 (96%) newly diagnosed contributors and 13 of the 14 laboratory abnormalities [##REF##12364413##3##]. As many patients had endocrine diseases, collaboration with endocrinologists appeared to be highly valuable for diagnosis and treatment.</p>", "<p>The prevalence of clinical bone-related fracture risks in postmenopausal women, as evaluated by the Dutch guidelines, was similar between patients with and without documented contributors to and it contributed to further specify the risk for fractures.</p>", "<p>Nearly 80% of patients had fall-related risk factors for fractures, as reported by others [##REF##16283067##16##]. Although it has not been shown until now that fall prevention strategies itself can prevent fractures, they reduce the risk of falls. [##REF##15031239##42##] A multidisciplinary, multifactorial intervention program reduces postoperative falls and injuries after femoral neck fracture and are therefore applied in our ongoing prevention program [##REF##17061151##43##].</p>", "<p>An interesting finding was the prevalence of MVD which was more than twice as high among patients with documented contributors for secondary osteoporosis compared to those without contributors, in spite of similar low BMD in both groups. MVDs, that are related to future fracture risk independent of BMD [##REF##17245546##11##], reflect bone failure independently of BMD and thus indicate other mechanisms of bone's decreased resistance to fracture than low BMD, such as changes in the bone turnover, alterations in micro architecture of bone and deficient mineralization, especially in the context of the high prevalence of calcium and vitamin D deficiency.</p>", "<p>In several recent publications differential diagnosis and search for contributors to secondary osteoporosis is advocated [##UREF##4##44##,##REF##18266020##45##]. Only limited data are available about collaboration between surgeons and internists in taking care for osteoporosis in patients presenting with a fracture. Some initiatives were very successful [##REF##12107657##46##], but in most instances the collaboration is failing [##REF##14668497##47##]. This study indicates that such collaborations add to better treatment of patients with a clinical fracture.</p>", "<p>This study has several limitations. Smoking history, which is part of the WHO FRAX tool, was not recorded as it is not part of the Dutch guideline. The sample size was relatively small, but the strength of the study was that consecutive patients were evaluated showing that even in a small group many contributors to secondary osteoporosis could be diagnosed. Some laboratory abnormalities needed further exploration, but were not followed up and so no definite diagnosis could be reported in these patients. VFA has several limitations. Not all vertebrae could be measured, mainly at the upper thoracic level. Identifying patients with MVD by VFA requires additional X-rays to differentiate deformities due to other conditions, such as Scheuerman's disease, degenerative changes or non-osteoporotic short vertebral height. However, the method has a high negative predictive value in predicting the absence of vertebral fractures on X-rays [##REF##16940447##27##]. Another limitation is that only patients with BMD-osteoporosis were included. Most patients with a fracture have no BMD-osteoporosis. The results of our study suggest that documentation of the prevalence of contributors to secondary osteoporosis should also be studied in patients with a clinical fracture without BMD osteoporosis.</p>" ]
[ "<title>Conclusion</title>", "<p>We conclude that more than one in two patients presenting with a clinical vertebral or non-vertebral fracture and BMD-osteoporosis have secondary contributors to osteoporosis, most of which were correctable. Identifying and correcting these associated disorders will enhance treatment effect aimed at reducing the risk of subsequent fractures in patients older than 50 years.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>The aetiology of osteoporotic fractures is multifactorial, but little is known about the way to evaluate patients with a recent clinical fracture for the presence of secondary osteoporosis.</p>", "<p>The purpose of this study was to determine the prevalence of contributors to secondary osteoporosis in patients presenting with a clinical vertebral or non-vertebral fracture. Identifying and correcting these contributors will enhance treatment effect aimed at reducing the risk of subsequent fractures.</p>", "<p>In a multidisciplinary approach, including evaluation of bone and fall-related risk factors, 100 consecutive women (n = 73) and men (n = 27) older than 50 years presenting with a clinical vertebral or non-vertebral fracture and having osteoporosis (T-score ≤-2.5) were further evaluated clinically and by laboratory testing for the presence of contributors to secondary osteoporosis.</p>", "<p>In 27 patients, 34 contributors were previously known, in 50 patients 52 new contributors were diagnosed (mainly vitamin D deficiency in 42) and 14 needed further exploration because of laboratory abnormalities (mainly abnormal thyroid stimulating hormone in 9). The 57 patients with contributors were older (71 vs. 64 yrs, p &lt; 0.01), had more vertebral deformities (67% vs. 44%, p &lt; 0.05) and a higher calculated absolute 10-year risk for major (16.5 vs. 9.9%, p &lt; 0.01) and for hip fracture (6.9 vs. 2.4%, p &lt; 0.01) than patients without contributors. The presence of contributors was similar between women and men and between patients with fractures associated with a low or high-energy trauma.</p>", "<p>We conclude that more than one in two patients presenting with a clinical vertebral or non-vertebral fracture and BMD-osteoporosis have secondary contributors to osteoporosis, most of which were correctable. Identifying and correcting these associated disorders will enhance treatment effect aimed at reducing the risk of subsequent fractures in patients older than 50 years.</p>" ]
[ "<title>Abbreviations</title>", "<p>BD: Bianca Dumitrescu; BMD: Bone mineral density; CI: confidence interval; DXA: Dual X-Ray absortiometry; ESR: Erythrocyte sedimentation rate; EULAR: European League against Rheumatism; FRAX: Fracture risk assessment tool; MEC: Medical Ethical Committee; MVD: Morphometric vertebral deformity; MVF: Morphometric vertebral fracture; OR: Odds ratio; PTH: Parathormone; TSH: Thyroid stimulating hormone; VFA: Vertebral fracture assessment; WHO: World Health Initiative.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>BD analyzed clinical and laboratory data for the diagnosis of contributors to secondary osteoporosis, performed vertebral fracture assessment, statistical analyses and wrote the manuscript. SvH implicated in the coordination of the study, involved in the treatment of patients included in the study, participated to sequence alignment and data presentation. RtB involved in the coordination of the study, involved in the treatment of patients included in the study. AN–K analyzed clinical and laboratory data for the diagnosis of contributors to secondary osteoporosis, coordinated data presentation. CW gathered laboratory and clinical data, performed statistical analysis. GU participated in the sequence alignment. SvdL analyzed clinical and laboratory data for the diagnose of contributors to secondary osteoporosis, coordinated data presentation. PG conceived the study, participated in the design of the study, coordinated the study, analyzed the data for correct diagnosis and drafted the manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2474/9/109/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>EULAR: BD received a EULAR (European League against Rheumatism) grant for research in the field of osteoporosis at the University Hospital of Maastricht. This manuscript is part of her PhD thesis work.</p>", "<p>Thank you to Gittie Willems, the osteoporosis nurse working at The Fracture and Osteoporosis Outpatient Clinic for her active involvement and for contributing to the data collection in this study.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Flow chart of patients included in the study (see text for details) in one year.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Calcium intake and serum serum levels of 25OHD3</bold>. Only 3 patients had sufficient intake of calcium and normal serum levels of 25-OHD3.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Prevalence of MVD defined according to the grading of Genant et al. in patients with contributors to secondary osteoporosis and in patients without contributors.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Characteristics of the patient population (N = 100)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Variable Median+/-SD</bold></td><td align=\"left\"><bold>All patients</bold></td><td align=\"left\"><bold>Women</bold></td><td align=\"left\"><bold>Men</bold></td><td align=\"left\"><bold>Fragility fracture</bold></td><td align=\"left\"><bold>High energy trauma</bold></td><td align=\"left\"><bold>With contributors</bold></td><td align=\"left\"><bold>Without contributors</bold></td></tr></thead><tbody><tr><td align=\"left\">Number</td><td align=\"left\">100</td><td align=\"left\">73</td><td align=\"left\">27</td><td align=\"left\">80</td><td align=\"left\">20</td><td align=\"left\">57</td><td align=\"left\">43</td></tr><tr><td align=\"left\">Women/men (n)</td><td align=\"left\">73/27</td><td align=\"left\">na</td><td align=\"left\">na</td><td align=\"left\">66/14</td><td align=\"left\">7/13**</td><td align=\"left\">40/17</td><td align=\"left\">33/10***</td></tr><tr><td align=\"left\">Caucasian ethnicity (n)</td><td align=\"left\">97</td><td align=\"left\">94</td><td align=\"left\">27</td><td align=\"left\">79</td><td align=\"left\">18</td><td align=\"left\">55</td><td align=\"left\">42</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\">68 ± 10</td><td align=\"left\">70 ± 9</td><td align=\"left\">62 ± 8*</td><td align=\"left\">69,1 ± 9</td><td align=\"left\">63.3 ± 9**</td><td align=\"left\">71 ± 10</td><td align=\"left\">64 ± 7***</td></tr><tr><td align=\"left\">Weight (kg)</td><td align=\"left\">66 ± 13</td><td align=\"left\">63 ± 13</td><td align=\"left\">73 ± 11*</td><td align=\"left\">65,4 ± 14</td><td align=\"left\">68.8 ± 11</td><td align=\"left\">64 ± 14</td><td align=\"left\">69 ± 11</td></tr><tr><td align=\"left\">Spine T-score</td><td align=\"left\">-2.88 ± .91</td><td align=\"left\">-2.94 ± 0.79</td><td align=\"left\">-2.73 ± 1.17</td><td align=\"left\">-2.9 ± 0.92</td><td align=\"left\">-2.8 ± 0.83</td><td align=\"left\">-2.85 ± 0.97</td><td align=\"left\">-2.93 ± 0.82</td></tr><tr><td align=\"left\">Hip T-score</td><td align=\"left\">-1.92 ± 0.9</td><td align=\"left\">-2.13 ± 0.92</td><td align=\"left\">-1.37 ± 0.70*</td><td align=\"left\">-2 ± 0.87</td><td align=\"left\">-1.6 ± 1.09</td><td align=\"left\">-2.1 ± 1.00</td><td align=\"left\">-0.66 ± 0.63***</td></tr><tr><td align=\"left\">Hip Z-score</td><td align=\"left\">-1.92 ± 0.8</td><td align=\"left\">-0.57 ± 0.85</td><td align=\"left\">-0.89 ± 0.73</td><td align=\"left\">-0.62 ± 0.85</td><td align=\"left\">-0.83 ± 0.72</td><td align=\"left\">-0.66 ± 0.97</td><td align=\"left\">-0.66 ± 0.63</td></tr><tr><td align=\"left\">BMD spine(g/sq cm)</td><td align=\"left\">0.772+/- 0.100</td><td align=\"left\">0.756 ± 0.085</td><td align=\"left\">0.810 ± 0.130*</td><td align=\"left\">0.765 ± 0.103</td><td align=\"left\">0.795 ± 0.094</td><td align=\"left\">0.776 ± 0.109</td><td align=\"left\">0.766 ± 0.091</td></tr><tr><td align=\"left\">BMD hip(g/sq cm)</td><td align=\"left\">0.718+/- 0.395</td><td align=\"left\">0.676 ± 0.115</td><td align=\"left\">0.825 ± 0.107*</td><td align=\"left\">0.700 ± 0.116</td><td align=\"left\">0.779 ± 0.160**</td><td align=\"left\">0.695 ± 0.143</td><td align=\"left\">0.749 ± 0.106</td></tr><tr><td align=\"left\">Calcium intake(mg/day)</td><td align=\"left\">852 ± 432</td><td align=\"left\">828 ± 448</td><td align=\"left\">915 ± 389</td><td align=\"left\">851 ± 467</td><td align=\"left\">854 ± 266</td><td align=\"left\">744 ± 343</td><td align=\"left\">993 ± 497***</td></tr><tr><td align=\"left\">Serum 25OH vitamin D (nmol/L)</td><td align=\"left\">66 ± 53</td><td align=\"left\">67 ± 60</td><td align=\"left\">63 ± 28</td><td align=\"left\">63 ± 57</td><td align=\"left\">75 ± 30</td><td align=\"left\">42</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Creatinine clearance (ml/min)</td><td align=\"left\">67 ± 23</td><td align=\"left\">62 ± 20</td><td align=\"left\">65 ± 23</td><td align=\"left\">65 ± 23</td><td align=\"left\">74 ± 26</td><td align=\"left\">45 ± 18</td><td align=\"left\">82 ± 24***</td></tr><tr><td align=\"left\">Fracture after fall from standing height (n)</td><td align=\"left\">80</td><td align=\"left\">66</td><td align=\"left\">14</td><td align=\"left\">na</td><td align=\"left\">na</td><td align=\"left\">45</td><td align=\"left\">35</td></tr><tr><td align=\"left\">N of contributors (n)</td><td align=\"left\">86</td><td align=\"left\">62</td><td align=\"left\">24</td><td align=\"left\">70</td><td align=\"left\">16</td><td align=\"left\">na</td><td align=\"left\">na</td></tr><tr><td align=\"left\">N with contributors (n)</td><td align=\"left\">57</td><td align=\"left\">40</td><td align=\"left\">17</td><td align=\"left\">45</td><td align=\"left\">12</td><td align=\"left\">na</td><td align=\"left\">na</td></tr><tr><td align=\"left\">N with bone-related fracture risks</td><td align=\"left\">54</td><td align=\"left\">42</td><td align=\"left\">12</td><td align=\"left\">44</td><td align=\"left\">10</td><td align=\"left\">33</td><td align=\"left\">21</td></tr><tr><td align=\"left\">N with fall-related fracture risks</td><td align=\"left\">79</td><td align=\"left\">57</td><td align=\"left\">22</td><td align=\"left\">65</td><td align=\"left\">14</td><td align=\"left\">46</td><td align=\"left\">33</td></tr><tr><td align=\"left\">Time Go Up and Go (min)</td><td align=\"left\">8.6 ± 7.9</td><td align=\"left\">8.4 ± 8.0</td><td align=\"left\">8.9 ± 8.0</td><td align=\"left\">8.2 ± 8.0</td><td align=\"left\">9.8+/-8.0</td><td align=\"left\">8.1+/-8.6</td><td align=\"left\">9.2+/-7.1</td></tr><tr><td align=\"left\">N with MVD &lt;0.80 (n/n measured)</td><td align=\"left\">53/93</td><td align=\"left\">35/66</td><td align=\"left\">18/27</td><td align=\"left\">42/73</td><td align=\"left\">11/20</td><td align=\"left\">36/54</td><td align=\"left\">17/39***</td></tr><tr><td align=\"left\">N with MVD &lt;0.75</td><td align=\"left\">29</td><td align=\"left\">22</td><td align=\"left\">7</td><td align=\"left\">24</td><td align=\"left\">11</td><td align=\"left\">19</td><td align=\"left\">10</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Contributors to secondary osteoporosis identified in men and women &gt;50 years with a recent clinical fracture (N = 100)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Contributors</bold></td><td align=\"left\"><bold>Total</bold></td><td align=\"left\"><bold>Known</bold></td><td align=\"left\"><bold>Newly diagnosed</bold></td><td align=\"left\"><bold>Fragility Fracture </bold><break/><bold>(N = 80)</bold></td><td align=\"left\"><bold>High-energy trauma </bold><break/><bold>(N = 20)</bold></td></tr></thead><tbody><tr><td align=\"left\">Endocrine disorders</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Serum 25-OHD<sub>3 </sub>s ≤50 nmol/l</td><td align=\"left\">42</td><td align=\"left\">0</td><td align=\"left\">42</td><td align=\"left\">37</td><td align=\"left\">5</td></tr><tr><td align=\"left\"> Hyperparathyroidism secondary to low calcium intake</td><td align=\"left\">2</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\"> Hyperthyroidism</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Hypogonadism (in men)</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">1</td></tr><tr><td align=\"left\"> Anorexia nervosa (in women)</td><td align=\"left\">2</td><td align=\"left\">2</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Diabetes mellitus</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Gastrointestinal disorders</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Lactose intolerance</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Connective tissue disorders</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Rheumatoid arthritis</td><td align=\"left\">2</td><td align=\"left\">2</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\"> Giant-cell arteritis</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Renal disorders</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Renal insufficiency without secondary hyperparathyroidism</td><td align=\"left\">11</td><td align=\"left\">5</td><td align=\"left\">6</td><td align=\"left\">7</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Renal insufficiency with secondary hyperparathyroidism</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Miscellaneous</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Severe immobility</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Pulmonary diseases</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Medication and life style</td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Exogenous hyperthyroidism</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\"> Alcohol abuse</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">2</td></tr><tr><td align=\"left\"><bold>Total number of contributors (N = 86)</bold></td><td align=\"left\"><bold>86</bold></td><td align=\"left\"><bold>34</bold></td><td align=\"left\"><bold>52</bold></td><td align=\"left\"><bold>69</bold></td><td align=\"left\"><bold>17</bold></td></tr><tr><td align=\"left\"><bold>Total number of patients with contributors to osteoporosis (N = 57)</bold></td><td align=\"left\"><bold>57</bold></td><td align=\"left\"><bold>27</bold></td><td align=\"left\"><bold>50</bold></td><td align=\"left\"><bold>45</bold></td><td align=\"left\"><bold>12</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Laboratory abnormalities that required further exploration in men and women more than 50 years of age with a recent clinical fracture (N = 100)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Laboratory abnormality</td><td align=\"left\">Total</td></tr></thead><tbody><tr><td align=\"left\">Exogenous hypervitaminosis D (&gt;220 nmol/l)</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Hypercalciuria in 24 hours urine</td><td align=\"left\">3</td></tr><tr><td align=\"left\">TSH 0.4–3.5 mU/L</td><td align=\"left\">13</td></tr><tr><td align=\"left\"> - &gt;3.5 mU/L</td><td align=\"left\">10</td></tr><tr><td align=\"left\"> - treated hypothyroidism</td><td align=\"left\">9</td></tr><tr><td align=\"left\">  -TSH &lt;0.4 mU/L</td><td align=\"left\">1</td></tr><tr><td align=\"left\">  -TSH &gt;3.5 mU/L</td><td align=\"left\">3</td></tr><tr><td align=\"left\"> - treated hyperthyroidism</td><td align=\"left\">3</td></tr><tr><td align=\"left\">  -TSH &gt;3.5 mU/L</td><td align=\"left\">1</td></tr><tr><td align=\"left\"> -TSH &lt;0.4 mU/L</td><td align=\"left\">2</td></tr><tr><td align=\"left\">Serum testosterone in men &lt;12 nmol/L (one measurement)</td><td align=\"left\">1</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"><bold>Total number of patients</bold></td><td align=\"left\">14</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Clinical risks for fractures recorded in patients with a recent clinical fracture according to the Dutch guidelines</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>CLINICAL RISKS OF FRACTURE</bold></td><td align=\"left\"><bold>Total</bold></td><td align=\"left\"><bold>Women</bold></td><td align=\"left\"><bold>Men</bold></td><td align=\"left\"><bold>Fragility fracture</bold></td><td align=\"left\"><bold>High trauma</bold></td><td align=\"left\"><bold>Contributors</bold></td><td align=\"left\"><bold>No contributors</bold></td></tr></thead><tbody><tr><td align=\"left\">Numbers of patients</td><td align=\"left\">100</td><td align=\"left\">73</td><td align=\"left\">27</td><td align=\"left\">80</td><td align=\"left\">20</td><td align=\"left\">57</td><td align=\"left\">43</td></tr><tr><td align=\"left\">BONE RELATED RISK FACTORS</td><td align=\"left\">54</td><td align=\"left\">42</td><td align=\"left\">12</td><td align=\"left\">44</td><td align=\"left\">10</td><td align=\"left\">33</td><td align=\"left\">21</td></tr><tr><td align=\"left\">History of clinical fracture after 50 years</td><td align=\"left\">31</td><td align=\"left\">23</td><td align=\"left\">8</td><td align=\"left\">25</td><td align=\"left\">6</td><td align=\"left\">19</td><td align=\"left\">12</td></tr><tr><td align=\"left\">History of clinical vertebral fracture</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Mother with fracture</td><td align=\"left\">12</td><td align=\"left\">9</td><td align=\"left\">3</td><td align=\"left\">10</td><td align=\"left\">2</td><td align=\"left\">8</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Low body weight (&lt;60 kg)</td><td align=\"left\">23</td><td align=\"left\">20</td><td align=\"left\">3</td><td align=\"left\">18</td><td align=\"left\">5</td><td align=\"left\">15</td><td align=\"left\">8</td></tr><tr><td align=\"left\">Severe immobility</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">3</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Glucocorticosteroids user</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">FALL RELATED RISK FACTORS</td><td align=\"left\">79</td><td align=\"left\">57</td><td align=\"left\">22</td><td align=\"left\">65</td><td align=\"left\">14</td><td align=\"left\">46</td><td align=\"left\">33</td></tr><tr><td align=\"left\">Mobility: Time Get up and Go test</td><td align=\"left\">24</td><td align=\"left\">21</td><td align=\"left\">3</td><td align=\"left\">20</td><td align=\"left\">4</td><td align=\"left\">12</td><td align=\"left\">12</td></tr><tr><td align=\"left\">Previous falls: 2 or more falls in the previous year</td><td align=\"left\">27</td><td align=\"left\">22</td><td align=\"left\">5</td><td align=\"left\">23</td><td align=\"left\">4</td><td align=\"left\">15</td><td align=\"left\">12</td></tr><tr><td align=\"left\">Medication use (benzodiazepines, antiepileptics)</td><td align=\"left\">16</td><td align=\"left\">14</td><td align=\"left\">2</td><td align=\"left\">14</td><td align=\"left\">2</td><td align=\"left\">13</td><td align=\"left\">3</td></tr><tr><td align=\"left\">Low activities of daily living</td><td align=\"left\">49</td><td align=\"left\">38</td><td align=\"left\">11</td><td align=\"left\">43</td><td align=\"left\">16*</td><td align=\"left\">34**</td><td align=\"left\">15</td></tr><tr><td align=\"left\">Osteoarthritis</td><td align=\"left\">48</td><td align=\"left\">40</td><td align=\"left\">8</td><td align=\"left\">44</td><td align=\"left\">4*</td><td align=\"left\">25</td><td align=\"left\">23</td></tr><tr><td align=\"left\">Snellen score-visual acuity less than 0.4</td><td align=\"left\">16</td><td align=\"left\">8</td><td align=\"left\">8</td><td align=\"left\">14</td><td align=\"left\">2</td><td align=\"left\">13</td><td align=\"left\">3</td></tr><tr><td align=\"left\">Urinary incontinence</td><td align=\"left\">19</td><td align=\"left\">17</td><td align=\"left\">2</td><td align=\"left\">19</td><td align=\"left\">0</td><td align=\"left\">13</td><td align=\"left\">6</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>*p &lt; 0.05 women vs. men, ** p &lt; 0.05 between fragility fracture and high-energy trauma, *** p &lt; 0.05 between group with and without contributor, Na: not applicable</p></table-wrap-foot>", "<table-wrap-foot><p>*p &lt; 0.05 between fragility fracture and high-energy traum fracture, **p &lt; 0.05 between groups with and without contributors</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2474-9-109-1\"/>", "<graphic xlink:href=\"1471-2474-9-109-2\"/>", "<graphic xlink:href=\"1471-2474-9-109-3\"/>" ]
[]
[{"source": ["Richtlijn Preventie van valincidenten bij ouderen"], "year": ["2004"], "publisher-name": ["Utrecht , Kwaliteitsinstitut voor de Gezondheidszorg"]}, {"surname": ["Kanis"], "given-names": ["AJ"], "source": ["WHO Fracture Risk Assessment Tool"], "edition": ["february, 2008"]}, {"surname": ["Favus"], "given-names": ["DMJ"], "article-title": [" PRIMER on the Metabolic Bone Diseases and Disorders of the Mineral Metabolism"], "source": ["Best Pract Res Clin Rheumatol"], "year": ["2006"], "edition": ["12"], "publisher-name": [" American Society for Bone and Mineral Research"], "fpage": ["1"]}, {"surname": ["B"], "given-names": ["DH"], "article-title": ["The role of vitamin D in fracture prevention"], "source": ["BoneKEy-Osteovision"], "year": ["2005"], "volume": ["2"], "fpage": ["6"], "lpage": ["10"]}, {"collab": ["US Department of Health and Human Services "], "article-title": ["2004 Surgeon General's Report on Bone Health and Osteoporosis: What It Means To You"], "year": ["2004"]}]
{ "acronym": [], "definition": [] }
47
CC BY
no
2022-01-12 14:47:26
BMC Musculoskelet Disord. 2008 Aug 5; 9:109
oa_package/68/bf/PMC2529301.tar.gz
PMC2529302
18710520
[ "<title>Background</title>", "<p>Pronation is a normal component of the stance phase of gait, however excessive pronation, when the rearfoot remains pronated beyond the midstance phase of gait [##REF##10870868##1##], may cause excessive myofascial and soft tissue stress [##REF##11358622##2##]. Low-dye (LD) taping is commonly used by physiotherapists in the treatment of lower limb symptoms related to altered or excessive pronation [##REF##11722292##3##,##REF##16676873##4##], or to help decide if orthotics may be indicated [##UREF##0##5##]. LD taping is suggested to limit foot pronation by raising the medial longitudinal arch and controlling the amount of rearfoot pronation occurring [##UREF##1##6##,##UREF##2##7##].</p>", "<p>The effectiveness of LD taping has been examined in many different ways, including static and dynamic measures. Static measures include assessing vertical navicular height (VNH) and the navicular drop test (ND) [##UREF##1##6##,##REF##12014823##8##]. Using these measures, it appears that LD taping increases VNH and reduces ND in stance [##REF##12014823##8##,##UREF##3##9##], implying a short-term reduction of pronation with LD taping. Dynamic analysis of the effect of anti-pronation taping on foot motion and alignment has been less commonly used, even though studies have questioned the actual validity of static measures in predicting dynamic foot function [##REF##10870868##1##]. A previous study [##UREF##2##7##] used two-dimensional (2D) video analysis to measure the pronation angle of the foot with and without tape. Their results did not show any significant difference in dynamic pronation under each of the conditions. In contrast, other authors [##REF##16306503##10##] who also used 2D video analysis, found the arch height ratio to increase, which indicated reduced pronation, in overpronated subjects after the application of LD taping whilst walking. There are no published trials examining the effect of LD taping using three-dimensional (3D) motion analysis. Previous 3D analysis of the effects of soft foot orthotics on overpronation however found significantly reduced overall rearfoot motion when the orthotics are used, and not just reduced pronation [##REF##8066110##11##]. While there has been research into the effect of LD taping on rearfoot pronation, the effect of LD taping on rearfoot supination and overall rearfoot motion is unclear.</p>", "<p>Numerous studies have used plantar pressure patterns as an indirect measure of foot pronation during walking, as it has been assumed that plantar pressure distribution reflects rearfoot position [##REF##15128190##12##]. The existing evidence suggests that LD taping reduces pronation, as indicated by shifts in midfoot pressure from medial to lateral, as well as changes in forefoot and hindfoot forces [##REF##11722292##3##,##REF##15128190##12##]. Many previous trials examining plantar pressure however did not individually calibrate the sensors for each individual, did not allow subjects wear their usual footwear, and may not have investigated truly normal gait due to subjects having to step onto a sensing platform [##REF##11722292##3##,##REF##15128190##12##].</p>", "<p>The purpose of this study was therefore to evaluate the immediate effect of LD taping, using 3D motion and plantar pressure distribution analysis. A population of healthy subjects with a navicular drop exceeding 10 mm were chosen to attempt to replicate the patient population who might receive LD taping.</p>" ]
[ "<title>Methods</title>", "<p>Ethical approval was obtained from the University of Limerick Research Ethics committee. Participants gave written informed consent prior to participation.</p>", "<title>Subjects</title>", "<p>A convenience sample of 28 healthy subjects volunteered to participate in this study. An initial screening session determined if subjects had excessive pronation, using the ND test, which is a commonly used method for measuring excessive pronation in healthy individuals, and which has good intra-rater reliability [##REF##12014823##8##,##REF##11359892##13##]. Excessive pronation was defined as navicular drop of &gt; 10 mm, similar to previous research [##REF##12014823##8##,##REF##11359892##13##,##REF##8473991##14##] and all subjects were screened by one investigator. Eight were excluded as they did not have a navicular drop of greater than 10 mm. Tape allergy testing was also performed at the initial screening, for which a piece of zinc oxide tape was applied to the right ankle, and left in situ for at least 24 hours. Subjects with an adverse skin reaction (redness, rash or discomfort) to tape, with a lower limb injury in the past six months, or who were unable to walk painfree were excluded.</p>", "<title>Study design</title>", "<p>A repeated measures crossover study design was used. Since the plantar pressure and 3D motion data could not be collected simultaneously due to the practical issues in using both pieces of equipment, the order of testing was structured to minimise the length of time required for testing. Therefore the sequence of testing was always as described in Table ##TAB##0##1##. This allowed each subject to be analysed using both systems separately with a requirement to be only taped once.</p>", "<title>Taping</title>", "<p>LD taping was applied only to the right foot of each subject [##REF##11722292##3##]. A standard LD taping technique using rigid 3.8 cm wide zinc oxide tape (Leukotape) was used, similar to other trials [##REF##11722292##3##,##UREF##1##6##,##REF##15128190##12##], while palpating subtalar joint neutral position (Figure ##FIG##0##1##). Feet were washed and dried in advance of taping to optimise tape adherence [##UREF##0##5##]. To enhance consistency, the same investigator applied all taping and followed a standardised protocol.</p>", "<title>Instrumentation: Plantar pressure Data</title>", "<p>The F-Scan (Tekscan Inc), a computerised insole sensor system, was used to measure plantar pressure. The sensor consists of a bipedal, thin shoe insole composed of 960 individual pressure-sensing locations, providing a spatial resolution of four sensors/cm<sup>2</sup>. The insole uses resistance-based technology and the inner surfaces are printed with electrical circuits and in between these circuits is a semiconductive ink whose electrical resistance change inversely proportionally to the pressure applied. Studies have found that the F-Scan has fair to good reliability. Ahroni et al. [##REF##9801080##15##] examined the reliability of the F-Scan in people with diabetes and found moderate ICC values of 0.755 and 0.751. Mueller and Strobe [##REF##11415614##16##] examined the reliability of the F-Scan in ten normal subjects over multiple steps and reported a pearson product moment correlation coefficient of 0.933 between force platform data and F-Scan data. An experimental comparison of the Pedar system and the F-Scan by Hsiao et al. [##REF##12167198##17##] also reported good reliability for both systems provided the limitations of using such measurement devices were identified and reduced where possible. The F-Scan insoles were measured for each individual's right shoe according to manufacturers guidelines. The sensor was then inserted into the subjects shoe and attached to the transducer device that is attached to a computer via a 9.25 m cable. Insole calibration was performed once for each subject as per manufacturers' guidelines. This calibration involved subjects initially walking &gt; 20 steps to allow the insole adjust to conditions in the shoe. The insole was then loaded with total body weight for 1 second by lifting the left foot off the ground, simulating the magnitude and speed of stance phase loading during gait (Figure ##FIG##1##2##). The same insole was used for each individual for each of his or her walking trials. Because of natural step-to-step variability [##UREF##4##18##], data from several footfalls was gathered to obtain a representative profile of the subject's foot. Plantar pressure data was collected over 10 metres at a frequency of 50 Hz. Subjects were asked to walk at their normal speed, looking straight ahead. Standardised instructions were given to each subject by the same investigator. Prior to testing, subjects were allowed practice to become comfortable with the procedure. Post-taping, subjects walked around for 2–3 minutes to adapt to the tape. A rest interval between walking trials was offered to all subjects to minimise possible fatigue. Because velocity has been shown to affect plantar pressure values [##UREF##5##19##], the time taken to complete the walks was also recorded.</p>", "<title>Instrumentation: Motion analysis</title>", "<p>Kinematic data was acquired using a CODA mpx64 (Charnwood Dynamics Ltd., Leicestershire, UK) motion analysis system. This system uses a laboratory-based coordinate system, and calculates joint angles based on skin marker positions without the need to define a 'zero' starting position for the rearfoot. The markers were applied by one investigator in line with both manufacturer guidelines and previous research [##REF##16730177##20##]. Markers were positioned on the lateral aspect of the knee joint line in the median frontal plane, the anterior aspect of the lateral malleolus, the posterior inferior lateral aspect of the heel, and the lateral aspect of the fifth metatarsal head. The markers were fixed to the skin with double-sided adhesive tape. The order of testing required removal and immediate replacement of some of these markers when LD tape was being removed prior to analysis of the 'untaped' condition, however the same investigator did this over a very short time period. During testing subjects walked barefoot across a 10-metre walkway at a comfortable 'normal' walking speed. Subjects were instructed to look at a distant mark to prevent them from looking down at the floor. The subject performed 4 gait cycles with the tape and 4 cycles without the tape, since previous research has recommended the use of at least 3 gait cycles to aid reliability [##REF##12535727##21##]. 3D motion data was collected at 200 Hz for 4 seconds while the subject was performing the walks, similar to previous research [##REF##16730177##20##]. Blinding of the data collector regarding subject condition during the testing procedure was not possible.</p>", "<title>Data Analysis</title>", "<p>Plantar pressure data from the entire stance phase (heel-strike to toe-off) was collected and analysed using Tekscan software. To avoid any acceleration and deceleration associated with the beginning and end of walks, the middle 3 stance phases of each 10 metre walk were analysed. The foot was divided into a grid with 6 distinct areas to display changes in plantar pressure distribution. The same grid was used for taped and untaped data of each subject, but to accommodate different sized feet, different grids had to be developed for each subject. Insole sensor cells occasionally developed \"shorts\" where they appeared to be loaded when they are not, and these were edited prior to analysis as per manufacturers' guidelines. Tekscan software calculated the average peak plantar pressure of the middle 3 stance phases in each of the 6 areas. Peak pressure was defined as the highest value recorded by a cluster of 4 cells over the entire stance phase [##UREF##6##22##,##REF##10758524##23##]. For kinematic data, the stance phase of gait had to be identified in the absence of a force plate to demarcate stance and swing phases. Therefore heel strike was identified using the lowest vertical component of the heel marker and verified with the stick figure diagram [##REF##16730177##20##]. Kinematic data was calculated and analysed by CODA software, before being extracted and entered into Microsoft Excel and averaged for all subjects. The kinematic data analysed included the following parameters at the subtalar joint during the stance phase of gait;</p>", "<p>• minimum displacement value, which indicated peak pronation.</p>", "<p>• maximum displacement value, which indicated peak supination.</p>", "<p>• total displacement which represented total subtalar joint ROM.</p>", "<p>• mean displacement value, which indicated mean joint position during stance.</p>", "<p>These kinematic values are as defined by the manufacturers and other researchers [##REF##16730177##20##].</p>", "<title>Statistical Analysis</title>", "<p>Statistical analysis was undertaken using SPSS 13.0 for Microsoft Windows (Chicago, IL). Data distribution was determined visually using histograms and using the Kolmogornov-Smirnov statistical test. Kinematic data, with the exception of minimum (pronation) values was normally distributed. Plantar pressure data, along with the pronation values from motion analysis, were non-normally distributed. Paired t-tests were carried out on normally distributed data to test for statistically significant differences between taped and untaped conditions. Wilcoxon-Signed Rank tests were carried out on non-normally distributed data to test for significant differences between taped and untaped conditions. The level of significance was set at p &lt; 0.05. The standard error of measurement (SEM) was calculated in line with previous research [##REF##8047565##24##].</p>" ]
[ "<title>Results</title>", "<title>Demographic Data</title>", "<p>20 subjects (6 M, 14 F) met the inclusion criteria. Their mean (+/- SD) age was 22.1 (+/- 5) years.</p>", "<title>Plantar pressure data</title>", "<p>LD taping resulted in statistically significant increases in peak plantar pressure in the lateral midfoot (p = 0.000), along with significant decreases in pressure in the medial forefoot (p = 0.014), and the medial (p = 0.000) and lateral hindfoot (p = 0.007) (Table ##TAB##1##2##). No significant changes occurred in the medial midfoot (p = 0.794) or lateral forefoot (p = 0.654) (Figure ##FIG##2##3##). The actual differences in peak plantar pressure values between taped and untaped conditions for all 6 areas of the foot are also detailed for each subject (see additional file ##SUPPL##0##1##).</p>", "<title>Kinematic data</title>", "<p>The means and standard deviations for pronation, supination, total ROM and joint position under both taped and untaped conditions are displayed in table ##TAB##2##3## and figure ##FIG##3##4##. There was a statistically significant reduction in both pronation (p = 0.006) and supination (p = 0.025) when LD taping was applied. As a result, there was also a significant reduction in total ROM after the application of LD tape (p = 0.000). However the mean rearfoot position was not significantly different between the test conditions (p = 0.188). The actual differences in kinematic values between taped and untaped conditions are also detailed for each subject (see additional file ##SUPPL##1##2##).</p>", "<title>Data reliability</title>", "<p>We did not perform a test-retest reliability study, which significantly limits interpretation of the reliability of the data. Instead, we used the actual study data to calculate values for the SEM, to give an approximate representation of the reliability of the data. Data for plantar pressure could not be used to generate a value for SEM. Kinematic data from each of the four trials was however analysed to obtain values for the SEM of the CODA system (see additional file ##SUPPL##2##3##).</p>" ]
[ "<title>Discussion</title>", "<p>The findings of this study suggest that LD taping results in reduced rearfoot motion, and changes in plantar pressure patterns, in a small sample of healthy subjects. In agreement with previous trials, LD taping resulted in an immediate short-term reduction in pronation [##REF##11722292##3##,##UREF##1##6##,##REF##12014823##8##, ####UREF##3##9##, ##REF##16306503##10####16306503##10##,##REF##15128190##12##]. This is the first trial that has shown this reduction in pronation to be present when measured by both plantar pressure and 3D motion analysis. Interestingly rearfoot motion in general, rather than simply pronation, appears to have been reduced with LD taping. This has not been reported previously, but is consistent with similar research demonstrating that addition of foot orthotics resulted in an overall reduction in rearfoot motion, rather than simply reduced pronation [##REF##8066110##11##]. This suggests that it may be inappropriate to refer to LD taping as 'anti-pronation' taping, as its effects are not solely on pronation ROM. In a wider context, this is important because the technique is used by up to 73% of physiotherapists [##UREF##7##25##], and it is commonly described as an 'anti-pronation' taping technique, with less consideration of it's effect on supination ROM [##UREF##8##26##].</p>", "<title>Plantar pressure</title>", "<p>Results of the current study indicate that taping caused a significant increase in peak plantar pressure in the lateral midfoot, no change in the medial midfoot or lateral forefoot, and significant decreases in the hindfoot (medial and lateral) and the medial forefoot. Since peak plantar pressures are located more medially in excessively pronated feet [##REF##11809575##27##], these results imply that there may be a trend towards reduced pronation in the midfoot and forefoot, but not in the hindfoot. The results are broadly in accordance with the results of previous similar studies [##REF##11722292##3##,##REF##15128190##12##]. Russo and Chipchase [##REF##11722292##3##] found very similar results in the midfoot and forefoot, however they reported contradictory findings in the hindfoot, where peak pressure was increased after taping. Lange et al. [##REF##15128190##12##] also agreed with the results of the current study, showing a significant increase in lateral midfoot pressure and a reduction in hindfoot pressure after LD taping. Vincenzino et al. [##UREF##0##5##] also demonstrated a significant reduction in hindfoot contact as well as a non-significant increase in lateral midfoot contact after 'augmented' LD taping, similar to the current study. They examined plantar contact area however, rather than plantar peak pressure. The slight inconsistencies between trials may be explained by differences in the pressure-sensor system used, as well as variations in the exact type of LD taping applied. Despite this, the changes observed in the current study are broadly consistent with those described in the literature.</p>", "<title>Kinematics</title>", "<p>Maximum pronation was found to decrease significantly (p &lt; 0.05) as a result of LD taping. This finding is in agreement with results found in other studies [##UREF##0##5##,##UREF##2##7##, ####REF##12014823##8##, ##UREF##3##9##, ##REF##16306503##10####16306503##10##,##UREF##8##26##]. The populations studied in these other trials, and the taping techniques used, were similar to those of the current study. Different outcome measures were however used in previous trials, with the majority being related to measures such as ND and VNH [##UREF##3##9##,##REF##16306503##10##]. This is the first study examining the effect of LD taping on rearfoot motion using more complex 3D analysis, however the findings regarding reduced pronation are in line with previous studies. The findings of a reduction in supination are interesting in that they appear to indicate that LD taping results in a general decrease in mobility of the rearfoot, rather than having a purely 'anti-pronation' effect, as has typically been described in the literature [##UREF##2##7##,##UREF##8##26##]. This is further highlighted by the fact that the mean position of the rearfoot during stance did not change significantly between conditions. The observed reduction in overall rearfoot motion has also been described with the use of foot orthotics, albeit using different methods of motion analysis [##REF##8066110##11##,##REF##12633779##28##]. The effects of LD taping and foot orthotics may be similar, however this has not been proven and further research is needed to clarify if the effects seen here with LD taping also occur with foot orthotics. In addition, previous research [##REF##3343628##29##] indicates that ankle taping reduces ankle joint motion in normal subjects. Although the taping technique and joint motion measured differs, their findings are in line with the current study.</p>", "<title>Mechanism of action</title>", "<p>The main proposed mechanism behind the clinical effectiveness of LD taping has been that it restricts rearfoot pronation [##REF##16306503##10##], thereby reducing medial loading and increasing lateral loading through the foot [##UREF##2##7##,##UREF##8##26##]. The findings of this study agree only in part with this proposal. While pronation was reduced, LD taping did not result in increased supination, but rather reduced supination. The motion of the rearfoot as a whole was reduced, and the mean position through stance did not alter, with LD taping. The changes in plantar pressure imply a reduction in pronation, particularly during loading of the midfoot and forefoot. The plantar pressure data does not inform us sufficiently about supination range however. It may be that LD taping acts as a controller of general foot hypermobility rather than having a specific 'anti-pronation' effect. These hypotheses require further research before being proven however.</p>", "<title>Future research</title>", "<p>The evidence suggests that the effects of LD taping are short lived, although the exact length of time it may be effective for is still unclear [##UREF##1##6##, ####UREF##2##7##, ##REF##12014823##8##, ##UREF##3##9####3##9##]. This study was limited to the short-term effects of LD taping on non-injured subjects. Obviously further research is required to evaluate if these findings are replicated in a painful population, and how long these effects are maintained. Furthermore, research using foot orthotics suggests that when rearfoot motion is reduced significantly, significant changes may also occur more proximally at the knee joint [##REF##8066110##11##]. Further research into the effects of LD taping on motion in other lower limb joints is warranted. Future use of both kinematic and plantar pressure data in studies examining the effects of LD taping may be warranted as the current study results imply that they inform us of related, but different, aspects of the technique.</p>", "<title>Limitations</title>", "<p>The main limitation relates to the fact that both 3D motion analysis and plantar pressure systems are known to be linked to variable data output [##REF##12535727##21##,##REF##11415636##30##]. The current study took steps to minimise this variation however, and the degree of variation is similar for both taped and untaped conditions. 3D motion analysis is a relatively new method of analysing the effect of LD taping on rearfoot motion. All surface marking systems carry a certain degree of error when estimating the motion of joints, however the CODA motion analysis system is sufficiently reliable if a number of gait cycles are used, similar to this study [##REF##16730177##20##,##REF##12535727##21##]. It is difficult to compare absolute values of plantar pressure systems across studies, and it is more appropriate to compare plantar pressure distributions under constant conditions, as in this study [##REF##9651891##31##]. Secondly, a strict protocol was followed when using the F-Scan in order to make the procedure reliable. The F-scan system is highly correlated with force platform measures [##REF##11415614##16##] and is sufficiently reliable [##REF##9801080##15##], particularly when a mean of 3 steps is taken as the representative value [##REF##11415614##16##], similar to recommendations for other pressure measurement systems [##UREF##9##32##]. Thirdly, other factors which could affect validity e.g. walking speed and surface contact [##REF##9651890##33##], were consistent between taped and untaped conditions. The use of footwear was different for each measurement type, but once again this was consistent between taping conditions. Ideally, the measurement of 3D motion and plantar pressure would occur simultaneously to ensure the gait cycle analysed was identical, and the effect of taping could not have changed. The desire to examine in-shoe plantar pressures obviously would not allow visualisation of the skin markers. Therefore, simultaneous data collection was not possible and correlations between changes in kinematics and plantar pressure distribution were neither possible nor appropriate. This potential bias was minimised by gathering multiple cycles for each measurement system, in line with recommendations regarding a suitable number for adequate reliability for each system [##REF##11415614##16##,##REF##12535727##21##]. This resulted in a different number of gait cycles being performed for plantar pressure and motion analysis, however the number of gait cycles did not vary between the taped and untaped conditions. The absence of a force plate also meant the authors had to visually gauge where heel-strike and toe-off occur. This method has, however, been recommended by the manufacturers and been described in previous research [##REF##16730177##20##]. The need to reposition motion analysis skin markers after the removal of LD tape requires that the kinematic results be interpreted with some caution, as there is a small risk that this could have resulted in slight changes in kinematic angles. Similar to some previous LD taping trials [##UREF##0##5##,##UREF##2##7##], the reliability of the investigators was not established in the current study, however this is a potential source of error. This is particularly important given the small magnitude of change between conditions and the high variability of the data. The SEM values for kinematic data exceeded the statistically significant difference observed between taped and untaped conditions. Therefore the data should be interpreted with caution, as some of the difference observed between groups could be due to simply measurement error. A clearer indication of the reliability of the study protocol would require a test-retest reliability study to be performed in advance. The sample size is however in line with previous LD taping trials [##UREF##0##5##,##UREF##2##7##,##UREF##3##9##,##REF##16306503##10##,##UREF##8##26##]. This high level of data variability is commonly noted in studies of plantar pressure, LD taping and lower limb kinematics [##UREF##0##5##,##REF##9801080##15##,##REF##11415614##16##]. The effect of taping was examined only during the stance phase, due to the fact that maximum pronation has been found to occur during the middle-to-late stance phase of the gait cycle [##REF##10870868##1##], and symptoms are usually related to weight bearing. The size of the change with LD taping was statistically significant, but we cannot say whether this would be clinically significant. We did not examine whether the taping was performed identically for each subject, however one person performed all taping to minimise error and the tape applied did not change between the two measurement techniques. The sample size was small, and a suitable power calculation was not performed due to the exploratory nature of the study, and this limits external validity. Subjects were not randomly selected, but were a sample of convenience. The amount of time subjects were given to become accustomed to the tape varied somewhat between 2 and 3 minutes, which is a potential source of error. Also, there is a very slight risk of a residual effect of taping even after its removal, which could potentially have affected the baseline 'untaped' kinematic data. Finally, neither subjects nor investigators were blinded to taping condition, as this was not feasible.</p>" ]
[ "<title>Conclusion</title>", "<p>While this relatively small study does have some limitations, we believe, as it is the first study to combine 3D kinematic and plantar pressure measurement of the effects of LD taping, that its results are noteworthy. The study demonstrated that LD taping reduced both pronation and supination in the rearfoot during the stance phase of gait in healthy subjects with a ND exceeding 10 mm. LD taping also significantly altered the plantar pressure pattern of the foot. Clinically, this may support the use of LD taping in the treatment of symptoms related to increased foot mobility. Despite the description of LD taping as an 'anti-pronation' taping technique, it may work by limiting overall motion at the rearfoot. Further research is needed, particularly in clinical populations and examining the effects of foot orthotics. Further research is also required to establish the effect of reduced rearfoot ROM on other joints of the lower limb and its implications for injured subjects. It is important that future similar studies clarify whether the changes observed are greater than measurement error, which the current study was unable to do. Studies will also need to be conducted to establish the length of time that this effect of LD taping on the rearfoot lasts in a clinical population.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Low-dye (LD) taping is commonly used to reduce rearfoot pronation. No studies have previously investigated the effectiveness of LD taping using both plantar pressure distribution (F-Scan) and 3-D (CODA) analysis of rearfoot motion.</p>", "<title>Methods</title>", "<p>20 healthy subjects with a navicular drop test exceeding 10 mm participated in the study. T tests were used to determine whether significant (p &lt; 0.05) differences in plantar pressure and rearfoot motion occurred with LD taping.</p>", "<title>Results</title>", "<p>LD taping resulted in statistically significant increases in peak plantar pressure in the lateral midfoot (p = 0.000), along with significant decreases in pressure in the medial forefoot (p = 0.014), and the medial (p = 0.000) and lateral hindfoot (p = 0.007). No significant changes occurred in the medial midfoot (p = 0.794) or lateral forefoot (p = 0.654). When assessed using motion analysis, taping resulted in a statistically significant decrease in rearfoot pronation (p = 0.006), supination (p = 0.025) and total rearfoot range of motion (p = 0.000). The mean rearfoot position during stance was not significantly different however (p = 0.188).</p>", "<title>Conclusion</title>", "<p>LD taping is associated with alterations in peak plantar pressure in the midfoot and forefoot that indicate reduced pronation with LD taping. However, LD taping appears to reduce both pronation and supination in the rearfoot, rather than simply reducing pronation, when assessed using 3D motion analysis. Therefore, it would appear that LD taping does indeed reduce pronation, by restricting rearfoot motion in general, rather than pronation specifically. The degree of change observed with LD taping was however very small, and further research is needed to clarify the clinical significance of these initial findings.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>KOS was involved in conception and design of the study, data analysis and interpretation, as well as drafting and editing the final document for publication. NK was involved in conception and design of the study, data analysis and interpretation, as well as drafting and editing the final document for publication. EON was involved in conception and design of the study, data collection, data analysis, as well as drafting and editing the final document for publication. UNM was involved in conception and design of the study, data collection, data analysis, as well as drafting and editing the final document for publication.</p>", "<title>Funding</title>", "<p>None</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2474/9/111/prepub\"/></p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>David Sainsbury and Aisling Roche for assistance with data collection. Dr. Jean Saunders and Ms. Patricia Gunning for statistical advice. Written consent was obtained from the study subject for publication of the photographs.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Low-dye taping technique used in the study.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Calibration procedure for the F-scan plantar pressure system.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Peak plantar pressure values for taped and untaped conditions for each region of the foot; medial forefoot (MFF), lateral forefoot (LFF), medial midfoot (MMF), lateral midfoot (LMF), medial hindfoot (MHF) and lateral hindfoot (LHF). These differences were statistically significant for the lateral midfoot (p = 0.000), the medial forefoot (p = 0.014), and the medial (p = 0.000) and lateral (p = 0.007) hindfoot.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Kinematic values for pronation, supination, total range of motion (ROM) and mean joint position for taped and untaped conditions. These differences were statistically significant for pronation (p = 0.006), supination (p = 0.025) and total ROM (p = 0.000).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Order of testing for both procedures, and taping condition of each.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Order</bold></td><td align=\"left\"><bold>Test procedure</bold></td><td align=\"left\"><bold>Taped</bold></td></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Plantar pressure</td><td align=\"left\">No</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Plantar pressure</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">3</td><td align=\"left\">3D motion analysis</td><td align=\"left\">Yes</td></tr><tr><td align=\"left\">4</td><td align=\"left\">3D motion analysis</td><td align=\"left\">No</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Mean (+/- SD) values for peak plantar pressure in taped and untaped conditions for each region of the foot; medial forefoot (MFF), lateral forefoot (LFF), medial midfoot (MMF), lateral midfoot (LMF), medial hindfoot (MHF) and lateral hindfoot (LHF).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\"><bold>Untaped</bold></td><td align=\"left\"><bold>Taped</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>MFF</bold></td><td align=\"left\">276.85 (+/- 79.66)</td><td align=\"left\">241.5 (+/- 131.44)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>LFF</bold></td><td align=\"left\">230.65 (+/- 105.43)</td><td align=\"left\">227.75 (+/- 108.90)</td></tr><tr><td align=\"left\"><bold>MMF</bold></td><td align=\"left\">57.2 (+/- 15.83)</td><td align=\"left\">58.7 (+/- 23.17)</td></tr><tr><td align=\"left\"><bold>LMF</bold></td><td align=\"left\">99.4 (+/- 52.79)</td><td align=\"left\">149.35 (+/- 65.79)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>MHF</bold></td><td align=\"left\">234.85 (+/- 88.20)</td><td align=\"left\">192.05 (+/- 43.05)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>LHF</bold></td><td align=\"left\">208 (+/- 63.73)</td><td align=\"left\">180.2 (+/- 35.49)<bold>*</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Mean (+/- SD) values for pronation, supination, total range of motion (ROM) and mean joint position for taped and untaped conditions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\"><bold>Taped</bold></td><td align=\"left\"><bold>Untaped</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Pronation #</bold></td><td align=\"left\">5.54 (+/- 4.27)</td><td align=\"left\">4.15 (+/- 3.76)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>Supination</bold></td><td align=\"left\">25.69(+/- 4.06)</td><td align=\"left\">27.56 (+/- 4.30)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>Total ROM</bold></td><td align=\"left\">20.15(+/- 3.64)</td><td align=\"left\">23.41 (+/- 3.92)<bold>*</bold></td></tr><tr><td align=\"left\"><bold>Mean Position</bold></td><td align=\"left\">18.05(+/- 3.50)</td><td align=\"left\">19.16 (+/- 3.48)</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Peak plantar pressure data for all 20 subjects (averaged) in taped and untaped conditions, for each region of the foot; medial forefoot (MFF), lateral forefoot (LFF), medial midfoot (MMF), lateral midfoot (LMF), medial hindfoot (MHF) and lateral hindfoot (LHF).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Kinematic data for all 20 subjects (averaged) in taped and untaped conditions.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p>Estimated standard error of measurement (SEM) values (in degrees) for kinematic data, for both taped and untaped conditions.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><bold>*</bold>indicates the difference was statistically significant (p &lt; 0.05)</p></table-wrap-foot>", "<table-wrap-foot><p><bold>*</bold>indicates the difference was statistically significant (p &lt; 0.05). # lower values for pronation represent increased pronation, and not reduced pronation.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2474-9-111-1\"/>", "<graphic xlink:href=\"1471-2474-9-111-2\"/>", "<graphic xlink:href=\"1471-2474-9-111-3\"/>", "<graphic xlink:href=\"1471-2474-9-111-4\"/>" ]
[ "<media xlink:href=\"1471-2474-9-111-S1.doc\" mimetype=\"application\" mime-subtype=\"msword\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2474-9-111-S2.doc\" mimetype=\"application\" mime-subtype=\"msword\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2474-9-111-S3.doc\" mimetype=\"application\" mime-subtype=\"msword\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Vincenzino", "McPoil", "Russell", "Peisker"], "given-names": ["B", "TG", "T", "S"], "article-title": ["Anti-pronation tape changes foot posture but not plantar ground contact during gait"], "source": ["The Foot"], "year": ["2006"], "volume": ["16"], "fpage": ["91"], "lpage": ["97"], "pub-id": ["10.1016/j.foot.2006.02.005"]}, {"surname": ["Ator", "Gunn", "McPoil", "Knecht"], "given-names": ["R", "K", "T", "H"], "article-title": ["The effect of adhesive taping on medial longitudinal arch support before and after exercise."], "source": ["Journal of Orthopaedic and Sports Physical Therapy"], "year": ["1991"], "volume": ["14"], "fpage": ["18"], "lpage": ["23"]}, {"surname": ["Harradine", "Herrington", "Wright"], "given-names": ["P", "L", "R"], "article-title": ["The effect of low dye taping upon rear foot motion and position before and after exercise"], "source": ["The Foot"], "year": ["2001"], "volume": ["11"], "fpage": ["57"], "lpage": ["60"], "pub-id": ["10.1054/foot.2000.0656"]}, {"surname": ["Del Rossi", "Fiolowski", "Horodyski", "Bishop", "Trimble"], "given-names": ["G", "P", "M", "M", "M"], "article-title": ["For how long do temporary techniques maintain the height of the medial longitudinal arch?"], "source": ["Physical Therapy in Sport"], "year": ["2004"], "volume": ["5"], "fpage": ["84"], "lpage": ["89"]}, {"surname": ["Akhlaghi", "Daw", "Pepper", "Potter"], "given-names": ["F", "J", "M", "MJ"], "article-title": ["In-shoe step-to-step pressure variations"], "source": ["The Foot"], "year": ["1994"], "volume": ["4"], "fpage": ["62"], "lpage": ["68"], "pub-id": ["10.1016/0958-2592(94)90031-0"]}, {"surname": ["Taylor", "Menz", "Keenan"], "given-names": ["AJ", "HB", "AM"], "article-title": ["The influence of walking speed on plantar pressure measurements using the two-step gait initiation protocol"], "source": ["The Foot"], "year": ["2004"], "volume": ["14"], "fpage": ["49"], "lpage": ["55"], "pub-id": ["10.1016/j.foot.2003.09.004"]}, {"surname": ["Cavanagh", "Ulbrecht", "Sammarco GJ"], "given-names": ["P", "J"], "article-title": ["Plantar pressure in the diabetic foot"], "source": ["The Foot in Diabetes"], "year": ["1991"], "publisher-name": ["Philadelphia, Lea and Febiger"], "fpage": ["54"], "lpage": ["70"]}, {"surname": ["Brown"], "given-names": ["J"], "article-title": ["Physiotherapists' and podiatrists' views on the effectiveness of treatments for plantar fasciitis"], "source": ["International Journal of Therapy and Rehabilitation"], "year": ["2005"], "volume": ["12"], "fpage": ["151"], "lpage": ["157"]}, {"surname": ["Vincenzino", "Fielding", "Howard", "Moore", "Smith"], "given-names": ["B", "J", "R", "R", "S"], "article-title": ["An investigation of the anti-pronation effect of the two taping methods after the application and exercise"], "source": ["Gait and Posture"], "year": ["1997"], "volume": ["5"], "fpage": ["1"], "lpage": ["5"], "pub-id": ["10.1016/S0966-6362(95)01061-0"]}, {"surname": ["Hughes", "Pratt", "Linge", "Clark", "Klenerman"], "given-names": ["J", "L", "K", "P", "L"], "article-title": ["Reliability of plantar pressure measurements: the emed system"], "source": ["Clinical Biomechanics"], "year": ["1991"], "volume": ["6"], "fpage": ["14"], "lpage": ["18"], "pub-id": ["10.1016/0268-0033(91)90036-P"]}]
{ "acronym": [], "definition": [] }
33
CC BY
no
2022-01-12 14:47:26
BMC Musculoskelet Disord. 2008 Aug 18; 9:111
oa_package/30/74/PMC2529302.tar.gz
PMC2529304
18710562
[ "<title>Background</title>", "<p>Phagocytosis of haemozoin (HZ, malarial pigment) or HZ-containing trophozoites alters functionality of human monocytes and macrophages. Monocyte ability to perform oxidative burst is compromised [##REF##1402649##1##], bacterial killing abolished [##REF##7877842##2##], antigen presentation altered [##REF##10583855##3##], and ability to differentiate to functional dendritic cells disturbed [##REF##15356156##4##]. Moreover, HZ-laden monocytes produce increased amounts of peroxidation products of polyunsaturated fatty acids (PUFAs) [##REF##12393662##5##] and stimulate generation of several cytokines, such as TNF, IL-1beta, MIP-1alpha and MIP-1beta [##REF##7943569##6##,##REF##7583361##7##].</p>", "<p>It has been shown [##REF##16272296##8##] that HZ/trophozoite-fed human monocytes produced increased amounts of TNF and upregulated mRNA/protein expression and activity of matrix metalloproteinase-9 (MMP-9), a proteolytic enzyme which degrades matrix proteins [##REF##10419448##9##,##REF##12730128##10##] and sheds TNF and IL-1beta from cell-bound precursors [##REF##7759957##11##,##REF##8663297##12##]. As TNF induces the synthesis of MMP-9 [##REF##9755853##13##], ingested HZ was found to generate a TNF-driven positive feedback loop enhancing production of TNF and activity of MMP-9, both blocked by a specific inhibitor of MMP-9.</p>", "<p>Here it is shown that HZ/trophozoite-fed human monocytes generated increased amounts of IL-1beta and enhanced expression and activity of MMP-9. The latter appears to be causally related to enhanced IL-1beta production, as both expression and activation were abrogated by anti-hIL-1beta Abs. It is also shown that upregulation of IL-1beta and MMP-9 was absent in monocytes fed with beta-haematin (lipid-free synthetic HZ) or delipidized HZ, indicating a role for HZ-generated lipid components. 15-HETE (15(S,R)-hydroxy-6,8,11,13-eicosatetraenoic acid), a potent lipoperoxidation derivative generated by HZ from arachidonic acid via haem-catalysis [##REF##12393662##5##] was identified as one mediator possibly responsible for increased IL-1beta production and MMP-9 activity.</p>" ]
[ "<title>Methods</title>", "<title>Materials</title>", "<p>All materials were from Sigma-Aldrich, St Louis, MO, unless otherwise stated. Cell culture media RPMI 1640, Macrophage-SFM medium, TRIzol, M-MLV, oligo-dT, sense and anti-sense primers, Platinum Taq DNA Polymerase were from Invitrogen, Carlsbad, CA; Panserin 601 monocyte medium was from PAN Biotech, Aidenbach, Germany; recombinant human (rh)IL-1beta, blocking anti-human (h)IL-1beta antibodies and Merck's inhibitor I, (N-hydroxy-1-(4-methoxyphenyl)sulfonyl-4-(4-biphenylcarbonyl)piperazine-2-carboxamide), a specific inhibitor of MMP-9/MMP-13 activity, were from Merck, Darmstadt, Germany; ELISA kit for IL-1beta assay and 15-HETE were from Cayman, Ann Arbor, MI; anti-D IgG were from Immuno AG, Vienna, Austria; Percoll was from Pharmacia, Uppsala, Sweden; Dynabeads M-450 CD2 Pan T and M-450 CD19 Pan B were from Dynal, Oslo, Norway; Diff-Quik parasite stain was from Baxter Dade AG, Dudingen, Switzerland; sterile plastics were from Costar, Cambridge, UK; bicinchoninic acid protein assay was from Pierce, Rockford, IL; anti-MMP-9 monoclonal antibodies were from Santa Cruz Biotechnology, Santa Cruz, CA; DNA-free kit was from Ambion, Austin, TX; Beacon Designer 2.1 software was from Premier Biosoft International, Palo Alto, CA; dNTPs were from Applied Biosystem, Foster City, CA. 4-hydroxynonenal (HNE) was from Biomol, Plymouth Meeting, PA. Beta-haematin (synthetic HZ) was prepared according to the Slater <italic>et al </italic>[##REF##1988933##14##] procedure, modified as indicated [##REF##17543124##15##].</p>", "<title>Cultivation of <italic>Plasmodium falciparum </italic>and isolation of trophozoite-parasitized RBCs and native or delipidized HZ</title>", "<p><italic>Plasmodium falciparum </italic>parasites (Palo Alto strain, Mycoplasma-free) were kept in culture as described [##REF##15356156##4##]. HZ and trophozoite-parasitized RBCs (trophozoites) isolated from cultures during the first two days after infection of RBCs were added to schizonts (multinucleated parasite form). After centrifugation at 5,000 <italic>g </italic>on a discontinuous Percoll-mannitol density gradient, native HZ was collected from the 0–40% interphase and trophozoites/schizonts from the 40–80% interphase [##REF##15356156##4##]. Native HZ was washed five times with 10 mM HEPES (pH 8.0) containing 10 mM mannitol at 4°C and once with PBS, and stored at 20% (vol/vol) in PBS at -20°C. For delipidized haemozoin, lipid extraction was performed as previously reported [##REF##12393662##5##]. After isolation, HZ and trophozoites enriched to 95–97% parasitaemia were washed twice and reincubated in RPMI 1640 for 1 h at 37°C before opsonization and phagocytosis.</p>", "<title>Preparation and handling of monocytes</title>", "<p>Human monocytes were separated by Ficoll centrifugation from freshly collected buffy coats discarded from blood donations by healthy adult donors of both sexes provided by the local blood bank (AVIS, Associazione Volontari Italiani Sangue, Torino, Italy) [##REF##1402649##1##]. Separated lymphomonocytes were resuspended in RPMI 1640 medium and plated on wells of six-well plates. Each well received 2 ml of cell suspension containing 8 × 10<sup>6 </sup>cells/ml in RPMI 1640. The plates were incubated in a humidified CO<sub>2</sub>/air-incubator at 37°C for 60 min. Thereafter, non-adherent cells were removed by three washes with RPMI 1640 and adherent cells reincubated at 37°C overnight in RPMI 1640. Shortly before starting phagocytosis, wells were washed with RPMI 1640 and Macrophage-SFM medium added (2 ml/well). Adherent cells prepared by this method were detached from the plates by scraping, stained with specific antibodies and analysed on a FACScan flow cytometer (Becton-Dickinson, San Jose, CA). As an average, monocytes (CD14<sup>+ </sup>cells) were 63.8 ± 5.7%, lymphocytes 36.2 ± 5.7% (mean values ± SD, n = 6) of all mononuclear cells. For selected experiments, lymphomonocytes were separated by Ficoll centrifugation from fresh buffy coats (see above) and monocytes immunopurified by depletion of non-monocytic cells from lymphomonocytes. Dynabeads M-450 CD2 Pan T and M-450 CD19 Pan B (Dynal) were added to the lymphomonocytes in a 2:1 ratio for 20 min at 4°C. B and T lymphocytes were removed by biomagnetic separation as specified by the manufacturer. The remaining monocytes were washed twice and resuspended in Macrophage-SFM medium. By this method monocytes (CD14<sup>+ </sup>cells) were 73.6 ± 9.5% pure, (mean values ± SD, n = 6, range 62–89.2%).</p>", "<title>Phagocytosis by adherent monocytes of opsonized trophozoites, native or delipidized HZ, beta-haematin, nonparasitized opsonized RBCs and latex particles</title>", "<p>To each well of a six-well plate with approx. 1 × 10<sup>6 </sup>adherent monocytes, 50 μl trophozoites (10% haematocrit), native or delipidized HZ (120 nmoles HZ haem, an amount comparable to 50 μl trophozoites on haem content basis), 50 μl beta-haematin (120 nmoles haem), 50 μl anti-D IgG-opsonized RBCs (10% haematocrit) and 50 μl amine-modified, red-fluorescent latex particles (2.5% solids, diameter 0.105 μm) were added. Trophozoites, native and delipidized HZ, beta-haematin and latex particles were opsonized with fresh autologous serum, and nonparasitized RBCs were opsonized with anti-D IgG as indicated [##REF##1402649##1##,##REF##12393662##5##]. After opsonization, all phagocytic meals were suspended in Macrophage-SFM medium. The plates were centrifuged at low speed for 5 seconds to start phagocytosis and incubated in a humidified CO<sub>2</sub>/air-incubator at 37°C for 3 hours. This time period maximized phagocytosis and was not sufficient to induce haem-oxygenase-mediated degradation of ingested haem [##REF##10376994##16##]. Thereafter, non-ingested cells, HZ, latex and beta-haematin particles were removed by four washes with RPMI 1640. The plates were then incubated in a humidified CO<sub>2</sub>/air-incubator at 37°C for the indicated times. In selected experiments, cells were incubated with rhIL-1beta (20 ng/ml), blocking anti-hIL-1beta antibodies (30 ng/ml) or Merck's inhibitor I, a specific inhibitor of MMP-9/MMP-13 activity (4 ng/ml) for 48 h.</p>", "<title>Assay of IL-1beta production</title>", "<p>After termination of phagocytosis, monocytes were further incubated with Panserin 601 monocyte medium in a humidified CO<sub>2</sub>/air-incubator at 37°C for 48 h in presence (4 ng/ml) or absence of Merck's inhibitor I, a specific inhibitor of MMP-9/MMP-13 activity. The level of active soluble IL-1beta was assayed in monocyte supernatants by ELISA. A standard calibration curve was generated with rhIL-1beta, according to the manufacturer's instructions.</p>", "<title>Assay of MMP-9 activity by gelatin zymography</title>", "<p>After termination of phagocytosis, monocytes were further incubated with Panserin 601 monocyte medium in a humidified CO<sub>2</sub>/air-incubator at 37°C for 48 h. Thereafter, the activity of MMP-9 was evaluated by gelatin zymography in the cell supernatants as indicated [##REF##16272296##8##,##REF##8074288##17##,##REF##14500672##18##]. Supernatants were loaded on 8% polyacrylamide gels containing 0.1% gelatin under non-denaturing and non-reducing conditions. Following electrophoresis, gels were washed and incubated for 18 h at 37°C in a collagenase buffer. Densitometric analysis of the bands considered to reflect total enzymatic activity of MMP-9, was performed using a computerized densitometer (Chemidoc, Biorad, Hercules, CA) with activity presented in relative units compared to background.</p>", "<title>Assay of MMP-9 protein expression by western blotting</title>", "<p>After termination of phagocytosis, monocytes were further incubated with Panserin 601 monocyte medium in a humidified CO<sub>2</sub>/air-incubator at 37°C for 48 h. Thereafter, cells were washed and lysed at 4°C in lysis buffer containing (mM): NaCl, 300; Tris, 50; 1% (vol/vol) Triton-X100; protease and phosphatase inhibitors: pepstatin, 50 ng/ml; leupeptin, 50 ng/ml; aprotinin, 10 μg/ml. The protein content in the lysate was measured by the bicinchoninic acid assay and 12 μg protein/lane were added to the loading buffer. The lysates samples were loaded on 8% polyacrylamide gels under denaturing and reducing conditions, with addition of Laemmli buffer, blotted on a polyvinylidene difluoride membrane, and probed with anti-MMP-9 monoclonal antibodies at 1/1,000 final dilution. Bands were visualized by enhanced chemiluminescence. Densitometric analysis of the bands was performed using a computerized densitometer (Chemidoc).</p>", "<title>Assay of IL-1beta mRNA expression by real-time quantitative RT-PCR</title>", "<p>After termination of phagocytosis, monocytes were further incubated with Panserin 601 monocyte medium in a humidified CO<sub>2</sub>/air-incubator at 37°C for 6 h (immunopurified monocytes) or 15 h (adherent monocytes). Total cellular RNA from 2 × 10<sup>6 </sup>cells was isolated from monocytes by TRIzol, according to the manufacturer's instructions, and eluted in 20 μl diethyl pyrocarbonate water. To remove any contaminating DNA, RNA was treated with Ambion's DNA-free kit (Ambion). Retrotranscription was performed using 6 μg of RNA, 200 U/μl of M-MLV and 25 μ/μl oligo-dT (Invitrogen). Real-time quantitative RT-PCR was performed with the i<italic>Cycler </italic>instrument (Bio-Rad) and data analysis was performed with iCycler iQ Real-Time Detection System Software version 3.0 (Bio-Rad). IL-1beta (GenBank accession no. <ext-link ext-link-type=\"gen\" xlink:href=\"NM_000576\">NM_000576</ext-link>) oligonucleotide sequences (forward: 5'-ACA GAT GAA GTG CTC CTT CCA-3', reverse: 5'-GTC GGA GAT TCG TAG CTG GAT-3') were identified using Beacon Designer Software package and designed to be intron-spanning allowing the differentiation between cDNA and DNA-derived PCR products. PCR amplification was carried out in 25 μl of reaction mixture. 1 μl of cDNA (corresponding to 10<sup>5 </sup>cells) and 400 nM primers were added to the amplification mixture (iQ SYBR Green Supermix, Bio-Rad). DNA polymerase was pre-activated for 2 min at 94°C, and the amplification was performed by a 40-cycle PCR (94°C, 30 s, 60°C, 30 s and 72°C, 30 s). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as reference gene to normalize cDNA across samples. Relative quantitation for IL-1 beta, expressed as -fold variation over untreated control cells, was calculated using the 2<sup>-ΔΔCT </sup>method. To validate the use of the 2<sup>-ΔΔCT </sup>method, serial dilutions of cDNA from monocytes stimulated for 6 or 15 h by 20 ng/ml rhTNF, were tested. Analysed transcripts exhibited high linearity amplification plots (r &gt; 0.98) and similar PCR efficiency (85.7% for IL-1 beta and 86.5% for GAPDH), confirming that the expression of each gene could be directly compared. The specificity of PCRs was confirmed by melt curve analysis. Values are means of triplicate measurements.</p>" ]
[ "<title>Results</title>", "<title>Enhancement of IL-1beta production by adherent monocytes fed with HZ or trophozoites</title>", "<p>Adherent monocytes were allowed to phagocytose HZ, trophozoites, nonparasitized opsonized RBCs and latex particles (control meals) during 3 h. As an average, each monocyte ingested 8–10 trophozoites, or HZ equivalent to 8–10 trophozoites in terms of ingested haem, or almost 8–10 non-parasitized anti-D IgG opsonized RBCs, as shown previously [##REF##1402649##1##,##REF##7819098##19##]. After termination of phagocytosis and elimination of noningested phagocytic meals by repeated washings, and further incubation during 48 h, IL-1beta production was measured by ELISA in cell supernatants. Compared to unfed control monocytes, IL-1beta production was increased approximately two-fold after phagocytosis of HZ or trophozoites, whereas phagocytosis of control meals did not affect significantly cytokine production (Figure ##FIG##0##1##). In selected experiments, adherent monocytes after the 3 h phagocytic period were further incubated for 48 h in presence of Merck's inhibitor I, a specific inhibitor of MMP-9/MMP-13 activity. Supplementation of this inhibitor did not affect cytokine production by HZ/trophozoite-fed monocytes.</p>", "<title>Enhancement of MMP-9 protein expression and enzyme activity in adherent monocytes after HZ phagocytosis and rhIL-1beta treatment. Abrogation of the HZ effect by anti-hIL-1beta antibodies</title>", "<p>Unfed, latex-fed and HZ-fed adherent monocytes after termination of phagocytosis were further incubated for 15 h (MMP-9 mRNA expression studies) or 48 h (MMP-9 activity and protein expression studies) in presence or absence of 20 ng/ml rhIL-1beta and 30 ng/ml blocking anti-hIL-1beta antibodies. Phagocytosis of HZ enhanced enzyme activity (measured in cell supernatants) and protein expression (measured in cell lysates), confirming previous data obtained by this group [##REF##16272296##8##] (Figure ##FIG##1##2##, panel A). rhIL-1beta added to unfed or latex-fed monocytes mimicked the HZ effect at the protein expression/enzyme activity level (Figure ##FIG##1##2##, panel A and panel B) and at the mRNA expression level, and further enhanced enzyme activity when added to HZ-fed monocytes (Figure ##FIG##1##2##, panel A). Blocking anti-hIL-1beta antibodies abrogated the enhancement of protein expression (Figure ##FIG##1##2##, panel A) and enzyme activity (Figure ##FIG##1##2##, panel B), and also inhibited enhancement of mRNA expression observed after HZ phagocytosis.</p>", "<title>Role of lipidic component of HZ in enhancement of IL-1beta production and MMP-9 activity in adherent monocytes after phagocytosis of HZ</title>", "<p>Previous work has shown that PUFAs stably adherent to the crystalline poly-haem core of native HZ are transformed by non-enzymatic haem catalysis into a number of potent lipoperoxidation derivatives [##REF##12393662##5##]. To ascertain whether lipids were involved in HZ-elicited activation of MMP-9, lipid-free beta-haematin (synthetic HZ) and delipidized native HZ were fed to adherent monocytes. After phagocytosis, monocytes were further incubated for 48 hours and cell supernatants analysed by ELISA for IL-1beta production and MMP-9 activity. Beta-haematin and delipidized HZ were unable to enhance IL-1beta production (Figure ##FIG##2##3##, panel A) and stimulate MMP-9 activity (Figure ##FIG##2##3##, panel B).</p>", "<title>Involvement of 15-HETE in HZ-mediated effects on IL-1beta production and MMP-9 activity in adherent monocytes and on IL-1beta mRNA expression in immunopurified monocytes</title>", "<p>Previous work has indicated that 15-HETE, a product of arachidonic acid peroxidation by HZ, is an active mediator of HZ/trophozoite effects in monocytes [##REF##10697856##20##]. As shown in Figure ##FIG##3##4##, panel A, 15-HETE added to adherent monocytes at 0.1–10 μM (final concentration) enhanced production of IL-1beta, measured in cell supernatants 48 h after addition. 15-HETE added in the same concentration range also stimulated MMP-9 activity, similarly measured in cell supernatants 48 h after addition (Figure ##FIG##3##4##, panel B). This second effect was not concentration-dependent. Both 15-HETE-mediated effects were comparable to those elicited by HZ/trophozoite phagocytosis. In selected experiments with immunopurified monocytes, IL-1beta mRNA expression measured 6 h after phagocytosis was increased 3,5-fold after HZ and 2-fold after addition of 10 μM 15-HETE (Figure ##FIG##4##5##). The stimulatory effect of 15-HETE appears to be specific, as 4-hydroxynonenal [4-HNE], another potent PUFA derivative generated by HZ activity [##REF##8690068##21##] was unable to stimulate IL-beta production and MMP-9 activity when added at 0.1 μM (final concentration) and downregulated both parameters when added at 1–10 μM (final concentration).</p>" ]
[ "<title>Discussion</title>", "<p>Matrix metalloproteinases (MMPs) are a family of zinc-dependent enzymes characterized by their ability to remodel/disrupt subendothelial matrix proteins and shed or activate cytokines from their precursors [##REF##10419448##9##, ####REF##12730128##10##, ##REF##7759957##11##, ##REF##8663297##12####8663297##12##,##REF##16441234##22##]. MMPs play physiological roles, for example in wound repair [##REF##16441234##22##], and are also involved in pathological processes such as cancer metastasis [##REF##16680569##23##] or neurological diseases [##REF##12203394##24##]. Basal transcription level of MMPs is generally low [##REF##17167774##25##], but it can be enhanced in various cell types including monocytes, by cytokines and growth factors, and by cell-cell or cell-matrix interactions [##REF##10419448##9##]. Recently, involvement of MMPs in malaria has been described. Deininger <italic>et al </italic>[##UREF##0##26##] found higher levels of MMP-1 and angiogenic proteins such as VEGF in post-mortem samples of brain tissues of patients dead from cerebral malaria. Van den Steen <italic>et al </italic>[##REF##16865090##27##] described higher MMP-9 expression in brain and other tissues of mice with cerebral malaria. The enzyme was apparently produced by cells of monocytic lineage. Lastly, present group [##REF##16272296##8##] has shown that human adherent monocytes fed with HZ or HZ-containing trophozoites displayed increased activity and mRNA/protein expression of MMP-9, and increased production of TNF. Since TNF induces expression of MMP-9, while MMP-9 sheds TNF from its membrane-bound precursor, interaction between MMP-9 and TNF was considered to start a positive feedback loop eventually enhancing the pathological effects of both molecules: for MMP-9 – disruption of subendothelial basal lamina and infiltration of mononuclear cells in brain, lung and kidney – [##UREF##1##28##, ####UREF##2##29##, ##REF##10476050##30####10476050##30##]; and for TNF, – fever, hypoglycaemia, circulatory failure, and placental pathology – [##REF##2657427##31##, ####REF##2283152##32##, ##REF##10479160##33##, ##REF##11298488##34##, ##REF##14504653##35####14504653##35##].</p>", "<p>Present data show that IL-1beta production was enhanced in HZ/trophozoite-fed adherent monocytes, and causally related to enhancement of MMP-9 mRNA and protein expression (measured in cell lysates) and MMP-9 enzyme activity (measured in cell supernatants). Short-term experiments were performed to establish which cytokine (TNF or IL-1beta) was the primary target of HZ stimulatory activity. Data indicate that while blocking anti-hIL-1beta antibodies significantly reduced TNF production by HZ-fed cells at 1 h (p &lt; 0.05) and at 2 h (p &lt; 0.02), blocking anti-hTNF Abs did not affect the short-term production of IL-1beta by HZ-fed cells. These data seem thus to suggest that the enhancement of IL-1beta formation occurred first followed by enhanced formation and activity of TNF and MMP-9, respectively.</p>", "<p>HZ and HZ-containing trophozoites contain large amounts of monohydroxy derivatives of polyunsaturated fatty acids (OH-PUFAs). OH-PUFAs are stable derivatives of PUFA peroxidation, here most likely non-enzymatically generated by haem-catalyzed lipid peroxidation carried out by the poly-haem moiety of HZ [##REF##12393662##5##]. High concentration of HZ and acidic conditions are likely to favour unspecific haem-catalyzed lipid peroxidation, leading to a complex pattern of oxygenated products. Six HETE isomers and two major isomers of HODE (hydroxyoctadeca-9Z,11E-dienoic acid, a linoleic acid derivative) were found in HZ and trophozoites [##REF##12393662##5##]. Of those molecules and isomers, only 12- and 15-HETE mimicked toxic effects of HZ/trophozoite phagocytosis in monocytes, such as inhibition of oxidative burst and inhibition of differentiation and maturation of monocytes to dendritic cells [##REF##17543124##15##]. HODE isomers were inactive. Native HZ was found to contain 0.24 mmole 15-HETE/mole haem. 15-HETE is further produced by ingested HZ, and HZ-fed monocytes were found to shed 15-HETE into the supernatant and to contain approximately 10 μM 15-HETE, under the realistic assumption of 10 RBC equivalents per monocyte and a monocyte volume of 500 fL [##REF##12393662##5##]. 4-HNE, another PUFA derivative [##REF##8690068##21##], supplemented here at 0.1 μM concentration did not stimulate IL-beta production or MMP-9 activity, while it downregulated both parameters at 1–10 μM concentration. Possible explanations may reside in the strong reactivity of 4-HNE with thiols, such as reduced glutathione, or amino-groups present in suspending buffers. Additionally, 4-HNE tends to concentrate in the cell membrane where it generates adducts with His and Cys residues of membrane proteins, possibly interfering with the 15-HETE transduction pathway.</p>", "<p>Based on present data, following sequence of events is likely. First, 15-HETE, possibly together with co-generated, similarly active 12-HETE, would induce production of IL-1beta and TNF via a yet undetermined transduction pathway. It is likely that the NF-kB pathway is involved in cytokine upregulation, as Jaramillo <italic>et al </italic>recently reported activation of the NF-κB pathway in HZ-fed murine macrophages [##REF##14530348##36##,##REF##15611273##37##]. Subsequently, increased IL-1beta and TNF would upregulate MMP-9 expression and activity. Indeed, literature data indicate the NF-KB pathway as essential for both TNF and IL-1beta induction, and the latter cytokines as potent upregulators of MMP-9 [##REF##15800029##38##,##REF##17244487##39##].</p>" ]
[ "<title>Conclusion</title>", "<p>Phagocytosis of HZ or trophozoite-parasitized RBCs was shown to induce enhanced production of TNF and IL-beta, and to increase mRNA and protein expression (both measured in cell lysates), and enzymatic activity of MMP-9 (measured in cell supernatants), a metalloproteinase involved in disruption of basal membranes. Present data indicate that lipid components attached to HZ were instrumental for enhanced production of IL-1beta and MMP-9 increase. In fact, the ability of HZ to non-enzymatically generate HETEs and the presence of HETEs in HZ-fed monocytes [##REF##12393662##5##], the lack of effects by feeding cells with delipidated HZ, and the recapitulation of HZ effects by supplementing exogeneous 15-HETE are converging indications that 15-HETE, an active member of the HETE family, may be causally involved in upregulation of IL1-beta and MMP-9. Thus, HZ-derived 15-HETE might be a molecule primarily responsible for cytokine induction and MMPs activation and possibly instrumental in inducing hallmarks of cerebral malaria such as localized haemorrhages and extravasation, migration and perivascular accumulation of phagocytic cells. Interestingly, it was recently shown that soluble factors released by trophozoite-parasitized RBCs significantly decreased electrical resistance of human brain-blood barrier endothelium, indicating a parasite-mediated perturbation of the brain monolayer barrier function [##REF##17330783##40##].</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>It has been shown previously that human monocytes fed with haemozoin (HZ) or trophozoite-parasitized RBCs displayed increased matrix metalloproteinase-9 (MMP-9) enzyme activity and protein/mRNA expression and increased TNF production, and showed higher matrix invasion ability. The present study utilized the same experimental model to analyse the effect of phagocytosis of: HZ, delipidized HZ, beta-haematin (lipid-free synthetic HZ) and trophozoites on production of IL-1beta and MMP-9 activity and expression. The second aim was to find out which component of HZ was responsible for the effects.</p>", "<title>Methods</title>", "<p>Native HZ freshly isolated from <italic>Plasmodium falciparum </italic>(Palo Alto strain, Mycoplasma-free), delipidized HZ, beta-haematin (lipid-free synthetic HZ), trophozoites and control meals such as opsonized non-parasitized RBCs and inert latex particles, were fed to human monocytes. The production of IL-1beta by differently fed monocytes, in presence or absence of specific MMP-9 inhibitor or anti-hIL-1beta antibodies, was quantified in supernatants by ELISA. Expression of IL-1beta was analysed by quantitative real-time RT-PCR. MMP-9 activity and protein expression were quantified by gelatin zymography and Western blotting.</p>", "<title>Results</title>", "<p>Monocytes fed with HZ or trophozoite-parasitized RBCs generated increased amounts of IL-1beta and enhanced enzyme activity (in cell supernatants) and protein/mRNA expression (in cell lysates) of monocyte MMP-9. The latter appears to be causally related to enhanced IL-1beta production, as enhancement of both expression and enzyme activity were abrogated by anti-hIL-1beta Abs. Upregulation of IL-1beta and MMP-9 were absent in monocytes fed with beta-haematin or delipidized HZ, indicating a role for HZ-attached or HZ-generated lipid components. 15-HETE (15(S,R)-hydroxy-6,8,11,13-eicosatetraenoic acid) a potent lipoperoxidation derivative generated by HZ from arachidonic acid via haem-catalysis was identified as one mediator possibly responsible for increase of both IL-1beta production and MMP-9 activity.</p>", "<title>Conclusion</title>", "<p>Results indicate that specific lipoperoxide derivatives generated by HZ may play a role in modulating production of IL-1beta and MMP-9 expression and activity in HZ/trophozoite-fed human monocytes. Results may clarify aspects of cerebral malaria pathogenesis, since MMP-9, a metalloproteinase able to disrupt the basal lamina is possibly involved in generation of hallmarks of cerebral malaria, such as blood-brain barrier endothelium dysfunction, localized haemorrhages and extravasation of phagocytic cells and parasitized RBCs into brain tissues.</p>" ]
[ "<title>Authors' contributions</title>", "<p>MP designed the research, performed the experiments and drafted the manuscript. VG performed the experiments and helped to draft the manuscript. GG helped with the real-time quantitative RT-PCR experiments. PA helped design the research, examined and interpreted the data and wrote the final manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported in the context of the Italian Malaria Network by grants from Compagnia di San Paolo-IMI, the University of Torino Intramural Funds and Regione Piemonte, Ricerca Sanitaria Finalizzata 2007 to PA. Thanks are due to Elena Valente, Giuliana Gremo and Oscar Akide-Ndunge for help with the parasite, cell cultures and handling of HZ; to Dr Evelin Schwarzer for discussions; and to Associazione Volontari Italiani Sangue (AVIS, Torino, Italy) for providing freshly discarded buffy coats. The authors have no conflicting financial interests.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>HZ and trophozoite phagocytosis enhances IL1-beta production by human adherent monocytes</bold>. Human adherent monocytes were unfed or fed with HZ, trophozoites and control meals (IgG-anti D-opsonized nonparasitized RBCs, latex particles). After 3 h phagocytosis and a further incubation during 48 h, IL1-beta levels were measured by ELISA in cell supernatants. Data are given as ng IL-1beta/ml supernatant (mean values ± SD of six independent experiments). Data were analysed for significance by Student's t-test. Significance of differences (column numbers): trophozoite-fed(3)/HZ-fed(5) vs unfed-(1)/nonparasitized RBC-fed(2) monocytes, p &lt; 0.05; latex-fed(4) vs unfed-(1)/nonparasitized RBC-fed(2) monocytes, n.s.; trophozoite-fed(3) vs HZ-fed(5) monocytes, n.s.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>HZ and trophozoite phagocytosis, and rhIL-1beta enhance MMP-9 protein expression (in cell lysates) and enzyme activity (in cell supernatants) in human adherent monocytes. Abrogation of the HZ effect by anti-hIL-1beta antibodies</bold>. Human adherent monocytes were unfed, fed with HZ or latex particles treated or not with rhIL-1beta (20 ng/ml) or blocking anti-hIL-1beta antibodies (30 ng/ml) as indicated. <bold>Panel A</bold>. Western blot with anti-MMP-9 antibodies and densitometric quantification of MMP-9 protein. After 3 h phagocytosis and a further incubation during 48 h, cell lysates were prepared, separated by PAGE (8% polyacrylamide) blotted and probed with anti-MMP-9 monoclonal antibodies (1/1000 final dilution). The 92-kDa band in the gel corresponds to pro-MMP-9. Data are given as arbitrary densitometric units (mean values ± SD of four independent experiments). <bold>Panel B</bold>. Gelatin zymography and densitometric quantification of MMP-9 enzyme activity. After 3 h phagocytosis and a further incubation during 48 h, cell supernatants were separated by PAGE (8% polyacrylamide gel containing 0.1% gelatin) under non-denaturing and non-reducing conditions. The 83-kDa negative bands in the gel correspond to MMP-9 enzyme activity. Data are given as arbitrary densitometric units (mean values ± SD of four independent experiments). Data (Panel A, Panel B) were analysed for significance by Student's t-test. Significance of differences (column/lane numbers): HZ-fed(7)/rhIL1beta(2)-stimulated vs control(1)/anti-hIL1beta-stimulated(3)/latex-fed(4) monocytes, p &lt; 0.01 (Panel A) or p &lt; 0.05 (Panel B). rhIL1beta-stimulated(2,5,8) vs HZ-fed(7,8) monocytes, n.s.; anti-hIL1beta-stimulated(3,6,9)/latex-fed(4) vs unfed(1) monocytes, n.s.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>IL-1beta production and MMP-9 enzyme activity (in cell supernatants) in human adherent monocytes unfed or fed with HZ, delipidized HZ or beta-haematin</bold>. Human adherent monocytes were unfed or fed with HZ, delipidized HZ (D-HZ) and beta-haematin. <bold>Panel A: </bold>IL-1beta production. After 3 h phagocytosis and a further incubation during 48 h, IL1-beta levels were measured by ELISA in cell supernatants. Data are given as ng IL-1beta/ml supernatant (mean values ± SD of four independent experiments). Data were analysed for significance by Student's t-test and differences between delipidized HZ or beta-haematin against unfed controls were not significant. <bold>Panel B: </bold>Gelatin zymography and densitometric quantification of MMP-9 enzyme activity. After 3 h phagocytosis and a further incubation during 48 h, cell supernatants were separated by PAGE and MMP-9 enzyme activity measured by gelatin zymography and densitometric quantification (see legend to Figure 2 for details). The 83-kDa negative band in the gel corresponds to MMP-9 enzyme activity. Data are given as arbitrary densitometric units (mean values ± SD of four independent experiments). Data (Panel A, panel B) were analysed for significance by Student's t-test. Significance of differences (column/lane numbers). HZ-fed(2) vs unfed(1)/D-HZ-(3)/beta-haematin(4)-fed monocytes, p &lt; 0.01; unfed(1) vs D-HZ(2)/beta-haematin(4)-fed monocytes, n.s.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>IL-1beta production and MMP-9 enzyme activity (in cell supernatants) in human adherent monocytes unfed or fed with HZ and treated or not with 15-HETE</bold>. Human adherent monocytes were fed or not with HZ and treated or not with 15-HETE added at time 0 at 0.1–10 μM (final concentration). <bold>Panel A</bold>. After 3 h phagocytosis and a further incubation during 48 h (HZ-fed monocytes) or 48 h after addition of 15-HETE, IL-1beta levels were measured by ELISA in cell supernatants. Data are given as ng IL-1beta/ml supernatant (mean values ± SD of four independent experiments). <bold>Panel B</bold>. After 3 h phagocytosis and a further incubation during 48 h (HZ-fed monocytes) or 48 h after addition of 15-HETE, cell supernatants were separated by PAGE and MMP-9 enzyme activity measured by gelatin zymography and densitometric quantification (see legend to Figure 2 for details). The 83-kDa negative bands in the gel correspond to MMP-9 enzyme activity. Data are given as arbitrary densitometric units (mean values ± SD of four independent experiments). Data were analysed for significance by Student's t-test. Significance of differences (column/lane numbers). Unfed(1) vs HZ-fed(5) monocytes, p &lt; 0.05; unfed(1) vs 15-HETE-treated (3,4,5) monocytes, p &lt; 0.05 (Panel A) and p &lt; 0.01 (Panel B).</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>IL-1beta mRNA expression in immunopurified human adherent monocytes unfed or fed with HZ and treated or not with 15-HETE</bold>. Human CD14<sup>+ </sup>immunopurified monocytes were unfed, fed with HZ or treated with 15-HETE added at time 0 at 1–10 μM (final concentration). 6 h after phagocytosis or addition of 15-HETE. mRNA expression was measured by real-time quantitative RT-PCR in cell lysates and expressed as -fold variation over untreated monocytes. Mean values ± SD of three independent experiments. Data were analysed for significance by Student's t-test. Significance of differences (column numbers). Unfed(1) vs HZ-fed(2) monocytes, p &lt; 0.01; unfed(1) vs 15-HETE 1 μM(3) treated monocytes, n.s.; unfed(1) vs 15-HETE 10 μM(4) treated monocytes, p &lt; 0.05.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1475-2875-7-157-1\"/>", "<graphic xlink:href=\"1475-2875-7-157-2\"/>", "<graphic xlink:href=\"1475-2875-7-157-3\"/>", "<graphic xlink:href=\"1475-2875-7-157-4\"/>", "<graphic xlink:href=\"1475-2875-7-157-5\"/>" ]
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[{"surname": ["Deininger", "Winkler", "Kremsner", "Meyermann", "Schluesener"], "given-names": ["MH", "S", "PG", "R", "HJJ"], "article-title": ["Angiogenic proteins in brains of patients who died with cerebral malaria"], "source": ["Neuroimmunol"], "year": ["2003"], "volume": ["142"], "fpage": ["101"], "lpage": ["111"], "pub-id": ["10.1016/S0165-5728(03)00250-9"]}, {"surname": ["Clark", "Tomlinson", "Boyd MF Jr"], "given-names": ["HC", "WJ"], "article-title": ["The pathologic anatomy of malaria"], "source": ["Malariology"], "year": ["1949"], "volume": ["2"], "publisher-name": ["Philadelphia, Saunders"], "fpage": ["874"], "lpage": ["903"]}, {"surname": ["Aikawa", "Suzuki", "Gutierrez", "Kreier JP Jr"], "given-names": ["M", "M", "Y"], "article-title": ["Pathology of malaria"], "source": ["Malaria, Pathology, Vector Studies, and Culture"], "year": ["1980"], "volume": ["1"], "publisher-name": ["New York, Academic"], "fpage": ["47"], "lpage": ["102"]}]
{ "acronym": [], "definition": [] }
40
CC BY
no
2022-01-12 14:47:26
Malar J. 2008 Aug 18; 7:157
oa_package/16/7b/PMC2529304.tar.gz
PMC2529305
18664257
[ "<title>Background</title>", "<p>Malaria remains one of the most important causes of morbidity and mortality in the world. Current methods of control are only partially effective and, therefore, the development of a vaccine which can provide a high degree of protection is a priority. Antibody-mediated immune responses to malaria antigens are known to be involved in protecting against disease [##REF##13880318##1##, ####REF##4934028##2##, ##REF##1994206##3##, ##REF##1928564##4####1928564##4##], but the antigens that induce protective antibodies have not been conclusively identified. Immuno-epidemiological studies from different laboratories have sometimes yielded conflicting results [##REF##12201577##5##, ####REF##10225865##6##, ##REF##8627050##7##, ##REF##14688102##8####14688102##8##]. This may be partly due to differences in malaria endemicity and the use of different study designs, reagents, assay protocols and statistical methodologies. In an attempt to make such studies more comparable, the Afro-Immuno Assay (AIA) network project was initiated. The network includes six African Institutions in Gabon, Ghana, Burkina Faso, Senegal, Tanzania, and Zimbabwe and three European Institutions from Denmark, The Netherlands and France. The Afro-Immuno Assay network has developed standardized enzyme immuno assays [##REF##16194274##9##, ####REF##18070896##10##, ##REF##17280744##11####17280744##11##] that ensure the use of the same reagents, protocols and statistical methods to assess the association between acquisition of malaria specific antibody responses to four potential malaria vaccine candidate antigens and possible protection from clinical malaria. Samples for the AIA multi-center project were retrospectively obtained from cohort studies in six different geographical and epidemiological settings, comprising low endemic to holoendemic areas. These antigens include the Glutamate Rich Protein (GLURP), the Merozoite Surface Protein 3 (MSP3) [##REF##15541030##12##], the 19-kilo Dalton region of the Merozoite Surface Protein 1 (MSP1<sub>19) </sub>[##REF##17023096##13##] and the Apical Membrane Antigen 1 (AMA1) [##REF##15542195##14##], which are all thought to induce protective antibody responses through various mechanisms [##REF##7629503##15##, ####REF##16914240##16##, ##REF##17923516##17##, ##REF##9423833##18####9423833##18##]. Vaccines incorporating these antigens are currently in clinical trials and are described in detail elsewhere [##REF##8627050##7##,##REF##1741018##19##, ####REF##1775153##20##, ##REF##1343716##21##, ##REF##2659533##22##, ##REF##14688092##23##, ##REF##7838184##24##, ##REF##10613831##25##, ##REF##8068948##26####8068948##26##]. It is likely that a future malaria vaccine will comprise multiple rather than single antigens and it is, therefore, useful to study natural immune responses to multiple malaria antigens in relation to incidence of malaria in a more standardized way. In this study, the standardized AIA ELISA procedures [##REF##16194274##9##, ####REF##18070896##10##, ##REF##17280744##11####17280744##11##], were used to assess the relationship between incidence of clinical malaria and naturally acquired isotype and IgG subclass responses to these four leading malaria vaccine candidate antigens, AMA1, MSP1<sub>19</sub>, MSP3 and GLURP in Ghanaian children from three to 10 years of age.</p>" ]
[ "<title>Materials and methods</title>", "<title>Study area, study population and morbidity surveillance</title>", "<p>Samples used in this study were obtained in March 2002 from a longitudinal study conducted in Dodowa, in which 352 children aged three to 10 years (in the active phase of acquiring immunity to malaria), were enrolled in a study, whose original aim had been to assess the role of cytokine regulation and immunity to malaria. Dodowa is a semi-rural town in the Dangme West District of the Greater Accra Region of Ghana, about 50 km from the capital Accra and is an area of moderate and stable malaria transmission with a seasonal peak. Bed net coverage in this area was low, about 10% [##REF##9217705##27##]. The study was approved by the Noguchi Memorial Institute's Ethical Review Board. After obtaining consent from parents, blood was obtained from each child and plasma stored at -20°C until use. Malaria episodes were detected using both active and passive surveillance implemented over a period of nine-months, spanning the entire malaria transmission season. Clinical and parasitological information was captured using a standard questionnaire. Each child was visited once a week and the child and the parents or guardians were asked about symptoms of malaria since the last visit and whether she/he had received any anti-malarial treatment. The child was then given a physical examination and the body temperature measured. Children with a history of fever within 48 hours and/or axillary temperature equal to or above 37.5°C had a rapid test for malaria parasitaemia using OptiMAL™ (DiaMed, FLOW Inc. Portland, Oregon) and then thick and thin blood films were prepared for microscopy for estimating the parasite density which was used later in the more specific malaria case definition employed for data analysis. Only children with measured or reported fever, and with a positive rapid test were treated with chloroquine (in accordance with the then prevailing national malaria treatment policy) and in the case of severe symptoms, the child was referred to the hospital. At monthly intervals, blood smears from finger pricks were obtained from all children irrespective of symptoms to estimate the prevalence of asymptomatic malaria infections. The parents of the children were instructed to report to the field team if the child had any symptoms of disease at any time. The qualifying case definition for malaria in the data analysis was reported fever and/or a measured temperature equal to or above 37.5°C, with parasitaemia ≥ 5,000 parasites/μl of blood; this case definition has been found to have 90% sensitivity and specificity in the study area [##REF##15501780##28##, ####REF##10720556##29##, ##REF##11500389##30####11500389##30##]. Positive and negative control plasma used in ELISA measurements were obtained from adult Liberians and Danes respectively.</p>", "<title>Malaria antigens</title>", "<p>AMA1 was from the <italic>Pichia pastoris </italic>expressed ectodomain of <italic>Plasmodium falciparum </italic>FVO strain comprising amino acids 25–545 [##REF##12117958##31##] (Donated by A Thomas, Biomedical Primate Research Centre). GLURP was an <italic>Escherichia coli </italic>recombinant protein containing the conserved non-repeat N-terminal region (amino acids 25–514) called R0 [##REF##7719909##32##] (Donated by M. Theisen, Statens Serum Institut). MSP1<sub>19 </sub>was a <italic>Baculovirus </italic>antigen of the C-terminal region of the merozoite protein surface 1, produced in insect cells infected with a recombinant Baculovirus containing a synthetic G-C enriched PfMSP1 gene (Palo Alto allele), coding for 43 N-terminal MSP1 precursor residues and 16 amino acid residues upstream of the \"classical\" MSP-1<sub>19 </sub>(NIS---FCS) [##REF##16814434##33##] (Donated by S. Longacre, Institut Pasteur). The MSP3 antigen used in this study was a long synthetic peptide called LR55 (amino acids 181 – 276) of the merozoite surface protein 3 [##REF##16299295##34##] (Donated by M. Theisen, Statens Serum Institut). All the antigens were provided through the AIA Project.</p>", "<title>Enzyme-linked immunosorbent assay (ELISA)</title>", "<p>Specific isotype and IgG subclass levels against GLURP, MSP1<sub>19</sub>, MSP3 and AMA1 were measured using indirect ELISA according to the AIA standard ELISA protocols [##REF##16194274##9##, ####REF##18070896##10##, ##REF##17280744##11####17280744##11##]. All antigens tested were optimized and shown to be stable for at least three weeks, when antigen-coated plates and serum/plasma dilutions are refrigerated. The subclass specific reagents used were selected on the basis of low cross reactivities among themselves. To control for inter-assay and day-to-day variations in the standardized ELISA procedure, three-fold serial dilutions of reference standard reagents (IgG, IgM and IgG1 to IgG4) were directly coated on each ELISA plate (Maxisorp Nunc, Denmark) at a start concentration of 1,000 ng/ml (100 μl/well). OD values for the test samples were converted into antibody units with the standard reference curves generated for each ELISA plate using a four parameter curve-fit Microsoft Excel-based application. Samples were re-tested if the coefficient of variation between duplicate absorbance values were higher than 15% and plates were also re-tested if the R-square value of the standard curve was less than 97%. The reference standards, PBS buffer blank, positive and negative control plasma pools that were included in each ELISA test plate allow for the determination of failed assay runs. The AIA ELISA procedure used in this study is described in detail elsewhere [##REF##18070896##10##].</p>", "<title>Statistical analysis</title>", "<p>Clinical data were double entered using Microsoft Fox Pro and immunological data using Excel. STATA version 9.2 (Statcorp, Texas) was used for statistical analysis. Children were considered to have a clinical malaria episode if they had parasitaemia of ≥ 5,000 parasites/μl, with a measured temperature ≥ 37.5°C or a history of fever in the last 48 hours [##REF##10225865##6##,##REF##15275531##35##,##REF##7855468##36##].</p>", "<p>For each antigen, Poisson regression was used to investigate the association between the levels of antibody measured at baseline and the incidence rate of the first (or only) episode of clinical malaria. The level of total IgG, IgM and each IgG subclass, were analysed for each antigen in turn. Antibody values were transformed to log base 2, so that the rate ratio represents the ratio of malaria incidence corresponding to a doubling of antibody level. To investigate whether the relationship between malaria incidence and antibody level was nonlinear, a likelihood ratio test was used to compare the fit of the model when antibody level was included as a categorical or a continuous variable. When there were zero antibody values, indicating levels below the detection limit, the zero (left censored) values were assigned a nominal value equal to half the smallest measured value for that variable. If the proportion of zero values was large, the variable was treated as categorical with the reference category containing the zero values and the positive values divided into three equal groups. A likelihood ratio test was used to determine the P-value for the association with malaria incidence. Age at enrolment was considered to be an important potential confounder, and was included in the regressions as a factor with categories defined by quintiles. To model seasonality in malaria incidence, the calendar month of surveillance was included in the models as a factor. To construct a parsimonious model using all the immunological variables, firstly a model was produced for each antigen; in this model each IgG subclass, total IgG and IgM were candidates for inclusion provided the P-value for association with malaria incidence was 0.1 or less when considered individually. Variables were then removed from the model if the P-value for the likelihood ratio test was more than 0.1, provided removal did not change coefficients of variables in the model by more than 10%. In a second stage, the variables included in these models were candidates for inclusion in a final model derived in a similar way. Baseline parasitaemia was not considered as a potential confounder, but the interaction between each immunological variable and the presence of parasitaemia at baseline in their effects on malaria incidence was examined. A consequence of using a more specific case definition in the analysis than was used to decide treatment during the study is that children could have received anti-malarial treatments during the period they are considered at risk in the analysis. To explore the impact of these drug treatments, a time dependent variable was defined, to allow for a reduced risk of malaria for a period of 28 days after each drug treatment, which was included as a covariate in the Poisson regression model. LOESS smoothing was applied in R software to plot antibody levels in relation to age. Spearman's rank correlation test was used to assess associations between antibody levels and age.</p>" ]
[ "<title>Results</title>", "<title>Pattern of <italic>P. falciparum </italic>infections and malaria in the study cohort</title>", "<p>Of the 352 children recruited for the original study, eight were lost to follow-up immediately after the baseline blood sampling. Of the 344 children followed up, sixty four (19%) had at least one episode of malaria (53 children had one episode, nine had two and two had three episodes). The incidence rate of malaria in the study cohort was 0.35 attacks per child per year (Table ##TAB##0##1##). The risk of clinical malaria decreased with increasing age [##REF##9647243##37##]. Sixty-six percent of the children had asymptomatic parasitaemia at baseline. Parasites were predominantly <italic>P. falciparum </italic>(95%) and the prevalence of parasitaemia measured each month was roughly constant (ranging from 50% to 65%). The incidence of clinical malaria, however, varied during the survey period, rising gradually from March to May, peaking in July and then decreasing until November (Figure ##FIG##0##1##).</p>", "<title>Relationship between age and antibody levels</title>", "<p>Several studies in malaria endemic regions have shown increasing antibody levels with age. This pattern is more pronounced the greater the intensity and duration of malaria transmission. In this study, the relationship between antibody levels and age were assessed for antibodies against MSP1<sub>19</sub>, AMA1, GLURP and MSP3. The levels of IgG and IgM to MSP1<sub>19</sub>, MSP3, AMA1 and GLURP increased with age (Spearman correlation coefficient (r<sub>s</sub>) 0.21 – 0.45; p &lt; 0.001, Figure ##FIG##1##2##). IgG levels to AMA1 however, gradually increased until six years of age, and then leveled off. For the four antigens tested, IgG1, IgG2 and IgG3 significantly increased with age (r<sub>s</sub>, 0.12 – 0.36; p &lt; 0.03) with the exception of IgG2 to AMA1. There was no evidence that the level of IgG4 was associated with age for any of the antigens. Like IgG to AMA1, IgG3 to MSP3 steadily increased with age until seven years of age, then leveled off (Figure ##FIG##2##3##).</p>", "<p>The levels of both IgG and IgG1 were highest for AMA1 and lowest for MSP3, whereas IgG levels to MSP1<sub>19 </sub>and GLURP were comparable (Figures ##FIG##1##2## and ##FIG##2##3##). The levels of IgG3 were higher in AMA1 than the comparable levels in GLURP, MSP1<sub>19 </sub>and MSP3 (Figure ##FIG##2##3##). In general, the levels of cytophilic IgG1 and IgG3 were higher than those of non-cytophilic IgG2 and IgG4, IgG4 levels being the lowest.</p>", "<title>Antibody levels in relation to protection from clinical malaria</title>", "<p>Total IgG to MSP3, MSP1<sub>19</sub>, and GLURP, and IgM to all four antigens tested (MSP3, MSP1<sub>19</sub>, GLURP, and AMA1) were associated with reduced malaria incidence in crude analyses (Table ##TAB##1##2##). The incidence of both clinical malaria and antibody levels were associated with age, age is therefore a potential confounder, and it is important to adjust for its effects. After adjusting for the effect of age, there was evidence of a significant association between total IgG to MSP3, MSP1<sub>19</sub>, GLURP and reduced risk of malaria (IgG to MSP3: rate ratio 0.69 (95%CI 0.53, 0.90) P = 0.01; IgG to MSP1<sub>19</sub>: 0.75 (0.61, 0.92) P = 0.01; IgG to GLURP: 0.79 (0.64, 0.98) P = 0.04); and IgM levels to AMA1, MSP3, MSP1<sub>19 </sub>were also significantly associated with reduced risk of malaria (Table ##TAB##1##2##).</p>", "<p>A large proportion of the measurements of IgG4 to MSP3 and AMA1 and IgG2 to AMA1 were zero (left-censored) values and so these variables were treated as categorical variables (Table ##TAB##2##3##). In the crude analysis, IgG1, IgG2 and IgG3 to MSP3, MSP1<sub>19</sub>, GLURP and also IgG4 to MSP3, were associated with a reduced risk of clinical malaria (Table ##TAB##2##3##, ##TAB##3##4##), but after adjustment for age, only one of these variables, IgG1 to MSP1<sub>19</sub>, remained significantly associated with malaria incidence (rate ratio 0.89 (95%CI 0.80, 0.99), P = 0.04, Table ##TAB##3##4##).</p>", "<p>When all immunological variables were considered simultaneously, only two variables were independently associated with reduced malaria incidence, IgG1 [(0.80 (0.66–0.96), p = 0.018)] to MSP1<sub>19</sub>, and IgM [(0.48 (0.32–0.72), p &lt; 0.001)] to MSP1<sub>19 </sub>(Table ##TAB##4##5##).</p>", "<p>The estimated rate ratio for the effect of antimalarial treatments given to children with parasitaemia &lt; 5000/uL was 0.25 (95%CI 0.06–1.05), indicating these children had a substantially reduced risk of being found positive with parasitaemia &gt;= 5000/ul during the 28 days following the treatment. But the estimates of rate ratios for other variables in the model were unchanged when this variable was included in the model suggesting treatment effects did not cause a bias in the estimation of the effects of immunological variables.</p>" ]
[ "<title>Discussion</title>", "<p>This study in Ghanaian children is one of a series of studies designed to assess, using standardized methods, the association of antibody levels to four leading asexual blood-stage malaria antigens (MSP1<sub>19</sub>, MSP3, AMA1 and GLURP) with the incidence of clinical malaria in different epidemiological settings. Previous results have been difficult to interpret due to different study protocols and analytical methods having been used [##REF##12201577##5##, ####REF##10225865##6##, ##REF##8627050##7####8627050##7##,##REF##16194274##9##,##REF##15542195##14##,##REF##10720556##29##,##REF##10768952##38##, ####REF##16474069##39##, ##REF##14977962##40####14977962##40##]. In this study, the prevalence of asymptomatic malaria parasitaemia was relatively high and stable, while incidence of clinical malaria fluctuated in parallel with the intensity of transmission and seasonal rainfall pattern. These patterns are typical of this area and have been reported in previous studies [##REF##10225865##6##,##REF##8512880##41##, ####REF##12160443##42##, ##REF##11349035##43####11349035##43##]. The variation in the incidence of clinical malaria during the study period may be due to the introduction of new parasites with different antigenic presentation into the population leading to clinical malaria in susceptible individuals. The risk of malaria decreased with age, while isotype and IgG subclass levels to the four antigens generally increased with age. This is consistent with the hypothesis that immunity to malaria is largely effected through antibody-mediated mechanisms and that protective antibody levels to relevant antigens increase with age-related exposure to the parasites [##REF##16368979##44##]. Increasing IgG and IgM levels with age may reflect greater cumulative exposure of older children but may also be due to older children having a more mature immune system [##REF##15275362##45##]. The association of IgM responses with reduced malaria incidence indicates a possible role in immunity in Ghanaian children. Although much emphasis has been placed on IgG as the important isotype in immunity against malaria, IgM, which has lower affinity but is multivalent, may afford protection via other mechanisms such as the blocking of merozoite invasion of erythrocytes, complement activation, agglutination of merozoites [##REF##8458956##46##].</p>", "<p>The association with malaria incidence of IgG responses to MSP3, MSP1<sub>19 </sub>and GLURP is consistent with data from several other immuno-epidemiological studies [##REF##12201577##5##,##REF##8627050##7##, ####REF##14688102##8##, ##REF##16194274##9####16194274##9##,##REF##15541030##12##,##REF##10720556##29##,##REF##11500389##30##,##REF##10768952##38##,##REF##9502606##47##, ####REF##10823777##48##, ##REF##15759156##49####15759156##49##] indicating that these antigens may be targets of protective antibodies [##REF##13880318##1##,##REF##4934028##2##,##REF##1928564##4##]. There was no evidence that IgG levels to AMA1 were associated with malaria incidence, and there was no evidence of an interaction with baseline parasitaemia in contrast with similar studies conducted in Kenya [##REF##15542195##14##]. As shown in other studies, the cytophilic antibody levels to the four antigens tested in this study were higher than the non-cytophilic ones, emphasizing their importance in anti-malaria immunity [##REF##16194274##9##,##REF##10720556##29##,##REF##10768952##38##,##REF##1548071##50##,##REF##1343695##51##]. IgG and cytophilic antibody levels were highest for AMA1, while the levels were relatively low for MSP3. These differences in specific antibody levels may be related to the number of immunogenic B-Cell epitopes exposed to the immune system and could also be related to the structure, location and function of the particular antigen(s). With the exception of IgG2 levels to AMA1, IgG2 levels to GLURP, MSP1<sub>19 </sub>and MSP3 increased with age which may suggest that IgG2 is involved in immunity against malaria. In recent studies, malaria antigen specific IgG2 have been shown to bind with high affinity to mutant Fcγ RII H131-&gt;R receptors [##REF##17587350##52##] on monocytes, granulocytes and B cells, thus affording protection against malaria through monocytes and or neutrophil mediated mechanisms in subjects expressing the mutant CD32 form [##UREF##0##53##]. There was however, no evidence found that IgG2 was associated with malaria incidence for any of the four antigens tested.</p>", "<p>The IgG subclasses, IgG1, IgG2 and IgG3 for MSP3, MSP1<sub>19</sub>, GLURP and IgG4 to MSP3 were associated with a reduced risk of malaria in un-adjusted analysis but of these only IgG1 to MSP1<sub>19 </sub>was independently associated with malaria incidence after adjustment for age. Other studies have shown the importance of IgG1 in clearing parasitaemia in children [##REF##12201577##5##,##REF##9502606##47##,##REF##16890498##54##] In a previous cohort study conducted in the same area, there was no association between antibody levels to MSP1<sub>19 </sub>and malaria incidence [##REF##10225865##6##]. This may be due to differences in antigen and antibody reagents used in the two studies; the MSP1<sub>19 </sub>used in this study was a Baculovirus product that included a synthetic G-C enriched PfMSP1 gene that coded for the 43 N-terminal MSP1 precursor residues and 16 amino acid residues upstream of the \"classical\" MSP-1<sub>19 </sub>(NIS---FCS) [##REF##16814434##33##] compared to the one produced in <italic>E. coli</italic>, which had been used in the previous study. Although antibodies to MSP1<sub>19 </sub>have been shown to be associated with both exposure and protection from disease, the fine specificities of such responses may contribute to protection [##REF##14977962##40##]. The antigen used in this study may have assessed antibodies of fine specificities that are protective [##REF##14977962##40##,##REF##15385530##55##,##REF##11292349##56##], whereas the antigen used in the previous study did not. It may, therefore, be important to assess in a standardized way the various MSP1<sub>19</sub>, and other antigens, that are produced in different expression systems, in order to select the most appropriate antigen/expression system for malaria vaccine development. In another study in this series, in Burkina Faso, the same antigens, reagents, ELISA procedure and analytical methods were used; none of the isotypes and subclasses to MSP1<sub>19 </sub>was associated with the incidence of clinical malaria. Since the same laboratory methods were used, the different outcomes of these two studies may be attributed to differences in malaria transmission or to the age of the children [##REF##16368979##44##]. In Burkina Faso, the malaria transmission season is much shorter, which may influence the induction of differing antibody types for controlling malaria as shown in recent studies conducted in areas with different malaria endemicities in Tanzania [##REF##16194274##9##,##REF##16368979##44##]. Although total IgG to GLURP and MSP3 were associated with the risk of malaria, none of the constituent subclasses was identified to be associated with protection. When the effects of all the immunological variables were considered simultaneously, only IgG1 and IgM to MSP1<sub>19 </sub>were independently associated with the incidence of clinical malaria, which may indicate the importance of MSP1<sub>19 </sub>in malaria vaccine development. Parasite growth inhibition assays would be required to confirm if this association reflects a functional role of MSP1<sub>19 </sub>in immunity.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, using standardized AIA ELISA, anti-MSP<sub>19 </sub>antibodies (IgG1 and IgM) have been shown to be the most strongly correlated with reduced risk of clinical malaria among the four malaria vaccine candidates tested. The standardized AIA ELISA developed for this project could be used to validate malaria vaccine candidate antigens, provide useful baseline information for clinical trials, and contribute to quality assured laboratory capacity in Africa.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Antigen-specific antibody-mediated immune responses play an important role in natural protection against clinical malaria, but conflicting estimates of this association have emerged from immuno-epidemiological studies in different geographical settings. This study was aimed at assessing in a standardized manner the relationship between the antibody responses to four malaria vaccine candidate antigens and protection from clinical malaria, in a cohort of Ghanaian children.</p>", "<title>Methods</title>", "<p>Standardized ELISA protocols were used to measure isotype and IgG subclass levels to Apical Membrane Antigen 1 (AMA1), Merozoite Surface Protein 1–19 (MSP1<sub>19</sub>), Merozoite Surface Protein 3 (MSP3) and Glutamate Rich Protein (GLURP) antigens in plasma samples from 352 Ghanaian children, aged three to 10 years with subsequent malaria surveillance for nine months. This is one of a series of studies in different epidemiological settings using the same standardized ELISA protocols to permit comparisons of results from different laboratories.</p>", "<title>Results</title>", "<p>The incidence rate of malaria was 0.35 episodes per child per year. Isotype and IgG subclasses for all antigens investigated increased with age, while the risk of malaria decreased with age. After adjusting for age, higher levels of IgG to GLURP, MSP1<sub>19</sub>, MSP3 and IgM to MSP1<sub>19</sub>, MSP3 and AMA1 were associated with decreased malaria incidence. Of the IgG subclasses, only IgG1 to MSP1<sub>19 </sub>was associated with reduced incidence of clinical malaria. A previous study in the same location failed to find an association of antibodies to MSP1<sub>19 </sub>with clinical malaria. The disagreement may be due to differences in reagents, ELISA and analytical procedures used in the two studies. When IgG, IgM and IgG subclass levels for all four antigens were included in a combined model, only IgG1 [(0.80 (0.67–0.97), p = 0.018)] and IgM [(0.48 (0.32–0.72), p &lt; 0.001)] to MSP1<sub>19 </sub>were independently associated with protection from malaria.</p>", "<title>Conclusion</title>", "<p>Using standardized procedures, the study has confirmed the importance of antibodies to MSP1<sub>19 </sub>in reducing the risk of clinical malaria in Ghanaian children, thus substantiating its potential as a malaria vaccine candidate.</p>" ]
[ "<title>Authors' contributions</title>", "<p>DD carried out field studies, developed assays and drafted the manuscript. AA performed the ELISA, compiled data and assisted in the manuscript writing. KAK and HL assisted with the ELISA. MT assisted with the assay development and manuscript writing, while PM, SB and ER wrote the analysis plan, performed the data analysis and together with RC, BDA and YDO contributed to the writing of the manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors are grateful to the children in Dodowa community for participation in this study. Dr. Soren Jepsen is acknowledged for supporting and encouraging the Network. Dr. Ramadhani Abdalla Noor is also acknowledged for the support and management contribution.</p>", "<p>This investigation received support of AMANET and Netherlands Ministry of Foreign Affairs (DGIS). The Wellcome Trust (grant 057736) is also acknowledged for funding the prospective study that generated plasma samples and morbidity survey data.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>The monthly point prevalence of asymptomatic malaria parasitaemia and incident rate of clinical malaria</bold>. The point prevalence of asymptomatic parasitaemia for each month is represented as a bar graph and the pattern of clinical malaria (incident rate) is shown as a line graph.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Baseline IgG and IgM levels in relation to age</bold>. The top and bottom panels represents total IgG or IgM levels to AMA1, GLURP, MSP1<sub>19 </sub>and MSP3 in relation to age of Ghanaian children, respectively. The line shows the LOESS smoothed estimate of the geometric mean.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Baseline IgG subclass levels in relation to age</bold>. The top, 2nd, 3<sup>rd </sup>and 4<sup>th </sup>panels represent IgG subclasses 1 to 4 against AMA1, GLURP, MSP1<sub>19 </sub>and MSP3 in relation to age of Ghanaian children, respectively. The line shows the LOESS smoothed estimate of the geometric mean.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Malaria incidence by age group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Age in yrs:</bold></td><td align=\"center\"><bold>No. of</bold><break/><bold> children</bold></td><td align=\"left\"><bold>Cum. incidence</bold><break/><bold> of malaria</bold></td><td align=\"left\"><bold>no episodes of malaria</bold><break/><bold> (years at risk)</bold></td><td align=\"left\"><bold>Incidence rate per </bold><break/><bold>child year (95%CI)</bold></td></tr></thead><tbody><tr><td align=\"center\">3</td><td align=\"center\">32</td><td align=\"left\">12/32 (38%)</td><td align=\"left\">15 (19.52)</td><td align=\"left\">0.79 (0.48–1.31)</td></tr><tr><td align=\"center\">4–5</td><td align=\"center\">86</td><td align=\"left\">24/86 (28%)</td><td align=\"left\">28 (54.75)</td><td align=\"left\">0.51 (0.35–0.74)</td></tr><tr><td align=\"center\">6–7</td><td align=\"center\">79</td><td align=\"left\">11/79 (14%)</td><td align=\"left\">13 (51.38)</td><td align=\"left\">0.25 (0.15–0.44)</td></tr><tr><td align=\"center\">8</td><td align=\"center\">48</td><td align=\"left\">6/48 (13%)</td><td align=\"left\">7 (31.11)</td><td align=\"left\">0.22 (0.11–0.47)</td></tr><tr><td align=\"center\">9–10</td><td align=\"center\">99</td><td align=\"left\">11/99 (11%)</td><td align=\"left\">14 (61.22)</td><td align=\"left\">0.23 (0.14–0.39)</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"center\">Total</td><td align=\"center\">344</td><td align=\"left\">64/344 (19%)</td><td align=\"left\">77 (217.48)</td><td align=\"left\">0.35 (0.28–0.44)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Association of total IgG and IgM with malaria incidence</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>IgG</bold></td><td align=\"center\"><bold>Crude IRR (95%CI)</bold></td><td align=\"center\"><bold>P-value</bold></td><td align=\"center\"><bold>IRR adjusted for age (95%CI)</bold></td><td align=\"center\"><bold>P-value</bold></td></tr></thead><tbody><tr><td align=\"left\">MSP3</td><td align=\"center\">0.59 (0.45, 0.76)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.69 (0.53, 0.90)</td><td align=\"center\">0.01</td></tr><tr><td align=\"left\">MSP1<sub>19</sub></td><td align=\"center\">0.74 (0.63, 0.88)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.75 (0.61, 0.92)</td><td align=\"center\">0.01</td></tr><tr><td align=\"left\">GLURP</td><td align=\"center\">0.68 (0.55, 0.82)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.79 (0.64, 0.98)</td><td align=\"center\">0.04</td></tr><tr><td align=\"left\">AMA1</td><td align=\"center\">0.96 (0.84, 1.09)</td><td align=\"center\">0.50</td><td align=\"center\">1.03 (0.89, 1.20)</td><td align=\"center\">0.67</td></tr><tr><td align=\"left\"><bold>IgM</bold>:</td><td/><td/><td/><td/></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">MSP3</td><td align=\"center\">0.62 (0.49, 0.80)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.76 (0.59, 0.97)</td><td align=\"center\">0.03</td></tr><tr><td align=\"left\">MSP1</td><td align=\"center\">0.59 (0.47, 0.75)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.68 (0.53, 0.88)</td><td align=\"center\">&lt; 0.01</td></tr><tr><td align=\"left\">GLURP</td><td align=\"center\">0.71 (0.58, 0.87)</td><td align=\"center\">&lt; 0.01</td><td align=\"center\">0.84 (0.68, 1.03)</td><td align=\"center\">0.09</td></tr><tr><td align=\"left\">AMA1</td><td align=\"center\">0.48 (0.34, 0.67)</td><td align=\"center\">&lt; 0.0001</td><td align=\"center\">0.63 (0.44, 0.91)</td><td align=\"center\">0.01</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Association of IgG2 and IgG4 with malaria incidence.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Antigen</bold></td><td align=\"center\" colspan=\"2\"><bold>Antibody</bold></td><td align=\"left\"><bold>Crude IRR (95%CI)</bold></td><td align=\"left\"><bold>P-value</bold></td><td align=\"left\"><bold>Age-adjusted IRR</bold></td><td align=\"left\"><bold>P-value</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>MSP3</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td/><td align=\"left\">IgG2*</td><td align=\"left\">(coded as log(2))</td><td align=\"left\">0.74 (0.57, 0.98)</td><td align=\"left\">0.04</td><td align=\"left\">0.85 (0.63, 1.15)</td><td align=\"left\">0.29</td></tr><tr><td/><td align=\"left\">IgG4</td><td align=\"left\">&lt; detection limit</td><td align=\"left\">1</td><td align=\"left\">0.01</td><td align=\"left\">1</td><td align=\"left\">0.20</td></tr><tr><td/><td/><td align=\"left\">&lt; median</td><td align=\"left\">0.45 (0.21, 0.95)</td><td/><td align=\"left\">0.56 (0.26, 1.19)</td><td/></tr><tr><td/><td/><td align=\"left\">≥ median</td><td align=\"left\">0.47 (0.28, 0.79)</td><td/><td align=\"left\">0.68 (0.40, 1.15)</td><td/></tr><tr><td align=\"left\"><bold>MSP1</bold><sub>19</sub></td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">IgG2*</td><td align=\"left\">(coded as log(2))</td><td align=\"left\">0.62 (0.45, 0.86)</td><td align=\"left\">&lt; 0.01</td><td align=\"left\">0.836 (0.606, 1.157)</td><td align=\"left\">0.275</td></tr><tr><td/><td align=\"left\">IgG4*</td><td align=\"left\">(coded as log(2))</td><td align=\"left\">1.07 (0.95, 1.21)</td><td align=\"left\">0.25</td><td align=\"left\">0.838 (0.608, 1.159)</td><td align=\"left\">0.278</td></tr><tr><td align=\"left\"><bold>GLURP</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">IgG2*</td><td align=\"left\">(coded as log(2))</td><td align=\"left\">0.65 (0.49, 0.86)</td><td align=\"left\">&lt; 0.01</td><td align=\"left\">0.79 (0.58, 1.08)</td><td align=\"left\">0.13</td></tr><tr><td/><td align=\"left\">IgG4*</td><td align=\"left\">(coded as log(2))</td><td align=\"left\">0.94 (0.81, 1.08)</td><td align=\"left\">0.37</td><td align=\"left\">1.01 (0.87, 1.16)</td><td align=\"left\">0.94</td></tr><tr><td align=\"left\"><bold>AMA1</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td align=\"left\">IgG2</td><td align=\"left\">&lt; detection limit</td><td align=\"left\">1</td><td align=\"left\">0.18</td><td align=\"left\">1</td><td align=\"left\">0.54</td></tr><tr><td/><td/><td align=\"left\">&lt; median</td><td align=\"left\">1.80 (0.86, 3.78)</td><td/><td/><td/></tr><tr><td/><td/><td align=\"left\">≥ median</td><td align=\"left\">1.78 (0.90, 3.51)</td><td/><td/><td/></tr><tr><td/><td align=\"left\">IgG4</td><td align=\"left\">&lt; detection limit</td><td align=\"left\">1</td><td align=\"left\">0.19</td><td align=\"left\">1</td><td align=\"left\">0.54</td></tr><tr><td/><td/><td align=\"left\">&lt; median</td><td align=\"left\">0.96 (0.47, 1.93)</td><td/><td/><td/></tr><tr><td/><td/><td align=\"left\">≥ median</td><td align=\"left\">0.62 (0.36, 1.07)</td><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Association of IgG1 and IgG3 with malaria incidence</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>IgG1:</bold></td><td align=\"center\"><bold>Crude IRR</bold><break/><bold> (95%CI)</bold></td><td align=\"center\"><bold>P-value</bold></td><td align=\"center\"><bold>IRR adjusted for age</bold><break/><bold> (95%CI)</bold></td><td align=\"center\"><bold>P-value</bold></td></tr></thead><tbody><tr><td align=\"left\"> MSP3</td><td align=\"center\">0.82 (0.72, 0.94)</td><td align=\"center\">&lt; 0.01</td><td align=\"center\">0.88 (0.76, 1.03)</td><td align=\"center\">0.11</td></tr><tr><td align=\"left\"> MSP1<sub>19</sub></td><td align=\"center\">0.86 (0.77, 0.97)</td><td align=\"center\">0.01</td><td align=\"center\">0.89 (0.80, 0.99)</td><td align=\"center\">0.04</td></tr><tr><td align=\"left\"> GLURP</td><td align=\"center\">0.85 (0.74, 0.98)</td><td align=\"center\">0.02</td><td align=\"center\">0.93 (0.81, 1.08)</td><td align=\"center\">0.35</td></tr><tr><td align=\"left\"> AMA1</td><td align=\"center\">0.95 (0.86, 1.06)</td><td align=\"center\">0.36</td><td align=\"center\">1.01 (0.90, 1.14)</td><td align=\"center\">0.83</td></tr><tr><td align=\"left\"><bold>IgG3:</bold></td><td/><td/><td/><td/></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\"> MSP3</td><td align=\"center\">0.90 (0.82, 0.98)</td><td align=\"center\">0.01</td><td align=\"center\">0.94 (0.86, 1.03)</td><td align=\"center\">0.17</td></tr><tr><td align=\"left\"> MSP1<sub>19</sub></td><td align=\"center\">0.90 (0.82, 0.99)</td><td align=\"center\">0.03</td><td align=\"center\">0.93 (0.84, 1.03)</td><td align=\"center\">0.18</td></tr><tr><td align=\"left\"> GLURP</td><td align=\"center\">0.83 (0.74, 0.93)</td><td align=\"center\">&lt; 0.01</td><td align=\"center\">0.91 (0.80,1.03)</td><td align=\"center\">0.14</td></tr><tr><td align=\"left\"> AMA1</td><td align=\"center\">0.97 (0.89, 1.06)</td><td align=\"center\">0.48</td><td align=\"center\">0.98 (0.90, 1.07)</td><td align=\"center\">0.73</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Adjusted rate ratios for immunological variables independently associated with malaria risk in the final model.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Crude IRR</bold><break/><bold> (95%CI)</bold></td><td align=\"left\"><bold>P-value</bold></td><td align=\"center\"><bold>IRR adjusted for age</bold><break/><bold> (95%CI)</bold></td><td align=\"left\"><bold>P-value</bold></td><td align=\"center\"><bold>IRR adjusted for effects of age</bold><break/><bold> and treatment (95%CI)</bold></td></tr></thead><tbody><tr><td align=\"left\">IgM to MSP1<sub>19</sub></td><td align=\"center\">0.44 (0.30–0.64)</td><td align=\"left\">P &lt; 0.001</td><td align=\"center\">0.49 (0.33–0.73)</td><td align=\"left\">P &lt; 0.001</td><td align=\"center\">0.48 (0.32–0.72)</td></tr><tr><td align=\"left\">IgG1 to MSP1<sub>19</sub></td><td align=\"center\">0.78 (0.65–0.93)</td><td align=\"left\">P = 0.005</td><td align=\"center\">0.80 (0.66–0.96)</td><td align=\"left\">P = 0.018</td><td align=\"center\">0.80 (0.67–0.97)</td></tr><tr><td align=\"left\">Treatment effect</td><td align=\"center\">0.28 (0.07–1.15)</td><td align=\"left\">P = 0.076</td><td align=\"center\">-</td><td align=\"left\">-</td><td align=\"center\">0.25 (0.06–1.05)</td></tr></tbody></table></table-wrap>" ]
[]
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[ "<table-wrap-foot><p>Malaria incidence rate ratios (IRR) with 95% confidence interval, corresponding to a doubling of baseline antibody level, before and after adjustment for the effect of age.</p></table-wrap-foot>", "<table-wrap-foot><p>For IgG 2 to AMA1 and for IgG4 to AMA1 and MSP3, antibody level was categorized.</p><p>*For IgG2 to MSP3, MSP1<sub>19 </sub>and GLURP, the association is expressed as the incidence rate ratio (IRR) corresponding to a doubling of baseline antibody level.</p></table-wrap-foot>", "<table-wrap-foot><p>The incidence rate ratio (IRR) corresponds to a doubling of baseline antibody level, before and after adjustment for effects of age</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1475-2875-7-142-1\"/>", "<graphic xlink:href=\"1475-2875-7-142-2\"/>", "<graphic xlink:href=\"1475-2875-7-142-3\"/>" ]
[]
[{"surname": ["Braga", "Scopel", "Komatsu", "daSilva-Nunes", "Ferreira"], "given-names": ["EM", "KKG", "NT", "M", "MU"], "article-title": ["Polymorphism of the Fc gamma receptor IIA and malaria morbidity"], "source": ["Journal of Molecular and Genetic Medicine"], "year": ["2005"], "volume": ["1"], "fpage": ["5"], "lpage": ["10"]}]
{ "acronym": [], "definition": [] }
56
CC BY
no
2022-01-12 14:47:26
Malar J. 2008 Jul 29; 7:142
oa_package/b7/95/PMC2529305.tar.gz
PMC2529306
18694508
[ "<title>Introduction</title>", "<p>First described by Freney <italic>et al. </italic>in 1988 [##UREF##0##1##], <italic>Staphylococcus lugdunensis </italic>is an unusually virulent coagulase-negative staphylococcus (CoNS) known primarily as a cause of endocarditis, especially in immunocompromised patients [##REF##8286628##2##]. It has also been associated with septic arthritis, osteomyelitis, peritonitis, brain abscesses, and infections of the skin and soft tissues, urinary tract, and prosthetic medical devices [##UREF##1##3##]. Its remarkable virulence has been attributed to the production of extracellular slime, which facilitates colonization and interferes with phagocytosis-associated activities of neutrophils [##REF##12409048##4##]. Some strains also produce a synergistic hemolysin that resembles the δ-hemolysin of <italic>S. aureus</italic>, consisting of three very similar 43-residue peptides closely related to the gonococcal-growth-inhibitor bacteriocin secreted by <italic>S. haemolyticus</italic>. Nucleic acid sequences related to the accessory gene regulator (the major determinant of virulence in <italic>S. aureus</italic>) have also been demonstrated in <italic>S. lugdunensis </italic>[##REF##8359673##5##].</p>", "<p>A MEDLINE search conducted with the keywords \"<italic>S. lugdunensis</italic>\" <italic>AND </italic>\"cerebrospinal fluid (CSF)-shunt infection\" <italic>OR </italic>\"central nervous system (CNS) infection\" yielded only four cases, which are summarized in Table ##TAB##0##1##. Cases #1 through #3 were <italic>S. lugdunensis </italic>ventriculo-peritoneal shunt (VPS) infections [##REF##11641620##6##,##REF##11531988##7##], and case #4 was a <italic>S. lugdunensis </italic>meningitis unrelated to implanted CSF drainage devices [##REF##11980968##8##]. The other two cases of <italic>S. lugdunensis </italic>infections reported in Table ##TAB##0##1## were recently diagnosed in the Pediatric Neurosurgery Unit of our Medical Center.</p>", "<p>This report analyzes the management of these patients in light of the few previously reported cases of <italic>S. lugdunensis </italic>CNS infections and summarizes the molecular characteristics of the isolates recovered from CSF and ventricular drain cultures.</p>" ]
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[]
[ "<title>Discussion</title>", "<p>To date, there have been no reports of <italic>S. lugdunensis </italic>ventriculitis associated with EVDs, which are widely used in neurosurgery for continuous intracranial pressure monitoring, injection of therapeutic agents, and temporary external drainage of CSF [##REF##12182415##9##]. EVD-associated CSF infections can be classified as ventriculostomy-related infections or ventriculitis [##REF##12182415##9##]. The former are generally associated with few clinical symptoms. The CSF is characterized by culture- or Gram-stain-positivity with progressive decrease in glucose and progressive increase in proteins. Progression to ventriculitis is heralded by high-grade fever and signs of meningitis (for example, nuchal rigidity, photophobia, deteriorating mental status, seizures, moribund appearance). The latter pattern was seen in both of our patients. Fever and meningeal signs were also reported in cases #2, 3 and 4 (Table ##TAB##0##1##), whereas the VPS infection in case #1 was associated with severe non-neurological symptoms (intra-abdominal sepsis with purulent peritonitis) [##REF##11531988##7##].</p>", "<p>Our patients and one of those reported by Elliot and colleagues [##REF##11641620##6##] had hospital-acquired infections, which developed 3 to 7 days after shunt insertion. The other infections shown in Table ##TAB##0##1## were evident at hospital admission and would thus seem to be community-acquired (although in one case [##REF##11531988##7##] the admission occurred 14 days after a previous hospitalization during which a CSF shunt had been inserted).</p>", "<p>EVD use in critically ill neurosurgical patients is on the rise and reported rates of infection associated with these devices vary widely (from 0 to 45%). The prevailing opinion is that the infecting agent is usually introduced during EVD placement [##REF##12182415##9##], which is consistent with the fact that most EVD-associated infections are caused by skin flora, particularly <italic>Staphylococcus epidermidis</italic>.</p>", "<p>Like other CoNS species, <italic>S. lugdunensis </italic>is considered part of the resident flora of the human skin and mucous membranes, although the preferred carriage site seems to be the perineum [##REF##12682121##14##]. In both of our patients, infection onset occurred a few days after surgery, the patients were being cared for in the same hospital room, and all isolates displayed the same pulsotype. These findings are suggestive of a common source, which unfortunately has not been identified.</p>", "<p>We cannot exclude the possibility that the infections were transmitted during manipulation of the catheters. Healthcare-provider hand cultures (data not shown) and patient skin cultures yielded no growth of <italic>S. lugdunensis</italic>. However, the staff surveillance cultures were collected after the second child had been infected, not during the time of EVD insertion, so the infection reservoir might have been missed. Environmental cultures and inguinal cultures of providers were not performed.</p>", "<p>Hellbacher and colleagues [##REF##16460545##13##] recently suggested that PFGE is unsuitable for analyzing outbreak situations involving <italic>S. lugdunensis</italic>. The homogeneity they observed among 39 isolates collected over 4 years suggests that <italic>S. lugdunensis </italic>is either a highly conserved species or that specific clones are more likely to cause invasive infections. Other investigators [##REF##12682121##14##], however, have found that PFGE with <italic>Sma</italic>I macrorestriction analysis is inappropriate for epidemiological investigations of <italic>S. lugdunensis </italic>infections only when the strains are β-lactamase-producers, since these isolates usually display a high level of genetic homogeneity. Identification of adequate typing tools for this bacterial population will probably require multimodal molecular characterization of a larger collection of <italic>S. lugdunensis </italic>strains.</p>", "<p>The standard approach to CSF shunt infections caused by a methicillin-resistant <italic>Staphylococcus </italic>spp. includes shunt removal and intravenous vancomycin [##REF##12182415##9##]. The previously reported CSF infections caused by <italic>S. lugdunensis </italic>were treated with intravenous vancomycin and/or oxacillin, alone or with rifampicin, and bacteriological and clinical cures were documented in all four [##REF##11641620##6##, ####REF##11531988##7##, ##REF##11980968##8####11980968##8##]. CSF shunt infections caused by methicillin-resistant staphylococci are common in our hospital, so when Gram-stain data indicated the possibility of staphylococcal ventriculitis, our patients were both treated empirically with vancomycin. Later, the isolates' methicillin resistance was confirmed by susceptibility testing and PCR analysis. Our patients were treated with intrathecal vancomycin (40 mg/day), and bacteriological and clinical cures were achieved in both cases. Since then, guidelines have been published in which considerably lower doses are recommended for children (for example &lt; 5 mg/24 hours) [##REF##15261509##15##]. Although no adverse effects were experienced by our patients, we have modified our protocol, and staphylococcal CSF shunt infections are now treated with IT vancomycin at the dosage indicated above and with drug levels closely monitored.</p>", "<p>The frequency of <italic>S. lugdunensis </italic>infections may well be underestimated. The species is likely to escape detection by screening tests or certain automated microbiological systems. Its ability to produce colony variations is well known, described in three of the 11 strains included in the initial description of <italic>S. lugdunensis </italic>in 1988 [##UREF##0##1##]. Awareness of this risk has led to the development of genetic tools for identifying this species [##REF##11531988##7##]. No colony variation was observed in either of our patients and all isolates consistently exhibited the normal <italic>S. lugdunensis </italic>phenotype in blood agar cultures.</p>" ]
[ "<title>Conclusion</title>", "<p>Our experience confirms that, unlike other CoNS which usually display low virulence, <italic>S. lugdunensis </italic>can cause severe CNS infections in patients with implanted CSF drainage devices. Accurate species-level identification of isolates causing staphylococcal CSF shunt infections is clearly essential for their successful treatment, but it is also fundamental for epidemiological surveillance and for improving our understanding of the pathophysiological factors affecting the clinical outcome of these infections. With the increasing use of invasive medical devices for management of neurosurgical patients, CSF shunt infections are likely to become more common. Failure to identify their causative agents can be particularly disastrous when the infection is due to <italic>S. lugdunensis</italic>.</p>", "<title>Nucleotide sequence accession numbers</title>", "<p>The sequences of the isolates evaluated in this study have been deposited in the GenBank database under accession nos. <ext-link ext-link-type=\"gen\" xlink:href=\"FM177467\">FM177467</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"FM177468\">FM177468</ext-link>, respectively, for the <italic>rpoB </italic>gene and under accession nos. <ext-link ext-link-type=\"gen\" xlink:href=\"FM177469\">FM177469</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"FM177470\">FM177470</ext-link>, respectively, for the 16 rRNA gene.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Introduction</title>", "<p><italic>Staphylococcus lugdunensis </italic>is an unusually virulent coagulase-negative staphylococcus that has rarely been implicated in central nervous system infections.</p>", "<title>Case presentation</title>", "<p>Two children hospitalized in the Neurosurgery Unit developed ventriculitis caused by methicillin-resistant <italic>Staphylococcus lugdunensis </italic>following placement of external ventriculostomy drains. The causative organisms were identified by molecular studies. The patients recovered without significant sequelae after high doses of intrathecal vancomycin.</p>", "<title>Conclusion</title>", "<p>Distinguishing <italic>Staphylococcus lugdunensis </italic>from other coagulase-negative staphylococcus species is crucial because it carries a substantial risk for severe central nervous system infections displayed by patients with implanted cerebrospinal fluid devices. Clinicians should not underestimate the importance of the isolation of this species from cerebrospinal fluid specimens.</p>" ]
[ "<title>Case presentation</title>", "<title>Patient 1</title>", "<p>A 7-year-old boy was hospitalized in the Pediatric Neurosurgery Unit for headache, vomiting, and right ocular dysmetria. Magnetic resonance imaging (MRI) revealed obstructive hydrocephalus caused by a posterior fossa tumor. The child was taken to the operating room for placement of an external ventriculostomy drain (EVD). CSF cultures yielded no growth. There was no improvement and, on the 10<sup>th </sup>day of hospitalization, he had a second operation for partial removal of the tumor (a medulloblastoma). A new EVD was inserted; cultures of the original EVD and CSF yielded no growth. Seven days later, the child developed severe headache, fever (39.5°C), vomiting, lethargy, and signs of EVD malfunction. Shunt puncture yielded cloudy CSF containing 900 leukocytes/mm<sup>3 </sup>(80% polymorphonuclear cells); 100 mg protein/dl); and 21 mg glucose/dl (blood glucose: 87 mg/dl). Cytocentrifuge Gram staining revealed Gram-positive cocci that were later identified by biochemical and molecular methods as <italic>Staphylococcus lugdunensis</italic>.</p>", "<p>Meanwhile, a presumptive diagnosis of ventriculitis was made [##REF##12182415##9##], a new EVD was inserted, and intrathecal (IT) vancomycin (40 mg/day) was started. Cultures of the CSF and the ventricular tip of the second EVD grew methicillin-resistant <italic>S. lugdunensis</italic>. Three blood cultures yielded no growth. Swabs of both inguinal folds and the surgical incision were cultured, but none yielded <italic>S. lugdunensis</italic>. Defervescence occurred after 2 days of IT vancomycin. After 14 days of vancomycin, the composition of the CSF was normal, and cultures of CSF and EVD were negative. The boy was discharged after 62 days of hospitalization, and no sign of CNS infection was noted at the 6-month follow-up visit.</p>", "<title>Patient 2</title>", "<p>A 2-month-old male was admitted to the Pediatric Neurosurgery Unit for rapid head growth with a tense anterior fontanel. He was placed in the same room where Patient 1 was still being cared for. Triventricular hydrocephalus was evident on cranial ultrasonography, and MRI with angiographic sequences disclosed a malformation involving Galen's vein that caused arteriovenous shunting and obstruction of CSF flow through the aqueduct. The malformation was treated successfully with intravascular embolization, but 1 week later, the infant developed seizures. Cerebral angiography confirmed occlusion of the aneurysmal malformation, but computed tomography revealed an intraventricular/subarachnoid hemorrhage. An EVD was placed for continuous intracranial pressure monitoring and collection of CSF specimens. All CSF cultures were negative, including the one obtained during EVD placement. Three days later, the child was febrile (39.2°C) and drowsy. A cloudy CSF specimen collected from the EVD contained 129 mg/dl protein, 20 mg/dl glucose (blood glucose: 113 mg/dl), 800 leukocytes/mm<sup>3 </sup>(60% were polymorphonuclear cells), and 5 to 10 Gram-positive cocci per microscopic field that were identified in biochemical and molecular assays as <italic>S. lugdunensis</italic>.</p>", "<p>The clinical picture was compatible with ventriculitis [##REF##12182415##9##], and IT vancomycin (40 mg/dose/day) was started immediately after EVD replacement and continued for 14 days. Cultures of the CSF and the tip of the original EVD grew methicillin-resistant <italic>S. lugdunensis</italic>. Three blood cultures were negative. Skin cultures (inguinal folds, surgical incision site) were all negative for <italic>S. lugdunensis</italic>. The infant's condition rapidly improved after vancomycin was started, and cultures of CSF collected 15 days later and of the second EVD showed no growth. The infant was discharged after 40 days of hospitalization. Six months later, he was well with no evidence of infection and no acute neurological signs.</p>", "<title>Microbiological diagnosis</title>", "<p>Our routine protocol for suspected CSF shunt infections includes Gram staining of cytocentrifuged CSF, aerobic culture at 35°C on MacConkey agar, microaerobic culture (35°C in room air with 5% CO<sub>2</sub>) on Columbia and chocolate agars, anaerobic culture (35°C) on Schaedler agar (all from bioMérieux, Marcy-L'Etoile, France), and 72 hours of aerobic and anaerobic cultures on brain-heart infusion broth supplemented with 5% NaCl.</p>", "<p>In these two patients, cultures of CSF and EVD tips on Columbia agar produced yellowish beta-hemolytic colonies (diameter: 0.8–2.5 mm) of Gram-positive cocci. Tube coagulase tests with rabbit plasma (Becton Dickinson Microbiology Systems, Sparks, MD) were negative. The isolates were positive for catalase, clumping factor, pyrrolidonyl arylamidase, and ornithine decarboxylase. They were identified as <italic>S. lugdunensis </italic>by the API ID32 STAPH (bioMérieux) and the VITEK 2 (bioMérieux, Inc, Hazelwood, MO) systems.</p>", "<p>Bacterial DNA was extracted from CSF specimens and culture isolates with the QIAmp DNA Mini kit (QIAGEN, Hilden, Germany). Species-level identification was confirmed by sequencing of the 16S rRNA gene (using the RIDOM entries) and the RNA polymerase B gene [##REF##11923353##10##]. Sequence analysis of the 16S rRNA gene revealed 100% homology with the prototype strain sequence of <italic>S. lugdunensis </italic>ATCC 43809 (<ext-link ext-link-type=\"gen\" xlink:href=\"Z26899\">Z26899</ext-link>). A partial sequence of the <italic>rpoB </italic>gene of each isolate revealed 99.5% homology with the prototype strain sequence of <italic>S. lugdunensis </italic>ATCC 43809 (<ext-link ext-link-type=\"gen\" xlink:href=\"EF173667\">EF173667</ext-link>).</p>", "<p><italic>A</italic>ntimicrobial susceptibility testing with the E-test (AB Biodisk, Solna, Sweden) yielded the following MICs (μg/ml): oxacillin, 256; vancomycin, 0.5; erythromycin, 0.03; ciprofloxacin, 0.03; clindamycin, 0.03; rifampicin, 4.0; quinupristin-dalfopristin, 0.5; linezolid, 1.0. Resistance was defined by Clinical Laboratory Standards Institute breakpoints [##UREF##2##11##]. The <italic>mecA </italic>gene (reflecting methicillin resistance) was detected by PCR, as described by Geha and colleagues [##REF##7929772##12##]. Pulsed-field gel electrophoresis (PFGE) analysis of <italic>Sma</italic>I- and <italic>Apa</italic>I-digested chromosomal DNA [##REF##16460545##13##] revealed identical profiles for all isolates recovered from Patients 1 and 2 (data not shown).</p>", "<title>Abbreviations</title>", "<p>CNS: Central nervous system; CoNS: Coagulase-negative staphylococcus; CSF: Cerebrospinal fluid; EVD: External ventriculostomy drain; IT: Intrathecal; VPS: ventriculo-peritoneal shunt; MRI: Magnetic resonance imaging; PFGE: Pulsed-field gel electrophoresis.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>TS and DR participated in the conception and design of the study, acquisition of data, analysis and interpretation of data, drafting of the manuscript and its critical revision. GT and DP managed both children as neurosurgeons. BF, TDI, BP and MS carried out the laboratory studies of patients. GF revised the manuscript. All authors read and approved the final version of the manuscript.</p>", "<title>Consent</title>", "<p>Written consent was obtained from each patient's parents for the publication of this report. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]
[ "<title>Acknowledgements</title>", "<p>This work was partially supported by grants from the Italian Ministry for the University and Scientific Research (Fondi Ateneo, Linea D-1 2006). We thank Marian Kent for her editorial assistance.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Clinical data and outcome of ventriculitis or meningitis caused by <italic>Staphylococcus lugdunensis</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Case #<break/>Gender<break/> – Age</td><td align=\"left\">Underlying<break/> disease</td><td align=\"left\">CSF<break/> shunt<sup>a</sup></td><td align=\"center\">Time to<break/> infection<sup>b</sup></td><td align=\"left\">Signs and<break/> symptoms</td><td align=\"left\">Antimicrobial<break/> therapy</td><td align=\"center\">Outcome</td><td align=\"left\">Reference<break/> (year)</td></tr></thead><tbody><tr><td align=\"left\">Case #1<break/> M – 74<break/> years</td><td align=\"left\">Ventriculomegaly</td><td align=\"left\">VPS</td><td align=\"center\">14 days</td><td align=\"left\">Fever,<break/>abdominal<break/> pain, sweating</td><td align=\"left\">Vancomycin<sup>c </sup>+<break/>rifampicin +<break/>cefuroxime,<break/>then<break/>vancomycin<sup>c </sup>+<break/>rifampicin, then<break/>ciprofloxacin +<break/> rifampicin</td><td align=\"center\">Recovered</td><td align=\"left\">Sandoe<break/> (2001)</td></tr><tr><td align=\"left\">Case #2<break/>F – 10<break/> months</td><td align=\"left\">Obstructive <break/>hydrocephalus</td><td align=\"left\">VPS</td><td align=\"center\">3 days</td><td align=\"left\">Fever,<break/>irritability, <break/>decreased<break/> activity</td><td align=\"left\">Oxacillin</td><td align=\"center\">Recovered</td><td align=\"left\">Elliott <break/>(2001)</td></tr><tr><td align=\"left\">Case #3<break/>F –<break/> 16 years</td><td align=\"left\">Aqueduct <break/>stenosis</td><td align=\"left\">VPS</td><td align=\"center\">2 years</td><td align=\"left\">Fever, lethargy,<break/>abdominal<break/> complaint</td><td align=\"left\">Vancomycin<sup>c</sup><break/>then oxacillin</td><td align=\"center\">Recovered</td><td align=\"left\">Elliott <break/>(2001)</td></tr><tr><td align=\"left\">Case #4<break/>M –<break/> 12 years</td><td align=\"left\">Obstructive<break/> hydrocephalus</td><td align=\"left\">ND<sup>d</sup></td><td align=\"center\">-</td><td align=\"left\">Fever,<break/>headache,<break/>vomiting,<break/> lethargy</td><td align=\"left\">Vancomycin<sup>c </sup>+<break/>cefotaxime,<break/>then oxacillin +<break/> rifampicin</td><td align=\"center\">Recovered</td><td align=\"left\">Kaabia<break/> (2002)</td></tr><tr><td align=\"left\">Case #5<break/>M – 7<break/> years</td><td align=\"left\">Tumor within the<break/>posterior cranial<break/> fossa</td><td align=\"left\">EVD</td><td align=\"center\">20 days</td><td align=\"left\">Fever,<break/>headache,<break/>vomiting,<break/> lethargy</td><td align=\"left\">Vancomycin<sup>e</sup></td><td align=\"center\">Recovered</td><td align=\"left\">This study</td></tr><tr><td align=\"left\">Case #6<break/>M – 2<break/> months</td><td align=\"left\">Malformation of <break/>Galen's vein</td><td align=\"left\">EVD</td><td align=\"center\">19 days</td><td align=\"left\">Fever, seizures,<break/>impaired <break/> consciousness</td><td align=\"left\">Vancomycin<sup>e</sup></td><td align=\"center\">Recovered</td><td align=\"left\">This study</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p><sup>a</sup>CSF shunt, cerebrospinal fluid shunt included VPS or EVD.</p><p><sup>b</sup>From shunt placement to infection onset.</p><p><sup>c</sup>Administered intravenously.</p><p><sup>d</sup>ND = not described.</p><p><sup>e</sup>Administered intrathecally.</p></table-wrap-foot>" ]
[]
[]
[{"surname": ["Freney", "Brun", "Bes", "Meugnier", "Grimont", "Grimont", "Nervi", "Fleurette"], "given-names": ["J", "Y", "M", "H", "F", "PAD", "C", "J"], "italic": ["Staphylococcus lugdunensis", " Staphylococcus schleiferi"], "source": ["Int J Syst Bacteriol"], "year": ["1988"], "volume": ["38"], "fpage": ["168"], "lpage": ["172"]}, {"surname": ["Bannerman", "Murray PR, Baron EJ, Jorgensen JH, Pfaller MA, Yolken RH"], "given-names": ["TL"], "italic": ["Staphylococcus, Micrococcus"], "source": ["Manual of Clinical Microbiology"], "year": ["2003"], "edition": ["8"], "publisher-name": ["American Society for Microbiology Press"], "fpage": ["385"], "lpage": ["404"]}, {"collab": ["Clinical Laboratory Standards Institute"], "source": ["Performance Standards for Antimicrobial Susceptibility Testing Seventeenth Informational Supplement M100-S17"], "year": ["2007"], "publisher-name": ["Wayne, PA: CLSI"]}]
{ "acronym": [], "definition": [] }
15
CC BY
no
2022-01-12 14:47:26
J Med Case Reports. 2008 Aug 11; 2:267
oa_package/c9/aa/PMC2529306.tar.gz
PMC2529307
18700010
[ "<title>Background</title>", "<p>ADP-glucose pyrophosphorylase (AGPase; EC 2.7.7.27) catalyses a rate-limiting step in starch synthesis, the formation of ADP-glucose from glucose-1-P and ATP. ADP-glucose is the predominant, if not sole, precursor for starch synthesis. While AGPase is a homotetramer in bacteria (including cyanobacteria), it is a heterotetramer in angiosperms and green algae. This heterotetramer comprises two identical large and two identical small subunits. They exhibit a high degree of identity to each other and to the cyanobacterial AGPase, pointing to an origin by gene duplication early in the evolution of plants and green algae (Figure ##FIG##0##1A##) (Additional file ##SUPPL##0##1##) [##REF##1318389##1##,##REF##17496118##2##]. The two subunits have complementary rather than redundant functions, and knockout mutations in either abolish more than 90% of AGPase activity in some experimental systems [##REF##985379##3##].</p>", "<p>Although both subunits are necessary for full AGPase activity, the angiosperm small subunit appears more conserved than the large subunit throughout its sequence (small subunits exhibit an average of 91.3% amino acid identity while large subunits an average of 70.8% identity) [##REF##17496118##2##]. However, mean percent identities might be misleading since the genes encoding both subunit genes of AGPase underwent a number of duplications after the initial duplication that generated the two subunits. The potential confusion due to the comparison of paralogs rather than orthologs can be overcome by methods that incorporate phylogeny, such as the use of maximum likelihood (ML) to estimate ω (the ratio of non-synonymous substitutions per non-synonymous site [K<sub>A</sub>] to synonymous substitutions per synonymous site [K<sub>S</sub>]). The ML estimate of ω for the large subunit is ~2.7-fold greater than the estimate for the small subunit [##REF##17496118##2##], suggesting a higher rate of amino acid replacement for the large subunit.</p>", "<p>Although ω provides a convenient and commonly used method to examine evolutionary constraints, it has typically been used to examine sequences that have diverged relatively recently. The rate of synonymous evolution is relatively high in plant nuclear genes [##REF##3480529##4##, ####REF##8816790##5##, ##UREF##0##6##, ##REF##10545473##7####10545473##7##] and estimates of K<sub>S </sub>appear saturated in analyses of some angiosperm gene families, even for relatively shallow evolutionary divergences [##REF##10471724##8##]. Hence, the accuracy of ω estimates for ancient divergences is unclear. Another potential problem for the use of ω is the assumption that mutations at synonymous sites are neutral. It has been suggested that synonymous sites are subject to both positive and purifying selection [##REF##8754221##9##, ####REF##16418745##10##, ##REF##16221894##11##, ##REF##17522087##12####17522087##12##]. The action of selection on synonymous sites may explain why adding among-sites rate variation for synonymous sites to models of codon evolution improves their fit to empirical data [##REF##16107593##13##,##UREF##1##14##]. Saturation and among-sites rate variation both have the potential to cause K<sub>S </sub>to be underestimated (and ω to be overestimated since ω = K<sub>A</sub>/K<sub>S</sub>); biased estimates of ω will lead to incorrect inferences regarding evolutionary constraints on the proteins being analyzed. Finally, ω cannot detect changes in the evolutionary rate when rates of synonymous and non-synonymous substitution increase or decrease simultaneously [##REF##15014159##15##].</p>", "<p>The almost 3-fold difference in evolutionary rates for the AGPase subunits is a paradox because random mutagenesis revealed that maize endosperm AGPase subunits expressed in bacteria are equally susceptible to activity-altering amino acid changes [##REF##17496118##2##]. Georgelis et al. [##REF##17496118##2##] proposed that the difference in evolutionary rates between AGPase subunits reflected, at least in part, the differences between the subunits in their tissue-expression patterns and the fact that the small subunit has to interact with multiple large subunits in plants. Here, we establish the pattern and timing of duplications in the AGPase gene family and estimate absolute rates of AGPase sequence evolution. Functional divergence has been observed among AGPase subunits based on biochemical criteria [##REF##12748181##16##, ####REF##14520572##17##, ##REF##10557246##18##, ##REF##16299180##19##, ##REF##18024561##20####18024561##20##]. One of our primary goals was to identify candidate sites for functional divergence. We identify specific AGPase sites apparently subject to either positive selection or branch-specific patterns of rate variation (types-I and -II divergence as defined by Gu [##REF##10605109##21##,##REF##11264396##22##]).</p>" ]
[ "<title>Methods</title>", "<title>Sequence Retrieval and Alignment</title>", "<p>Full-length AGPase sequences from plants were retrieved from the NCBI database and the DOE Joint Genome Institute (JGI) web site, and the source and accession numbers of all sequences are presented in Additional file ##SUPPL##9##10##. DNA and protein sequence alignments were obtained using the MEGA software [##UREF##7##68##] with BLOSUM matrix followed by manual inspection. The poorly aligned N-termini (~70–80 amino acids for the large subunit and ~40 amino acids for the small subunit) were excluded from alignment. The large subunit amino acid numbers used correspond to the protein encoded by the maize <italic>Shrunken-2 </italic>(<italic>Sh2</italic>) gene (Accession #: P55241) while the small subunit amino acid numbers used correspond to the protein, encoded by the maize <italic>Brittle-2 </italic>(<italic>Bt2</italic>) gene (Accession #: AAQ14870).</p>", "<title>Phylogenetic analysis</title>", "<p>Estimates of AGPase gene trees based upon nucleotide data were obtained using the GARLI (Genetic Algorithm for Rapid Likelihood Inference) software [##UREF##8##69##]. Estimates of phylogeny estimated for protein sequence alignments were obtained either using neighbor joining in the MEGA software or ML in the RAxML software [##REF##16928733##70##], using the JTT model [##REF##1633570##71##] in both cases (to estimate distances or calculate likelihoods). Bootstrap support [##UREF##9##72##] was calculated using 100 replicates.</p>", "<p>Branch lengths were estimated by ML, with those form amino acid trees based estimated using AAML, using the Dayhoff (PAM) model [##UREF##10##73##] with Γ-distributed rates. Nonsynonymous substitutions per nonsynonymous site (K<sub>A</sub>) and synonymous substitutions per synonymous site (K<sub>S</sub>) and the ratio of these values (ω = K<sub>A</sub>/K<sub>S</sub>) were estimated using CODEML. AAML and CODEML are programs in the PAML (Phylogenetic Analysis by Maximum Likelihood) package [##REF##9367129##74##].</p>", "<title>Reconciled tree analysis</title>", "<p>Reconciled tree analyses map a gene tree onto a species tree [##UREF##11##75##,##REF##9918954##76##], and most commonly used procedure for doing this is gene tree parsimony, which minimizes the number of duplication events needed to explain a specific gene tree given the species tree [##UREF##5##32##]. However, some error is typically associated with estimates of phylogeny for individual genes, and accommodating error in gene trees is difficult in reconciled tree analyses [##UREF##11##75##]. Chen et al. [##REF##11108472##77##] suggested an algorithm that rearranged gene trees to increase congruence with the species tree when nodes in the gene tree were poorly supported to limit the impact of error in the gene tree. We implemented this idea manually, by rearranging nodes in the gene tree with limited support (those with bootstrap support &lt; 70%; see [##UREF##12##78##]) to increase the congruence with the species tree. This yielded two estimates of the numbers of duplications, one based on the optimal gene trees and a conservative estimate based on well-supported nodes.</p>", "<title>Estimation of the absolute rate of evolution</title>", "<p>Estimates of the absolute rate of amino acid evolution and synonymous site evolution for each subunit were obtained using the \"tip procedure\", which uses the average number of amino acid substitutions per site along unique paths from each tip (extant sequence) to a dated speciation event (allowing us to avoid pseudoreplication of rate estimates). In addition to this method, absolute rates of amino acid substitution were also obtained by estimating the age of each node after smoothing rates using penalized likelihood in r8s (\"PL method\") [##REF##12538260##79##]. The PL method was used because the tip method is biased towards recent branches, although the PL method also has the potential to be affected by saturation, especially for synonymous sites.</p>", "<title>Detection of branch-specific patterns of rate variation among sites and positive selection</title>", "<p>Type-I and type-II functional divergence among large or small subunit groups was examined using the DIVERGE software [##REF##11934757##48##], which implements the tests suggested by Gu [##REF##11264396##22##,##REF##16864604##47##] that can be used to determine whether the coefficients of divergence (θ<sub>I </sub>and θ<sub>II</sub>) are significantly greater than zero. Amino acid sites likely to have undergone types-I or -II divergence were detected as those with a posterior probability &gt; 0.5–0.6.</p>", "<p>Sites subject to positive selection were identified using the site, branch and branch-site models implemented in CODEML, using the model comparisons recommended by Yang et al. [##REF##11080365##80##]. The first comparison was between model M1a, which includes two ω values (one for sites subject to purifying selection with ω &lt; 1 and one for neutral sites with ω = 1) and model M2a (which adds positively selected sites [with ω &gt; 1] to model M1a). The second comparison was between model M7, which assumes values of ω at different sites are β-distributed, and model M8 (which adds positively selected sites to model M7). We also searched for positive selection by estimating different values of ω for each branch and using branch-site models. For the first test, we compared a model specified as model = 2 NSsites = 2 to model M1a [##REF##12032247##81##]. For the second test, we compared a model specified as model = 2 NSsites = 2 to a model whose specifications are model = 2 NSsites = 2 fix_omega = 1 and omega = 1. When the likelihood ratio test was significant, the Bayes empirical Bayes method was used to calculate posterior probabilities that sites were subject to positive selection [##REF##15689528##82##].</p>" ]
[ "<title>Results</title>", "<title>Patterns of AGPase gene duplication</title>", "<p>It is well known that genes can have three possible fates after duplication [##REF##11073452##23##, ####UREF##2##24##, ##UREF##3##25##, ##REF##15252449##26##, ##REF##17267422##27####17267422##27##]: (1) nonfunctionalization, in which one duplicate is lost, (2) subfunctionalization, in which the functions of the original single-copy gene are partitioned between the duplicates, and (3) neofunctionalization, in which one duplicate gains a novel function. The latter two processes can result in paralogs that persist for a substantial length of time (although a few exceptions have been proposed, such as pseudogene resurrection [##REF##17267422##27##]). Throughout this work, the term duplication will be limited to the description of the latter two processes. Although gene loss can be as important as duplication for shaping genomes [##REF##15130831##28##], we have avoided making major conclusions based on gene loss since many organisms included in these analyses lack complete genome sequences.</p>", "<p>Inclusion of AGPase sequences from the moss <italic>Physcomitrella patens </italic>[##REF##18079367##29##], which has 7 large subunit and 4 small subunit genes, placed a major constraint on the earliest divergence within the large subunit family since the moss sequences were intermixed with angiosperm sequences. This indicates that the earliest duplication in the large subunit family occurred prior to the divergence of angiosperms and mosses, more than 400 million years (MY) ago [##UREF##4##30##]. Since the rate of synonymous evolution in angiosperms varies from ~2 × 10<sup>-9 </sup>to ~10 × 10<sup>-9 </sup>synonymous substitutions per synonymous site per year [##REF##3480529##4##, ####REF##8816790##5##, ##UREF##0##6##, ##REF##10545473##7####10545473##7##], values of K<sub>S </sub>in excess of 2 are expected for some comparisons, which may make estimates of K<sub>S </sub>problematic [##REF##17975267##31##].</p>", "<p>Since many divergence times for plants can be constrained to reasonable ranges, it should be possible to estimate absolute rates of AGPase subunit amino acid evolution and establish whether they correlate with estimates of ω. However, this requires differentiating between speciation and gene duplication events in AGPase phylogeny. Gene family phylogenies reflect both speciation and duplication events, and these events can be distinguished by reconciled tree analyses if the gene and species trees are known. Gene tree parsimony [##UREF##5##32##] is the most commonly used reconciled tree method, and the only approach practical for even moderately sized phylogenies at this time. Reconciling the AGPase gene trees with the best available estimate of the land plant species tree (Additional file ##SUPPL##1##2##) revealed 11–14 large subunit duplications (Figure ##FIG##0##1B##) and 5–7 small subunit duplications (Figure ##FIG##0##1C##). It may be appropriate to view the lower estimates, which are based upon well-supported nodes, as the primary results since they are based on modified versions of the gene trees (Additional file ##SUPPL##2##3A, B##) in which the topology was rearranged near poorly supported nodes to increase congruence with the species tree (Methods). In contrast, the higher estimates were based only on reconciling the optimal estimates of the gene trees (Figure ##FIG##0##1B, C##) with the species tree. Regardless, both analyses indicate that the large subunit genes underwent a larger number of duplications than the small subunit genes.</p>", "<p>AGPase large subunits have narrower tissue-specificity than small subunits [##REF##7700228##33##, ####REF##9469935##34##, ##REF##15821022##35##, ##REF##16275672##36##, ##REF##16957017##37##, ##REF##15598655##38####15598655##38##], and the large subunit phylogeny appears more complex (with four major clades some of which include both monocots and eudicots; Figure ##FIG##0##1B##) than the small subunit phylogeny (Figure ##FIG##0##1C##). Large subunit group 1 genes are predominantly expressed in leaves, group 2 genes are expressed both in source and sink tissues, group 3 genes are expressed sink tissues (these genes are subdivided into group 3a in eudicots and group 3b in monocots), and group 4 corresponds to a clade of two sequences that have not been characterized yet in terms of function and expression patterns (Figure ##FIG##0##1B##). Some of the major large subunit clades arose prior to the divergence of monocots and eudicots, and the optimal placement of the AGPase large subunit sequences from <italic>Physcomitrella </italic>suggests that the first duplication in the large subunit happened around 400 MY ago (Figure ##FIG##0##1A##). In contrast, there is no evidence that angiosperm small subunits underwent a duplication prior to the divergence of monocots and eudicots, and we have divided them into a monocot clade (group 1) genes and a eudicot clade (group 2). These results emphasize that the large subunit underwent a larger number of duplications than did the small subunit and that only large subunit duplications began before the divergence of monocots and eudicots.</p>", "<title>Absolute rates of AGPase evolution</title>", "<p>Absolute rates of amino acid evolution for AGPase subunits were estimated by examining terminal branch lengths for divergences that reflect speciation events with known divergence times (these divergence times are presented in Additional file ##SUPPL##1##2##). This approach is called the tip procedure since it involves only terminal branches (Methods), and it revealed that the average rate of evolution for the large subunit was 2.7-fold faster than that of the small subunit (Figure ##FIG##1##2##). This rate difference was both congruent with the difference in ML estimates of ω [##REF##17496118##2##] and highly significant (<italic>P </italic>= 0.0006 by Student's unpaired <italic>t</italic>-test). Our conclusions were unchanged if we limited consideration to strongly supported duplication events (those retained when bootstrap support was considered; see Additional file ##SUPPL##2##3##).</p>", "<p>Estimates of the absolute rate of amino acid substitution for AGPase subunits obtained by the penalized likelihood (PL) method (Figure ##FIG##2##3##) were very similar to those obtained using the tip procedure (Figure ##FIG##1##2##). Using gene trees in which poorly supported nodes were rearranged to minimize number of duplications yielded similar results (Additional file ##SUPPL##3##4##, Figure ##FIG##1##2##). Thus, very similar estimates of the absolute rate of amino acid substitution were obtained despite the different assumptions made by the tip procedure and the PL method.</p>", "<p>The absolute rate of synonymous evolution was estimated using ML estimates of K<sub>S </sub>(Additional file ##SUPPL##4##5A, B##). The tip procedure resulted in virtually identical rates for both large and small subunit genes (6.5 × 10<sup>-9 </sup>synonymous substitutions per synonymous site per year; Additional file ##SUPPL##4##5C##). PL rate estimates were 5.5 × 10<sup>-9</sup>± 0.3 × 10<sup>-9 </sup>and 6.3 × 10<sup>-9 </sup>± 0.2 × 10<sup>-9 </sup>(mean ± standard error) synonymous substitutions per synonymous site per year for the large and small subunit, respectively (data not shown). The slightly lower estimates based upon PL are consistent with saturation being a problem, but presumably only for the deepest branches in the tree. All of these values are well within the range of previous estimates for a variety of angiosperm genes (which range from approximately 2 × 10<sup>-9 </sup>to 10 × 10<sup>-9 </sup>synonymous substitutions per synonymous site per year and exhibit some variation among lineages [##REF##3480529##4##, ####REF##8816790##5##, ##UREF##0##6##, ##REF##10545473##7####10545473##7##,##REF##16973872##39##,##REF##16702410##40##]). This suggests that there are little to no constraints on the synonymous sites of angiosperm AGPase genes and, when combined with estimate of the absolute rate of sequence evolution, that there was minimal bias in our estimates of ω.</p>", "<title>Does the large subunit show temporary or permanent elevation of ω?</title>", "<p>Estimates of the mean rate of evolution for AGPase, whether based upon ω [##REF##17496118##2##] or the absolute rate of amino acid substitution (Figure ##FIG##1##2##), show a substantially higher rate for the large subunit. The existence of these rate differences despite identical sensitivities to mutations when expressed in bacteria suggests that there are important differences <italic>in planta</italic>. Transient increases in the evolutionary rate might explain the observed rate differences if they are more common for the large subunit. Large-scale analyses provide evidence for transient increase in the rate of non-synonymous evolution. Indeed, paralogs with a recent origin (defined as those with K<sub>S </sub>&lt; 0.1) accumulate more non-synonymous mutations per non-synonymous site (and thus have a higher K<sub>A</sub>) relative to the number of the synonymous mutation per synonymous site than older duplicates [##REF##11073452##23##]. Thus, ω is expected to be elevated for paralogs with K<sub>S </sub>&lt; 0.1 relative to those with K<sub>S </sub>&gt; 0.1. Lynch and Conery [##REF##11073452##23##] interpreted this phenomenon as reflecting a temporary relaxation of constraints, positive selection, or a combination of both phenomena. Thus, it is important to consider the potential impact of the elevation of ω on our analyses of the evolutionary processes that shaped the AGPase gene family.</p>", "<p>The large subunit underwent more gene duplications than did the small subunit (Figure ##FIG##0##1##). Thus, the higher mean estimates of ω for the large subunit might reflect a larger number of periods during which ω is elevated (due to temporary relaxation of constraints and/or positive selection) rather than permanent relaxation of constraints for the large subunit. To distinguish between transient and permanent relaxation of constraint we tested whether the non-synonymous rate was increased after duplication and if the increase is sufficient to explain the observed differences in the mean rate. Branches in both large and small subunit gene trees (Figure ##FIG##0##1##) were placed into two groups, the first of which (class 1) contained branches that follow a duplication event with K<sub>S </sub>= ~0.1 (these branches are shown in Figure ##FIG##0##1B, C## and Additional file ##SUPPL##2##3##). The second group (class 2) contained all other branches (branches that follow either a speciation event or a duplication and have K<sub>S </sub>&gt; 0.1). We examined two nested models using the likelihood ratio test (LRT) [##REF##7288891##41##, ####REF##7679448##42##, ##UREF##6##43##, ##REF##9092465##44##, ##REF##12679544##45####12679544##45##] to determine whether ω for class 1 branches is higher than ω for class 2 branches for either subunit using CODEML (included in PAML software). The more complex model, which assumes two different ω values (one ω for class 1 and one ω for class 2), was favored over the null hypothesis model, which assumes a single ω for both classes, for both the small subunit (2δ<italic>ln</italic>L = 18.4; <italic>P </italic>&lt; 0.001) and the large subunit (2δ<italic>ln</italic>L = 7.48; <italic>P </italic>= 0.005) (for details see Table ##TAB##0##1##). The ω estimate for short branches following duplications was 1.3 to 1.5-fold greater than the ω estimate for all the other branches when the large subunit was examined and 2 to 2.8-fold greater for the small subunit (Table ##TAB##0##1##). These results support periods of increased ω after duplications in the AGPase gene family, due to the temporary relaxation of constraints, positive selection, or both. However, these results also indicate that this phenomenon cannot explain the differential rates of amino acid sequence divergence of the two AGPase subunits, since estimates of ω for the large subunit are 2.6-fold greater than estimates for the small subunit (Table ##TAB##0##1##). Instead these results suggest that the small subunit is permanently subject to greater purifying selection than is the large subunit.</p>", "<title>What is the role of positive selection in AGPase evolution?</title>", "<p>To examine the potential role of positive selection in AGPase evolution, we used ML to compare two distinct sets of models. The first model set contains a neutral (null) model M1a allowing two categories of sites, one with ω = 0 and one with ω = 1, and model M2a that adds an extra category of sites with ω &gt; 1. The second includes a neutral (null) model M7 assuming that ω is β-distributed among sites and model M8 that adds an extra category of sites with ω &gt; 1 [##REF##15514074##46##]. Neither of the models with positive selection was significantly better than the null model based upon the LRT for either of the subunits when the tests were applied to the complete trees for the small subunit (Figure ##FIG##0##1C##) or large subunit (Figure ##FIG##0##1B##) (data not shown). Likewise, neither of the models that include positive selection was significantly better when the tests were applied to the individual groups (Figure ##FIG##0##1B, C##) within each subunit (groups 1, 2, 3A, and 3B for the large subunit as well as groups 1, and 2 for the small subunit) (data not shown). These results indicate either that positive selection has not played a role in the evolution of the large and small subunit of AGPase in the angiosperms or that these tests have insufficient power.</p>", "<p>To increase the power of our tests for positive selection, we used branch-site models to examine the potential for positive selection at specific sites in all tree branches separately (Methods). There are branches in the large or small subunit tree on which specific sites may be subject to positive selection (Figure ##FIG##3##4##), with a total of 0.8 and 0.2 sites/branch potentially affected by positive selection for the large and the small subunit respectively (Additional file ##SUPPL##5##6##). However, the limited number of sites potentially affected by positive selection suggests that purifying selection is the major force in the evolution of both AGPase subunits in angiosperms. Thus, positive selection cannot explain the different rates of amino acid evolution for the AGPase subunits.</p>", "<title>Functional divergence of AGPase subunits</title>", "<p>Gu [##REF##10605109##21##,##REF##11264396##22##,##REF##16864604##47##] proposed that specific sites in proteins have the potential to undergo two distinct types of divergences after gene duplication (e.g., divergence among the different groups within the large and small subunits), and these types were designated type-I and type-II divergence. Sites that have undergone type-I divergence are conserved in one group but variable in another group while type-II sites are fixed in both groups but differ between groups [##REF##10605109##21##,##REF##11264396##22##].</p>", "<p>Tests for types-I and -II divergence based upon the relevant coefficients of divergence (θ), which correspond to the probability that a specific site has undergone type-I or -II divergence in a pairwise comparison, were proposed by Gu [##REF##10605109##21##,##REF##11264396##22##,##REF##11934757##48##]. These tests for types-I and -II establish whether the relevant θ value is significantly greater than zero. We conducted all possible pairwise comparisons among small (groups 1 and 2) and large (groups 1,2, 3a and 3b) subunits with the exception of group 4 of large subunits (that were excluded because the group included only two sequences). All of type-I coefficients (θ<sub>I</sub>) of functional divergence were significantly greater than zero while none of the type-II coefficients (θ<sub>II</sub>) were significantly greater than zero (Table ##TAB##1##2##). The estimates of θ<sub>I </sub>are also much larger than the estimates of θ<sub>II</sub>, suggesting that type-I divergence is the dominant pattern of sequence evolution for AGPase large and small subunit groups.</p>", "<title>Sites contributing to functional divergence among AGPase groups</title>", "<p>We identified the sites likely to be involved in the changes of functional constraints between groups revealed by the significant values of θ<sub>I </sub>using a posterior probability analysis (Additional file ##SUPPL##6##7##). A greater number of large subunit sites appear to have undergone type-I divergence than the number of small subunit sites. Specifically, for an alignment with 453 amino acids we found evidence that 78 large subunit sites and 13 small subunit sites show evidence for type-I divergence (Additional file ##SUPPL##7##8##). These sites appear randomly distributed with respect to secondary structure (data not shown), but pairwise comparisons among groups in the large and the small subunit reveal some non-random patterns in the distribution of sites that are conserved in one group but not another. For instance, large subunit group 1 proteins (leaf isoforms) had more conserved sites in the N-terminus while group 3a proteins (sink isoforms) exhibited more conserved in the C-terminus. The large subunit crystal structure has not been elucidated yet. However, the high degree of amino acid sequence identity (~43%) and similarity (~61%) [##REF##17496118##2##] (Additional file ##SUPPL##0##1##) to the small subunit along with structure modelling (unpublished data) strongly suggest that the 3D structure of the large subunit is almost identical to the known structure of the small subunit. The N-terminal domain includes the active site that resembles a Rossman fold while the C-terminal domain is a β-helix that extensively interacts with the N-terminal domain based on the structure of the potato tuber small subunit elucidated by Jin et al. [##REF##15692569##49##]. Both domains are important for stability and catalytic/allosteric properties of the enzyme [##REF##15692569##49##, ####REF##11339804##50##, ##REF##11237727##51##, ##REF##9628013##52####9628013##52##], but the non-random spatial distribution of type I sites clearly suggests these sites should be targeted in mutational studies focused on the analysis of AGPase structure/function relationships.</p>", "<p>In contrast to the significant estimates of θ<sub>I</sub>, estimates of θ<sub>II </sub>(the type-II coefficient) were not significantly greater than zero. However, the power of the test for type-II divergence is unclear. To determine whether there was any evidence for type-II divergence we examined the sequences to determine whether any specific sites show evidence for this type of divergence. The likelihood that these sites reflect <italic>bona fide </italic>instances of type-II divergence would be increased if they correspond to sites that have been identified in mutagenesis studies. The posterior ratio test, using a cut-off 2 (i.e., a posterior probability of 0.5), identified a few potential type-II sites (Additional file ##SUPPL##8##9##). Some sites were also identified by analyses of positive selection and one has an important function revealed by mutagenesis (see Discussion), so it is reasonable to postulate that at least some of these sites reflect genuine instances of type-II divergence that have contributed to the specialization among the large and small subunits.</p>" ]
[ "<title>Discussion</title>", "<p>The large and the small subunit of AGPase in plants exhibit considerable sequence identity [##REF##1318389##1##,##REF##17496118##2##] and they reflect a gene duplication that occurred prior to the divergence of land plants and green algae. Both subunits are equally sensitive to activity-altering amino acids, at least when expressed in a bacterial system [##REF##17496118##2##]. However, the small subunit of angiosperms is more conserved than large subunit based upon estimates of the rate of evolution (both estimates of ω [##REF##17496118##2##] and estimates of the absolute rate of amino acid evolution). These results suggest saturation has not had a major impact upon the estimation of ω for angiosperm AGPases, and this may reflect, at least in part, the limited codon bias of these genes (data not shown) since codon bias can have a major impact on the estimation of K<sub>S </sub>[##REF##10471724##8##]. They also suggest that estimation of the absolute rate of amino acid evolution provides a valid method that can be used as an alternative to ω analysis when the use of ω is not appropriate (e.g., ancient divergences, especially for gene families with strong codon bias, and for gene families where there is evidence for strong selection on synonymous sites). Taken as a whole, these results confirm that plant AGPases represent a genuine paradox: the large and small subunits exhibit similar sensitivities to activity-altering changes but differ almost 3-fold in their rates of non-synonymous evolution.</p>", "<p>Although a temporary elevation of ω after duplication was observed for both large and small subunit gene families, this transient elevation cannot account for the overall difference in ω value (and the related difference in the overall rate of amino acid substitutions) between the two subunits. Additionally, although both subunits appear to have been subject to positive selection the observed rate differences are too large to be explained by postulating that they reflect greater differences in positive selection. Based upon the falsification of these hypotheses, we conclude that the small subunit has been evolving under stronger purifying selection than the large subunit.</p>", "<p>Consistent with the numbers of large and small subunit genes found in the sequenced plant genomes [##REF##17496118##2##,##REF##15821022##35##,##REF##15598655##38##], reconciled tree analyses indicated that large subunit genes underwent more duplications than small subunit genes. Both phylogeny and molecular clock analyses indicate that the initial duplication in the large subunit family of angiosperms occurred <italic>ca</italic>. 400 MY ago, close to the divergence of angiosperms and bryophytes [##UREF##4##30##] (Figure ##FIG##2##3A## and Additional file ##SUPPL##3##4A##). In contrast, the oldest retained small subunit duplicates date back to 120–140 MY ago, after the divergence of monocots and eudicots (Figure ##FIG##2##3B## and Additional file ##SUPPL##3##4B##). The reason why the large subunit had more ancient and duplications than did the small subunit remains enigmatic. Macroevolutionary models that can be applied to gene families are poorly developed, so it remains possible that the observed difference is coincidental. Alternatively, the large subunit might have a greater ability to undergo subfunctionalization after duplication. The fact that 7 large subunit genes and 4 small subunit genes can be identified in the <italic>Physcomitrella </italic>genome suggests similar patterns in both mosses and angiosperms. However, more rigorous tests to distinguish among these scenarios must await both the acquisition of additional data from additional deep-branching land-plant lineages (e.g., liverworts and hornworts) and the development of better models of gene family macroevolution.</p>", "<p>Studies of expression patterns of AGPase genes in several species, including rice, <italic>Arabidopsis</italic>, potato, tomato and barley, have shown that the large subunit is tissue specific while the small subunit is more broadly expressed [##REF##7700228##33##, ####REF##9469935##34##, ##REF##15821022##35##, ##REF##16275672##36##, ##REF##16957017##37##, ##REF##15598655##38####15598655##38##]. Based on these studies, the major large subunit groups (Figure ##FIG##0##1B##) are likely to be expressed in different tissues in most or all plants. The tissue-specificity of large subunit genes suggests the expression patterns of these genes might undergo subfunctionalization after duplication, as predicted by the \"DDC model\" of gene duplication [##REF##10101175##53##]. The DDC model predicts that duplicated genes are preserved by complementary changes in their expression pattern (e.g., a broadly expressed gene might undergo duplication and have one duplicate expressed in a specific tissue like leaves while the other duplicate is expressed elsewhere). Although the potential for subfunctionalization due to changes in gene expression to preserve duplicated genes is generally accepted [##REF##16140417##54##,##REF##16280546##55##], it also remains possible that distinct AGPase genes have specialized in terms of protein function (e.g., their pH optima might have shifted based upon the specific tissues in which the paralogs are expressed). The relative contributions of subfunctionalization and specialization or neofunctionalization to gene family evolution are open questions [##REF##17988397##56##,##REF##15654095##57##], and it is unclear that there is any reason why the large subunit would be more likely to undergo specialization at either the protein or gene expression level. Such a model, which postulates that subfunctionalization of gene expression was followed by specialization, is similar to a combined model called subneofunctionalization [##REF##15654095##57##]. The subneofunctionalization model postulates that subfunctionalization occurs shortly after duplication while neofunctionalization is a more prolonged process [##REF##17988397##56##,##REF##15654095##57##]. If the combined model were applied to AGPases, the initial preservation of paralogous AGPase genes immediately after duplication might reflect subfunctionalization but this process would be followed by adaptation to the more specialized domains of expression in which each of the paralogs are expressed. Either a neofunctionalization or a subneofunctionalization model would be consistent with the evidence that different large subunit groups have functionally diverged from each other at the protein level (Figure ##FIG##3##4##, Table ##TAB##1##2##). However, subneofunctionalization provides a means to directly link the divergence of expression patterns and divergence at the protein level. Corroborating the subneofunctionalization will require correlating gene expression and sequence divergence for a large number of plants.</p>", "<p>Differences in their patterns of expression represent the major difference between the large and small subunits that could explain the differences in their rates of evolution, since broadly expressed genes are more conserved than tissue-specific genes [##REF##11682310##58##, ####REF##10666707##59##, ##REF##15201397##60####15201397##60##]. However, this raises the question of why broadly expressed genes, like AGPase small subunit genes, exhibit slower rates of evolution. Although it may simply be that mutations in broadly expressed genes have a greater impact on fitness or because these genes have to function in multiple cellular environments [##REF##11682310##58##], a simpler explanation might be that small subunit genes have to function with multiple large subunit genes. Georgelis et al. [##REF##17496118##2##] presented data consistent with this possibility, since they showed that the effects of several amino acid changes in the maize endosperm small subunit on enzyme activity depended on the identity of the large subunit [maize endosperm large subunit (SHRUNKEN-2)(SH2) and maize embryo large subunit (AGPLEMZM) were used]. Both SH2 and AGPLEMZM are members of group 3b (Figure ##FIG##0##1B##), so these results suggest that even fairly similar large subunit genes can interact differently with small subunits.</p>", "<p>In addition to the potential for subfunctionalization due to changes in gene expression, the observation that estimates of θ<sub>I </sub>for large subunit groups were significantly greater than zero suggests that it will be possible to attribute differences among AGPase genes to specific amino acid changes. We found 99 candidate sites of the large subunit likely to have been involved in rate shifts (either type-I or -II divergence; Additional files ##SUPPL##7##8## and ##SUPPL##8##9##). At least some of these putative rate shift residues are likely to have contributed to functional changes among the different large subunit groups. The estimate of θ<sub>I </sub>for the small subunit groups 1 (monocot) and 2 (eudicot) is also significantly greater than zero. It was possible to find evidence for 13 type-I candidate sites in the small subunit alignment. Like the large subunit, the estimate of θ<sub>II </sub>for the small subunit was not significantly greater than zero. Nonetheless, there were two potential type-II sites could be identified (Additional file ##SUPPL##8##9##).</p>", "<p>A total of 21 candidate sites for positive selection could be identified in the large subunit branches following duplications that led to different groups (Branch numbers: 4,6,9,11,12,13,16 shown in Additional file ##SUPPL##5##6##), and six of these sites overlapped with the set of sites that appear to have undergone either type-I (sites 341, 364, 445) or type-II (sites 106, 114, 382) divergence. Biochemical and genetic studies confirm that at least one of the sites (sites 106) is important for AGPase activity. This site is a threonine (T) in large subunit groups 3a and 3b but a lysine (K) in groups 1 and 2 and in all small subunits. The potato tuber large subunit, which falls into group 3a, has a T at site 106 and it forms an inactive complex if it is combined with an inactivated potato tuber small subunit [##REF##15632142##61##]. Changing this T in the potato tuber large subunit to a K actually results (the T106K mutant) in a complex with some activity with the same inactivated potato tuber small subunit [##REF##15632142##61##]. These results were interpreted as evidence that the large subunit lost its catalytic ability partly because of the K to T change. Although the K residue at site 106 may be necessary for catalysis if the small subunit is inactive, another model that explains the data would be one in which the wild-type large subunits (which have a T at site 106) require prior catalysis by the small subunit before they perform catalysis by themselves. Such a catalytic mechanism has been proposed for the <italic>Escherichia coli </italic>AGPase [##REF##363717##62##]. The T residue at site 106 is absolutely conserved in large subunit groups 3 and the branch-site model suggests positive selection for the T immediately following the duplication that generated group 3 (Additional file ##SUPPL##5##6##). This suggests that this change was important for enzymatic activity and beneficial for the plants. Indeed, the overall activity of a complex that includes the T106K mutant of the potato tuber large subunit and the wild-type potato tuber small subunit showed significantly reduced activity relative to wild-type potato tuber AGPase [##REF##17137579##63##].</p>", "<p>One potential type-II site (site 507) and four sites that represent candidates for positive selection on branches that immediately follow duplications (sites 104, 230, 441, 445) have been shown to be important for the allosteric properties of AGPase [[##REF##15692569##49##,##REF##9733546##64##,##REF##11524424##65##], Hannah personal communication]. However, most of the candidate sites for either rate shifts or positive selection do not have a known function.</p>", "<p>The existence of type-I and type-II divergence among AGPase subunit groups along with the detection of positively selected sites after duplications that led to different groups in the large subunit provide evidence for functional divergence especially among the large subunit groups. Our data are consistent with biochemical studies showing that the four possible AGPase complexes in <italic>Arabidopsis</italic>, which have a single functional small subunit gene and four distinct large subunit genes (belonging to different groups in Figure ##FIG##0##1B##), have different kinetic and allosteric properties [##REF##12748181##16##]. There is further evidence for functional divergence among plant AGPases, since the maize and barley endosperm AGPases are less dependent than potato tuber AGPase on the allosteric activator 3-PGA for activity and the maize endosperm AGPase is more heat labile than potato tuber AGPase [##REF##14520572##17##, ####REF##10557246##18##, ##REF##16299180##19##, ##REF##18024561##20####18024561##20##]. Functional divergence among the different subunit groups was also suggested by Georgelis et al. [##REF##17496118##2##], who showed that all groups of large subunit genes have ω values (which range from 0.073 to 0.132) that exceed those for small subunit genes (which range from 0.027 to 0.054). These results are consistent with the rate shifts within the large and the small subunit families that were observed in the present study and they further imply that the various groups of plant AGPases have undergone functional divergence. The present study also identifies specific residues that are likely to have contributed towards that divergence. Site directed mutagenesis of these candidate sites is likely to shed some light on their functions and reveal the proportion of candidate sites that reflect type II error for tests to identify sites subject to rate shifts and positive selection.</p>", "<p>A number of angiosperm AGPases have been successfully expressed in <italic>E. coli </italic>and purified, including maize endosperm AGPase [##REF##8650177##66##], potato tuber AGPase [##REF##8380404##67##], and all possible <italic>Arabidopsis </italic>AGPase complexes [##REF##12748181##16##]. The most straightforward candidate sites to test are the type-II sites and those subject to positive selection, where it is possible to change the relevant residue either to the amino acid present in the other group (for type-II sites) or the ancestral amino acid (for positively selected sites). Testing type-I sites may be more challenging, since there is not a clear way to swap the residues present in a member of the focal group of proteins with that in a different group. However, it is reasonable to predict that any of the residues present in the group of proteins for which the site is variable should alter the biochemical activity of a protein in which the site is invariant. Regardless of the specific strategies for generating mutants, mutant large or small subunits could be expressed in <italic>E. coli </italic>along with a wild-type version of the other subunit, the relevant complex purified, and the properties of the enzyme determined to allow the impact of the mutations to be studied. This will allow elucidation of the importance of the sites in enzyme activity. Ultimately, it will be interesting to determine whether these sites are important for the kinetic and allosteric properties of AGPase, for enzyme stability for the pH optimum, or for multiple properties.</p>" ]
[ "<title>Conclusion</title>", "<p>Herein, we validated and extended the observation, initially based upon estimates of ω [##REF##17496118##2##], that the AGPase large subunit accumulated non-synonymous substitutions more rapidly than the small subunit in angiosperms by estimating absolute rates of amino acid change. The earliest duplication in the large subunit family of angiosperms was close to the time that angiosperms and mosses diverged (~400 MY ago;[##UREF##4##30##]). The large subunit underwent a larger number of duplications than the small subunit, which only began to duplicate after the divergence of monocots and eudicots. We suggest that the large subunit evolved faster due to permanently relaxed constraints since positive selection and sporadic episodes of relaxed constraints cannot account for the different rates of evolution between the large and the small subunit.</p>", "<p>Large subunit genes exhibit narrower tissue specificity than small subunit genes in terms of their gene expression patterns [##REF##7700228##33##, ####REF##9469935##34##, ##REF##15821022##35##, ##REF##16275672##36##, ##REF##16957017##37##, ##REF##15598655##38####15598655##38##], and they are likely to have experienced subfunctionalization in terms of expression patterns. However, we use analyses of rate shifts and positive selection to demonstrate that different groups of both large and small subunits are likely to have diverged at the protein level. We have identified candidate amino acid sites with the potential to account for the functional divergence and described strategies for site-directed mutagenesis experiments that could shed light into the specific roles of these sites.</p>" ]
[ "<title>Background</title>", "<p>ADP-glucose pyrophosphorylase (AGPase), which catalyses a rate limiting step in starch synthesis, is a heterotetramer comprised of two identical large and two identical small subunits in plants. Although the large and small subunits are equally sensitive to activity-altering amino acid changes when expressed in a bacterial system, the overall rate of non-synonymous evolution is ~2.7-fold greater for the large subunit than for the small subunit. Herein, we examine the basis for their different rates of evolution, the number of duplications in both large and small subunit genes and document changes in the patterns of AGPase evolution over time.</p>", "<title>Results</title>", "<p>We found that the first duplication in the AGPase large subunit family occurred early in the history of land plants, while the earliest small subunit duplication occurred after the divergence of monocots and eudicots. The large subunit also had a larger number of gene duplications than did the small subunit. The ancient duplications in the large subunit family raise concern about the saturation of synonymous substitutions, but estimates of the absolute rate of AGPase evolution were highly correlated with estimates of ω (the non-synonymous to synonymous rate ratio). Both subunits showed evidence for positive selection and relaxation of purifying selection after duplication, but these phenomena could not explain the different evolutionary rates of the two subunits. Instead, evolutionary constraints appear to be permanently relaxed for the large subunit relative to the small subunit. Both subunits exhibit branch-specific patterns of rate variation among sites.</p>", "<title>Conclusion</title>", "<p>These analyses indicate that the higher evolutionary rate of the plant AGPase large subunit reflects permanent relaxation of constraints relative to the small subunit and they show that the large subunit genes have undergone more gene duplications than small subunit genes. Candidate sites potentially responsible for functional divergence within each of the AGPase subunits were investigated by examining branch-specific patterns of rate variation. We discuss the phenotypes of mutants that alter some candidate sites and strategies for examining candidate sites of presently unknown function.</p>" ]
[ "<title>Authors' contributions</title>", "<p>NG conducted the experiments and carried out the analyses. NG, ELB and LCH conceived and designed the experiments. NG, ELB and LCH wrote the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Rebecca Kimball and members of the Kimball, Braun and Hannah laboratories for many useful comments and discussions. This research was supported by National Science Foundation Grants IOB-0444031, DBI-0077676, DBI-0606607, and IOB-9982626 and USDA Grant 2006-35100-17220.</p>" ]
[ "<fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><bold>Reconciled large and small subunit trees</bold>. A) Amino acid tree of the large and small subunits from angiosperms, <italic>Physcomitrella patens </italic>and <italic>Chlamydomonas reinhardtii</italic>. The topology of the tree was determined by ML using aligned amino acid sequences using PhyML. Branch lengths reflect numbers of amino acid substitutions per site. The branches within groups have been replaced by the grey triangles. ML bootstrap values are indicated above branches, and the bar shows the number of amino acid substitutions per site. B) Angiosperm large subunit reconciled tree. C) Angiosperm small subunit reconciled tree. The topology of the trees shown in B) and C) was determined by ML using aligned cDNA sequences using GARLI. Branch lengths reflect numbers of amino acid substitutions per site as estimated by AAML and the scale bar shows the number of amino acid substitutions per site. ML bootstrap values &gt; 50% are indicated above branches. Reconciled tree analyses (using the gene trees shown and the species tree shown in Additional file ##SUPPL##0##1##) were conducted using GENETREE. Black boxes at nodes indicate duplication events. The arrow in B) indicates the divergence of <italic>Physcomitrella patens </italic>from angiosperms. The trees in B) and C) were rooted with the AGPase large and small subunit from <italic>Chlamydomonas reinhardtii </italic>respectively. Thicker lines indicate branches that follow duplication events and have K<sub>S </sub>&lt; 0.1 (based upon ML estimates of synonymous branch lengths).</p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>Absolute rate of evolution of the large and the small subunit of AGPase from angiosperms (measured in aass MY<sup>-1</sup>)</bold>. The blue bars indicate the average rates (amino acid substitutions per site per million years; aass MY<sup>-1</sup>) estimated from the most recent dated speciation events to present sequences of the trees shown in Figure 1B and 1C. The red bars indicate the average aass MY<sup>-1 </sup>estimated from the most recent dated speciation events to present sequences of the trees shown in Additional file ##SUPPL##2##3A## and ##SUPPL##1##2B##. The yellow bars indicate the average aass MY<sup>-1 </sup>estimated from all branches in Figure 3A and 3B. The green bars indicate the average aass MY<sup>-1 </sup>estimated from all branches in Additional file ##SUPPL##3##4A## and ##SUPPL##2##3B##. The error bars indicate 2× standard error.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>Phylogenetic trees of the large and the small subunits from angiosperms after rate-smoothing</bold>. The trees in A) and B) are the trees shown in Figure 1B and 1C respectively after rate-smoothing. Rate-smoothing was done by using penalized likelihood as implemented in the r8s software [##REF##12538260##79##]. Black boxes indicate duplication events.</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p><bold>Positive selection in the large and the small subunit of the angiosperms</bold>. The trees shown in A) and B) have the same topology as the trees shown in Figure 1B and 1C, although the shown here are unrooted. White circles indicate branches where positive selection was detected only by Test 1 but not Test 2 (described in Methods). Black circles indicate branches where positive selection was detected by both Test 1 and Test 2. The branches where positive selection is detected are numbered.</p></caption></fig>" ]
[ "<table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Temporary relaxation of purifying selection, positive selection, or both after duplications in the large and small subunit of AGPase from angiosperms.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\">Branches</th><th align=\"center\">Large subunit</th><th align=\"center\">p-value</th><th align=\"center\">Small subunit</th><th align=\"center\">p-value</th></tr></thead><tbody><tr><td align=\"center\">Class 1 (Figure 1)</td><td align=\"center\">0.114</td><td align=\"center\">0.005</td><td align=\"center\">0.058</td><td align=\"center\">&lt; 0.001</td></tr><tr><td align=\"center\">Class 2 (Figure 1)</td><td align=\"center\">0.086</td><td/><td align=\"center\">0.029</td><td/></tr><tr><td align=\"center\">All branches (Figure 1)</td><td align=\"center\">0.090</td><td/><td align=\"center\">0.033</td><td/></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">Class 1 (Additional file ##SUPPL##2##3##)</td><td align=\"center\">0.125</td><td align=\"center\">&lt; 0.001</td><td align=\"center\">0.081</td><td align=\"center\">&lt; 0.001</td></tr><tr><td align=\"center\">Class 2 (Additional file ##SUPPL##2##3##)</td><td align=\"center\">0.086</td><td/><td align=\"center\">0.029</td><td/></tr><tr><td align=\"center\">All branches (Additional file ##SUPPL##2##3##)</td><td align=\"center\">0.089</td><td/><td align=\"center\">0.034</td><td/></tr></tbody></table></table-wrap>", "<table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Type I and II functional divergence between large and small subunit groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th align=\"center\" colspan=\"6\">Large subunit</th><th align=\"center\">Small subunit</th></tr><tr><th/><th colspan=\"7\"><hr/></th></tr><tr><th/><th align=\"center\">Group 3b/</th><th align=\"center\">Group 3b/</th><th align=\"center\">Group 3b/</th><th align=\"center\">Group 3a/</th><th align=\"center\">Group 3a/</th><th align=\"center\">Group 2/</th><th align=\"center\">Group 1/</th></tr><tr><th/><th align=\"center\">Group 3a</th><th align=\"center\">Group 2</th><th align=\"center\">Group 1</th><th align=\"center\">Group 2</th><th align=\"center\">Group 1</th><th align=\"center\">Group 1</th><th align=\"center\">Group 2</th></tr></thead><tbody><tr><td/><td/><td/><td align=\"center\" colspan=\"2\">Type I divergence</td><td/><td/><td/></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\">Theta-I</td><td align=\"center\">0.261*</td><td align=\"center\">0.383*</td><td align=\"center\">0.448*</td><td align=\"center\">0.220*</td><td align=\"center\">0.331*</td><td align=\"center\">0.273*</td><td align=\"center\">0.263*</td></tr><tr><td align=\"center\">SE</td><td align=\"center\">0.069</td><td align=\"center\">0.095</td><td align=\"center\">0.097</td><td align=\"center\">0.060</td><td align=\"center\">0.072</td><td align=\"center\">0.076</td><td align=\"center\">0.084</td></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\" colspan=\"2\">TypeII divergence</td><td/><td/><td/></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\">Theta-II</td><td align=\"center\">0.016</td><td align=\"center\">0.072</td><td align=\"center\">0.080</td><td align=\"center\">0.016</td><td align=\"center\">0.058</td><td align=\"center\">0.001</td><td align=\"center\">0.028</td></tr><tr><td align=\"center\">SE</td><td align=\"center\">0.061</td><td align=\"center\">0.052</td><td align=\"center\">0.054</td><td align=\"center\">0.059</td><td align=\"center\">0.061</td><td align=\"center\">0.053</td><td align=\"center\">0.033</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>Alignment of large and small subunits AGPases from angiosperms with protein domains highlighted</bold>. The blue domain indicates the hypervariable N terminus of the large and the small subunit. The pink and green domains indicate the catalytic domain and the β-helix domain respectively. The yellow domain indicates the loop that connects the catalytic to the β-helix domain.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold>Species tree</bold>. Times of divergence are indicated in million of years (MY) at nodes [##REF##8896372##83##, ####REF##10759555##84##, ##REF##10908680##85##, ##REF##11674868##86##, ##UREF##13##87##, ##REF##15489274##88##, ##REF##15499401##89##, ##UREF##14##90##, ##REF##16677401##91##, ##REF##16632644##92####16632644##92##]. All divergence times were examined for consistency with the fossil record [##REF##21652316##93##].</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p><bold>Reconciled large and small subunit trees</bold>. A) Angiosperm large subunit reconciled tree. B) Angiosperm small subunit reconciled tree. The topology of the trees shown in A) and B) was determined by ML using aligned cDNA sequences analyzed by GARLI. Nodes with bootstrap values &lt; 70% (Figure ##FIG##0##1##) were then rearranged to minimize the number of duplications (to increase congruence with the species tree). Branch lengths reflect numbers of amino acid substitutions per site, estimated AAML (with the scale bar showing the number of amino acid substitutions per site). Reconciled tree analyses were conducted using GENETREE and the species tree in Additional file 1. Black boxes indicate duplication events. The trees in A) and B) were rooted with the AGPase large and small subunit from <italic>Chlamydomonas reinhardtii </italic>respectively. Thicker lines indicate branches with K<sub>S </sub>&lt; 0.1 following duplication events (using ML estimates of synonymous branch lengths).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p><bold>Phylogenetic trees of the large and the small subunits from angiosperms after rate-smoothing</bold>. The trees in parts A) and B) of this figure are rate-smoothed versions of the gene trees shown in Additional file 3A and 3B that were rearranged to increase congruence with the species tree. Rate-smoothing was done by using the PL method implemented in the r8s software. Black boxes indicate duplication events.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional file 5</title><p><bold>Average number of synonymous substitutions per site per year</bold>. The trees in A) and B) have the topology of the trees shown in Figure ##FIG##0##1B## and ##FIG##0##1C## respectively. The length of the branches represents the number of synonymous substitutions per site as estimated by the free model of CODEML. The bars correspond to the number of synonymous substitutions per site. The numbers of synonymous substitutions per site per year, shown in C), were estimated from the most recent dated speciation events to present sequences of the trees shown in A) and B). The error bars represent 2× standard error.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional file 6</title><p><bold>Amino acid sites in the large and the small subunit of AGPase from angiosperms under positive selection</bold>. Large subunit site numbers correspond to the amino acid sequence encoded by <italic>Shrunken-2 </italic>(NCBI accession number: P55241). Small subunit site numbers correspond to the amino acid sequence encoded by <italic>Brittle-2 </italic>(NCBI accession number: AAQ14870).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional file 7</title><p><bold>Distribution of type I sites along the large (A) and the small (B) subunit</bold>. The cut-off value of posterior probability is empirical and it was set to 0.5 for all group comparisons except for group 1-group 3b and group 2-group 3b where the cut-off value was set to 0.6, since theta was greater for these pairs. The Y-axis corresponds to posterior probability. The X-axis corresponds to the number of the amino acid site based on the subunits encoded by <italic>Shrunken-2 </italic>(A) (NCBI accession number: P55241) and <italic>Brittle-2 </italic>(B) (NCBI accession number: AAQ14870).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional file 8</title><p><bold>Type-I sites in the large and the small subunit of AGPase from angiosperms</bold>. Type-I functional divergence between large and small subunit groups was estimated by DIVERGE. Large subunit site numbers correspond to the amino acid sequence encoded by <italic>Shrunken-2 </italic>(NCBI accession number: P55241). Small subunit site numbers correspond to the amino acid sequence encoded by <italic>Brittle-2 </italic>(NCBI accession number: AAQ14870).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional file 9</title><p><bold>Type-II sites in the large and the small subunit of AGPase from angiosperms</bold>. Type-II functional divergence between large and small subunit groups was estimated by DIVERGE. Large subunit site numbers correspond to the amino acid sequence encoded by <italic>Shrunken-2 </italic>(NCBI accession number: P55241). Small subunit site numbers correspond to the amino acid sequence encoded by <italic>Brittle-2 </italic>(NCBI accession number: AAQ14870).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional file 10</title><p>AGPase subunit accession numbers</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>The branches of the trees shown in Figure 1B and Additional file ##SUPPL##2##3A## (large subunit) or Figure 1C and Additional file ##SUPPL##2##3B## (small subunit) were classified into two groups. One group includes the short branches (K<sub>S </sub>&lt; 0.1) following a duplication event (post-duplication; class 1) and the other group includes all the other branches (class 2). The overall ω value of each group and the overall ω value of all branches were estimated by CODEML. The LRT was used to compare the model with two different ω values (one for class1 branches and one for class 2 branches) to the null hypothesis model with a single ω value.</p></table-wrap-foot>", "<table-wrap-foot><p>Coefficients of type I (θ<sub>I</sub>) and II (θ<sub>II</sub>) functional divergence were estimated using DIVERGE. * denotes statistical significance at 5% level of confidence. SE: standard error.</p></table-wrap-foot>" ]
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{ "acronym": [], "definition": [] }
93
CC BY
no
2022-01-12 17:11:36
BMC Evol Biol. 2008 Aug 12; 8:232
oa_package/b0/25/PMC2529307.tar.gz
PMC2529308
18680585
[ "<title>Background</title>", "<p>More than one sixth of humanity currently lives on the Indian subcontinent. This population is spread across up to 40,000 endogamous and semi-endogamous culturally, linguistically, and socially differentiated groups [##REF##8908794##1##]. The majority of these groups or populations are castes, but they also include nearly 500 'scheduled tribes' [##UREF##0##2##] and ca. 500 'scheduled castes' [##UREF##1##3##]. Thus, the Indian subcontinent is an ideal region for studying the relationships between culture, geography and genes, and for developing interdisciplinary models concerning the demographic history of <italic>Homo </italic>sapiens or anatomically modern humans (AMH). Moreover, the large number of deep-rooting mtDNA lineages emerging from the basal nodes of both superhaplogroup M and N (including R) [##REF##15467980##4##, ####REF##15890867##5##, ##REF##15890885##6##, ##REF##15772853##7##, ##REF##16776823##8##, ##REF##18223312##9##, ##REF##16361303##10##, ##UREF##2##11####2##11##] indicate that the Indian subcontinent was probably the first major outcome of the dispersals of AMH from Africa. Furthermore, these deep-rooted mtDNA haplogroups generally cross cultural and social boundaries; this suggests a common origin to the highly diverse peoples of the Indian sub-continent, with indigenous or autochthonous diversification of the maternal gene pool [##REF##10574762##12##, ####REF##12536373##13##, ##REF##15339343##14##, ##REF##17187379##15##, ##UREF##3##16####3##16##].</p>", "<p>These results have been generally corroborated by data from the Y chromosome [##REF##16415161##17##,##REF##16400607##18##] and autosomal DNA [##REF##12536373##13##,##REF##16266407##19##,##REF##17194221##20##]. The only exception, for mtDNA, are the Tibeto-Burman speakers of north-eastern India, who share about half of their maternal genetic heritage with populations living further east of India [##REF##15339343##14##,##REF##12678055##21##]. It has been argued, that following the initial colonization of Indian subcontinent, maternal gene flow from the west has been rather limited and largely restricted to the western states of contemporary India and Pakistan [##REF##15339343##14##,##REF##17187379##15##,##REF##15077202##22##]. Consequently, the haplogroup richness of the Indian subcontinent appears to have formed <italic>in situ</italic>, and date back to some point in the later Pleistocene, most probably between 40 Ka and 60 Ka ago. Furthermore, this high level of genetic diversity may also be linked to the possibility that the South Asian population in the Pleistocene was demographically large in global terms. Comparisons of relative regional population sizes through time, deduced by Bayesian coalescent inference methods applied to global mtDNA complete sequence data, indicate that between approximately 45 Ka and 20 Ka ago most of humanity lived in Southern Asia [##REF##18093996##23##].</p>", "<p>Two language families, Indo-European and Dravidian, account for the majority of linguistic diversity in India. However, apart from a number of linguistic isolates, there are two other major families – Tibeto-Burman and Austro-Asiatic (AA). The origin of the Austro-Asiatic language family is a highly debated issue. Building on archaeological and linguistic evidence, and the assumption that rice domestication was a single event, the currently preferred hypothesis places the origin of this language family in Southeast Asia [##UREF##4##24##, ####REF##12714734##25##, ##UREF##5##26####5##26##]. The alternative model, based on genetic evidence (that shows multiple domestications of rice varieties [##UREF##6##27##]), and comparative phonology, advocates an East Indian cradle for the AA language group [##UREF##7##28##]. The AA language family tree has two basic branches – Munda and Mon-Khmer. The former is distributed exclusively in the Indian subcontinent; the latter is predominantly Southeast Asian, although there are a few Indian representatives (Khasian and Nicobarese) [##UREF##5##26##].</p>", "<p>The genetic origin(s) of extant AA speakers, however, may or may not coincide with the origin of the language group. Studies of mtDNA diversity have shown that the AA speakers from Southeast Asia and the Indian subcontinent carry mtDNAs of different sources [##REF##15772853##7##,##REF##18223312##9##,##REF##15339343##14##,##UREF##3##16##,##REF##14525929##29##,##REF##17381059##30##]. Although the data on Southeast Asian populations, which speak languages of the Mon-Khmer branch of the AA tree, are still somewhat limited, it seems safe to conclude, that their mtDNA characteristics are similar to those of the surrounding Southeast Asian populations, and distinct from AA tribes of India (Munda-speakers) [##REF##17381059##30##]. Similarly, the Indian tribes speaking different Munda languages show generally the same mtDNA haplogroup composition as the Indo European and Dravidic groups of India [##REF##18223312##9##,##REF##15339343##14##,##UREF##3##16##,##REF##14525929##29##]. In contrast, the Y chromosomes of Indian and Southeast Asian AA speaking populations share a common marker, M95, which defines a single branch (O2a) in the overwhelmingly East Asian specific tree of haplogroup O. This evidence provides a strong basis for proposing a Southeast Asian origin of the paternal lineages of the Munda speaking populations of India [##REF##12536373##13##,##REF##16415161##17##,##REF##16400607##18##].</p>", "<p>The AA speaking populations of Myanmar, which is a likely dispersal route, or original location, for the ancestral populations of Munda speakers of India, have not yet been sampled for their mtDNA. It is still possible that some of the mtDNA clades present among the AA speakers of India (and in their neighbours) could, in fact, be due to gene flow to India from further east. In an attempt to identify mtDNA lineages that would reveal a phylogeographic distribution similar to that of the Y chromosome marker M95, we analyzed mtDNA samples representing all the major linguistic groups of India, with a particular focus to haplogroup R derived lineages.</p>", "<p>The first thorough study of complete mtDNA sequences from India [##REF##15467980##4##] identified numerous indigenous clades emerging directly from the roots of superhaplogroups N, R and U, such as N5, R5-R8, R30, R31, U2a-d and U7. West Eurasian specific haplogroups HV, JT, N1, and U (xU2a-d, U7) occur at lower frequencies, suggesting limited but phylogeographically well detectable gene flow into the Indian subcontinent, most probably from west and northwest Eurasia [##REF##15339343##14##]. Here we have now extended the complete mtDNA sequencing by determining 35 new complete sequences, in order to further refine the phylogeny of the Indian subcontinent-specific segment of haplogroup R. Furthermore, to explore the correlations between genes, languages and geography in Indian subcontinent, we have carried out high resolution genotyping and phylogeographic detailed analyses on R7, which occurs at high frequency among the Austro-Asiatic (Munda) speaking groups of India.</p>" ]
[ "<title>Methods</title>", "<p>To refine the phylogeny of superhaplogroup R we sequenced complete mitochondrial genomes of 35 samples selected from different regions and language groups of India (Table ##TAB##0##1##). The results were incorporated into a phylogenetic tree [see Additional file ##SUPPL##0##1##]; for detailed tree for hg R7 see Fig. ##FIG##0##1##) together with previously published complete mtDNA sequence data from India [##REF##15467980##4##]. For haplogroup R7 we performed a high-resolution survey of phylogenetically diagnostic markers, using information from complete mtDNA sequences. We studied ~12,000 samples collected from all over Indian subcontinent [see Additional file ##SUPPL##5##6##]. These samples cover all the language groups and most of the Indian states and union territories. The samples were screened for the presence of R7 mtDNAs based on HVS-I information (motif: 16260-16261-16319-16362). Previously this motif has, together with the restriction enzyme AluI cutting site polymorphism at np. 10143, been used to define haplogroup R20 [##REF##15339343##14##]. However, with the support of new complete mtDNA sequences information the lineage with this HVSI motif was subsequently named R7 [##REF##15467980##4##] and we follow this update of the nomenclature. Further, the identified R7 samples were analyzed for coding region markers by sequencing. Sequencing was carried out in ABI 3730 and 3730XL DNA Analyzers (Applied Biosystems, USA) and mutations were scored against the rCRS [##REF##10508508##33##]. To minimize errors, both strands were double-sequenced. Principal component analysis (PCA) of R subgroups was performed using POPSTR, kindly provided by H. Harpending. Median-joining and reduced median networks were reconstructed with NETWORK program (version 4.1) [##REF##10331250##34##]<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.fluxus-engineering.com\">http://www.fluxus-engineering.com</ext-link>. Reduced median and median-joining procedures were applied sequentially. Coalescence time has been calculated between nucleotide positions 16090–16365 (HVS-I) considering one transition equals to 20,180 years [##REF##8808611##31##], while for the coding region estimates we employed the rate calibrated by Kivisild et al. [##REF##16172508##32##] considering substitution rate estimate for protein-coding synonymous changes of 3.5 × 10<sup>-8</sup>, which gives 6,764 years per synonymous transition. Standard deviation of the rho estimate (σ) was calculated as in Saillard et al. [##REF##10924403##35##]. Haplogroup isofrequency maps were generated by using Surfer 7 of Golden Software (Golden Software Inc., Golden, Colorado), following the Kriging procedure. To determine whether language or geography has the strongest impact on genetic differentiation, spatial autocorrelation, SAAP [##UREF##8##36##] and Mantel [##UREF##9##37##] tests were performed using ARLEQUIN version 2.0 [##UREF##10##38##]. For Mantel test genetic distance matrixes were generated from ARLEQUIN, and geographic distance calculated from latitude and longitude information. For language groups linguistic distances (ranging from 10–100) assigned manually to each branch, based on published linguistic information and vocabulary match [##UREF##5##26##, ####UREF##6##27##, ##UREF##7##28####7##28##,##UREF##11##39##,##UREF##12##40##].</p>" ]
[ "<title>Results and Discussion</title>", "<p>The inclusion of our 35 novel sequences (Table ##TAB##0##1##) into the phylogeny of haplogroup R allows the recognition of eight new subclades within six haplogroup R branches unique to the Indian subcontinent (Fig. ##FIG##0##1##, [see Additional file ##SUPPL##0##1##]). We refine here the internal topology of haplogroups R5, R6 and R8, and describe two novel sub-clades of hg R7, to be discussed below in detail. Subclade R5a is defined by a deletion at nucleotide positions (np) 522–523 and one control region mutation at np16266. R6a is defined by two control region substitutions (at sites 16129 and 16266). In haplogroup R7, two new subclades R7a and R7b can be identified (for details see further down). A new subclade of R8, called R8a, is defined by a single coding region substitution at np 5510. Haplogroup R30 splits into two subclades R30a and R30b, the former supported by ten coding region substitutions and the latter by 24 coding and control region mutations. Similarly, in haplogroup R31 a new subclade R31a can be distinguished by 17 control and coding region mutations. Coalescent estimates suggest an ancient branching pattern in hgs R30 and R31, dating back almost to the earliest diversification of the superhaplogroup R itself. This most probably occurred soon after the out of Africa dispersals into the Indian subcontinent [see Additional file ##SUPPL##0##1##].</p>", "<p>Comparison of patterns of haplogroup distribution in relation to linguistic groups reveals that the frequency of the R7 clade is several times greater among AA (Munda) speakers than among Dravidian and Indo-European speaking populations (Table ##TAB##1##2##, [see Additional file ##SUPPL##1##2##]). Geographically, the distribution of R7 in India is centered on the AA \"heartland\" (Bihar, Jharkhand, and Chhattisgarh) [see Additional file ##SUPPL##2##3##]. Similar to R7, haplogroup R6 is significantly more frequent among the AA speakers than among other linguistic groups (Table ##TAB##1##2##, [see Additional file ##SUPPL##1##2##]). PC analysis based on frequency data of the hg R subclades confirms that the majority of Munda speaking populations cluster separately from others mainly because of higher hg R7 frequency (Fig. ##FIG##1##2##). However, only 50.6% of the variation can be explained by the first two principal components. Interestingly, hg R6 is placed within the main cluster, which is comprised of populations from all language groups. Based on these preliminary results we focused on R7 as a potential AA-associated marker.</p>", "<p>In general, the elevated frequency of hg R7 among the AA speakers of India can be explained by two alternative scenarios. Firstly, one may consider a possible origin of R7 among AA (Munda) speakers, possibly already outside India. Under this scenario the presence of R7 in some Dravidian and Indo-European speaking communities would be explained by its later introgression from the Munda communities, or by language shift of some Munda speaking groups into Dravidian/Indo-European languages. Secondly, an origin of R7 may lie among non-AA populations of India, with the presently observable higher frequency of R7 among AA resulting from founder effect(s) due to random genetic drift. To test these two scenarios, we carried out a detailed analysis of R7 mtDNAs in populations speaking different subgroups of AA languages, as well as among IE and Dravidian-speaking populations of Indian subcontinent.</p>", "<p>Complete mtDNA sequence-based topology of hg R7 divulges two deep-rooted subclades (Fig. ##FIG##0##1##). R7a is defined by four and R7b by six coding region mutations and, in addition, by two control-region substitutions (146 and 16311). We calculated the time to the most recent common ancestor (MRCA) for all R7 major sub-clades (Fig. ##FIG##0##1## and ##FIG##2##3##, Table ##TAB##2##3##), applying different calibration methods [##REF##17381059##30##,##REF##8808611##31##]. All the AA individuals coalesce to the founder R7a1 that dates back to between approximately 3 Ka and 7 Ka ago, depending on the mutation rate used. The coalescent times of R7 variation among Dravidians and Indo-Europeans are older. In other words, the only R7 lineage found by us in AA speakers of India – R7a1 – is nested within the R7 lineages found among Dravidian and Indo-European speakers of India (Table ##TAB##2##3##).</p>", "<p>Geographically, the distribution of R7a frequency is concentrated towards Bihar, Jharkhand and Chhattisgarh States, while R7b has its frequency peak in Andhra-Pradesh (Fig. ##FIG##3##4a## and ##FIG##3##4b##). The frequency of R7a is higher among AA (Munda) speakers, while R7b is most common among Dravidian speakers from Andhra-Pradesh, although the overall frequency of R7b is much lower than that of R7a (Fig. ##FIG##3##4c## and ##FIG##3##4d##). A Mantel test showed a significant correlation between genes and geography for the Indian R sub-clades, but no such correlation for the relationship between genes and languages (Table ##TAB##3##4##). The spatial autocorrelation analysis favoured a clinal pattern for the distribution of hg R7 [see Additional file ##SUPPL##3##4##]. At the local (i.e. district) level, R7 is present in Bihar, Jharkhand, Chhattisgarh, Madhya-Pradesh and the northern districts of Andhra-Pradesh (Adilabad, Warangal and Khammam), whereas elsewhere in India it is virtually absent, including among other AA groups inhabiting Orissa and Maharashtra states [see Additional file ##SUPPL##4##5##].</p>", "<p>The overall higher than average frequency of R7 among the AA speakers of India may superficially be seen as supporting the model that places the origin of this haplogroup among AA speakers, possibly even outside India, assuming the language phylum would have arisen elsewhere. Indirectly, such a scenario would be also supported by the Y chromosome evidence (haplogroup O2a, for details, see Introduction). However, the much higher diversity of R7a and R7b sub-clades among non Austro-Asiatic populations of India suggests that the source of haplogroup R7 is not among the maternal ancestors of all Austro-Asiatic tribal groups, but that they acquired this haplogroup via local admixture, together with the rest of the South Asian mtDNA lineages that make up their extant maternal lineage pool. Furthermore, the presence of only a single recent founder branch of R7, i.e. R7a1, among widely dispersed AA populations of India supports the founder event scenario by introgression of this lineage from the local non-AA populations before the range expansion of Munda speaking populations within India. If indeed R7 did have its origin among some so far unsampled populations of the present-day Myanmar or Cambodia, we would then expect to see different sub-divided AA populations losing by drift different sub-branches of R7a and R7b (to explain their reduced diversity), and the admixed Dravidian and Indo-European speaking populations would be expected to have obtained a subset of the R7 variation observed in AA speakers, which is not the case. While the occurrence of R7a1 among Dravidian and Indo-European-speaking populations living close to the AA populations (Fig. ##FIG##2##3##) could be explained by language shift or secondary admixture with AA speakers, sub-haplogroup R7b appears to be restricted to Dravidian-speakers of the southern part of India (Fig. ##FIG##3##4b## and ##FIG##3##4d##). Nevertheless, this haplogroup is also reported in two Indo-European populations (Kolcha and Rathwa) whose local tradition speaks about their ancient split from the Gond (Gondi subfamily of Dravidian language group) population of Central India and further migration to Gujarat. Thus, from the data and analyses shown here, it is most parsimonious to conjecture that R7 originated in India among non-AA, possibly in Dravidian speaking populations.</p>", "<p>To test further the two hypotheses, a Dravidian origin for R7 with admixture and founder effects, versus an external AA origin of R7, we examined whether the spread of R7 among the different Munda sub-groups in India, as defined by the language trees [##UREF##5##26##,##UREF##6##27##], is uniform. This would be expected if R7 was present among the ancestral AA speakers prior to the diversification of the language family into numerous branches. Consistent with the non-AA origin of R7, we found the distribution of R7a1 among AA populations to be profoundly skewed towards the Kherwari sub-branch of the North Munda languages which accounts for ~90% of the AA R7 samples (Fig. ##FIG##4##5##). Conversely, R7 is very rare in the South Munda group. It is completely absent in Koraput Munda speakers and marginally present only in the Kharia tribe of Madhya Pradesh (in total 3 out of 431 South Munda samples) (Fig. ##FIG##4##5##). This finding yet again strengthens the argument that only a subset of Indian AA groups has acquired one sublineage of R7a1 <italic>in situ </italic>after their arrival to Indian subcontinent from local non-AA groups through admixture. Thus, we fail to find from the evidence of the extant maternal lineage pool of the Austro Asiatic speakers of India any major lineages that show signs of potential origin outside India. Overall, the enigma of the origins and demographic past of the AA speakers in India remains, for while the East Asian contribution to their paternal gene pool seems evident, the maternal side of their genetic heritage appears to be autochthonous to Indian subcontinent. This suggests that introduction and spread of AA speakers into India involved a complex and sex-differentiated demography, involving both exogenous males and local females.</p>", "<p>In brief, our high-resolution study of haplogroup R7 suggests that this haplogroup originated in India among non-AA population most probably Dravidian, and that the Munda (mainly Kherwari group) speaking populations have acquired a subset of it only relatively recently. The highest frequency of haplogroup R7 among Austro-Asiatic tribal groups can be explained, thus, by their regional admixture with other local Indian subcontinental populations followed by random genetic drift, rather than being a genetic marker of their own. The spread of R7 as well as other ancient sub-clades of haplogroup R in India follows predominantly the geographic rather than linguistic landscape of the subcontinent. The geographic correlations are further manifested in the distribution patterns of the sub-clades: R7a being more common in northern India while R7b is more frequent in the southern parts of the subcontinent. Because Dravidian speakers harbour all the twigs of R7 identified so far, the haplogroup may have arisen among the matrilineal ancestry of the present day Dravidian speakers. However, it is important to caution that autochthonous basal mtDNA lineages in South as well as Southeast and East Asia appear to be significantly more ancient than any linguistic reconstruction offers to present day language families. This would imply that linguistically significant relationships among Indian populations may be superimposed on, and masking, demographic events of much greater antiquity. Our results also remind us, once again, that phylogenetically established within-haplogroup diversity is more informative than mere frequency in establishing the direction of gene flow between populations, language groups and geographically defined regions.</p>" ]
[ "<title>Results and Discussion</title>", "<p>The inclusion of our 35 novel sequences (Table ##TAB##0##1##) into the phylogeny of haplogroup R allows the recognition of eight new subclades within six haplogroup R branches unique to the Indian subcontinent (Fig. ##FIG##0##1##, [see Additional file ##SUPPL##0##1##]). We refine here the internal topology of haplogroups R5, R6 and R8, and describe two novel sub-clades of hg R7, to be discussed below in detail. Subclade R5a is defined by a deletion at nucleotide positions (np) 522–523 and one control region mutation at np16266. R6a is defined by two control region substitutions (at sites 16129 and 16266). In haplogroup R7, two new subclades R7a and R7b can be identified (for details see further down). A new subclade of R8, called R8a, is defined by a single coding region substitution at np 5510. Haplogroup R30 splits into two subclades R30a and R30b, the former supported by ten coding region substitutions and the latter by 24 coding and control region mutations. Similarly, in haplogroup R31 a new subclade R31a can be distinguished by 17 control and coding region mutations. Coalescent estimates suggest an ancient branching pattern in hgs R30 and R31, dating back almost to the earliest diversification of the superhaplogroup R itself. This most probably occurred soon after the out of Africa dispersals into the Indian subcontinent [see Additional file ##SUPPL##0##1##].</p>", "<p>Comparison of patterns of haplogroup distribution in relation to linguistic groups reveals that the frequency of the R7 clade is several times greater among AA (Munda) speakers than among Dravidian and Indo-European speaking populations (Table ##TAB##1##2##, [see Additional file ##SUPPL##1##2##]). Geographically, the distribution of R7 in India is centered on the AA \"heartland\" (Bihar, Jharkhand, and Chhattisgarh) [see Additional file ##SUPPL##2##3##]. Similar to R7, haplogroup R6 is significantly more frequent among the AA speakers than among other linguistic groups (Table ##TAB##1##2##, [see Additional file ##SUPPL##1##2##]). PC analysis based on frequency data of the hg R subclades confirms that the majority of Munda speaking populations cluster separately from others mainly because of higher hg R7 frequency (Fig. ##FIG##1##2##). However, only 50.6% of the variation can be explained by the first two principal components. Interestingly, hg R6 is placed within the main cluster, which is comprised of populations from all language groups. Based on these preliminary results we focused on R7 as a potential AA-associated marker.</p>", "<p>In general, the elevated frequency of hg R7 among the AA speakers of India can be explained by two alternative scenarios. Firstly, one may consider a possible origin of R7 among AA (Munda) speakers, possibly already outside India. Under this scenario the presence of R7 in some Dravidian and Indo-European speaking communities would be explained by its later introgression from the Munda communities, or by language shift of some Munda speaking groups into Dravidian/Indo-European languages. Secondly, an origin of R7 may lie among non-AA populations of India, with the presently observable higher frequency of R7 among AA resulting from founder effect(s) due to random genetic drift. To test these two scenarios, we carried out a detailed analysis of R7 mtDNAs in populations speaking different subgroups of AA languages, as well as among IE and Dravidian-speaking populations of Indian subcontinent.</p>", "<p>Complete mtDNA sequence-based topology of hg R7 divulges two deep-rooted subclades (Fig. ##FIG##0##1##). R7a is defined by four and R7b by six coding region mutations and, in addition, by two control-region substitutions (146 and 16311). We calculated the time to the most recent common ancestor (MRCA) for all R7 major sub-clades (Fig. ##FIG##0##1## and ##FIG##2##3##, Table ##TAB##2##3##), applying different calibration methods [##REF##17381059##30##,##REF##8808611##31##]. All the AA individuals coalesce to the founder R7a1 that dates back to between approximately 3 Ka and 7 Ka ago, depending on the mutation rate used. The coalescent times of R7 variation among Dravidians and Indo-Europeans are older. In other words, the only R7 lineage found by us in AA speakers of India – R7a1 – is nested within the R7 lineages found among Dravidian and Indo-European speakers of India (Table ##TAB##2##3##).</p>", "<p>Geographically, the distribution of R7a frequency is concentrated towards Bihar, Jharkhand and Chhattisgarh States, while R7b has its frequency peak in Andhra-Pradesh (Fig. ##FIG##3##4a## and ##FIG##3##4b##). The frequency of R7a is higher among AA (Munda) speakers, while R7b is most common among Dravidian speakers from Andhra-Pradesh, although the overall frequency of R7b is much lower than that of R7a (Fig. ##FIG##3##4c## and ##FIG##3##4d##). A Mantel test showed a significant correlation between genes and geography for the Indian R sub-clades, but no such correlation for the relationship between genes and languages (Table ##TAB##3##4##). The spatial autocorrelation analysis favoured a clinal pattern for the distribution of hg R7 [see Additional file ##SUPPL##3##4##]. At the local (i.e. district) level, R7 is present in Bihar, Jharkhand, Chhattisgarh, Madhya-Pradesh and the northern districts of Andhra-Pradesh (Adilabad, Warangal and Khammam), whereas elsewhere in India it is virtually absent, including among other AA groups inhabiting Orissa and Maharashtra states [see Additional file ##SUPPL##4##5##].</p>", "<p>The overall higher than average frequency of R7 among the AA speakers of India may superficially be seen as supporting the model that places the origin of this haplogroup among AA speakers, possibly even outside India, assuming the language phylum would have arisen elsewhere. Indirectly, such a scenario would be also supported by the Y chromosome evidence (haplogroup O2a, for details, see Introduction). However, the much higher diversity of R7a and R7b sub-clades among non Austro-Asiatic populations of India suggests that the source of haplogroup R7 is not among the maternal ancestors of all Austro-Asiatic tribal groups, but that they acquired this haplogroup via local admixture, together with the rest of the South Asian mtDNA lineages that make up their extant maternal lineage pool. Furthermore, the presence of only a single recent founder branch of R7, i.e. R7a1, among widely dispersed AA populations of India supports the founder event scenario by introgression of this lineage from the local non-AA populations before the range expansion of Munda speaking populations within India. If indeed R7 did have its origin among some so far unsampled populations of the present-day Myanmar or Cambodia, we would then expect to see different sub-divided AA populations losing by drift different sub-branches of R7a and R7b (to explain their reduced diversity), and the admixed Dravidian and Indo-European speaking populations would be expected to have obtained a subset of the R7 variation observed in AA speakers, which is not the case. While the occurrence of R7a1 among Dravidian and Indo-European-speaking populations living close to the AA populations (Fig. ##FIG##2##3##) could be explained by language shift or secondary admixture with AA speakers, sub-haplogroup R7b appears to be restricted to Dravidian-speakers of the southern part of India (Fig. ##FIG##3##4b## and ##FIG##3##4d##). Nevertheless, this haplogroup is also reported in two Indo-European populations (Kolcha and Rathwa) whose local tradition speaks about their ancient split from the Gond (Gondi subfamily of Dravidian language group) population of Central India and further migration to Gujarat. Thus, from the data and analyses shown here, it is most parsimonious to conjecture that R7 originated in India among non-AA, possibly in Dravidian speaking populations.</p>", "<p>To test further the two hypotheses, a Dravidian origin for R7 with admixture and founder effects, versus an external AA origin of R7, we examined whether the spread of R7 among the different Munda sub-groups in India, as defined by the language trees [##UREF##5##26##,##UREF##6##27##], is uniform. This would be expected if R7 was present among the ancestral AA speakers prior to the diversification of the language family into numerous branches. Consistent with the non-AA origin of R7, we found the distribution of R7a1 among AA populations to be profoundly skewed towards the Kherwari sub-branch of the North Munda languages which accounts for ~90% of the AA R7 samples (Fig. ##FIG##4##5##). Conversely, R7 is very rare in the South Munda group. It is completely absent in Koraput Munda speakers and marginally present only in the Kharia tribe of Madhya Pradesh (in total 3 out of 431 South Munda samples) (Fig. ##FIG##4##5##). This finding yet again strengthens the argument that only a subset of Indian AA groups has acquired one sublineage of R7a1 <italic>in situ </italic>after their arrival to Indian subcontinent from local non-AA groups through admixture. Thus, we fail to find from the evidence of the extant maternal lineage pool of the Austro Asiatic speakers of India any major lineages that show signs of potential origin outside India. Overall, the enigma of the origins and demographic past of the AA speakers in India remains, for while the East Asian contribution to their paternal gene pool seems evident, the maternal side of their genetic heritage appears to be autochthonous to Indian subcontinent. This suggests that introduction and spread of AA speakers into India involved a complex and sex-differentiated demography, involving both exogenous males and local females.</p>", "<p>In brief, our high-resolution study of haplogroup R7 suggests that this haplogroup originated in India among non-AA population most probably Dravidian, and that the Munda (mainly Kherwari group) speaking populations have acquired a subset of it only relatively recently. The highest frequency of haplogroup R7 among Austro-Asiatic tribal groups can be explained, thus, by their regional admixture with other local Indian subcontinental populations followed by random genetic drift, rather than being a genetic marker of their own. The spread of R7 as well as other ancient sub-clades of haplogroup R in India follows predominantly the geographic rather than linguistic landscape of the subcontinent. The geographic correlations are further manifested in the distribution patterns of the sub-clades: R7a being more common in northern India while R7b is more frequent in the southern parts of the subcontinent. Because Dravidian speakers harbour all the twigs of R7 identified so far, the haplogroup may have arisen among the matrilineal ancestry of the present day Dravidian speakers. However, it is important to caution that autochthonous basal mtDNA lineages in South as well as Southeast and East Asia appear to be significantly more ancient than any linguistic reconstruction offers to present day language families. This would imply that linguistically significant relationships among Indian populations may be superimposed on, and masking, demographic events of much greater antiquity. Our results also remind us, once again, that phylogenetically established within-haplogroup diversity is more informative than mere frequency in establishing the direction of gene flow between populations, language groups and geographically defined regions.</p>" ]
[]
[ "<title>Background</title>", "<p>Human genetic diversity observed in Indian subcontinent is second only to that of Africa. This implies an early settlement and demographic growth soon after the first 'Out-of-Africa' dispersal of anatomically modern humans in Late Pleistocene. In contrast to this perspective, linguistic diversity in India has been thought to derive from more recent population movements and episodes of contact. With the exception of Dravidian, which origin and relatedness to other language phyla is obscure, all the language families in India can be linked to language families spoken in different regions of Eurasia. Mitochondrial DNA and Y chromosome evidence has supported largely local evolution of the genetic lineages of the majority of Dravidian and Indo-European speaking populations, but there is no consensus yet on the question of whether the Munda (Austro-Asiatic) speaking populations originated in India or derive from a relatively recent migration from further East.</p>", "<title>Results</title>", "<p>Here, we report the analysis of 35 novel complete mtDNA sequences from India which refine the structure of Indian-specific varieties of haplogroup R. Detailed analysis of haplogroup R7, coupled with a survey of ~12,000 mtDNAs from caste and tribal groups over the entire Indian subcontinent, reveals that one of its more recently derived branches (R7a1), is particularly frequent among Munda-speaking tribal groups. This branch is nested within diverse R7 lineages found among Dravidian and Indo-European speakers of India. We have inferred from this that a subset of Munda-speaking groups have acquired R7 relatively recently. Furthermore, we find that the distribution of R7a1 within the Munda-speakers is largely restricted to one of the sub-branches (Kherwari) of northern Munda languages. This evidence does not support the hypothesis that the Austro-Asiatic speakers are the primary source of the R7 variation. Statistical analyses suggest a significant correlation between genetic variation and geography, rather than between genes and languages.</p>", "<title>Conclusion</title>", "<p>Our high-resolution phylogeographic study, involving diverse linguistic groups in India, suggests that the high frequency of mtDNA haplogroup R7 among Munda speaking populations of India can be explained best by gene flow from linguistically different populations of Indian subcontinent. The conclusion is based on the observation that among Indo-Europeans, and particularly in Dravidians, the haplogroup is, despite its lower frequency, phylogenetically more divergent, while among the Munda speakers only one sub-clade of R7, i.e. R7a1, can be observed. It is noteworthy that though R7 is autochthonous to India, and arises from the root of hg R, its distribution and phylogeography in India is not uniform. This suggests the more ancient establishment of an autochthonous matrilineal genetic structure, and that isolation in the Pleistocene, lineage loss through drift, and endogamy of prehistoric and historic groups have greatly inhibited genetic homogenization and geographical uniformity.</p>" ]
[ "<title>Electronic database information</title>", "<p>Accession numbers for data presented herein are as follows (for the complete mtDNA sequence accession numbers FJ004804-FJ004838 and for the HVS-I region sequence accession numbers FJ010662- FJ010785).</p>", "<title>Authors' contributions</title>", "<p>GC, MK, EM, DS–R, VKS, AS, BPN, AK, NA, CBM, BT, SP, RR, PS, SB, SVe, SVa, IK, AB, DS, AS, MR, VC and AGR carried out the mtDNA genotyping. GC, MK, EM, DS–R, VKS, AS, and BPN carried out the mtDNA sequencing analysis. AT, KT and LS contributed to the analysis and interpretation of the data. AT provided complete sequence information of Sindhi sample. GC, MK, EM, MM and TK analyzed the data. TK, GC, MM and RV were responsible for conceiving and designing the study. GC, MK, MM, TK, RV, AT, RF, KT and LS wrote the paper. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Jaan Lind, Ille Hilpus and Tuuli Reisberg for technical assistance. This work was supported by Estonian Basic Research grant SF0182474 and Estonian Centre of Excellence Grant TK10 (to RV), Tartu University grant PBGMR06901 (to TK), Estonian Science Foundation Grant 5807 (To EM), and UKIERI grant RG47772 (to TK and KT). LS and KT were supported by CSIR, Government of India.</p>" ]
[ "<fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><bold>The most parsimonious tree of haplogroup R7 complete mtDNA sequences observed in the Indian subcontinent.</bold> This tree was redrawn manually from the output of median joining/reduced network obtained using NETWORK program (version 4.1) [##REF##10331250##34##]<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.fluxus-engineering.com\">http://www.fluxus-engineering.com</ext-link>. The samples were selected through a preliminary sequence analysis of the control region in order to include the widest possible range of R7 variation, language and geographical groups. Coalescent times were calculated by a calibration method described elsewhere [##REF##8808611##32##]. 16182C, 16183C and 16519 polymorphisms were omitted. Suffixes A, C, G, and T indicate transversions, recurrent mutations are underlined. Synonymous (s) and non-synonymous (ns) mutations are distinguished. DRA-Dravidian, AA-Austro-Asiatic, IE-Indo-European. The ethnic affiliation of the samples is as follows: Lam, Lambadi; As, Asur; Mw, Mawasi; Tor45, Pakistan; Ho, Ho; Ori&amp;A, Oraon; G19, Kanwar; G39, Santhal; G66, Gond; KO, Koya. Two sequences, T35 (Thogataveera) and C35 (Brahmin), were taken from the literature [##REF##15467980##4##].</p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>Principal component (PC) analysis of R5-8, R30 and R31 lineages in Indian populations.</bold> Munda group and a few Indo-European/Dravidian populations collected from Bihar, Jharkhand and Chhattisgarh states, predominantly cluster with haplogroup R7. Haplogroup frequencies were obtained from published sources [##REF##15339343##14##] and our unpublished data.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>The reduced-median network of 152 mtDNAs belonging to haplogroup R7.</bold> Each sample represented on the diagram has been sequenced for the HVS-I region and genotyped for the coding region mutations that are indicated. Circle sizes are proportional to the number of mtDNAs with that haplotype. Recurrent mutations are underlined.</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p><bold>The frequency distribution of R7a and R7b clades in Indian subcontinent.</bold> The upper panel (a, b) shows the spatial distribution (%) of these clades in Indian populations. Isofrequency maps were generated by using Surfer7 of Golden Software (Golden Software Inc., Golden, Colorado), following the Kriging procedure. These isofrequency maps illustrate the geographic spread of the respective mtDNA haplogroups. It should be cautioned, however, that these illustrative maps should not be used to predict the frequency of the clade in geographical areas with missing data. The lower panel (c, d) depicts the frequencies of R7a and R7b in different social and language groups. DRA-Dravidian, AA-Austro-Asiatic, IE-Indo-European.</p></caption></fig>", "<fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p><bold>The frequency distribution of haplogroup R7 in different branches of the Austro-Asiatic language family of India</bold>[##UREF##5##26##].</p></caption></fig>" ]
[ "<table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Geographical, Linguistic and Haplogroup Affiliations of Completely Sequenced mtDNAs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\">Si No.</th><th align=\"center\">Sample code</th><th align=\"center\">Haplogroup</th><th align=\"center\">Population</th><th align=\"center\">Location</th><th align=\"center\">Lingustic affiliation</th></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"center\">Kol77</td><td align=\"center\">R5a1</td><td align=\"center\">Koli</td><td align=\"center\">Gujarat</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">2</td><td align=\"center\">Ben46</td><td align=\"center\">R5a1a</td><td align=\"center\">Bengal</td><td align=\"center\">West Bengal</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">3</td><td align=\"center\">Up41</td><td align=\"center\">R5a1a</td><td align=\"center\">Middle caste</td><td align=\"center\">Uttar Pradesh</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">4</td><td align=\"center\">Kall43</td><td align=\"center\">R5a2b</td><td align=\"center\">Kallar</td><td align=\"center\">Tamil Nadu</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">5</td><td align=\"center\">K35</td><td align=\"center\">R5a2b</td><td align=\"center\">Kota</td><td align=\"center\">Tamil Nadu</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">6</td><td align=\"center\">Ori74</td><td align=\"center\">R5a2b2</td><td align=\"center\">Oraon</td><td align=\"center\">Orissa</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">7</td><td align=\"center\">Mo38</td><td align=\"center\">R5a2b3</td><td align=\"center\">Moor</td><td align=\"center\">Sri Lanka</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">8</td><td align=\"center\">Gu35</td><td align=\"center\">R5a2b3</td><td align=\"center\">Gujarat</td><td align=\"center\">Gujarat</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">9</td><td align=\"center\">Pn32</td><td align=\"center\">R5a2b4</td><td align=\"center\">Paniya</td><td align=\"center\">Kerala</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">10</td><td align=\"center\">Mal33</td><td align=\"center\">R5a2b4</td><td align=\"center\">Malayan</td><td align=\"center\">Kerala</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">11</td><td align=\"center\">Ko 5</td><td align=\"center\">R6a1a</td><td align=\"center\">Koya</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">12</td><td align=\"center\">Ko31</td><td align=\"center\">R6a1a</td><td align=\"center\">Koya</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">13</td><td align=\"center\">Lam43</td><td align=\"center\">R7a1</td><td align=\"center\">Lambadi</td><td align=\"center\">Andhra-Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">14</td><td align=\"center\">As426</td><td align=\"center\">R7a1</td><td align=\"center\">Asur</td><td align=\"center\">Jharkhand</td><td align=\"center\">Austro-Asiatic</td></tr><tr><td align=\"center\">15</td><td align=\"center\">Mw1</td><td align=\"center\">R7a1a</td><td align=\"center\">Mawasi</td><td align=\"center\">Chhattisgarh</td><td align=\"center\">Austro-Asiatic</td></tr><tr><td align=\"center\">16</td><td align=\"center\">Tor45</td><td align=\"center\">R7a1a</td><td align=\"center\">Sindhi</td><td align=\"center\">Pakistan</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">17</td><td align=\"center\">Ho433</td><td align=\"center\">R7a1b1</td><td align=\"center\">Ho</td><td align=\"center\">Jharkhand</td><td align=\"center\">Austro-Asiatic</td></tr><tr><td align=\"center\">18</td><td align=\"center\">Ori7</td><td align=\"center\">R7a1b1</td><td align=\"center\">Oraon</td><td align=\"center\">Jharkhand</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">19</td><td align=\"center\">Ori37</td><td align=\"center\">R7b1a</td><td align=\"center\">Oraon</td><td align=\"center\">Orissa</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">20</td><td align=\"center\">A474</td><td align=\"center\">R7a1b2</td><td align=\"center\">Oraon</td><td align=\"center\">Jharkhand</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">21</td><td align=\"center\">G39</td><td align=\"center\">R7a1b2</td><td align=\"center\">Santhal</td><td align=\"center\">Bihar</td><td align=\"center\">Austro-Asiatic</td></tr><tr><td align=\"center\">22</td><td align=\"center\">G19</td><td align=\"center\">R7a1b2</td><td align=\"center\">Kanwar</td><td align=\"center\">Madhya-Pradesh</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">23</td><td align=\"center\">KO18</td><td align=\"center\">R7b</td><td align=\"center\">Koya</td><td align=\"center\">Andhra-Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">24</td><td align=\"center\">KO55</td><td align=\"center\">R7b1a</td><td align=\"center\">Koya</td><td align=\"center\">Andhra-Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">25</td><td align=\"center\">G66</td><td align=\"center\">R7b1a</td><td align=\"center\">Gond</td><td align=\"center\">Madhya-Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">26</td><td align=\"center\">Ko74</td><td align=\"center\">R8a</td><td align=\"center\">Koya</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">27</td><td align=\"center\">Lam10</td><td align=\"center\">R8a1a1</td><td align=\"center\">Lambadi</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">28</td><td align=\"center\">Ko30</td><td align=\"center\">R8a1a2</td><td align=\"center\">Koya</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">29</td><td align=\"center\">Ko37</td><td align=\"center\">R8a1a2</td><td align=\"center\">Koya</td><td align=\"center\">Andhra Pradesh</td><td align=\"center\">Dravidian</td></tr><tr><td align=\"center\">30</td><td align=\"center\">CoB41</td><td align=\"center\">R8a1b</td><td align=\"center\">Konkanastha Brahmin</td><td align=\"center\">Maharashtra</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">31</td><td align=\"center\">CoB23</td><td align=\"center\">R30</td><td align=\"center\">Konkanastha Brahmin</td><td align=\"center\">Maharashtra</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">32</td><td align=\"center\">Sin49</td><td align=\"center\">R30a</td><td align=\"center\">Sinhalese</td><td align=\"center\">Sri Lanka</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">33</td><td align=\"center\">Pun47</td><td align=\"center\">R30b</td><td align=\"center\">Punjab</td><td align=\"center\">Punjab</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">34</td><td align=\"center\">Raj25</td><td align=\"center\">R31a1</td><td align=\"center\">Rajput</td><td align=\"center\">Rajasthan</td><td align=\"center\">Indo-European</td></tr><tr><td align=\"center\">35</td><td align=\"center\">Raj48</td><td align=\"center\">R31a1</td><td align=\"center\">Rajput</td><td align=\"center\">Rajasthan</td><td align=\"center\">Indo-European</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Frequency of Autochthonous R Subgroups Among Different Language Groups of India.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th align=\"center\">R5</th><th align=\"center\">R6</th><th align=\"center\">R7</th><th align=\"center\">R8</th><th align=\"center\">R30</th><th align=\"center\">R31</th><th align=\"center\">Total Samples</th></tr></thead><tbody><tr><td align=\"center\"><bold>Austro-Asiatic</bold></td><td align=\"center\">1.12%</td><td align=\"center\">4.27%</td><td align=\"center\">5.90%</td><td align=\"center\">2.64%</td><td align=\"center\">0.61%</td><td align=\"center\">0.00%</td><td align=\"center\">983</td></tr><tr><td align=\"center\"><bold>Indo-European</bold></td><td align=\"center\">3.62%</td><td align=\"center\">1.70%</td><td align=\"center\">0.58%</td><td align=\"center\">1.61%</td><td align=\"center\">2.63%</td><td align=\"center\">0.85%</td><td align=\"center\">2240</td></tr><tr><td align=\"center\"><bold>Dravidian</bold></td><td align=\"center\">3.65%</td><td align=\"center\">1.69%</td><td align=\"center\">1.37%</td><td align=\"center\">1.64%</td><td align=\"center\">2.15%</td><td align=\"center\">0.32%</td><td align=\"center\">2190</td></tr><tr><td align=\"center\"><bold>Tibeto-Burman</bold></td><td align=\"center\">1.74%</td><td align=\"center\">0.00%</td><td align=\"center\">0.00%</td><td align=\"center\">0.58%</td><td align=\"center\">0.58%</td><td align=\"center\">0.00%</td><td align=\"center\">172</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Coalescent times of hg R7 subclades estimated from HVS-I data.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Clade</th><th align=\"center\">Number of Samples</th><th align=\"center\">Motif (Coding region)</th><th align=\"center\"><italic>rho </italic>(ρ)</th><th align=\"center\"><italic>sigma </italic>(σ)</th><th align=\"center\">Time (SD)</th></tr></thead><tbody><tr><td align=\"left\"><bold>R7</bold></td><td align=\"center\">152</td><td align=\"center\">1442-6248-7870-9051-9110-10289-13105-13830</td><td align=\"center\">0.796</td><td align=\"center\">0.31</td><td align=\"center\">16.064 (6.260)</td></tr><tr><td align=\"left\"><bold>R7(Austro-Asiatic)</bold></td><td align=\"center\">47</td><td align=\"center\">1442-6248-7870-9051-9110-10289-13105-13830</td><td align=\"center\">0.234</td><td align=\"center\">0.102</td><td align=\"center\">4.723 (2.059)</td></tr><tr><td align=\"left\"><bold>R7(Indo-European)</bold></td><td align=\"center\">29</td><td align=\"center\">1442-6248-7870-9051-9110-10289-13105-13830</td><td align=\"center\">0.793</td><td align=\"center\">0.306</td><td align=\"center\">16.005 (6.185)</td></tr><tr><td align=\"left\"><bold>R7(Dravidian)</bold></td><td align=\"center\">76</td><td align=\"center\">1442-6248-7870-9051-9110-10289-13105-13830</td><td align=\"center\">1.145</td><td align=\"center\">0.536</td><td align=\"center\">23.101 (10.822)</td></tr><tr><td align=\"left\"><bold>R7a(Overall)</bold></td><td align=\"center\">107</td><td align=\"center\">10143-10915-13404-15346</td><td align=\"center\">0.389</td><td align=\"center\">0.102</td><td align=\"center\">7.848 (2.064)</td></tr><tr><td align=\"left\"><bold>R7a(Dravidian)</bold></td><td align=\"center\">37</td><td align=\"center\">10143-10915-13404-15346</td><td align=\"center\">0.514</td><td align=\"center\">0.151</td><td align=\"center\">10.363 (3.037)</td></tr><tr><td align=\"left\"><bold>R7a(Indo-European)</bold></td><td align=\"center\">24</td><td align=\"center\">10143-10915-13404-15346</td><td align=\"center\">0.5</td><td align=\"center\">0.24</td><td align=\"center\">10.090 (4.757)</td></tr><tr><td align=\"left\"><bold>R7b(Overall)</bold></td><td align=\"center\">45</td><td align=\"center\">1804-2282-8557-12432-14064-15942</td><td align=\"center\">0.797</td><td align=\"center\">0.292</td><td align=\"center\">16.052 (5.891)</td></tr><tr><td align=\"left\"><bold>R7b(Dravidian)</bold></td><td align=\"center\">39</td><td align=\"center\">1804-2282-8557-12432-14064-15942</td><td align=\"center\">0.744</td><td align=\"center\">0.268</td><td align=\"center\">15.006 (5.402)</td></tr><tr><td align=\"left\"><bold>R7a1(Overall)</bold></td><td align=\"center\">86</td><td align=\"center\">12406-13674</td><td align=\"center\">0.337</td><td align=\"center\">0.115</td><td align=\"center\">6.805 (2.311)</td></tr><tr><td align=\"left\"><bold>R7a1(Austro-Asiatic)</bold></td><td align=\"center\">47</td><td align=\"center\">12406-13674</td><td align=\"center\">0.234</td><td align=\"center\">0.102</td><td align=\"center\">4.723 (2.059)</td></tr><tr><td align=\"left\"><bold>R7a1(Indo-European)</bold></td><td align=\"center\">23</td><td align=\"center\">12406-13674</td><td align=\"center\">0.522</td><td align=\"center\">0.246</td><td align=\"center\">10.529 (4.663)</td></tr><tr><td align=\"left\"><bold>R7a1(Dravidian)</bold></td><td align=\"center\">16</td><td align=\"center\">12406-13674</td><td align=\"center\">0.375</td><td align=\"center\">0.153</td><td align=\"center\">7.568 (3.090)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Mantel correlation test of Autochthonous R Subgroups to assess the significance of correlations between gene and geography, or language.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"center\">Haplogroup</th><th align=\"center\">Gene vs Geography</th><th align=\"center\">p</th><th align=\"center\">Gene vs Language</th><th align=\"center\">p</th></tr></thead><tbody><tr><td align=\"center\"><bold>R5</bold></td><td align=\"center\">0.1276</td><td align=\"center\">0.0475</td><td align=\"center\">0.1748</td><td align=\"center\">0.2</td></tr><tr><td align=\"center\"><bold>R6</bold></td><td align=\"center\">0.2654</td><td align=\"center\">0.037</td><td align=\"center\">0.13248</td><td align=\"center\">0.19</td></tr><tr><td align=\"center\"><bold>R7</bold></td><td align=\"center\">0.299</td><td align=\"center\">0.023</td><td align=\"center\">0.219</td><td align=\"center\">0.225</td></tr><tr><td align=\"center\"><bold>R8</bold></td><td align=\"center\">0.211496</td><td align=\"center\">0.01753</td><td align=\"center\">0.23248</td><td align=\"center\">0.31</td></tr><tr><td align=\"center\"><bold>R30</bold></td><td align=\"center\">0.189917</td><td align=\"center\">0.127</td><td align=\"center\">0.1348</td><td align=\"center\">0.28</td></tr><tr><td align=\"center\"><bold>R31</bold></td><td align=\"center\">0.172</td><td align=\"center\">0.1873</td><td align=\"center\">0.141</td><td align=\"center\">0.25</td></tr></tbody></table></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Phylogenetic tree of 22 Indian complete mtDNA sequences of superhaplogroup R. The tree includes data reported [[##REF##15467980##4##] and references there in] Suffixes A, C, G, and T indicate transversions, \"d\" signifies a deletion; recurrent mutations are underlined. 16182C, 16183C and 16519 polymorphisms are omitted in phylogenetic reconstruction. The sample code, geographic and linguistic affiliations are described in Table ##TAB##0##1##. The sub-tree of haplogroup R7 sequences is displayed in Fig. ##FIG##1##2##.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Haplogroup R5-8, R30 and R31 frequency plots with 95% credible regions. Data calculated from the posterior distribution of the proportion of a haplogroup/sub-haplogroup in the population. Linguistic affiliations of the populations are indicated by colors.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p>Map of Indian subcontinent depicting the spatial frequency distribution of mtDNA haplogroup R7. Isofrequency maps were generated by using Surfer7 Golden software (Golden Software Inc., Golden, Colorado), following the Kriging procedure. The spread of R7 in India is centered around the AA \"heartland\" (Bihar, Jharkhand, and Chhattisgarh). Dots indicate the sampling locations.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p>Spatial Autocorrelation Analyses Correlograms of haplogroup R7 in Indian subcontinent. The Moran's I coefficient was calculated with five distance classes in binary weight matrix. Significant values are shown as black (p &lt; .05) whereas nonsignificant values as blank circles. Distances are given in Kilometers (KM's).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional file 5</title><p>Map of India showing the frequency distribution (%) of haplogroup R7 at the district level. Only 2,200 samples were available at this resolution. Nevertheless, it is still evident that the frequency peak of R7 is observed in Bihar, Jharkhand, Chhattisgarh, Madhya-Pradesh and the northern districts of Andhra-Pradesh (Adilabad, Warangal and Khammam).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional file 6</title><p>Details of the samples studied for hg R7 in the present study. Data shown are from the present work and from literature: [##REF##15467980##4##,##REF##10574762##12##,##REF##15339343##14##,##REF##12678055##21##,##REF##15077202##22##,##REF##11381027##41##, ####REF##12969624##42##, ##REF##11702215##43##, ##REF##15469422##44##, ##REF##15749372##45##, ##REF##16893451##46####16893451##46##].</p></caption></supplementary-material>" ]
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[{"surname": ["Singh"], "given-names": ["KS"], "collab": ["Ed"], "article-title": ["The Scheduled Tribes"], "source": ["People of India"], "year": ["1997"], "publisher-name": ["Oxford, Oxford University Press"]}, {"surname": ["Singh"], "given-names": ["KS"], "collab": ["Ed"], "article-title": ["The Scheduled Castes"], "source": ["People of India"], "year": ["2002"], "publisher-name": ["New Dehli, Oxford University Press"]}, {"surname": ["Petraglia", "Allchin"], "given-names": ["M", "B"], "person-group": ["Petraglia M, Allchin B"], "article-title": ["Human evolution and culture change in the Indian subcontinent"], "source": ["The Evolution and History of Human Populations in Indian subcontinent"], "year": ["2007"], "publisher-name": ["Springer, Netherlands"], "fpage": ["393"], "lpage": ["443"]}, {"surname": ["Chaubey", "Metspalu", "Karmin", "Thangaraj", "Rootsi", "Parik", "Solnik", "Selvi Rani", "Singh", "Naidu", "Reddy", "Metspalu", "Singh", "Kivisild", "Villems"], "given-names": ["G", "M", "M", "K", "S", "J", "A", "D", "VK", "BP", "AG", "E", "L", "T", "R"], "article-title": ["Language shift by indigenous population: A Model Genetic Study in Indian subcontinent"], "source": ["Int J Hum Genet"], "year": ["2008"], "volume": ["8"], "fpage": ["41"], "lpage": ["50"]}, {"surname": ["Higham", "Thosarat"], "given-names": ["CFW", "R"], "source": ["Prehistoric Thailand: From early settlement to Sukothai"], "year": ["1998"], "publisher-name": ["River Books, Bangkok"]}, {"surname": ["Diffloth"], "given-names": ["G"], "article-title": ["Austroasiatic languages"], "source": ["Encyclopedia Britannica, online edition"], "year": ["2005"], "ext-link": ["http://www.britannica.com/eb/article-9109792"]}, {"surname": ["Fuller"], "given-names": ["DQ"], "person-group": ["Petraglia M, Allchin B"], "article-title": ["Non-human genetics, agricultural origins and historical linguistics in Indian subcontinent"], "source": ["The Evolution and History of Human Populations in Indian subcontinent"], "year": ["2007"], "publisher-name": ["Springer, Netherlands"], "fpage": ["393"], "lpage": ["443"]}, {"surname": ["Witzel"], "given-names": ["M"], "person-group": ["Osada T"], "article-title": ["Central Asian roots and acculturation in Indian subcontinent: linguistic and archaeological evidence from Western Central Asia, the Hindukush and northwestern Indian subcontinent for early Indo-Aryan language and religion"], "source": ["Liguistics, Archaeology and the Human Past"], "year": ["2005"], "publisher-name": ["Research Institute for Humanity and Nature, Kyoto"], "fpage": ["87"], "lpage": ["211"]}, {"surname": ["Sokal", "Oden"], "given-names": ["RR", "NL"], "article-title": ["Spatial autocorrelation in biology"], "source": ["Biol J Linn Soc"], "year": ["1978"], "volume": ["10"], "fpage": ["199"], "lpage": ["249"]}, {"surname": ["Mantel"], "given-names": ["NA"], "article-title": ["The detection of disease clustering and a generalized regression approach"], "source": ["cer Res"], "year": ["1967"], "volume": ["27"], "fpage": ["209"], "lpage": ["220"]}, {"surname": ["Schneider", "Roessli", "Excoffier"], "given-names": ["S", "D", "L"], "source": ["Arlequin ver. 2.000: A software for population genetics data analysis"], "year": ["2000"], "publisher-name": ["Genetics and Biometry Laboratory, University of Geneva, Geneva, Switzerland"]}, {"surname": ["Krishnamurti"], "given-names": ["B"], "source": ["The Dravidian Languages"], "year": ["2003"], "publisher-name": ["Cambridge University Press"], "fpage": ["1"], "lpage": ["574"]}, {"surname": ["Driem"], "given-names": ["G"], "article-title": ["Languages of the Himalayas"], "source": ["an ethnolinguistic handbook Leiden"], "year": ["1997"], "publisher-name": ["New York ; K\u00f6ln : Brill"]}]
{ "acronym": [], "definition": [] }
46
CC BY
no
2022-01-12 17:11:36
BMC Evol Biol. 2008 Aug 4; 8:227
oa_package/38/11/PMC2529308.tar.gz
PMC2529309
18667061
[ "<title>Background</title>", "<p>A number of useful evolutionary parameters can be estimated from between species comparisons of genome-wide divergence patterns: the magnitude of positive and negative (purifying) selection, variation in selection across different lineages, chromosomes, gene families and individual genes as well as the number of genes involved in the speciation process and adaptation to new environments.</p>", "<p>Comparative approaches have revealed that genes involved in immunity or in host defenses tend to exhibit the highest rate of evolution in the genome of different species. The arms race between hosts and parasites is generally invoked to explain this rapid evolution of genes involved in immune defense [##REF##15367941##1##]. Pathogens continuously evolve to escape the defense of the host and the host, in turn, responds by modifying its defense. At the molecular level, this cycle of environmental changes means that new mutations are continuously tested and fixed by selection if adaptive, which translates into higher rates of molecular evolution in genes controlling immunity in the hosts.</p>", "<p>While accelerated evolution of genes involved in immunity is common in mammals, the relative rate of evolution of those genes may vary from one phylogenetic lineage to another. This is the case for hominids (human and chimpanzee) compared to rodents (mouse and rats) where genes involved in immune defense show an accelerated rate of evolution in murids compared to hominids, suggesting that the immune system of murids has undergone more extensive specific innovations [##REF##16136131##2##].</p>", "<p>Hosts belonging to different lineages can therefore represent different environments for parasites to adapt. How do parasites preferentially infecting different host lineages respond to these different environments? Do parasites infecting different host lineages show lineage specific rates of evolution?</p>", "<p><italic>Plasmodium </italic>is a practical case study for genome evolution of parasites specifically infecting different host lineages. The genomes of two parasite species infecting only hominids (<italic>Plasmodium falciparum </italic>[##REF##12368864##3##], <italic>P. reichenowi </italic>[##REF##17159978##4##]) and three species preferentially infecting rodent hosts (<italic>P. yoelii yoelii</italic>, <italic>P. chabaudi </italic>and <italic>P. berghei</italic>) [##REF##15637271##5##] are now partially or completely sequenced. Although these two groups of species might be subject to similar selective pressures acting either on the genome as a whole or on genes with similar function across species, some aspects of their genomes, such as genes associated with evading host immunity, may evolve in a unique manner.</p>", "<p>In this paper we systematically analyze and compare the rate of evolution of protein-coding genes in the parasites infecting hominids (hereafter called the hominid parasite lineage) and in those infecting rodents (rodent or murid parasite lineage) (Figure ##FIG##0##1##). We explore and compare the adaptive rate of evolution of genes in both groups based on their function and timing or level of expression, factors that may explain variation in the rate of evolution among different genes between the different lineages.</p>", "<p>As for their two mammal host lineages (i.e. hominids and rodents) [##REF##16136131##2##], our study reveals, in particular, that the evolution of the hominid lineage parasite genomes was less constrained than the evolution of those parasites infecting the murid lineage, which likely reflects a lower effective population size in hominid parasites (specifically <italic>P. falciparum</italic>).</p>" ]
[ "<title>Methods</title>", "<title>Data sequences and alignments</title>", "<p>For rodent malaria species, protein and nucleotide sequences for annotated genes for <italic>P. berghei, P. chabaudi </italic>and <italic>P. yoelii yoelii </italic>were obtained from The Plasmodium Genome Resource Database (Plasmodb [##UREF##1##19##]). Orthologous genes between the three species were obtained with BlastN using the criterion of best hits with scores of E &lt; 1*10<sup>-15 </sup>and at least 70% similarity in length. Only the groups of genes for which only one gene of each species corresponded to these criteria were conserved. All groups of coding sequences were aligned using Clustal W version 1.82 [##REF##3243435##20##] (default parameters) using amino acid sequences followed by back-translation into nucleotides sequences using the original sequence provided by Plasmodb.</p>", "<p>For hominid malaria species, protein and nucleotides sequences for annotated genes were obtained for <italic>P. falciparum </italic>only. For <italic>P reichenowi</italic>, Plasmodb provided only nucleotide contigs (release 09 July 2004) of a partial genome shotgun of approximately onefold coverage. The assembled contiguous sequences cover slightly less than one third of the <italic>P. reichenowi </italic>genome. Orthologous genes between the two species and their alignment were obtained following several steps. First, <italic>P. falciparum </italic>and <italic>P. reichenowi </italic>orthologues were obtained using BlastN with scores of E &lt; 1*10<sup>-15 </sup>and at least 70% similarity in length. Only groups of genes where we obtained only one gene of each species were kept. These groups were then aligned using ClustalW (V. 1.82) using default parameters. All the alignments were then very carefully checked by eye and corrected when necessary. Introns were deleted.</p>", "<title>Synonymous and non-synonymous substitution rate analyses</title>", "<p>For both the rodent and hominid malaria gene groups, maximum likelihood estimates of rates of non-synonymous (<italic>dN</italic>) and synonymous (<italic>dS</italic>) substitutions, averaged over all branches, were obtained using PAML version 3.14 [##REF##9367129##21##]. We used a codon-based model of sequence evolution with <italic>dN </italic>and <italic>dS </italic>considered as free parameters and the average nucleotide frequencies estimated from the data at each codon position (F3 × 4 MG model). The transition/tranversion bias <italic>K </italic>was estimated for each group of orthologous sequence. Because estimates of <italic>dS </italic>&gt; 1 are more prone to error [##REF##15084679##22##], only genes with <italic>dS </italic>≤ 1 were used for statistical calculations, yielding 843 and 3060 valid orthologues for hominid and rodents malaria groups respectively. For each gene, Likelihood Ratio Tests (LRT) were used to test whether the estimated <italic>dN/dS </italic>(ω) ratio differed significantly from 1 [##REF##9580986##23##]. The tests were performed as bilateral tests of the hypothesis <italic>H</italic><sub>o</sub>: <italic>dN/dS </italic>=1 versus the alternative hypothesis <italic>H</italic><sub>1</sub>: <italic>dN/dS ≠ 1 </italic>for each group of sequence. Twice the difference of the log likelihood estimated for each hypothesis was then compared to a χ<sup>2 </sup>distribution with one degree of freedom (<italic>df</italic>).</p>", "<title>Substitution rates and genomic features</title>", "<p>To learn more about both synonymous and non-synonymous substitution patterns and their possible causes, we analyzed the effect of several genomic features such as the GC content, the level and timing of expression of genes and the function of proteins.</p>", "<p>The biological process of the annotated proteins of <italic>P. falciparum </italic>was determined using GO (Gene ontology) annotations. A biological process is a series of events accomplished by one or more ordered assemblies of molecular functions. Examples of broad biological process terms are cellular physiological process or signal transduction. Classification of proteins was made using the software GENERIC GENE ONTOLOGY (GO) TERM MAPPER [##UREF##2##24##]. Because no such classification was available for any of the rodent species, <italic>P. yoelii yoelii </italic>genes were classified as their orthologous <italic>P. falciparum </italic>genes defined in The TIGR <italic>Plasmodium yoelii yoelii </italic>Genome Annotation Database.</p>", "<p>For gene expression, we retrieved the mRNA abundance for genes of <italic>P. falciparum </italic>for different stages (rings, trophozoites, shizonts, merozoites, gametocytes and sporozoites) from [##REF##15520293##25##]. For the rodent lineage, information on expression was available for <italic>P. berghei </italic>on a lower number of stages (asexual blood stages, gametocytes, ookinetes, oocysts and sporozoites) [##REF##15637271##5##]. Such data precluded any possible rigorous comparison between the two lineages of parasites because of a lack of overlap between stages analyzed in the hominid and rodent lineages. We therefore decided to determine the timing of expression of rodent genes by using their orthology with <italic>P. falciparum</italic>, simply considering orthologous genes to be expressed at similar stages. We computed the breadth of expression for each gene in each lineage as the total number of different stages in which a gene is expressed.</p>", "<p>GC content was computed using CODONW [##UREF##3##26##]. GC content quantifies the proportion of GC inside the gene.</p>" ]
[ "<title>Results and Discussion</title>", "<p>By taking the complete set of coding genes of <italic>P. falciparum </italic>and aligning it to a partial genome shotgun of <italic>P. reichenowi </italic>(covering approximately 1/3 of the entire genome) available in PlasmoDB, we identified a set of 843 pairs of genes with unambiguous orthology for which it was possible to generate high quality sequence alignments covering virtually the entire coding region (see Methods and Additional file ##SUPPL##0##1##). The same procedure retrieved 3060 triplets of orthologues for rodent malaria parasites <italic>P. yoelii yoelii</italic>, <italic>P. berghei </italic>and <italic>P. chabaudi</italic>, distributed throughout these genomes (Additional file ##SUPPL##1##2##).</p>", "<title>Average rates of evolution: evolutionary constraints and selection on amino-acid sites within the hominid and murid lineages</title>", "<p>Analyzing estimates of ω (<italic>dN/dS</italic>) in both hominid (ω<sub>Hominids</sub>) and rodent (ω<sub>rodents</sub>) parasite species independently made it possible to study how evolutionary constraints and selection vary across both clades. In the hominid lineage, the average ω<sub>Hominids </sub>was estimated at 0.21 (Table ##TAB##0##1##), which is in congruence with previous estimates [##REF##17159978##4##]. This excludes four genes with estimates of ω higher than 500 that had very low observed <italic>dS </italic>estimates (including these 4 genes, the average was ~4.68) and five genes with an undefined ratio (<italic>dS </italic>= 0). Among the 843 pairs of orthologues analyzed between <italic>P. falciparum </italic>and <italic>P. reichenowi</italic>, only ten pairs displayed a higher ratio than 1 but none were statistically significant (<italic>p-values </italic>less than 0.05 as determined by a likelihood ratio test). Most genes were conserved with ω significantly lower than one. A total of 719 genes, 85% of the genes analyzed, were in fact determined to be under purifying selection with ω significantly less than one.</p>", "<p>The average ω <sub>Murids </sub>for the 3060 genes analyzed in <italic>P. yoelii yoelii, P. chabaudi and P. berghei</italic>, was estimated as 0.13 (Table ##TAB##0##1##), significantly lower than in parasites infecting hominids (Wilcoxon test over all genes, <italic>p-value </italic>&lt; 10<sup>-4</sup>). Only 4 genes displayed a ratio greater than 1, but none were significant. In fact, more than 98% of the genes displayed a ratio significantly lower than 1.</p>", "<p>Several observations suggest that the difference observed between the hominid and the rodent lineage is not due to the number of species aligned in each lineage nor their phylogenetic distances. First, the ω obtained between any pair of rodent parasite species, are similar, and lower than the estimate obtained for <italic>P. falciparum </italic>and <italic>P. reichenowi </italic>(data from [##REF##15637271##5##]: <italic>P. yoelii yoelii </italic>and <italic>P. chabaudi</italic>: ω = 0.11, <italic>P. yoelii yoelii </italic>and <italic>P. berghei</italic>: ω = 0.16 and <italic>P. chabaudi </italic>and <italic>P. berghei</italic>: ω = 0.13). Second, the marked difference between hominid and rodent malaria parasites still held when comparisons between lineages are made in a paired way using only those genes from hominid and rodent lineages of known orthology (orthology between <italic>P. falciparum and P. yoelii yoelii </italic>was retrieved from [##REF##12368865##6##]; Wilcoxon rank sum test; n (genes) = 263; <italic>p-value </italic>= 0.002). Therefore, there is an excess of about 25% of amino-acid altering substitutions, relative to synonymous substitutions, in the hominid lineage compared to the rodent lineage.</p>", "<p>Interestingly, the host lineages of these two groups of parasites show similar differences in their rate of molecular evolution. Hominids show an average ω of 0.20, over the whole genome, while murids show an average ratio close to 0.13 [##REF##16136131##2##,##REF##18077382##7##]. As in the case of <italic>Plasmodium </italic>parasites, the difference between both estimates is highly significant. This difference in the rate of molecular evolution among the host lineages could be the result of two different processes: 1) a relaxation in the selective constraints acting on the amino-acid sequence in the host hominid lineage, relative to the murid lineage, subsequent to a reduction in their effective population size or 2) an acceleration in the global rate of adaptive evolution in hominids. Genetic evidence favors the first hypothesis [##REF##7714912##8##,##REF##16136131##2##,##UREF##0##9##]. Does the same explanation hold for parasites?</p>", "<p>Comparing genetic variation at synonymous and non-synonymous sites within (polymorphism) and between species (divergence) can help distinguish between the two hypotheses above [##REF##1904993##10##]. When most variation is neutral, the ratio of the number of nonsynonymous to synonymous polymorphisms observed within populations should be the same as the ratio of divergence between species [##REF##1904993##10##]. Mu and collaborators [##REF##17159981##11##] analyzed genome-wide polymorphism of 5 <italic>P. falciparum </italic>isolates distributed globally. Jeffares and collaborators also analyzed polymorphisms using three different African isolates [##REF##17159978##4##]. In total, we obtained divergence and polymorphism data for 518 genes using Mu et al [##REF##17159981##11##]'s data and 839 genes using Jeffares et al. [##REF##17159978##4##]'s data. The ratio of the number of nonsynonymous to synonymous polymorphism was 2.3 for the first dataset and 2.7 for the second one. Comparatively, the ratio of divergence we observed among <italic>P. falciparum-P. reichenowi </italic>was only 1.1 and 1.3, respectively, thus indicating a 2-fold increase in the number of nonsynonymous polymorphisms. Although some amino-acid substitutions observed among species are likely adaptive, this observation supports selection being less efficient in removing segregating deleterious amino-acids in <italic>P. falciparum</italic>. A reduced effective size in hominid parasites compared to the murid lineage might be one explanation for the observed difference in ω. This scenario is supported by different studies suggesting the existence of an historical low effective population size in <italic>P. falciparum</italic>[##REF##12036741##12##]. Unfortunately, no such population study exists for rodent malaria species. The evolution of both hominids and their malaria parasites appears to be less constrained than that of murids, which might reflect, for both the host and the parasite, a small historical effective size and an evolution mainly driven by slightly deleterious mutations [##REF##10570982##13##].</p>", "<title>Variation in evolutionary rates across functionally different genes</title>", "<p>We then asked whether specific groups of genes evolved differently from the rest of the genome. In particular, we searched for groups of genes that could have experienced an accelerated evolution in one lineage compared to the other, thus leading to a higher difference in their amino-acid substitution rate between lineages than expected given the difference observed across the genome.</p>", "<p>To do so, we searched for variation in ω among different functional categories of genes. No functional annotation was directly available for the rodent lineage, so we classified rodent genes using their orthology with <italic>P. falciparum </italic>(see methods). Practically, because the amino-acid divergence was, on average, higher in the hominid lineage compared to the rodent lineage (see above section: Average rates of evolution), we sought categories that showed a significantly lower or higher difference than expected on average. To do so, we used the following Linear Model: ω ~<italic> L + Ca + L*Ca + constant</italic>, where ω corresponds to the ratio computed for each gene, <italic>L </italic>to the lineage (Hominid or Rodent) and <italic>Ca </italic>to the category to which the gene belongs (the category of interest or the rest of the genome). A difference higher than the average ω for certain categories between the hominid and the rodent lineages should translate into a significant interaction (<italic>L*Ca</italic>). We considered only those categories that contained at least 5 genes in both hominid and rodent lineages. Overall, the ω ratio was correlated among categories between hominid and rodent lineages (<italic>r</italic><sup>2 </sup>= 0.34; <italic>p-value </italic>= 0.0019). We found no category of genes showing an accelerated evolution in one lineage compared to the other, relative to the rest of the genome (Fig. ##FIG##1##2## and Table ##TAB##1##2##). Such a result can be interpreted in several ways. First, categories that could have been affected by such acceleration were not included in the analysis because of a lack of data. This could be the case, for instance, for categories of genes like those involved in host-parasite interactions like VAR genes which are not found in rodent parasite genomes. In our dataset, we had to exclude this category because of an insufficient number of genes belonging to it. Second, an evolutionary acceleration in the amino-acid substitution rate may not affect entire categories of genes but only some of them expressed, for instance, at only some particular stages of the parasite, thus rendering it more difficult to detect them.</p>", "<title>Variation in evolutionary rates, timing and frequency of expression</title>", "<p>We finally addressed how variation in evolutionary rates could evolve relative to the timing and the breadth of expression of the genes. Do genes expressed at a particular stage show lineage specific evolution?</p>", "<p>As shown in Fig. ##FIG##2##3A##, hominid and rodent genes show similar patterns of evolution relative to their timing of expression. While no significant difference was observed between categories of genes for hominids (<italic>p-</italic>value = 0.3), a difference was found between categories for rodents: (<italic>p-</italic>value = 0.013). For both lineages, genes that are expressed at the merozoite stage look the most constrained while those expressed at the gametocyte stage appear the least constrained. We did not find any group of genes showing an accelerated evolution in one lineage compared to the other relative to the rest of the genome.</p>", "<p>The classification we used for Fig. ##FIG##2##3A## was nevertheless very broad. It included genes that were expressed at one particular stage but those same genes could also be expressed at other stages. Because this could preclude the detection of stage-specific evolution, we then re-analyzed our data keeping only the genes that were expressed at one stage. Doing so, we observed a significant difference in the rate of evolution among the different categories in the hominid lineage (<italic>p-</italic>value = 0.017) but no difference was observed in the rodent lineage (<italic>p-</italic>value = 0.59, Fig. ##FIG##2##3B##). We observed an overall difference between hominid and rodent parasites primarily due to the loci expressed at the sporozoite stage that showed an accelerated evolution in the hominid lineage compared to the rodent one (<italic>p-</italic>value = 0.018). This result suggests that genes only expressed at the sporozoite stage might be key genes in the infection process of the mammal host by malaria parasites and experienced higher adaptive evolution in the hominid lineage than in the rodent one.</p>", "<p>We then analyzed the relationship between the breadth of expression and evolutionary rates. Genes expressed at only one parasitic stage (see methods for details) were characterized as unique and those expressed at all stages were characterized as ubiquitous. For both the hominid and rodent lineages, we observed a significant relationship between the breadth of expression and the rate of non-synonymous substitutions. On average, stage specific proteins (expressed at only one stage) evolve at a higher rate relative to ubiquitous ones (expressed at all stages) (Fig. ##FIG##3##4## and Table ##TAB##2##3##). In contrast, synonymous variation shows a very different trend regarding the breadth of expression. The rate of synonymous substitutions increases with the breadth of expression: proteins expressed in a larger number of stages evolve at higher rates at synonymous sites. The obvious corollary of these observations is a negative relationship between the breadth of expression and <italic>dN/dS </italic>as shown in Figure ##FIG##3##4##. Note that we obtained similar relationships for rodents using data on the expression of <italic>P. berghei </italic>[##REF##15637271##5##] except that we did not find any significant relationship between <italic>dS </italic>and the breadth of expression (data not shown).</p>", "<p>Our results are congruent with previous studies reporting relationships between breadth of expression and the rate of gene evolution in other organisms [##REF##16176987##14##,##REF##14595100##15##]. In both hominid and rodent lineages, highly expressed genes are generally more constrained than less expressed genes [##REF##16044241##16##]. This observation is often attributed to the fact that proteins that are expressed in more diverse cellular environments are subjected to stronger functional constraints [##REF##16176987##14##,##REF##14595100##15##,##REF##15978954##17##]. These results are consistent with the observation in both hominid and rodent parasites of a positive relationship between GC content at position 1 and 2 of codons (GC1-2) and the level of expression as well as a negative relationship between GC1-2 and <italic>dN/dS </italic>(Table ##TAB##2##3##). Highly and universally expressed genes are more GC-rich than lowly expressed genes which might thus reflect a codon bias, in particular for GC-rich codons. In other words, amino acids encoded by GC-rich residues are preferred and conserved in protein coding genes of the genus <italic>Plasmodium</italic>.</p>", "<p>The positive relationship observed between the rate of synonymous substitutions and expression can potentially be explained by translational selection acting on synonymous codon sites of highly expressed genes [##REF##16044241##16##]. Alternatively, an increase in transcription may simply increase the level of spontaneous mutations as demonstrated in certain bacteria [##REF##12888512##18##].</p>" ]
[ "<title>Results and Discussion</title>", "<p>By taking the complete set of coding genes of <italic>P. falciparum </italic>and aligning it to a partial genome shotgun of <italic>P. reichenowi </italic>(covering approximately 1/3 of the entire genome) available in PlasmoDB, we identified a set of 843 pairs of genes with unambiguous orthology for which it was possible to generate high quality sequence alignments covering virtually the entire coding region (see Methods and Additional file ##SUPPL##0##1##). The same procedure retrieved 3060 triplets of orthologues for rodent malaria parasites <italic>P. yoelii yoelii</italic>, <italic>P. berghei </italic>and <italic>P. chabaudi</italic>, distributed throughout these genomes (Additional file ##SUPPL##1##2##).</p>", "<title>Average rates of evolution: evolutionary constraints and selection on amino-acid sites within the hominid and murid lineages</title>", "<p>Analyzing estimates of ω (<italic>dN/dS</italic>) in both hominid (ω<sub>Hominids</sub>) and rodent (ω<sub>rodents</sub>) parasite species independently made it possible to study how evolutionary constraints and selection vary across both clades. In the hominid lineage, the average ω<sub>Hominids </sub>was estimated at 0.21 (Table ##TAB##0##1##), which is in congruence with previous estimates [##REF##17159978##4##]. This excludes four genes with estimates of ω higher than 500 that had very low observed <italic>dS </italic>estimates (including these 4 genes, the average was ~4.68) and five genes with an undefined ratio (<italic>dS </italic>= 0). Among the 843 pairs of orthologues analyzed between <italic>P. falciparum </italic>and <italic>P. reichenowi</italic>, only ten pairs displayed a higher ratio than 1 but none were statistically significant (<italic>p-values </italic>less than 0.05 as determined by a likelihood ratio test). Most genes were conserved with ω significantly lower than one. A total of 719 genes, 85% of the genes analyzed, were in fact determined to be under purifying selection with ω significantly less than one.</p>", "<p>The average ω <sub>Murids </sub>for the 3060 genes analyzed in <italic>P. yoelii yoelii, P. chabaudi and P. berghei</italic>, was estimated as 0.13 (Table ##TAB##0##1##), significantly lower than in parasites infecting hominids (Wilcoxon test over all genes, <italic>p-value </italic>&lt; 10<sup>-4</sup>). Only 4 genes displayed a ratio greater than 1, but none were significant. In fact, more than 98% of the genes displayed a ratio significantly lower than 1.</p>", "<p>Several observations suggest that the difference observed between the hominid and the rodent lineage is not due to the number of species aligned in each lineage nor their phylogenetic distances. First, the ω obtained between any pair of rodent parasite species, are similar, and lower than the estimate obtained for <italic>P. falciparum </italic>and <italic>P. reichenowi </italic>(data from [##REF##15637271##5##]: <italic>P. yoelii yoelii </italic>and <italic>P. chabaudi</italic>: ω = 0.11, <italic>P. yoelii yoelii </italic>and <italic>P. berghei</italic>: ω = 0.16 and <italic>P. chabaudi </italic>and <italic>P. berghei</italic>: ω = 0.13). Second, the marked difference between hominid and rodent malaria parasites still held when comparisons between lineages are made in a paired way using only those genes from hominid and rodent lineages of known orthology (orthology between <italic>P. falciparum and P. yoelii yoelii </italic>was retrieved from [##REF##12368865##6##]; Wilcoxon rank sum test; n (genes) = 263; <italic>p-value </italic>= 0.002). Therefore, there is an excess of about 25% of amino-acid altering substitutions, relative to synonymous substitutions, in the hominid lineage compared to the rodent lineage.</p>", "<p>Interestingly, the host lineages of these two groups of parasites show similar differences in their rate of molecular evolution. Hominids show an average ω of 0.20, over the whole genome, while murids show an average ratio close to 0.13 [##REF##16136131##2##,##REF##18077382##7##]. As in the case of <italic>Plasmodium </italic>parasites, the difference between both estimates is highly significant. This difference in the rate of molecular evolution among the host lineages could be the result of two different processes: 1) a relaxation in the selective constraints acting on the amino-acid sequence in the host hominid lineage, relative to the murid lineage, subsequent to a reduction in their effective population size or 2) an acceleration in the global rate of adaptive evolution in hominids. Genetic evidence favors the first hypothesis [##REF##7714912##8##,##REF##16136131##2##,##UREF##0##9##]. Does the same explanation hold for parasites?</p>", "<p>Comparing genetic variation at synonymous and non-synonymous sites within (polymorphism) and between species (divergence) can help distinguish between the two hypotheses above [##REF##1904993##10##]. When most variation is neutral, the ratio of the number of nonsynonymous to synonymous polymorphisms observed within populations should be the same as the ratio of divergence between species [##REF##1904993##10##]. Mu and collaborators [##REF##17159981##11##] analyzed genome-wide polymorphism of 5 <italic>P. falciparum </italic>isolates distributed globally. Jeffares and collaborators also analyzed polymorphisms using three different African isolates [##REF##17159978##4##]. In total, we obtained divergence and polymorphism data for 518 genes using Mu et al [##REF##17159981##11##]'s data and 839 genes using Jeffares et al. [##REF##17159978##4##]'s data. The ratio of the number of nonsynonymous to synonymous polymorphism was 2.3 for the first dataset and 2.7 for the second one. Comparatively, the ratio of divergence we observed among <italic>P. falciparum-P. reichenowi </italic>was only 1.1 and 1.3, respectively, thus indicating a 2-fold increase in the number of nonsynonymous polymorphisms. Although some amino-acid substitutions observed among species are likely adaptive, this observation supports selection being less efficient in removing segregating deleterious amino-acids in <italic>P. falciparum</italic>. A reduced effective size in hominid parasites compared to the murid lineage might be one explanation for the observed difference in ω. This scenario is supported by different studies suggesting the existence of an historical low effective population size in <italic>P. falciparum</italic>[##REF##12036741##12##]. Unfortunately, no such population study exists for rodent malaria species. The evolution of both hominids and their malaria parasites appears to be less constrained than that of murids, which might reflect, for both the host and the parasite, a small historical effective size and an evolution mainly driven by slightly deleterious mutations [##REF##10570982##13##].</p>", "<title>Variation in evolutionary rates across functionally different genes</title>", "<p>We then asked whether specific groups of genes evolved differently from the rest of the genome. In particular, we searched for groups of genes that could have experienced an accelerated evolution in one lineage compared to the other, thus leading to a higher difference in their amino-acid substitution rate between lineages than expected given the difference observed across the genome.</p>", "<p>To do so, we searched for variation in ω among different functional categories of genes. No functional annotation was directly available for the rodent lineage, so we classified rodent genes using their orthology with <italic>P. falciparum </italic>(see methods). Practically, because the amino-acid divergence was, on average, higher in the hominid lineage compared to the rodent lineage (see above section: Average rates of evolution), we sought categories that showed a significantly lower or higher difference than expected on average. To do so, we used the following Linear Model: ω ~<italic> L + Ca + L*Ca + constant</italic>, where ω corresponds to the ratio computed for each gene, <italic>L </italic>to the lineage (Hominid or Rodent) and <italic>Ca </italic>to the category to which the gene belongs (the category of interest or the rest of the genome). A difference higher than the average ω for certain categories between the hominid and the rodent lineages should translate into a significant interaction (<italic>L*Ca</italic>). We considered only those categories that contained at least 5 genes in both hominid and rodent lineages. Overall, the ω ratio was correlated among categories between hominid and rodent lineages (<italic>r</italic><sup>2 </sup>= 0.34; <italic>p-value </italic>= 0.0019). We found no category of genes showing an accelerated evolution in one lineage compared to the other, relative to the rest of the genome (Fig. ##FIG##1##2## and Table ##TAB##1##2##). Such a result can be interpreted in several ways. First, categories that could have been affected by such acceleration were not included in the analysis because of a lack of data. This could be the case, for instance, for categories of genes like those involved in host-parasite interactions like VAR genes which are not found in rodent parasite genomes. In our dataset, we had to exclude this category because of an insufficient number of genes belonging to it. Second, an evolutionary acceleration in the amino-acid substitution rate may not affect entire categories of genes but only some of them expressed, for instance, at only some particular stages of the parasite, thus rendering it more difficult to detect them.</p>", "<title>Variation in evolutionary rates, timing and frequency of expression</title>", "<p>We finally addressed how variation in evolutionary rates could evolve relative to the timing and the breadth of expression of the genes. Do genes expressed at a particular stage show lineage specific evolution?</p>", "<p>As shown in Fig. ##FIG##2##3A##, hominid and rodent genes show similar patterns of evolution relative to their timing of expression. While no significant difference was observed between categories of genes for hominids (<italic>p-</italic>value = 0.3), a difference was found between categories for rodents: (<italic>p-</italic>value = 0.013). For both lineages, genes that are expressed at the merozoite stage look the most constrained while those expressed at the gametocyte stage appear the least constrained. We did not find any group of genes showing an accelerated evolution in one lineage compared to the other relative to the rest of the genome.</p>", "<p>The classification we used for Fig. ##FIG##2##3A## was nevertheless very broad. It included genes that were expressed at one particular stage but those same genes could also be expressed at other stages. Because this could preclude the detection of stage-specific evolution, we then re-analyzed our data keeping only the genes that were expressed at one stage. Doing so, we observed a significant difference in the rate of evolution among the different categories in the hominid lineage (<italic>p-</italic>value = 0.017) but no difference was observed in the rodent lineage (<italic>p-</italic>value = 0.59, Fig. ##FIG##2##3B##). We observed an overall difference between hominid and rodent parasites primarily due to the loci expressed at the sporozoite stage that showed an accelerated evolution in the hominid lineage compared to the rodent one (<italic>p-</italic>value = 0.018). This result suggests that genes only expressed at the sporozoite stage might be key genes in the infection process of the mammal host by malaria parasites and experienced higher adaptive evolution in the hominid lineage than in the rodent one.</p>", "<p>We then analyzed the relationship between the breadth of expression and evolutionary rates. Genes expressed at only one parasitic stage (see methods for details) were characterized as unique and those expressed at all stages were characterized as ubiquitous. For both the hominid and rodent lineages, we observed a significant relationship between the breadth of expression and the rate of non-synonymous substitutions. On average, stage specific proteins (expressed at only one stage) evolve at a higher rate relative to ubiquitous ones (expressed at all stages) (Fig. ##FIG##3##4## and Table ##TAB##2##3##). In contrast, synonymous variation shows a very different trend regarding the breadth of expression. The rate of synonymous substitutions increases with the breadth of expression: proteins expressed in a larger number of stages evolve at higher rates at synonymous sites. The obvious corollary of these observations is a negative relationship between the breadth of expression and <italic>dN/dS </italic>as shown in Figure ##FIG##3##4##. Note that we obtained similar relationships for rodents using data on the expression of <italic>P. berghei </italic>[##REF##15637271##5##] except that we did not find any significant relationship between <italic>dS </italic>and the breadth of expression (data not shown).</p>", "<p>Our results are congruent with previous studies reporting relationships between breadth of expression and the rate of gene evolution in other organisms [##REF##16176987##14##,##REF##14595100##15##]. In both hominid and rodent lineages, highly expressed genes are generally more constrained than less expressed genes [##REF##16044241##16##]. This observation is often attributed to the fact that proteins that are expressed in more diverse cellular environments are subjected to stronger functional constraints [##REF##16176987##14##,##REF##14595100##15##,##REF##15978954##17##]. These results are consistent with the observation in both hominid and rodent parasites of a positive relationship between GC content at position 1 and 2 of codons (GC1-2) and the level of expression as well as a negative relationship between GC1-2 and <italic>dN/dS </italic>(Table ##TAB##2##3##). Highly and universally expressed genes are more GC-rich than lowly expressed genes which might thus reflect a codon bias, in particular for GC-rich codons. In other words, amino acids encoded by GC-rich residues are preferred and conserved in protein coding genes of the genus <italic>Plasmodium</italic>.</p>", "<p>The positive relationship observed between the rate of synonymous substitutions and expression can potentially be explained by translational selection acting on synonymous codon sites of highly expressed genes [##REF##16044241##16##]. Alternatively, an increase in transcription may simply increase the level of spontaneous mutations as demonstrated in certain bacteria [##REF##12888512##18##].</p>" ]
[ "<title>Conclusion</title>", "<p>Our knowledge about the evolution of parasites responsible for malaria is increasing rapidly thanks to the availability of several completely sequenced genomes from species belonging to different lineages. As shown in the present study, different questions can be directly answered by comparing genomes from multiple <italic>Plasmodium </italic>species. Our comparative analysis suggests that, while there are a few aspects that are distinct among lineages, patterns of molecular evolution in the hominid parasite lineage are generally consistent with those observed in the rodent parasite lineage. In the murid lineage the most rapidly evolving genes are those involved in host-parasite interactions and those that are the least expressed. However, the evolution of the hominid lineage appears to be less constrained likely reflecting their historical lower effective size and an evolution driven by slightly deleterious mutations.</p>", "<p>While we tried to be as exhaustive as possible in our comparison of the evolution of the genome of both species, this study is still imperfect and incomplete. A definitive study will require the use of a high-quality complete sequence for <italic>P. reichenowi </italic>as well as more population data on the rodent lineages. Analyses of polymorphisms in natural populations of both hominid and rodent lineages (in the rodent lineage this information is specifically lacking) is critical to better understand the nature and intensity of selection acting on different categories of genes.</p>", "<p>Because for parasites with complex life-cycles (like <italic>Plasmodium sp</italic>.), the vector and the vertebrate host constitute very different environment, an interesting analysis would be to study the evolution of the genes exclusively expressed in the stages infecting the mammal host versus those only expressed in the stages infecting the mosquito vector. Such an analysis would however require the collection of more detailed expression profiles in both lineages.</p>" ]
[ "<title>Background</title>", "<p>Malaria kills more people worldwide than all inherited human genetic disorders combined. To characterize how the parasites causing this disease adapt to different host environments, we compared the evolutionary genomics of two distinct groups of malaria pathogens in order to identify critical properties associated with infection of different hosts: those parasites infecting hominids (<italic>Plasmodium falciparum </italic>and <italic>P. reichenowi</italic>) versus parasites infecting rodent hosts (<italic>P. yoelii yoelii</italic>, <italic>P. berghei</italic>, and <italic>P. chabaudi</italic>). Adaptation by the parasite to its host is likely highly critical to the evolution of these species.</p>", "<title>Results</title>", "<p>Our comparative analysis suggests that patterns of molecular evolution in the hominid parasite lineage are generally similar to those of the rodent lineage but distinct in several aspects. The most rapidly evolving genes in both lineages are those involved in host-parasite interactions as well as those that show the lowest expression levels. However, we found that, similar to their respective mammal host lineages, parasite genomes infecting hominids are generally less constrained, evolving at faster rates, and accumulating more deleterious mutations than those infecting murids, which may reflect an historical lower effective size of the hominid lineage and relaxed host-driven selective pressures.</p>", "<title>Conclusion</title>", "<p>Our study highlights for the first time the differences in trends and rates of evolution in <italic>Plasmodium </italic>lineages infecting different hosts and emphasizes the potential importance of the variation in effective size between lineages to explain variation in selective constraints among genomes.</p>" ]
[ "<title>Authors' contributions</title>", "<p>FP: conceived of the study, designed it, performed the sequence alignment and all the statistical analyses, wrote the manuscript. KMcG and JK.: participated in the sequence alignment and the analyses. PA: conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The <italic>P. reichenowi </italic>sequence data were produced by the Pathogen Sequencing Group at the Welcome Trust Sanger Institute and can be obtained from <ext-link ext-link-type=\"ftp\" xlink:href=\"ftp://ftp.sanger.ac.uk/pub/pathogens/P_reichenowi/\">ftp://ftp.sanger.ac.uk/pub/pathogens/P_reichenowi/</ext-link>. Authors thank the Welcome Trust for allowing them to use <italic>P. reichenowi </italic>sequence data. We also thank Nicolas Galtier for very useful discussion. F.P. is supported by CNRS, P. Awadalla is supported by the National Academies and Keck Futures Initiative, the National Institute of Health and the Human Frontiers in Science Program.</p>" ]
[ "<fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><bold>Schematic representation of the phylogenetic relationship between hominid and rodent <italic>Plasmodiu</italic>m lineages (adapted from</bold>[##REF##12435139##27##]<bold>).</bold></p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>Evolutionary rates (<italic>dN/dS</italic>) and Gene Ontology Processes in hominid (blue bars) and rodent (red bars) <italic>Plasmodium </italic>lineages</bold>. The numbers between parentheses are the number of genes belonging to each group. The first number corresponds to the hominid lineage; the second corresponds to the rodent one.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>A, B. Evolutionary rates (dN/dS) and timing of expression</bold>. A. for all genes expressed at one stage (but that may also be expressed at another stage). B. for the genes that are only expressed at one particular stage. Blue squares: hominid lineage; Red squares: rodent lineage.</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Substitution rates (<italic>dN/dS</italic>, <italic>dN</italic>, <italic>dS</italic>) and breadth of expression in hominid and rodent lineages.</p></caption></fig>" ]
[ "<table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Evolutionary rates in hominid and rodent's <italic>Plasmodium </italic>lineage</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th align=\"left\"><italic>Hominid lineage</italic></th><th align=\"left\"><italic>Rodent (murid) lineage</italic></th></tr></thead><tbody><tr><td align=\"left\"><italic>dN</italic></td><td align=\"left\">0.012 ± 0.00044</td><td align=\"left\">0.026 ± 0.00041</td></tr><tr><td align=\"left\"><italic>dS</italic></td><td align=\"left\">0.057 ± 0.0013</td><td align=\"left\">0.20 ± 0.0027</td></tr><tr><td align=\"left\"><italic>dN/dS (ω)</italic></td><td align=\"left\">0.21 ± 0.0068</td><td align=\"left\">0.13 ± 0.0023</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Go categories and relative divergence rates (<italic>dN/dS</italic>) in hominid and murid lineages</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Go categories within \"biological process\"</th><th align=\"left\"><italic>dN/dS (ω) </italic><break/>hominid</th><th align=\"left\"><italic>dN/dS (ω) </italic><break/>murid</th><th align=\"left\"><italic>p-value</italic></th></tr></thead><tbody><tr><td align=\"left\">GO: Organelle organization and biogenesis</td><td align=\"left\">0.093797</td><td align=\"left\">0.136622</td><td align=\"left\">0.34</td></tr><tr><td align=\"left\">GO: Carbohydrate metabolism</td><td align=\"left\">0.094282</td><td align=\"left\">0.061524</td><td align=\"left\">0.17</td></tr><tr><td align=\"left\">GO: Energy pathways</td><td align=\"left\">0.095999</td><td align=\"left\">0.06409</td><td align=\"left\">0.25</td></tr><tr><td align=\"left\">GO: Protein transport</td><td align=\"left\">0.1000621</td><td align=\"left\">0.056915</td><td align=\"left\">0.10</td></tr><tr><td align=\"left\">GO: Cell organization and biogenesis</td><td align=\"left\">0.102521</td><td align=\"left\">0.11164</td><td align=\"left\">0.75</td></tr><tr><td align=\"left\">GO: Cytoskeleton organization and biogenesis</td><td align=\"left\">0.106537</td><td align=\"left\">0.123027</td><td align=\"left\">0.68</td></tr><tr><td align=\"left\">GO: Protein biosynthesis</td><td align=\"left\">0.10841</td><td align=\"left\">0.076408</td><td align=\"left\">0.092</td></tr><tr><td align=\"left\">GO: Transcription</td><td align=\"left\">0.1093033</td><td align=\"left\">0.085371</td><td align=\"left\">0.49</td></tr><tr><td align=\"left\">GO: Cell growth and/or maintenance</td><td align=\"left\">0.112558</td><td align=\"left\">0.078195</td><td align=\"left\">0.02</td></tr><tr><td align=\"left\">GO: Cytoplasm organization and biogenesis</td><td align=\"left\">0.115938</td><td align=\"left\">0.124335</td><td align=\"left\">0.80</td></tr><tr><td align=\"left\">GO: Catabolism</td><td align=\"left\">0.117659</td><td align=\"left\">0.107771</td><td align=\"left\">0.70</td></tr><tr><td align=\"left\">GO: Transport</td><td align=\"left\">0.118198</td><td align=\"left\">0.076005</td><td align=\"left\">0.059</td></tr><tr><td align=\"left\">GO: Amino acid and derivative metabolism</td><td align=\"left\">0.118466</td><td align=\"left\">0.105378</td><td align=\"left\">0.64</td></tr><tr><td align=\"left\">GO: Protein metabolism</td><td align=\"left\">0.118917</td><td align=\"left\">0.09077</td><td align=\"left\">0.06</td></tr><tr><td align=\"left\">GO: Biosynthesis</td><td align=\"left\">0.120925</td><td align=\"left\">0.09066</td><td align=\"left\">0.054</td></tr><tr><td align=\"left\">GO: Physiological process</td><td align=\"left\">0.12305</td><td align=\"left\">0.08881</td><td align=\"left\">0.0001</td></tr><tr><td align=\"left\">GO: Cell proliferation</td><td align=\"left\">0.125543</td><td align=\"left\">0.055601</td><td align=\"left\">0.03</td></tr><tr><td align=\"left\">GO: Metabolism</td><td align=\"left\">0.12623</td><td align=\"left\">0.0897</td><td align=\"left\">0.0001</td></tr><tr><td align=\"left\">GO: Nucleobase <italic>et al*.</italic></td><td align=\"left\">0.128449</td><td align=\"left\">0.07981</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">GO: Cell cycle</td><td align=\"left\">0.137136</td><td align=\"left\">0.055177</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\">GO: Response to stress</td><td align=\"left\">0.140927</td><td align=\"left\">0.064359</td><td align=\"left\">0.03</td></tr><tr><td align=\"left\">GO: DNA metabolism</td><td align=\"left\">0.154415</td><td align=\"left\">0.062976</td><td align=\"left\">0.0028</td></tr><tr><td align=\"left\">GO: Protein modification</td><td align=\"left\">0.170329</td><td align=\"left\">0.116675</td><td align=\"left\">0.21</td></tr><tr><td align=\"left\">GO: Lipid metabolism</td><td align=\"left\">0.171853</td><td align=\"left\">0.124668</td><td align=\"left\">0.17</td></tr><tr><td align=\"left\">GO: Biological_process unknown</td><td align=\"left\">0.2353</td><td align=\"left\">0.17011</td><td align=\"left\">0.001</td></tr><tr><td align=\"left\">GO: Cell communication</td><td align=\"left\">0.2439164</td><td align=\"left\">0.159463</td><td align=\"left\">0.17</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Relationship between gene expression, GC content and substitutions rates in both hominid's and rodent's <italic>Plasmodium </italic>parasites</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th align=\"left\">Hominid lineage</th><th align=\"left\">Rodent lineage</th></tr></thead><tbody><tr><td align=\"left\">Expression-<italic>dN/dS</italic></td><td align=\"left\">Rho = -0.26; <italic>p </italic>= 0</td><td align=\"left\">Rho = -0.26; <italic>p </italic>= 0</td></tr><tr><td align=\"left\">Expression-<italic>dN</italic></td><td align=\"left\">Rho = -0.13; <italic>p </italic>= 0.008</td><td align=\"left\">Rho = -0.15; <italic>p </italic>= 0</td></tr><tr><td align=\"left\">Expression-<italic>dS</italic></td><td align=\"left\">Rho = 0.18; <italic>p </italic>= 0</td><td align=\"left\">Rho = 0.113; <italic>p </italic>= 0.00001</td></tr><tr><td align=\"left\">Expression-GC1</td><td align=\"left\">Rho = 0.28; <italic>p </italic>= 0</td><td align=\"left\">Rho = 0.31; <italic>p </italic>= 0</td></tr><tr><td align=\"left\">Expression-GC2</td><td align=\"left\">Rho = 0.37; <italic>p </italic>= 0</td><td align=\"left\">Rho = 0.35; <italic>p </italic>= 0</td></tr><tr><td align=\"left\">GC1-<italic>dN/dS</italic></td><td align=\"left\">Rho = -0.37; <italic>p </italic>= 0</td><td align=\"left\">Rho = -0.38; <italic>p </italic>= 0</td></tr><tr><td align=\"left\">GC2-<italic>dN/dS</italic></td><td align=\"left\">Rho = -0.43; <italic>p </italic>= 0</td><td align=\"left\">Rho = -0.38; <italic>p </italic>= 0</td></tr></tbody></table></table-wrap>" ]
[]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><italic>P. falciparum </italic>genes analyzed in the study and rates of evolution (<italic>dS</italic>, <italic>dN</italic>, <italic>dN/dS</italic>).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Orthologous genes of rodent Plasmodium species and their rates of evolution (<italic>dS</italic>, <italic>dN</italic>, <italic>dN/dS</italic>).</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>Maximum likelihood estimates of the rates of evolution (± standard error) in protein coding genes for hominid (<italic>P. falciparum – P. rechenowi</italic>) and rodent (<italic>P. yoelii yoelii </italic>– <italic>P. berghei </italic>– <italic>P. chabaudi</italic>) lineages. The difference between <italic>dN/dS </italic>in hominid and rodent lineages is significant. Note that the ratio of the means is not equivalent to the mean of the ratios.</p></table-wrap-foot>", "<table-wrap-foot><p>*Nucleobase, nucleoside, nucleotide and nucleic acid metabolism.</p><p>Only those categories of biological processes with at least 5 genes in both lineages are listed. The <italic>p-value </italic>of the test comparing the average <italic>dN/dS </italic>(ω) ratio between hominid and murid lineages is given for each category (<italic>p-value</italic>). None of the category showed an accelerated evolution in hominid or rodent, given the average genome difference between lineages.</p></table-wrap-foot>", "<table-wrap-foot><p>A spearman rank test was used to analyze the correlation between variables (Rho: spearman correlation coefficient; <italic>p</italic>: <italic>p-</italic>value).</p></table-wrap-foot>" ]
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[{"surname": ["Keightley", "Lercher", "Eyre-Walker"], "given-names": ["PD", "MJ", "A"], "article-title": ["Evidence for widespread degradation of gene control regions in hominid genomes"], "source": ["Plos Biology"], "year": ["2005"], "volume": ["3"], "issue": ["2"], "fpage": ["282"], "lpage": ["288"], "pub-id": ["10.1371/journal.pbio.0030042"]}, {"article-title": ["The Plasmodium Genome Ressource Database"], "ext-link": ["http://www.plasmodb.org/"]}, {"article-title": ["Generic Gene Ontology (GO) Term Mapper"], "ext-link": ["http://go.princeton.edu/cgi-bin/GOTermMapper"]}, {"article-title": ["CodonW"], "ext-link": ["http://codonw.sourceforge.net/"]}]
{ "acronym": [], "definition": [] }
27
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2022-01-12 17:11:36
BMC Evol Biol. 2008 Jul 30; 8:223
oa_package/f9/10/PMC2529309.tar.gz
PMC2529310
18655705
[ "<title>Background</title>", "<p>Hormone signaling is a very important feature in metazoans, allowing communication between cells or organs within the organism. Two components of these signaling systems are of particular importance, the hormone and its receptor. The nuclear hormone receptor (NR) superfamily includes ligand dependent transcription factors that play a central role in various physiological processes as diverse as reproduction, development, and control of homeostasis [##REF##15520817##1##,##UREF##0##2##]. They share a common structural organization and exhibit a highly conserved DNA binding domain (DBD) and a moderately conserved ligand-binding domain (LBD). Some members of this superfamily are liganded receptors (24 among the 48 genes encoding NRs in the human genome) but many lack identified ligand and are therefore called \"orphan\" [##REF##17132848##3##]. Some orphan receptors are 'true' orphans in the sense that they do not possess a <italic>bona fide </italic>ligand-binding pocket (LBP), like the members of the NR4 subfamily (for instance, NURR1, DHR38 or NGFI-B. For review, see [##REF##17132856##4##]), and are regulated by other mechanisms [##REF##17132856##4##]. Alternatively, the crystal structures of several orphan receptors such as HNF4 were found to have a phospholipid constitutively bound to a large ligand binding pocket [##REF##12193589##5##,##REF##12220494##6##]. The functional and evolutionary implications of these constitutive ligands remain discussed. Other orphan nuclear receptors have a ligand binding pocket and thus have the potential to bind compounds. It is still not known whether those receptors have natural ligands, still to be discovered. Undoubtedly, the existence of such orphan receptors with physiological or developmental activities constitutes both a major challenge for understanding nuclear receptor evolution and a potential opportunity for pharmacology [##REF##15520817##1##].</p>", "<p>The existence of orphan and liganded members in the NR family raises the question of the evolution regarding their ligand binding ability. Whether the ancestral NR was liganded or orphan and more generally how NR ligand binding ability evolved has been recently debated [##REF##10918302##7##, ####REF##11331759##8##, ##REF##15242336##9##, ##REF##15707893##10##, ##REF##14500980##11##, ##REF##16690796##12##, ##REF##16839186##13##, ##REF##12655646##14####12655646##14##]. In general, it is still unclear if there is a correlation between the evolution of the hormone repertoire and NRs. Moreover the mechanisms underlying this coevolution are of particular interest [##REF##10918302##7##,##REF##16690796##12##,##UREF##1##15##, ####REF##17702911##16##, ##UREF##2##17##, ##REF##9192646##18##, ##REF##9460643##19####9460643##19##].</p>", "<p>Among the scenarios of NR evolution that have been proposed, one suggests that the ancestral NR was a ligand-independent transcription factor which acquired the ability to be regulated by ligands several times during evolution [##REF##10918302##7##,##REF##9192646##18##, ####REF##9460643##19##, ##REF##17678875##20####17678875##20##]. This hypothesis was based on the observation that compounds of similar chemical nature bind to divergent NRs and on the contrary compounds of very different nature bind to closely related receptors. For instance, orphan receptors are found in all families of NRs, and steroid receptors are not monophyletic but are located in two different subfamilies within the NR superfamily: the ecdysteroid as well as the sex steroid receptors. Interestingly, the evolution of sex steroid hormone receptors has also been used as an argument for an alternative hypothesis, the ligand exploitation model [##REF##11331759##8##,##REF##14500980##11##] (for an alternative view, see [##REF##17639030##21##,##REF##12063181##22##]). Phylogenetic trees show that sex steroid hormone receptors are grouped with ERRs as the NR3 subfamily, following the official nomenclature [##REF##10219237##23##]. They contain receptors that bind estradiol (ERs), that form the NR3A group as well as mineralocorticoids (MRs), glucocorticoids (GRs), progesterone (PRs), and androgen (ARs) that form the NR3C group. All known ligands in this subfamily can be seen as variations around the archetypical sterol skeleton. Consequently, Thornton et al. suggested in the ligand exploitation model that the ancestral steroid receptor was a high affinity estradiol receptor [##REF##11331759##8##,##REF##14500980##11##] and the other steroid receptors that originated later on, experienced, following gene duplication, shifts in their binding affinities to eventually bind to their extant ligand. The model in fact suggests that the newly duplicated receptors (here NR3C) exploit as ligands chemical species that serve as intermediary compounds in the \"ancestral ligand\" synthesis pathway (here the estradiol synthesis pathway) [##REF##11331759##8##]. According to this view, orphan receptors, like ERRs, secondarily lost the ability to have their activity regulated by a ligand and became orphan. Interestingly, within the NR3 family, two receptor subfamilies, ERRs and ERs, appear to be ancient since they are found in a wide variety of metazoans including deuterostomes and protostomes, whereas, up to now, MRs, GRs, PRs and ARs have been found only in vertebrates. The only non-vertebrate ERs that have been described so far were from mollusks and were shown to be unable to bind estradiol [##REF##14500980##11##,##REF##16690796##12##,##REF##16782100##24##, ####REF##17324427##25##, ##REF##17438828##26####17438828##26##]. Since the ligand exploitation model implies an ancestral estradiol-binding ER and since all liganded ER found so far come from vertebrates, and to improve taxonomic sampling, the ER orthologues from the basal vertebrate lamprey and the invertebrate chordate amphioxus were characterized here. Indeed, lamprey and amphioxus are located at key positions in the chordate phylum [##REF##16495997##27##, ####REF##16733532##28##, ##REF##16049193##29##, ##UREF##3##30####3##30##]. Moreover, amphioxus (<italic>Brachiostoma floridae</italic>) is much less derived than urochordates in its morphology as well as in its genome organization [##UREF##3##30##]. Indeed, amphioxus and vertebrates share a similar general body plan whereas urochordate morphology is more derived. For instance, during metamorphosis of some urochordates, the tadpole-like larva transforms into an adult that looks so different that it was first considered as a mollusk [##REF##12766941##31##]. Moreover the urochordate genome is fast evolving [##REF##16495997##27##], with for instance the loss of the clustering of the hox genes [##REF##15844201##32##]. There is no ER in the sequenced genome of <italic>Ciona intestinalis </italic>[##REF##12481130##33##] or in the sea urchin [##REF##17054934##34##], one ER was previously cloned in lamprey [##REF##11331759##8##], only one ER was found in the amphioxus genome [##REF##18562680##35##]. These reasons make lamprey and amphioxus excellent models to study the evolution of estrogen signaling pathway at the origin of vertebrates. In this study, we cloned the unique ER from amphioxus (amphiER) and characterized it, as well as the previously cloned but uncharacterized lamprey ER (lampER). AmphiER is an orphan receptor, showing no affinity to the estrogen hormone estradiol, when in contrast, the lamprey ER behaves as a \"classical\" vertebrate ER. As no ER from invertebrates studied so far binds estradiol, we propose that the ancestral ER (and the ancestral steroid receptor) was not a receptor for estradiol and gained later on during evolution the ability to bind the hormone.</p>" ]
[ "<title>Methods</title>", "<title>Cloning of amphiER</title>", "<p>An initial piece of amphiER was obtained by degenerate PCR on different RT reactions from total RNA extracted either from developing B. floridae embryos and larvae (at 13 h–15 h, 28 h, 36 h, 48 h or 3 d–4 d of development) or from B. floridae adults. The oligonucleotides used were as follows: forward primer 5'-TGYGARGGITGYAARGCITTYTT-3' and reverse primer 5'-GTRCAYTSRTTIGTIGCIGGRCA-3'.</p>", "<p>The touchdown PCR program used was as follows:</p>", "<p>5' 94 degrees</p>", "<p>5× (30\" 94 degrees, 1' 55 degrees, 1' 72 degrees)</p>", "<p>5× the same cycle, but at 50 degrees annealing temperature</p>", "<p>5× the same cycle, but at 45 degrees annealing temperature</p>", "<p>5× the same cycle, but at 40 degrees annealing temperature</p>", "<p>25× the same cycle, but at 37 degrees annealing temperature</p>", "<p>7' 72 degrees</p>", "<p>All degenerate PCRs irrespective of the RT reaction template used yielded a 83 bp fragment of amphiER. The fragment was sequenced on both strands and used for the design of oligonucleotides for 5' and 3' RACE experiments with the Invitrogen GeneRacer Kit. The template for the RACE experiments was pooled total RNA from 13 h–15 h B. floridae embryos and from B. floridae adults. In addition to the oligonucleotides provided by the kit, for the 3' RACE, the following primers were used:</p>", "<p>3' RACE, 1st PCR: 5'-AACGGAGCATTCAGCAAGGTC-3'</p>", "<p>3' RACE, 2nd PCR: 5'-GCATTCAGCAAGGTCAGACAG-3'</p>", "<p>5' RACE, 1st strand cDNA synthesis: 5'-ATGTAATCTGTCTGACCTTGC-3'</p>", "<p>5' RACE, 1st PCR: 5'-CTGTCTGACCTTGCTGAATGC-3'</p>", "<p>5' RACE, 2nd PCR: 5'-TCTGACCTTGCTGAATGCTCC-3'</p>", "<p>The protocols for the 1st and 2nd round of PCR experiments are given in the Invitrogen GeneRacer Kit. The 3' and 5' RACE products were subsequently sequenced on both strands and used for the design of oligonucleotides for the full-length cloning of amphiER: forward primer 5'-CGGCGAAGCGAAGAAGATCGAG-3' and reverse primer 5'-CTTAACCGATACTAACGGAACAG-3'. The full-length amphiER was obtained by PCR on pooled RT reactions from total RNA extracted from B. floridae 13 h–15 h embryos, 3 d–4 d larvae and B. floridae adults. The PCR protocol used was as follows:</p>", "<p>10' 94 degrees</p>", "<p>5× (30\" 94 degrees, 30\" 55 degrees, 2' 72 degrees)</p>", "<p>35× the same cycle, but at 50 degrees annealing temperature</p>", "<p>10' 72 degrees</p>", "<p>The full-length amphiER clone resulting from this PCR is 2279 bp long, was cloned into the pCR2.1 vector (Invitrogen) and subsequently sequenced on both strands.</p>", "<title>Plasmid constructs and reagents</title>", "<p>Full length amphiER were amplified by polymerase chain reaction (PCR) and the obtained fragments were inserted into a pSG5 vector between EcoR1 sites. Lamprey ER was a generous gift from JW Thornton. Human pSG5-ERα and pSG5-ERβ and the 3xERE-Luc luciferase reporter construct have been described previously [##REF##10953052##87##]. The pS2-Luc reporter construct encompasses an 1100 bp estrogen-responsive region of the human pS2 promoter inserted into the pGL3 basic vector (Promega). Chimeras comprising the GAL4 DNA-binding domain fused with the LBD of the human ERα (residues 251 to 595), the LBD of amphiER (residues 364 to 705), the LBD of lampER (residues 234 to 554) have been cloned in the pG4MpolyII vector. 17β-estradiol, genistein, 3β-androstenediol, resveratrol, cholesterol, cholic acid, chenodeoxycholic acid, 22<sup>®</sup>-hydroxycholesterol, 20-Hydroxyecdysone, pregnenolone, trans-Dehydroandrosterone (DHEA), corticosterone, progesterone, 4-androstene-3,17-dione, estrone, testosterone, 5α-androstan-17β-ol-3-one and 1a,25-Dihydroxyvitamin D3 (calcitriol) were purchased from Sigma. Enterolactone was a generous gift from Dr Sari Mäkelä [##REF##17628008##88##].</p>", "<title>Phylogenetic analysis of NR3</title>", "<p>Protein sequences of NR3 family members were obtained from GenBank by BLAST search using <italic>Homo sapiens </italic>ERα as a query. Eight additional sequences from the closely related RXR group were also obtained to serve as outgroup sequences. For accession numbers of the sequences used, see Additional file ##SUPPL##7##8##.</p>", "<p>The retrieved sequences were aligned using the muscle 3.6 program [##REF##15034147##89##] and the resulting alignment was manually corrected with SEAVIEW [##REF##9021275##90##]. Phylogenetic tree was calculated by maximum likelihood as implemented in PhyML version 2.4.3 under a JTT substitution matrix plus a eight-category gamma rate correction (α estimated) and with the proportion of invariant sites estimated. Both the DBD and the LBD were used. Robustness was assessed by bootstrap analysis (1,000 repetitions) [##UREF##12##91##].</p>", "<p>The Bayesian inference was done using the program MrBayes 3.1.2 [##REF##12116651##92##]. Two simultaneous independent runs were performed. For each run, one chain was sampled every 100 generations for 1,000,000 generations after the burn-in cycles, until the average SD of split frequencies was &lt;0.01; additionally, the potential scale reduction factors of the parameters were close to or equal to 1, which indicates that the runs had most probably converged. The neighbour-joining (Poisson correction) and maximum parsimony trees were done with Phylo_win [##REF##9021275##90##].</p>", "<title>Likelihood-based tests of alternative topologies placing amphiER at all possible positions in the tree</title>", "<p>The 149 trees were built by reconnecting amphiER from the maximum likelihood tree, into the 149 possible positions. The branch length and the different parameters of the obtained trees were re-estimated using PhyML. Likelihood-based tests of the 149 alternative topologies were calculated using CONSEL: site-wise log-likelihood values, available as output of PhyML, were used to calculate the P-values of the different positions according to the AU test with the software R.</p>", "<title>Ancestral sequence reconstruction</title>", "<p>The aminoacid sequence of the ancestral AncSRa and AncSRb was inferred only for the most conserved part of the alignment, <italic>i.e. </italic>the DBD and LBD (defined as in Figure ##FIG##0##1##). The ancestral sequences were reconstructed by maximum likelihood as implemented in PAML [##REF##17483113##48##], under the JTT substitution model and a gamma distribution with 8 categories of rates across sites, using the tree described in Additional file ##SUPPL##0##1A## for AncSRa and a the same topology truncated of the mollusk ER sequences and the amphioxus NR3 sequences, after reestimation of the branch lengths using phyml (JTT+γ) for AncSRb.</p>", "<title>Electrophoretic Mobility Shift Assay (EMSA)</title>", "<p>EMSAs were performed as previously described [##REF##16538655##93##]. Where indicated, a 10- and 100-fold molar excess of 30-bp unlabeled oligonucleotides (a consensus ERE and a non-related probe) were added as competitors. The sequence of the probe containing the consensus ERE is 5'-CGGGCCG<underline>AGGTCA</underline>CAG<underline>TGACCTC</underline>GGCCCGT-3' and the sequence of the non-related probe is 5'-CTAGTCCTAGGTCTAGAGAATTCA-3'.</p>", "<title>Cell culture and transfections</title>", "<p>Human embryonic kidney 293 cell culture and transfections using Lipofectamine Plus reagent (Invitrogen) were done according to the manufacturer's recommendations and as previously described [##REF##12774125##94##]. Briefly, 200 ng of the chimeras comprising the GAL4 DNA-binding domain fused with the LBD of either human ERα, lampER or AmphiER (or LBD for the control) were co-transfected together with 100 ng of reporter plasmid and 10 ng of a β-galactosidase expression vector, included as a control for transfection efficiency. For the mammalian double hybrid assays, the GAL4-amphiER-LBD chimera was transfected with 200 ng of the coactivator SRC1 fused to the strong activation domain VP16. Three to five hours post-transfection, serum and hormones (as indicated in the figures) were added to the cells which were incubated for an additional 48 hours before harvest and luciferase and β-galactosidase activities were determined. Results show the mean ± s.e.m. (n = 3) of representative experiments. Human HeLa cervical cancer cells and CV-1 green monkey kidney cells were routinely maintained in Dulbecco's modified eagle's medium (Invitrogen), supplemented with 10% fetal bovine seum, 1% v/v L-glutamine and 1% v/v penicillin/streptomycin. Cells were seeded in 12 or 24 well plates one day prior to transfection. Transient transfections were carried out using the Lipofectamine Plus reagent according to instructions of the manufacturer (Invitrogen) in culture media devoid of serum, phenol-red and antibiotics. Briefly, 1 ng of ERα, ERβ or AmphiER expression vectors were co-transfected together with 100 ng 3xERE-Luc (or 200 ng pS2-Luc where indicated) and 20 ng of a β-galactosidase expression vector, included as a control for transfection efficiency. In the co-expression experiments, AmphiER was co-transfected together with ERα or ERβ in ratios of 0.5:1, 1:1 and 1:5, respectively. Three hours post-transfection, serum and hormones (as indicated in the figures) were added to the cells which were thereafter incubated for an additional 48 hours before harvest and luciferase and β-galactosidase activities were determined. Figures represent results from at least three independent experiments performed in duplicates. Data is presented as mean +/- SD of fold induction of relative luciferase values corrected against β-galactosidase activity, where activity obtained from transfected reporter plasmid alone and treated with vehicle, was arbitrarily set to 1.</p>", "<title>Limited proteolytic digestion</title>", "<p>These assays were done as previously described [##REF##16839186##13##].</p>" ]
[ "<title>Results</title>", "<title>Cloning of the ER from amphioxus (amphiER)</title>", "<p>Using degenerate primers designed to match motifs in the most conserved part of vertebrate ERs in the DNA binding domain, a single gene fragment from total RNA of an adult <italic>Branchiostoma floridae </italic>was amplified, cloned and sequenced. Rapid amplification of cDNA ends (RACE) was utilized to obtain the full-length cDNA. From this sequence, a new set of specific primers were designed and used to amplify the full length open reading frame of this gene. The obtained cDNA [GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"ACF16007\">ACF16007</ext-link>] is 2118 bp long and encodes a 705 aa long putative protein (Figure ##FIG##0##1##) that harbors the classical features of an ER with the 5 main functional domains (Figure ##FIG##1##2A##), among which a highly conserved DNA binding domain (DBD) and a less conserved ligand binding domain (LBD). The DBD shares an 82% sequence identity with the human ERα one (83% with human ERβ) and much less with the other NR3 receptors (&lt;62%). The same pattern is observed for the LBD, although this domain is less conserved since it exhibits only 34% amino acid identity with human ERα (35% for human ERβ) and about 20% with other steroid receptors (Figure ##FIG##1##2A##). The three other domains, namely the A/B region in the N-terminal part, the hinge between DBD and LBD, and the short C-terminal end of the protein, are more divergent, which is a general pattern for NRs [##UREF##0##2##] (Figure ##FIG##1##2A##). The recent release of the amphioxus genome confirmed the presence of a single ER gene [##REF##18562680##35##]. In contrast the previously described lamprey ER is more similar to the human ERα with its DBD sharing a 93% sequence identity (93% for human ERβ) and its LBD sharing 55% sequence identity (56% for human ERβ) [##REF##11331759##8##].</p>", "<title>Phylogenetic analysis of ERs</title>", "<p>The orthology relationships of the amphioxus and lamprey ER sequences were studied in a phylogenetic analysis of the NR3 family using an exhaustive dataset comprising 69 members of the NR3 subfamily as well as sequences of RXRs as an outgroup. The dataset included the 6 currently known mollusk ER sequences (from <italic>Nucella lapillus, Crassostrea gigas, Marisa cornuarietis, Thais clavigera, Octopus vulgaris, Aplysia californica</italic>), as well as the 2 NR3 sequences previously known from amphioxus (1 ERR [GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"AAU88062\">AAU88062</ext-link>] and 1 NR3C [JGI: 201600], retrieved from a previous work [##REF##15876195##36##] or from the complete genome sequence [##REF##18562680##35##]). In the resulting phylogenetic tree, the sequence of lamprey ER branches within the ER clade with a high bootstrap support (95%), at the expected position before the split of vertebrate ERα and ERβ (Figure ##FIG##1##2B##, and for a tree presenting all sequences, see Additional file ##SUPPL##0##1A##), as previously shown [##REF##11331759##8##]. The sequence of amphiER branches within the ERs and is located at the base of the vertebrate estrogen receptor group, before the split of ERα and ERβ but after the split of the mollusk ERs (bootstrap value of 81%, Figure ##FIG##1##2B##). However its precise position within the ER group is poorly supported (bootstrap value of 42%).</p>", "<p>Such low bootstrap supports reveal either the weakness of the phylogenetic signal contained in ER proteins, or the presence of two incompatible signals in the data, one supporting the observed position of amphiER within ERs, and the other supporting another position. Whereas the weakness of the signal is not testable, the long branch leading to amphiER in the tree suggests that sites that have undergone a large number of substitutions may account for one of the two signals. Such sites may be saturated to the point that phylogenetic methods are not able to correctly recover their evolution, a situation leading to the long branch attraction artifact [##UREF##4##37##]. It is therefore important to correctly characterize sites that support the ER position of amphiER: if only fast-evolving sites support this hypothesis, it is probably due to long branch attraction, and an alternative branching should be favored. Alternatively, if slowly-evolving sites support this position, one can confidently identify amphiER as a <italic>bona fide </italic>ER. To characterize sites with respect to their evolutionary rates and the amphiER position they favor, both site likelihoods and site evolutionary rates were computed for all possible positions of amphiER.</p>", "<p>First, AmphiER was pruned from the tree shown in Figure ##FIG##1##2B##, and then re-grafted in all 149 remaining branches. This yielded 149 topologies, for which site likelihoods and site evolutionary rates could be computed using PhyML-aLRT. This allowed us to obtain an evolutionary rate per site averaged over all possible positions of amphiER, and therefore independent from the precise position of amphiER in the tree. Additionally, as likelihoods were computed for each of the 149 positions, these positions could be compared according to the Approximately Unbiased test (AU test, implemented in Consel [##UREF##5##38##]). Out of all the 149 resulting trees, 26 could not be distinguished with the AU test and had a likelihood significantly better than all the other ones (p-value &gt; 0,05). Of these 26 topologies, all but three place amphiER within the ER clade (\"ER\" trees). The remaining topologies (\"alter-ER\" trees) place amphiER either at the base of the NR3C clade (comprising the ARs, PRs, MRs and GRs), within the NR3C or at the base of (ER, NR3C) (Figure ##FIG##2##3A##). Because site evolutionary rates had been computed, sites having a higher likelihood for the \"alter-ER\" trees could be compared with sites favoring the \"ER\" topologies with respect to their evolutionary rates. Interestingly, the sites pleading for the \"alter-ER\" trees evolve significantly faster than the sites pleading for the \"ER\" trees (mean evolutionary rates of 1.20 and 0.90, p-value &lt; 10<sup>-5 </sup>with a Wilcoxon-test or p &lt; 0.001 with a an unpaired t-test). This suggests that the \"alter-ER\" signal in the alignment is probably due to long branch attraction to the NR3C subtree, which might also be at the origin of the low bootstrap support (42%) for the position of amphiER. Conversely, this suggests that amphiER should be considered as an ER, as the signal at the origin of this position does not seem to be artifactual.</p>", "<p>An additional test can be run to further confirm this hypothesis, and consists in reestimating the phylogeny using only slowly-evolving sites. For that purpose, the distribution of expected relative evolutionary rates across sites of the alignment was plotted, as found by phyml-aLRT [##REF##16785212##39##,##REF##14530136##40##] (Figure ##FIG##2##3B##). Fastest-evolving sites were removed from the dataset based on three different rate thresholds (2.5, 2 or 1.5, Figure ##FIG##2##3B## and ##FIG##2##3C##), and trees were reconstructed based on the alignments containing only the remaining slowly-evolving sites. These operations did not impact the monophyly of ERs (Figure ##FIG##2##3C##) or the statistical support. This shows that the clustering of amphiER with vertebrate ERs does not come from saturated sites, which argues against long branch attraction being at the origin of this position [##REF##17288577##41##]. Accordingly, complementary phylogenetic analyses with different methods (bayesian, Neighbor-joining, parsimony) gave similar results (see Additional file ##SUPPL##0##1##). From these studies we conclude that amphiER does indeed belong to the ER subfamily, which is confirmed by the general conservation of the exon-intron structure of amphiER with human ERα, especially at two exon-intron splice sites in the DBD and in the LBD after helix 3 (Figure ##FIG##1##2C## and short red strokes in Figure ##FIG##0##1##) [##REF##15059999##42##].</p>", "<title>Chordate ERs, including amphioxus ER and lamprey ER, are able to bind estrogen specific response elements (ERE)</title>", "<p>To test whether the lamprey ER and the amphioxus ER are able to bind DNA on specific estrogen response elements (EREs), electrophoresis mobility shift assays were performed using a radiolabeled consensus ERE sequence (see Additional file ##SUPPL##1##2##). These experiments show that, like vertebrate ERs, amphiER and lampER are able to bind DNA specifically on a consensus ERE. This binding is specific, since a 100-fold excess of non-specific DNA was not able to compete for binding, whereas a 100-fold excess of cold ERE completely suppressed it (see Additional file ##SUPPL##1##2##, compare lanes 15 and 17, as well as lanes 19 and 21). ERs contain two major conserved signatures in the DBD, the P-box (CEGCKA), responsible for the binding specificity to response elements, and the D-box, also involved in the DNA binding specificity of the ER dimers (Figure ##FIG##1##2A##). The P-box is highly conserved in all known ERs, including amphiER and lamprey ER and is different from other NR3 members. AmphiER and lamprey ER also have a well conserved D-box, amphiER D-box containing just a few conservative mutations, (<italic>e.g. </italic>a mutation of an alanine in glycine, Figure ##FIG##1##2A##). Since the three characterized mollusk ERs (from <italic>A. californica, O. vulgaris </italic>and <italic>Thais clavigera </italic>[##REF##14500980##11##,##REF##16690796##12##,##REF##16782100##24##]) also bind EREs and since the P-box and D-box are well conserved in all known ERs, including those from mollusks, ERE binding appears to be a feature specific to all ERs.</p>", "<title>Lamprey ER, but not amphioxus ER, is able to induce transactivation of a reporter gene in response to estradiol stimulation</title>", "<p>The transactivation ability of lamprey ER and amphiER was then compared with that of human ERα. AmphiER failed to induce transcription of a reporter construct containing a consensus ERE in front of a minimal promoter in transfected mammalian cells after stimulation by the natural vertebrate ER ligand, estradiol (E<sub>2</sub>) as well as a wide variety of other vertebrate ER ligands (the natural agonist 3β-androstenediol [##REF##9048584##43##], and the phytoestrogens resveratrol [##REF##9391166##44##] and enterolactone [##REF##12270221##45##]) (Figure ##FIG##3##4A## and ##FIG##3##4B##). In order to improve the detection sensitivity, was also tested the transactivation capacity of amphiER in response to E<sub>2 </sub>as a construct containing only the LBD fused to the GAL4 DNA-binding domain. In this case again, no activation was detected (Figure ##FIG##3##4C##). In agreement with this result, no recruitment of the coactivator SRC1 (an homologue of which is present in the amphioxus genome, see Discussion) was detected in mammalian two-hybrid assay (Figure ##FIG##3##4D##). However lampER is activated by E<sub>2</sub>, with an intensity comparable to humanERα (Figure ##FIG##3##4C##), which suggests that the lamprey ER is a high affinity E<sub>2</sub>-dependant transcription factor.</p>", "<p>Since amphiER is able to bind DNA but is unable to activate transcription of a reporter gene, the dominant negative capacity of the amphioxus protein was tested. A dose-dependent decrease in the reporter gene activity was clearly visible in 2 different cell lines when increasing amounts of the amphiER plasmid were added together with constant amounts of human ERα or ERβ in transient transfection experiments. This decrease was observed both with synthetic consensus EREs (Figures ##FIG##4##5A## and ##FIG##4##5B##. See also Additional file ##SUPPL##2##3##) and with the natural ERE present in the classical ER pS2 target gene (Figure ##FIG##4##5C##). Apparently, amphiER is able to compete with human ERα or ERβ for binding to the ERE sites present in the reporter constructs, and in doing so, prevents ERα and ERβ from inducing transcription, which results in a decrease in reporter gene activity. Thus, in contrast to <italic>Aplysia, Octopus </italic>or <italic>Thais </italic>ER [##REF##14500980##11##,##REF##16690796##12##,##REF##16782100##24##], amphiER does not display constitutive transcriptional activity under our experimental conditions and rather exhibits an inhibitory effect (Figure ##FIG##3##4##). This clearly shows that the absence of transcriptional activity observed here is not an artifact linked to a poor expression of the construct but rather reflects the inability of amphiER to activate transcription in mammalian cells.</p>", "<title>LampER is an estradiol receptor whereas amphiER is not able to bind ER ligands except the synthetic compound Bisphenol A</title>", "<p>In order to confirm that lamprey ER is an E<sub>2 </sub>receptor and to better understand the molecular basis behind the inability of the amphiER to become transcriptionally activated by estradiol stimulation, E<sub>2 </sub>binding by lamprey ER and amphiER was tested <italic>in vitro</italic>. For that purpose, limited proteolysis assay allows to assess whether addition of different putative ligands can induce a conformational change in amphiER [##REF##16839186##13##]. Using this method the ligand induced conformational change of the LBD is revealed by the alteration of the receptor sensitivity toward proteolytic digestion by trypsin. As expected, E<sub>2 </sub>was able to protect human ERα from proteolysis (Figure ##FIG##5##6A##). Interestingly lamprey ER was also protected from proteolysis by E<sub>2 </sub>even at the lowest concentration tested (Figure ##FIG##5##6A##), thus confirming the results of the transactivation assays that the lamprey ER is a high affinity E<sub>2 </sub>receptor. In contrast no protection of amphiER by estradiol was observed, even at very high ligand concentrations (10<sup>-3</sup>M) (Figure ##FIG##5##6A##). Since estradiol does not protect amphiER from proteolysis, several other classical ER ligands were tested, such as the synthetic ER agonists diethylstilbestrol [##REF##17132854##46##], 4-hydroxy-tamoxifene [##REF##17132854##46##] or bisphenol A (BPA) [##REF##17628395##47##], the natural agonist 3β-androstenediol [##REF##9048584##43##], the phytoestrogen enterolactone [##REF##12270221##45##] or the synthetic ER antagonist ICI-182780 [##REF##17132854##46##]. All compounds were able to bind to human ERα (Figure ##FIG##5##6B## to ##FIG##5##6G##) as expected, whereas none but BPA was able to bind to amphiER (Figure ##FIG##5##6G##). However, BPA did not induce transactivation by amphiER in mammalian cells reporter assay, and did not induce recruitment of the coactivator SRC1 either (see Additional file ##SUPPL##3##4##).</p>", "<p>In order to rule out the possibility that amphiER is activated by a compound related to E<sub>2</sub>, a large panel of 14 other steroids and cholesterol derivatives were tested for their ability to bind and activate amphiER. None of the tested compounds, even at high doses, had any effect on amphiER transcription activity (see Additional file ##SUPPL##4##5A##). Accordingly, no recruitment of the coactivator SRC1 by amphiER was detected in mammalian two hybrid assays (See Additional file ##SUPPL##4##5B##). The most probable explanation is thus the lack of binding by those compounds to amphiER (See Additional file ##SUPPL##5##6##).</p>", "<p>Taken together these results show that the ER from lamprey behaves as a \"classical\" ER since it binds DNA on a classical ERE and is activated by binding E<sub>2</sub>. On the other hand, though the single ER from amphioxus is able to bind the ERE, it does not bind any tested ER ligand and cholesterol derivative, except bisphenol A. However, no transcriptional activity was detected upon stimulation by any of the tested ligands. Since none of the mollusk ERs sequenced up to now binds E<sub>2 </sub>either, our data suggest that E<sub>2</sub>-binding by ER is restricted to vertebrates, implying that vertebrates specifically gained the ability to be regulated by E<sub>2 </sub>(see Discussion).</p>", "<title>Ancestral reconstruction of steroid receptors</title>", "<p>In previous analyses that discussed the evolution of ERs, it was argued that estradiol binding was an ancient function of all sex steroid receptors (SRs, comprised of ERs and NR3C members) and that the binding to other steroids was more recent, with estradiol binding ability getting restricted to ERs [##REF##11331759##8##,##REF##14500980##11##]. Those conclusions were based on the reconstruction of the ancestral SR sequence [##REF##14500980##11##]. Since the finding that estradiol binding is not shared by all ERs but restricted to vertebrate ERs contradicts this hypothesis, and to get better insight into this apparent contradiction, the sequence of the ancestral steroid receptor was reestimated. When the ancestor (AncSR1) was first \"resurrected\", only one non-vertebrate sequence was available [##REF##11331759##8##,##REF##14500980##11##]. The impact of more non-vertebrate sequences (including amphiER) was thus tested on the reconstruction of the ancestor of steroid receptors. The sequence of the ancestral steroid receptor (AncSRa), at the node grouping ERs and NR3C, was inferred using PAML 4 [##REF##17483113##48##], from the alignment described previously (the study was restricted to DBD and LBD) and the topology shown in Figure ##FIG##1##2B##. The predicted sequence resembles AncSR1 (Figure ##FIG##0##1##) with 12 out of 18 amino acids involved in ligand binding [##REF##9600906##49##] being ER-like (Figure ##FIG##0##1##). However, important differences were noticed between AncSR1 and AncSRa. First one of the 3 amino acids making direct contacts with E<sub>2 </sub>is different in AncSR1 and AncSRa: at this position, AncSR1 is vertebrate-ER like (a His residue is present at position 524 of humanERα, located in helix H10–H11, in green in the alignment, Figure ##FIG##0##1##) whereas the amino acid is different in AncSRa and is mollusk-ER like (Tyr instead of His) and mutations at this site have been shown to impair ERα activity in human [##REF##8702724##50##,##REF##12069588##51##]. Second, when a phylogenetic tree is built including both ancestral sequences AncSR1 and AncSRa as well as various NR3 sequences, AncSRa branches deep in steroid receptors as expected since it was built on the same dataset (Figure ##FIG##6##7##. For a complete tree presenting all leaves, see Additional file ##SUPPL##6##7##) but AncSR1 branches close to the vertebrate ERs, which is surprising. In order to determine whether the taxonomic sampling, and not details of the alignment, was responsible for these differences, we calculated a second SR ancestral sequence (AncSRb) using a smaller dataset: taxon sampling was reduced by removing most of the non-vertebrate steroid receptor sequences from the alignment (the amphioxus ER, the amphioxus NR3C as well as 5 out of the 6 mollusk ER sequences were removed to obtain a dataset closer to the one used in [##REF##11331759##8##]). In this case, AncSRb branches next to AncSR1, closer to the ER clade than to AncSRa (Figure ##FIG##6##7##). This result clearly shows that the reconstruction of ancestral sequences is influenced by the set of sequences available and that restricted taxonomic sampling biases ancestral SR sequences towards vertebrate ER sequences. Therefore conclusions based on such an analysis (specifically that the ancestral ER was able to bind estradiol) should be considered as only tentative, since taxonomic sampling of available steroid sequences is very much vertebrate-centered. Overall, taken together, our data do no support the hypothesis that the ancestral steroid receptor was an estradiol receptor.</p>" ]
[ "<title>Discussion</title>", "<title>The amphioxus ER does not bind estradiol</title>", "<p>In this paper we cloned and functionally characterized the lamprey and amphioxus orthologues of the human estrogen receptors. Our results show that lampER binds estradiol whereas amphiER does not. We propose that 3 types of ERs can be distinguished, depending on their ligand binding properties: vertebrate ERs (including lamprey) are the only <italic>bona fide </italic>estradiol receptors, mollusk ERs do not bind estradiol and are constitutively active transcription factors and amphiER does not bind estradiol and is transcriptionally silent in mammalian cells. This is supported by two points: (i) the experimental approach developed here is biologically relevant since the binding of bisphenol A (BPA) to amphiER was observed using the same experimental conditions as for E<sub>2 </sub>suggesting that amphiER is correctly folded and that a ligand binding pocket is likely to be present. (ii) One of the three key amino acid positions within the LBP of amphiER (Cys 531, located between the helixes H5 and H6, in green in Figure ##FIG##0##1##) diverges from vertebrate ERs (Arg 394 in human ERα), whilst the two other key positions (Glu 490 and His 659 in amphiER, located in helix H3 and H10–H11 respectively, in green in Figure ##FIG##0##1##) are conserved with the vertebrate ERs (amino acids corresponding to Glu 353 and His 524 in human ERα), suggesting that potential contacts between amphiER and estradiol are impaired. Accordingly, a recent <italic>in silico </italic>study of amphiER ligand binding ability confirmed an \"unusual ligand recognition in amphioxus ER\" [##REF##18471435##52##].</p>", "<p>It was unexpected that no effect of the synthetic ER agonist BPA was detected in the transactivation assay of the receptor in mammalian cells since BPA induces a conformational change of amphiER. This apparent absence of coactivator recruitment (see Additional file ##SUPPL##3##4B##) resulting in no transcriptional activity in response to BPA can be interpreted in several ways: (i) because of the different geometry of the ligand-binding pocket in amphiER, BPA behaves as an antiestrogen (partial agonist or even a partial antagonist) and blocks the transcriptional activation properties of amphiER, for instance by inducing a conformational change that does not allow coactivator recruitment (like human ERα and 4-raloxifen, [##REF##9338790##53##]) or by excluding amphiER from nucleus (like ICI-182,780 with human ERα, [##REF##17991486##54##]). (ii) Alternatively, the coactivator interface of amphiER does not fit with mammalian coactivators, resulting in artifactual loss of activation. However, the conservation of the amino acids involved in co-activator interaction, compared to human ERα does not support this hypothesis (sites indicated with a star in Figure ##FIG##0##1##, as described in [##REF##9875847##55##]). Among the divergent sites, at a position implicated in the charge clamp necessary for coactivator contact, amphiER contains an aspartate (D677) instead of a glutamate in humanERα (E542). Importantly, the divergence (E-&gt;D) is conservative and preserves the negative charge of the amino acid, which is important for interaction with the lysine from helix 3 (conserved in amphiER) to form this charge clamp [##REF##9744270##56##]. In addition, a unique orthologue of the p160 family of coactivators was found in the amphioxus genome [##REF##18562680##35##] and its overall conservation with its 3 human ohnologues (genes that have been duplicated during the two rounds of whole genome duplications in the chordate lineage [##UREF##6##57##]) is good. (iii) Interaction between mammalian chaperones like HSP90 and amphiER is impaired, leading to improper binding to the hormone [##REF##9183567##58##]. Taking these results into account, it will be interesting to test the effect of BPA on the subcellular localization of amphiER and to study if other related compounds are able to bind and/or activate amphiER. In addition, it will be important, when cell cultures from amphioxus are available, to check the activity of amphiER in a monospecific transient transfection assay. It should be remembered that some orphan receptors such as ERRs are thought to have no natural ligands even if they are able to bind synthetic compounds [##REF##11864604##59##]. More generally, the precise status of amphiER in terms of ligand binding remains an open question. It is nevertheless clear, and this is an important issue for the current evolutionary debate, that amphiER is not able to bind estradiol.</p>", "<title>Is there any receptor for estradiol in amphioxus?</title>", "<p>The observation that amphiER does not bind E<sub>2 </sub>is indeed a surprising observation since E<sub>2 </sub>was detected in amphioxus by RIA, the hormonal production being correlated with breeding season [##UREF##1##15##]. Several aspects of steroid metabolism were described in amphioxus [##REF##6541606##60##] and the homologues of many enzymes necessary for estradiol synthesis in mammals were cloned from amphioxus ovaries [##UREF##1##15##,##REF##16098224##61##]. Of particular interest is the report of an aromatase gene (CYP19) in amphioxus, which suggests that the crucial step in estradiol synthesis is indeed possible in amphioxus. These experimental data were recently confirmed by the analysis of the complete amphioxus genome sequence [##REF##18562680##35##]. It may be that, in amphioxus, the active sex hormone is an E<sub>2</sub>-derivative [##REF##15026175##62##] or another sex hormone, like in the case of androgens in lamprey [##REF##16359676##63##], and this derivative is still to be discovered. In a similar way, we recently demonstrated that the amphioxus TR orthologue does not bind T<sub>3 </sub>or T<sub>4</sub>, the classical thyroid hormones, but deaminated derivatives TRIAC and TETRAC, which are able to induce amphioxus metamorphosis [##REF##18514519##64##].</p>", "<p>A second possibility is that E<sub>2 </sub>itself has a central role in sex maturation in amphioxus, and that the functional estrogen receptor in amphioxus is different from amphiER. Several candidates are possible. First, there is another steroid receptor in amphioxus (amphiNR3C in Figures ##FIG##0##1## and ##FIG##1##2##) [##REF##18562680##35##] that exhibits several ER-like features. Its P- and D-boxes are closer to ERs than to vertebrate NR3C (Figure ##FIG##1##2A##). The sequence identity of its LBD with human ERα (37%) and with NR3C members (35%) are similar. Moreover, most of the amino acids involved in ligand binding are more ER-like than AR-, PR- or MR-like (Figure ##FIG##0##1##). However it is the only NR3C receptor (orthologous to AR, PR, MR and GR) found in the amphioxus genome. Thus if amphiNR3C plays the role of an estradiol receptor, this suggests an absence of a \"classical\" steroid receptor able to bind testosterone, progesterone or corticoids. Alternatively, a non-nuclear receptor could mediate E<sub>2 </sub>action in amphioxus. Indeed, several non-genomic effects of estradiol were reported in mammals involving GPCRs (for reviews see [##UREF##7##65##, ####REF##17113980##66##, ##REF##12843413##67####12843413##67##]). For instance, very recently, a high affinity receptor for the steroid androstenedione linked to the membrane, was described in lamprey [##REF##17596561##68##] and a GPCR with high affinity for progestines was isolated from sea trout [##REF##17082257##69##].</p>", "<title>Implications for the evolution of ERs</title>", "<p>The absence of E<sub>2 </sub>binding by the amphioxus estrogen receptor has interesting consequences for the evolution of SRs and ERs. Indeed, only the well characterized gnathostome ERs and the lamprey ER (studied here) have been shown to mediate E<sub>2 </sub>action. Outside vertebrates, all the ERs studied so far (in mollusks and amphioxus) do not bind E<sub>2 </sub>[##REF##14500980##11##,##REF##16690796##12##,##REF##16782100##24##, ####REF##17324427##25##, ##REF##17438828##26####17438828##26##]. Parsimony implies that the function of estradiol in the bilaterian ancestor was not mediated by ER and that ER had another function. Only later during evolution, in the vertebrate lineage, ER would then have gained the ability to be activated by E<sub>2 </sub>and to mediate the hormonal action of this compound (Figure ##FIG##7##8##). The alternative scenario (ancestral E<sub>2 </sub>binding and independent loss of either ER itself or E<sub>2 </sub>binding to ER in mollusks and invertebrate deuterostomes) is more costly in terms of evolutionary events, even if the hypothesis of an NR3C orthologue binding E<sub>2 </sub>is taken into account. Thus, taken together, our results do not support previous scenarios of steroid receptor evolution based on a reconstruction of the ancestral steroid hormone receptor AncSR1 [##REF##11331759##8##,##REF##14500980##11##].</p>", "<p>To describe the evolution of a protein, being able to study ancestral sequences at different nodes of a phylogeny would obviously provide historically relevant information that is not available otherwise [##REF##15143319##70##]. However such sequences have disappeared long ago and can only be statistically estimated. The accuracy and bias of these estimations therefore need to be investigated. Indeed, functional studies of ancestral sequences are of any value only if the ancestral reconstruction is reliable enough. The confidence associated with the previously published ancestral steroid receptor is quite low. Indeed, the overall accuracy of the reconstruction of the LBD (AncSR1) was only 62% [##REF##14500980##11##]. This is similar for the ancestor inferred here (AncSRa) on an enriched dataset, with an overall accuracy of the DBD+LBD of 70%. Moreover, amino acid uncertainty was high at many sites of AncSRa and AncSR1: more than 60 sites have more than 1 possible amino acid with a probability superior to 0.2. If one were to make an exhaustive study, one would need to reconstruct and test more than 10<sup>24 </sup>potential proteins (if all possible combinations of amino acids with probability &gt; 0.2 were tested). In fact several of the sites involved in ligand binding have low probabilities. Examples of more reliable reconstructions of nuclear receptors have been published, <italic>e.g. </italic>the ancestor of MR and GR (mineralocortoid and glucocorticoid receptors) in which the overall accuracy of the LBD was above 99%, with no disrupting mutation at any site [##REF##16601189##71##]. The reconstructed ancestor of RARs also showed a high average confidence (99% [##REF##16839186##13##]). This discrepancy between results obtained on the ancestor of all steroid receptors or merely of MR and GR for instance, can be explained by the higher sequence divergence observed among all SRs than simply among subfamilies MR and GR (see branch length in Figure ##FIG##1##2B##). Consequently, the uncertainty associated to the sequence of the ancestral steroid receptor as estimated with nowadays methods is probably too high to provide a firm basis for evolutionary conclusions. Moreover, the phylogenetic reconstruction of ancestral sequences has been shown to be biased towards the most frequent (and more stable) amino acids, resulting in an under-estimation of the less frequent amino acids (the stability of the ancestral protein is then over-estimated [##REF##16789817##72##]). In reconstructions of ancient proteins, where the evolutionary signal has been lost due to a high number of substitutions, such biases might be problematic. Thus, current reconstruction methods do not seem powerful enough to infer a biologically meaningful ancestral steroid receptor given the amount of divergence between sequences.</p>", "<p>Nonetheless, all these reservations put aside, it is surprising that the previously reconstructed ancestral SR, is vertebrate ER-like. As almost all the extant sequences used as matrix for the reconstruction came from vertebrates and led to the estimation of a \"vertebrate-like\" ancestral sequence, the same ancestral steroid receptor as previously published [##REF##14500980##11##] was estimated, but adding more sequences from various taxa. This reconstruction was done using a phylogeny equivalent to the one previously published [##REF##14500980##11##]. Using this approach, the new AncSRa is more divergent from vertebrate ERs than AncSR1. Interestingly AncSR1 was shown to bind E<sub>2 </sub>with a very low affinity (250 times lower affinity than human ERα [##REF##14500980##11##]), suggesting that AncSRa may be an even worse estradiol receptor.</p>", "<p>The bias of AncSR1 towards vertebrate ERs is explained by a lack of non-vertebrate sequences used for the reconstruction. Indeed, removing some non-vertebrate sequences from our dataset leads to an estimation of an ancestral steroid receptor that is more \"vertebrate ER\"-like (AncSRb in Figure ##FIG##6##7##). The clustering of AncSRb with the ER clade and the exclusion of AncSRa from the ER clade were supported with good statistical values (minimum of Chi2-based and SH-like supports of 0.83 and 0.89, respectively). Those data show that AncSR1 reconstruction was probably sensitive to the vertebrate bias in the data set (Figure ##FIG##6##7##). Overall, we suggest that analysis based on ancestral reconstructions should be taken as tentative, especially in case of low statistical confidence and limited taxonomic sampling. In case of the ancestral steroid receptor, even if exhaustive taxonomic sampling is necessary, phylogenetic signal is weak and the resulting confidence is quite low. Thus we think that even if the ancestral sequence built here is biologically more relevant than previously calculated ones (because of better taxonomic sampling), it remains quite uncertain. Consequently conclusions regarding the ancestral steroid receptor should be based mostly on comparative characterization of extant receptors. In that case, all the data based on invertebrate ER receptors (from mollusks and amphioxus) support an ancestor of steroid receptors that was not able to bind estradiol. This conclusion will obviously require the functional characterization of ERs from other protostome phyla in order to carefully check if this observation is general. Thus, available data converge towards a re-evaluation of the ancestral status of estrogen receptors.</p>", "<title>Sequence conservation reflects functional constraints: ligand binding ability is more recent than DNA-binding ability</title>", "<p>From our and previous studies, only vertebrate ERs are able to bind and activate transcription under estradiol stimulation [##REF##14500980##11##,##REF##16690796##12##,##REF##16782100##24##, ####REF##17324427##25##, ##REF##17438828##26####17438828##26##]. The LBD of amphiER is more divergent from its vertebrate counterparts (ca. 34% amino acid identities) than the LBD of other liganded amphioxus nuclear receptors such as amphiRAR (ca. 58%), which has been shown to bind the same ligand as its vertebrate homologue [##REF##10918302##7##,##REF##16839186##13##]. This suggests that a conserved functional feature (e.g. binding to the same ligand) is reflected in the sequence conservation of the LBD.</p>", "<p>The same observation can be done concerning the DBD since all ERs, including amphiER, have a highly conserved DBD and are able to bind EREs. Thus, for this domain also, a conservation of the function is reflected in sequence conservation.</p>", "<p>Accordingly with this notion, the LBD of invertebrate ERs is highly divergent but their DNA binding domain, as well as other functionally important domains not directly linked to ligand binding such as the dimerization interface, or the amino acids responsible for interaction with the co-activators [##REF##9875847##55##] are well conserved. This is true for amphiER as well as mollusk ERs. This strongly suggests that amphiER is a <italic>bona fide </italic>NR regulating ERE-containing genes in an E<sub>2</sub>-independent manner. Post-translational modifications such as phosphorylation or the presence/absence of other receptor-interacting proteins such as transcriptional coactivators have been shown to regulate unliganded nuclear receptors [##REF##17032747##73##]. Whether one of these mechanisms acts to regulate the activity of invertebrate ERs or if those receptors have unknown ligands still to be identified remains to be explored. Anyway our observations strongly suggest that for ERs, the DNA binding function of the receptor as well as its interaction with co-regulators have been conserved due to selective pressure. Interestingly, when studying the AncSRa, the P- and D-boxes in the DBD are ER/ERR-like (Figure ##FIG##0##1##), suggesting that ER/ERR DNA binding ability is ancestral, in accordance with the fact that these are the only receptors of the NR3 family found in invertebrates. This difference in the selection pressure between DBD and LBD has been proposed to be a general evolutionary pattern for the whole NR family [##REF##10918302##7##]. The plasticity of the ligand binding ability of NRs was recently illustrated in the case of RXR-USP where the ability of the receptor to be regulated by a ligand was suggested to have been subject to several successive episodes of gain and loss during evolution [##REF##17673910##74##].</p>", "<title>Evolution of endocrine systems: refinement of the ligand exploitation model</title>", "<p>The ligand exploitation model hypothesizes how new hormones and new receptors appear during evolution. It suggests that the ancestral ligand is the last metabolite of a synthesis pathway [##REF##11331759##8##]. According to this model, the ancestral steroid ligand was estradiol (and the ancestral SR bound estradiol). During evolution, other steroid receptors appeared by duplication of the ancestral ER and gained the ability to bind other steroids, intermediate in the synthesis pathway (like testosterone or progesterone).</p>", "<p>Our findings on the evolution of ERs do not support the ligand exploitation model, since our data strongly suggest that the ancestral ER did not bind estradiol. However, as estradiol has been detected in deuterostomes as well as protostomes (for instance in vertebrates, amphioxus, echinoderms, mollusks, for review, see [##UREF##8##75##]), steroid signaling may have been already present in bilaterian ancestor. However, up to now, the ancestral steroid molecule remains to be determined. If estradiol is an ancient hormone, it then probably bound another receptor and later on ER gained the ability to recognize it, as did other steroid receptors for their extant ligand. Thus the evolution of steroid system intermingles two distinct processes, the evolution of the receptor on one hand, and the evolution of the ligand on the other.</p>", "<p>The receptor can evolve by point mutations and change its affinity for a ligand towards another. This idea was convincingly exemplified in the case of corticoid receptors (the ancestor of MR and GR) for which it was recently demonstrated that ability of the ancestral vertebrate corticoid receptor to bind gnathostome-specific hormone aldosterone (a MR ligand) was a by-product of its ability to bind the ancestral ligand 11-deoxycorticosterone (DOC) [##REF##16601189##71##]. GR gained the ability to bind cortisol only in the gnathostome lineage, in parallel to endogenous synthesis of the hormone [##REF##17702911##16##,##REF##16601189##71##]. This detailed study shows that a receptor binding a given ligand can acquire affinity for compounds present in the cell that are structurally close to its natural ligand: this refines the ligand exploitation model, since new ligands are not necessarily precursors of ancient ligands, simply compounds present in the cell and structurally close to the ancestral ligand. Similar conclusions were drawn previously in the case of RAR evolution [##REF##16839186##13##]. It has to be emphasized that the pool of available compounds is also subject to evolutionary changes in parallel. For instance, the spatiotemporal production of estradiol is variable in the different vertebrate groups (reviewed in [##REF##12650719##76##]). Glucocorticoids differ in mouse (cortisol) and in human (corticosterone), with both hormones being GR ligands [##REF##17132855##77##]. There are several androgens in teleost fishes, with 11-ketotestosterone being teleost-specific [##REF##16107211##78##]. As there are 2 androgen receptors (ARs) in teleost fishes, from a whole genome duplication [##REF##15496914##79##], the study of the ligand-binding ability of those ARs is a potentially interesting case for the evolution of endocrine systems. As highlighted by Bridgham et al. (2006), lamprey does not produce cortisol [##REF##16601189##71##]. In accordance, their genomes do not contain the sequence corresponding to the enzyme responsible for cortisol synthesis (11b-hydroxylase) and in general classical steroids except estradiol are rarely found in lamprey. This suggests that the steroids actually found in lamprey are different from the ones found in mammals (reviewed in [##REF##17931674##80##]). Those cases exemplify the largely underestimated diversity of endocrine systems: except for lamprey and some teleost fishes, the hormonal pool of animals remains largely unknown. As proposed for the study of steroid receptors, a comparative approach should be applied to determine the metabolism of steroids in poorly studied animals. Indeed, the hormonal pool of such animals is usually evaluated from the presence/absence of putative orthologues of mammalian enzymes. As the enzymatic machinery involved in hormonal metabolism has a very labile activity (reviewed in [##UREF##9##81##,##REF##18348179##82##]), equating orthology with functional identity might be unreliable.</p>", "<p>The evolution of steroid receptors can be replaced in the more general context of ligand-nuclear receptor co-evolution. The evolution of the NR1H subfamily, that includes receptors for other steroidal compounds, like the major transcriptional regulator of bile salt synthesis farnesoid × receptor (FXR), the pregnane × receptor (PXR), the vitamin D receptor (VDR) or liver × receptor (LXR)/ecdysone receptor (Ecr), has been extensively studied and is not in line with the ligand exploitation model [##UREF##10##83##, ####UREF##11##84##, ##REF##17997857##85####17997857##85##]. For instance, comparative functional studies of FXRs from various chordate species showed that the vertebrate FXRs bind \"late\" cholesterol derivatives (from a complex synthesis pathway) but are thought to have evolved from an ancestral FXR that bound early cholesterol derivatives (from a simpler synthesis pathway) [##UREF##10##83##].</p>", "<p>In other cases, the evolution of ligand binding is more \"chaotic\" with close orthologs having a selective ligand binding ability that varies extensively (vertebrate VDRs are very well conserved when PXRs have the widest ligand repertoire of all NRs) [##UREF##10##83##].</p>", "<p>These complex histories are probably linked to specific function of some of those NRs, considered as xenotoxic compounds \"sensors\". This tight relationship with the unstable environment probably makes receptors like FXR and especially PXR more prone to fast evolution [##REF##16863441##86##]. Yet they illustrate the impressive variety of scenarios of NR evolution.</p>" ]
[ "<title>Conclusion</title>", "<p>In this article, we demonstrated that vertebrate ERs (including lamprey ER) are estradiol receptors whilst non-vertebrate ER (including amphioxus ER) are not. The most parsimonous scenario proposes that the ancestral ER was not able to bind estradiol and that it had another function. It later gained the ability to be regulated by estradiol, specifically in the vertebrate lineage. However, additional critical data remains to be discovered in poorly studied taxa [##REF##15026175##62##]. To fully understand the evolution of steroid signaling pathway, a larger number of taxa need to be targeted for detailed comparative studies. More precisely, ERs and other steroid receptors should be cloned from widely distributed taxa, especially in protostomes. Enzymes involved in steroidogenesis should also be cloned and characterized, to understand the evolution of steroid availability. In order to avoid the blinders of a \"vertebrate-centered\" view, it is of particular importance to establish the steroid hormone repertoire of an enlarged animal panel, including more protostomes. The description of various endocrine systems will certainly be relevant to the early evolution of hormone signaling.</p>" ]
[ "<title>Background</title>", "<p>The origin of nuclear receptors (NRs) and the question whether the ancestral NR was a liganded or an unliganded transcription factor has been recently debated. To obtain insight into the evolution of the ligand binding ability of estrogen receptors (ER), we comparatively characterized the ER from the protochordate amphioxus (<italic>Branchiostoma floridae</italic>), and the ER from lamprey (<italic>Petromyzon marinus</italic>), a basal vertebrate.</p>", "<title>Results</title>", "<p>Extensive phylogenetic studies as well as signature analysis allowed us to confirm that the amphioxus ER (amphiER) and the lamprey ER (lampER) belong to the ER group. LampER behaves as a \"classical\" vertebrate ER, as it binds to specific DNA Estrogen Responsive Elements (EREs), and is activated by estradiol (E<sub>2</sub>), the classical ER natural ligand. In contrast, we found that although amphiER binds EREs, it is unable to bind E<sub>2 </sub>and to activate transcription in response to E<sub>2</sub>. Among the 7 natural and synthetic ER ligands tested as well as a large repertoire of 14 cholesterol derivatives, only Bisphenol A (an endocrine disruptor with estrogenic activity) bound to amphiER, suggesting that a ligand binding pocket exists within the receptor. Parsimony analysis considering all available ER sequences suggest that the ancestral ER was not able to bind E<sub>2 </sub>and that this ability evolved specifically in the vertebrate lineage. This result does not support a previous analysis based on ancestral sequence reconstruction that proposed the ancestral steroid receptor to bind estradiol. We show that biased taxonomic sampling can alter the calculation of ancestral sequence and that the previous result might stem from a high proportion of vertebrate ERs in the dataset used to compute the ancestral sequence.</p>", "<title>Conclusion</title>", "<p>Taken together, our results highlight the importance of comparative experimental approaches vs ancestral reconstructions for the evolutionary study of endocrine systems: comparative analysis of extant ERs suggests that the ancestral ER did not bind estradiol and that it gained the ability to be regulated by estradiol specifically in the vertebrate lineage, before lamprey split.</p>" ]
[ "<title>Authors' contributions</title>", "<p>MP, HE, MS, SB and VL contributed to the conception and design of the study. MS cloned amphiER. MP performed the EMSA, limited proteolysis experiments, part of the transactivaton assays and the bioinformatics study. KP and IP performed the rest of the transactivation assays. MP and VL wrote the manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Bastien Boussau for help with phylogenetic analysis and for comments on the manuscript. We are grateful to Joseph Thornton for the gift of the lamprey ER clone, Gerard Benoit and Gabriel Markov for critical reading of the manuscript. This work was supported by the EU funded CASCADE network of excellence, CNRS, UCB Lyon 1, ENS de Lyon and MENRT.</p>" ]
[ "<fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><bold>Sequence alignment of several ERs including the amphioxus ER and the lamprey ER, as well as ancestral steroid receptors</bold>. The DBD is highlighted with light grey. The 12 helices from the LBD are indicated, based on the known 3D structure of human ERα [##REF##9600906##49##]. Amino acids from human ERα making direct hydrogen bonds with E<sub>2 </sub>are indicated in green. Amino acids making hydrophobic bonds with E<sub>2 </sub>are highlighted in purple. Amino acids known to be involved in co-activator interaction have been indicated with a star on top of each site [##REF##9875847##55##]. The more divergent A/B domain as well as the F domain have been omitted from the alignment. However, the numbering of the sites along the alignment starts at the beginning of each protein. The exon-intron limits of amphiER and humanERα have been indicated with small red strokes. The sequences of AncSRa and AncSRb have been inferred in this study. The sequence of AncSR1 was retrieved from a previous analysis [##REF##14500980##11##].</p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>There is a single ER in amphioxus</bold>. (A) Schematic representation of the different domains of amphiER. Percent identity of the amphioxus ER with other sequences from the NR3 subfamily in the DNA- and ligand-binding domains is indicated. Amino acid sequence of the highly conserved P-box and D-box in the DBD are shown. (B) Maximum likelihood (ML) tree obtained from the analysis of the amino acid sequences of the DBD and the LBD of a wide range of NR3 under a JTT+γ +i model. Bootstrap percentages obtained after 1,000 ML replicates are shown above selected branches. Scale bar indicates number of changes per site. The tree was rooted by selected RXR sequences. (C) The exon-intron structure of amphiER is conserved with that of human ERα, except two minor differences: the first human exon corresponds to the first two amphioxus exons and the last two human exons correspond to the last amphioxus exon.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p><bold>Phylogenetic analysis of amphiER</bold>. (A) Summary of the 26 best placements of amphiER within a phylogenetic tree comprised of 69 NR3 and 8 RXR sequences. 23/26 topologies (\"ER-tree\") place amphiER within the ER clade, the 3 remaining topologies (\"alter-ER tree\") place amphiER either at the base of (ER, NR3C) or within the NR3C family (close to an AR) or at the base of the NR3 family. The mean evolutionary rate of the sites supporting one of the 23 \"ER-tree\" topologies (0,9) or for the \"alter-ER tree\" topologies (1,2) are indicated in (B). (B) Distribution of the site relative evolutionary rates. Rates were estimated using an 8 class discretized gamma distribution. The vertical dotted lines correspond to different tentative threshold (2.5, 2, 1.5), above which sites have been discarded due to their high evolutionary rate, before reestimating the phylogeny of the consecutive alignment. (C) Estimation of the minimum of Chi2-based and SH-like supports, available in the aLRT-PHYML software, for the branches defining the monophyly of ERs as well as the position of amphiER. 4 trees were inferred using an alignment on which the fastest evolving sites were removed (no site removed, 34, 53 and 82 sites removed out of 323, with a mean evolutionary rate threshold above 2.5, 2.0 and 1.5, respectively).</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p><bold>LampER is activated by E<sub>2 </sub>whereas amphiER is not activated by ER agonists</bold>. The pSG5 constructs containing either amphiER (A), human ERα or human ERβ (B), were tested in transfected Cos 7 cells for their ability to activate the co-transfected cognate ERE-luc reporter plasmid after E<sub>2 </sub>stimulation (10<sup>-6</sup>M). (C) GAL4-LBD constructs from several chordate ERs were tested in transfectec 293 cells for their ability to activate a (17m)5x-G-luc reporter plasmid in the presence of increasing doses of E<sub>2 </sub>(10<sup>-9</sup>M to 10<sup>-6</sup>M). (D) Mammalian two-hybrid SRC1 recruitment assay. The GAL4-amphiER-LBD chimera was used with the coactivator SRC1 fused to the strong activation domain VP16 to transfect 293 cells in the presence of increasing doses of E<sub>2 </sub>(10<sup>-9</sup>M to 10<sup>-6</sup>M).</p></caption></fig>", "<fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p><bold>The amphioxus ER acts as a dominant negative estrogen receptor</bold>. A pSG5 construct containing human ERα (A) or human ERβ (B) was tested in transfected HeLa cells for its ability to activate the co-transfected cognate ERE-luc reporter plasmid after E<sub>2 </sub>stimulation (10<sup>-6</sup>M) in presence of increasing doses of the amphiER construct. (C) A pSG5 construct containing human ERβ was tested in transfected HeLa cells for its ability to activate the co-transfected ps2 promoter after E<sub>2 </sub>stimulation (10<sup>-6</sup>M) in the presence of increasing doses of the amphiER construct.</p></caption></fig>", "<fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p><bold>Limited proteolysis of lampER with E<sub>2 </sub>and of amphiER with various ER ligands</bold>. Human ERα was used as a positive control. lane 1: undigested protein, lanes 2–5, 6–9: digested protein in the absence (lane 2 and 6) or presence (lanes 3–5 and 7–9) of ligand (10<sup>-3</sup>M to 10<sup>-5</sup>M). 2 different trypsine doses are shown, indicated by thick or thin bars above each panel. The ligands are (A) estradiol, (B) 3β-Androstane-diol, (C) 4-hydroxytamoxifen, (D) diethylstibestrol, (E) enterolactone, (F)ICI-182780 and (G) bisphenol A.</p></caption></fig>", "<fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p><bold>The reconstruction of the ancestral sequence of steroid receptors is sensitive to taxonomic sampling</bold>. The ancestral sequence of ER and NR3C was inferred using either a complete dataset (AncSRa) or a partial dataset (AncSRb) where 5 mollusk ER sequences as well as amphiER and amphiNR3C were omitted. The position of those sequences within the phylogenetic tree calculated with the complete dataset was compared. The position of a previously described ancestor (AncSR1) is indicated as well. Triangles represent the different NR clades. For the complete tree, presenting all the 80 sequences present in the tree, see Additional file ##SUPPL##6##7##.</p></caption></fig>", "<fig id=\"F8\" position=\"float\"><label>Figure 8</label><caption><p><bold>Model of evolution of the ligand binding ability of ERs</bold>. On a classical phylogenetic tree of bilaterians, data available on the binding ability of all known ERs have been indicated. Two hypotheses are compared in terms of parsimony, whether the ancestral ER was liganded (in blue) or not (<italic>ie </italic>an orphan receptor) (in red). This result displays different costs in terms of parsimony: one unique event of gain specifically in vertebrate for the \"ancestral ER orphan\" hypothesis against at least two parallel events of loss of binding for the \"ancestral ER binding E<sub>2</sub>\" hypothesis. In addition, three events of loss of the ER gene in urochordates, echinoderms and insects+nematodes are implied by the current distribution of the gene across metazoans.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>Phylogenetic analysis of NR3 sequences using several methods</bold>. Phylogenetical trees of an alignment comprising 69 NR3 sequences as well as RXR sequences were inferred using the maximum likelihood method (ML) (A), Bayesian analysis (B), neighbour-joining method (C) and maximum parsimony method (MP) (D) based on an elision alignment of the DBD and LBD of 77 NR3 and RXRs (accession numbers are given in Additional file ##SUPPL##7##8##). Labels above each branch show percentages of bootstrap values after 1000 replicates (A), posterior probabilities (B), percentages of bootstrap values after 500 replicates (C) or 100 replicates (D). The fastest evolving sites (with an evolutionary rape above 2, as indicated in the Figure ##FIG##2##3A##) were removed from the alignment before computing phylogeny by maximum parsimony, to preserve the branching of mollusk ERs within the ER clade. In (A) nodes with bootstrap values below 50% are presented as polytomies, as in the Figure ##FIG##1##2B##.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold>DNA binding characterization of chordate ERs</bold>. Various chordate members of the NR3 family, namely human ERα, human ERβ, mouse ERRα, amphiER and lamprey ER, were synthesized <italic>in vitro </italic>and allowed to bind to a <sup>32</sup>P-labeled consensus ERE probe in an EMSA. Lane 1, empty vector (pSG5) reticulocytes lysates. Lanes 2–5, human ERα. Lanes 6–9, human ERβ. Lanes 10–13, mouse ERRα. Lanes 14–17, amphiER. Lanes 18–21, lamprey ER. Lanes 3–5, 7–9, 11–13, 15–17, 19–21, unlabeled non-specific oligonucleotide (NS) or ERE were added at indicated molar excess as competitors to test the specificity of the binding. The arrows indicated the gel shift induced by amphiER binding the ERE probe. The asterisk indicates free ERE probe.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p><bold>The amphioxus ER acts as a dominant negative estrogen receptor in CV1 cells</bold>. A pSG5 construct containing human ERα (A) or human ERβ (B) was tested in transfected CV1 cells for its ability to activate the co-transfected cognate ERE-luc reporter plasmid after E<sub>2</sub>, genistein or β-Androstane-diol stimulation (10<sup>-6</sup>M) in presence of increasing doses of the amphiER construct.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p><bold>The amphioxus ER is not activated by BPA</bold>. (A) GAL4-LBD constructs from several chordate ERs were tested in transfected 293 cells for their ability to activate a (17 m)5x-G-luc reporter plasmid in the presence of increasing doses of BPA (10<sup>-9</sup>M to 10<sup>-6</sup>M). (B) Representation of the mammalian two-hybrid SRC1 recruitment assay. The GAL4-amphiER-LBD chimera was used with the coactivator SRC1 fused to the strong activation domain VP16 to transfect 293 cells in the presence of increasing doses of BPA (10<sup>-9</sup>M to 10<sup>-6</sup>M).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional file 5</title><p><bold>amphiER is not activated by cholesterol derivatives</bold>. (A) The GAL4-amphiER-LBD chimera was tested in transfected 293 cells for its ability to activate a (17 m)5x-G-luc reporter plasmid in the presence of various cholesterol derivatives at a high concentration (1 μM) (black). The empty vector (white) was used as a negative control and the GAL4-humanERα-LBD in the presence of E<sub>2 </sub>was used as a positive control (B) Representation of the mammalian two-hybrid SRC1 recruitment assay. The GAL4-amphiER-LBD chimera was used with the coactivator SRC1 fused to the strong activation domain VP16 to transfect 293 cells in the presence of various cholesterol derivatives at 1 μM. The empty vector (white) was used as a negative control.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional file 6</title><p><bold>Limited proteolysis of amphiER with various cholesterol derivatives</bold>. lane 1: undigested protein, lanes 2–4, 5–7: digested protein in the absence (lane 2 and 5) or presence (lanes 3–4 and 6–7) of ligand (10<sup>-3</sup>M and 10<sup>-4</sup>M). 2 different trypsine doses are shown, indicated by thick or thin bars above each panel. The ligands are cholic acid (A), Chenodeoxycholic acid (B), 22R-OH-cholesterol (C), cholesterol (D), 4-androstene-3,17-dione (E), DHEA (F), corticosterone (G), progesterone (H), pregnenolone (I), estrone (J), testosterone (K), 5α-androstane-dione (L), 20-hydroxyecdysone (M) and calcitriol (N).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional file 7</title><p><bold>Phylogenetic tree of NR3 sequences as well as ancestral sequences</bold>. Complete tree corresponding to the simplified one presented in the figure ##FIG##6##7##. The ancestral sequence of ER and NR3C was inferred using either a complete dataset (AncSRa) or a partial dataset (AncSRb) where 5 mollusk ER sequences as well as amphiER and amphiNR3C were omitted. The position of those sequences within the phylogenetic tree calculated with the complete dataset was compared. The position of a previously described ancestor (AncSR1) is indicated as well. Minimum of Chi2-based and SH-like supports are shown for each branch.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional file 8</title><p><bold>Accession number of sequences used for phylogenetic analyses</bold>. AR: androgen receptor; ER: estrogen receptor; ERR: estrogen related receptor; GR: glucocorticoid receptor; MR: mineralocorticoid receptor; PR: progesterone receptor; RXR: retinoid × receptor.</p></caption></supplementary-material>" ]
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[{"surname": ["Laudet", "Gronemeyer"], "given-names": ["V", "H"], "source": ["The Nuclear Receptor FacsBook"], "year": ["2005"], "publisher-name": ["London: Academic Press;"]}, {"surname": ["Mizuta", "Kubokawa"], "given-names": ["T", "K"], "article-title": ["Presence of sex steroids and cytochrome P450 (CYP) genes in amphioxus"], "source": ["Endocrinology"], "year": ["2007"]}, {"surname": ["Fox", "Bridgham", "Bovee", "Thornton"], "given-names": ["JE", "JT", "TF", "JW"], "article-title": ["An evolvable oestrogen receptor activity sensor: development of a modular system for integrating multiple genes into the yeast genome"], "source": ["Yeast (Chichester, England)"], "year": ["2007"], "volume": ["24"], "fpage": ["379"], "lpage": ["390"], "pub-id": ["10.1002/yea.1466"]}, {"surname": ["Schubert", "Escriva", "Xavier-Neto", "Laudet"], "given-names": ["M", "H", "J", "V"], "article-title": ["Amphioxus and tunicates as evolutionary model systems"], "source": ["Trends in ecology & evolution (Personal edition)"], "year": ["2006"], "volume": ["21"], "fpage": ["269"], "lpage": ["277"]}, {"surname": ["Felsenstein"], "given-names": ["J"], "article-title": ["Cases in which parsimony or compatibility methods will be positively misleading"], "source": ["Systematic zoology"], "year": ["1978"], "volume": ["27"], "fpage": ["401"], "lpage": ["410"], "pub-id": ["10.2307/2412923"]}, {"surname": ["Shimodaira", "Hasegawa"], "given-names": ["H", "M"], "article-title": ["CONSEL: for assessing the confidence of phylogenetic tree selection"], "source": ["Bioinformatics (Oxford, England)"], "year": ["2001"], "volume": ["17"], "fpage": ["1246"], "lpage": ["1247"], "pub-id": ["10.1093/bioinformatics/17.12.1246"]}, {"surname": ["Ohno"], "given-names": ["S"], "source": ["Evolution by gene duplication"], "year": ["1970"], "publisher-name": ["Berlin, New York,: Springer-Verlag;"]}, {"surname": ["Losel", "Wehling"], "given-names": ["R", "M"], "article-title": ["Nongenomic actions of steroid hormones"], "source": ["Nature reviews"], "year": ["2003"], "volume": ["4"], "fpage": ["46"], "lpage": ["56"], "pub-id": ["10.1038/nrm1009"]}, {"surname": ["Lafont", "Mathieu"], "given-names": ["R", "M"], "article-title": ["Steroids in aquatic invertebrates"], "source": ["Ecotoxicology (London, England)"], "year": ["2007"], "volume": ["16"], "fpage": ["109"], "lpage": ["130"]}, {"surname": ["Markov", "Paris", "Bertrand", "Laudet"], "given-names": ["G", "M", "S", "V"], "article-title": ["The evolution of the ligand/receptor couple: A long road from comparative endocrinology to comparative genomics"], "source": ["Molecular and Cellular Endocrinology"], "year": ["2008"], "comment": [" in press "], "pub-id": ["10.1016/j.mce.2008.06.011"]}, {"surname": ["Reschly", "Ai", "Ekins", "Welsh", "Hagey", "Hofmann", "Krasowski"], "given-names": ["EJ", "N", "S", "WJ", "LR", "AF", "MD"], "article-title": ["Evolution of the bile salt nuclear receptor FXR in vertebrates"], "source": ["Journal of lipid research"], "year": ["2008"]}, {"surname": ["Reschly", "Ai", "Welsh", "Ekins", "Hagey", "Krasowski"], "given-names": ["EJ", "N", "WJ", "S", "LR", "MD"], "article-title": ["Ligand specificity and evolution of liver \u00d7 receptors"], "source": ["J Steroid Biochem Mol Biol"], "year": ["2008"]}, {"surname": ["Felsenstein"], "given-names": ["J"], "article-title": ["Confidence limits on phylogenies: An approach using the bootstrap"], "source": ["Evolution"], "year": ["1985"], "volume": ["39"], "fpage": ["783"], "lpage": ["791"], "pub-id": ["10.2307/2408678"]}]
{ "acronym": [], "definition": [] }
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2022-01-12 17:11:36
BMC Evol Biol. 2008 Jul 25; 8:219
oa_package/cd/70/PMC2529310.tar.gz
PMC2529311
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The Swine Leukocyte Antigen (SLA) system encodes molecules for self-nonself discrimination and is associated with immune responses and disease resistance. Three lines of pigs defined by their SLA-DRB1 alleles were developed at the University of Guelph for xenotransplantation and immune response studies. The aim of this project was to explore the potential association between defined SLA-DRB1 alleles and gene transcriptional patterns of other immune-related genes in blood mononuclear cells.</p>", "<title>Findings</title>", "<p>Three SLA-DRB1 alleles were characterized using a RT-PCR-based sequencing method. The loci represented included a new allele, DRB1*04ns01. Next, microarray heterologous (bovine-porcine) hybridization together with qPCR were used to explore differential gene expression between SLA-DRB1-defined groups. Microarray analysis showed significant (p &lt; 0.01) differential expression for 5 genes, mostly related to inflammation. Genes varied according to the comparison analyzed. Further testing with qPCR revealed the same trend of differential expression for 4 of the genes, although statistical significance was reached for only one.</p>", "<title>Conclusion</title>", "<p>A new SLA-DRB1 allele was characterized. A potential association was found between SLA-DRB1 alleles and inflammation-related genes. However, the influence of other genes cannot be ruled out. These preliminary findings agree with other studies linking MHC haplotypes and inflammation processes, including autoimmune disease. The study provides an initial view of the biological interactions between the SLA complex and other immune-related genes. Future studies will focus on characterization of SLA-haplotypes associated with these particular alleles and the dynamics of the immune response to antigenic challenges.</p>" ]
[ "<title>Findings</title>", "<p>The highly polymorphic MHC-encoded molecules are crucial for self-nonself discrimination in vertebrates. They constitute the major barrier for transplantation, contain numerous genes involved in immunological and non-immunological functions and are associated with resistance or susceptibility to various diseases. The two main classes, I and II, are involved in antigen presentation to T-cells. However, a large number of the genes in the MHC, like class III genes, are not directly related to this function [##UREF##0##1##,##REF##12500978##2##]. A total of 152 loci have been annotated within this region. In pigs, known as the SLA, the DRB genes show extensive polymorphism in exon 2 and the 135 available sequences identified to date are distributed into at least 10 confirmed allele groups [##REF##16305679##3##].</p>", "<p>Different SLA haplotypes have been associated with variation in immune response and disease, as well as reproduction and production traits [##REF##9638803##4##]. Therefore, SLA-defined pigs constitute an invaluable resource to study immune response, disease resistance and production traits, as well as an important large animal model for biomedical research [##REF##17039361##5##,##REF##17384736##6##]. Three lines of commercial Yorkshire pigs with defined SLA-DRB1 genotypes were produced at the University of Guelph for xenotransplantation and immune response research [##REF##16768723##7##,##REF##17493529##8##]. The aims of this study were to characterize the SLA-DRB1 alleles in these three pig lines and explore differential transcriptional activity between the three groups using heterologous (bovine probes – porcine targets) cDNA microarray and qPCR.</p>", "<title>Animals and samples</title>", "<p>Animal use was approved by the Animal Care Committee of the University of Guelph. Thirty-five pigs were included in the study (n = 6 for microarray analysis and n = 29 for qPCR). Pigs came from crossings of an outbred population selected for only by specific SLA-DRB1 alleles. Age of pigs ranged between 3–6 months and all pigs were in good general health at the time of sampling. Venous blood was collected in EDTA coated BD Vacutainer<sup>® </sup>collection tubes (BD – Canada, Oakville, ON, Canada) and processed immediately after collection. MNCs were isolated using Histopaque-1077 (Sigma-Aldrich Canada Ltd., Oakville, ON, Canada) and total RNA was extracted using TRIzol™ reagent (Invitrogen Canada Inc., Burlington, ON, Canada). Total RNA was treated with DNA-<italic>free </italic>(Ambion Inc., TX, USA) to eliminate genomic DNA contamination. Concentration and quality were assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA).</p>", "<title>SLA-DRB1 alleles characterization</title>", "<p>The SLA-DRB1 alleles were characterized according to Ho et al. (2006). Briefly, total RNA was reverse transcribed with the ThermoScript™ RT-PCR system using oligo d(T) primers (Invitrogen). A specific SLA-DRB1 coding region was amplified with PfuUltra™ Hotstart High-Fidelity DNA Polymerase (Stratagene, La Jolla, CA, USA) using a final concentration of 3 mM of MgCl<sub>2 </sub>and annealing temperature at 55°C. PCR products were gel-extracted with the QIAquick Gel Extraction Kit (Qiagen, Mississauga, ON, Canada) and purified PCR products were cloned using the Zero Blunt<sup>® </sup>TOPO<sup>® </sup>PCR cloning kit (Invitrogen). At least 7 colonies per animals were sent for sequencing. Each allele was characterized by sequencing using both forward and reverse primers, from at least two pigs per litter. The complete coding sequence was obtained by overlapping the forward and reverse fragments. Comparison with currently available sequences (GenBank and EBI-IPD-MHC, SLA section databases) was performed using BLAST [##REF##2231712##9##] and ClustalW [##REF##7984417##10##]. Two lines carried the SLA-DRB1*0502 and SLA-DRB1*0701 alleles respectively (Smith et al, 2005) as determined by a 100% homology between the sequences obtained from test samples and the corresponding published sequences. The third line had a novel allele, differing in one bp at position +118 in exon 2 with SLA-DRB1*0403. This difference corresponds to a point mutation substituting a cytosine for a guanine, which translates in the substitution of an arginine for a glycine residue in the protein encoded by this new allele (Figure ##FIG##0##1##). Sequences were submitted to GenBank [Genbank: <ext-link ext-link-type=\"gen\" xlink:href=\"EU087426\">EU087426</ext-link>, <ext-link ext-link-type=\"gen\" xlink:href=\"EU087427\">EU087427</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"EU087428\">EU087428</ext-link>] and the EBI IPD-MHC (SLA section) which resulted in the assignment of the SLA-DRB1*04ns01 provisional name to the new allele, approved by the MHC Nomenclature Committee.</p>", "<title>cDNA Microarray experiments</title>", "<p>Complete and detailed information on microarray experimental protocols, the datasets and the platform were submitted to GEO (accession number GSE7908). Experiments are described according to the MIAME standard [##REF##11726920##11##]. Heterologous hybridizations (bovine probes – porcine targets) were performed to compare the three groups representing defined SLA-DRB1 alleles (n = 2 pigs per group). A loop design was used for reciprocal comparisons. Six comparisons with dye-swap on the same slide were performed for a total of 12 microarrays. Data was analyzed using Acuity 4.0 (Molecular Devices Corp., Sunnyvale, CA, USA) and normalized with the LOWESS algorithm [##REF##11842121##12##]. After normalization, data was filtered based on flags, percentage of saturated pixels, background and intensity uniformity, and signal to noise ratio. The log-ratios of expression were calculated as the base 2 logarithm of the ratios of background-corrected intensity medians of red dye over green dye intensities. A gene was considered to be differentially expressed if it had an absolute value of log-intensity ratio higher or equal to 0.8, representing a fold-change of 1.7 in transcript quantity. Statistical analysis was performed using the Student's t-test with FDR correction for multiple comparisons [##UREF##1##13##]. Statistical significance was set at p = 0.01. We had previously validated the use of this in-house immune-endocrine bovine microarray with porcine targets [##REF##15261689##14##]. In this study, hybridization resulted in ~90% positive signals (~170 features) in agreement with those previous observations. However, the presence of positive signals of hybridization does not imply that all spots will provide valid results. As previously mentioned, we performed careful filtering to ensure that only consistent data was subject to further analysis. Results from microarray data analysis are summarized in Table ##TAB##0##1## and Figure ##FIG##1##2##. The *0502 allele group showed higher transcriptional activity for <italic>CCL4 </italic>and <italic>IL1B </italic>in all comparisons. The *0701 allele group showed less <italic>SLA-DQA </italic>transcripts in all comparisons. Transcripts amounts for <italic>TLR2 </italic>and <italic>CASP1 </italic>were higher in the *0502 and *0701 allele groups respectively, when compared to the *04ns01 group. The small number of genes consistently detected as differentially expressed reflects the tendency of heterologous hybridization to reduce the effective size of a given microarray. Although optimal results are obtained with homologous hybridizations, the use of heterologous microarray hybridization is still a valid approach to assess gene expression profiles given that measures are taken to preserve the quality of the data obtained [##REF##17449825##15##]. In addition, results were verified using qPCR as stated in the next section.</p>", "<p>Numerous associations have been established in swine between SLA haplotypes and features such as immune response and disease [##REF##8431799##16##,##REF##8344404##17##], reproduction [##REF##2513834##18##] and production traits [##REF##1901842##19##]. Many of these traits are not directly regulated by individual SLA genes but could rather be under the influence of non-classical MHC genes or controlled by downstream pathways yet to be described. The involvement of other closely linked genes, whose variants are in linkage disequilibrium (LD) can not be discarded [##REF##16791278##20##,##REF##16085407##21##]. For example, it has been found that differential expression of <italic>LTB </italic>(also known as TNF beta) in MHC class II-defined B cell lines is associated with certain MHC class II haplotypes but not others. This association could be explained by LD between <italic>LTB </italic>and MHC haplotypes or by the influence of polymorphism in the MHC class II molecules and their interactions on the control of gene expression [##REF##10363722##22##]. Another example is represented by <italic>BRD2 </italic>in humans. This transcription factor, without an established immune function and located in the MHC class II region, is strongly linked to the MHC in most vertebrates [##REF##15546336##23##].</p>", "<p>Although it is not possible from the results in this study to establish a direct causal relationship between particular SLA-DRB1 alleles and differential transcription of inflammatory genes observed, it is undeniable that there seems to be an association. These observations will be better explained by the characterization of the haplotypes linked to these alleles and further exploration of the immune response in animals with defined MHC haplotypes.</p>", "<title>Quantitative RT-PCR</title>", "<p>To verify differential expression observed in the microarray data, qPCR calibrator-normalized relative quantification with efficiency correction in the LightCycler<sup>® </sup>1.5 system and the Relative Quantification Software v. 1.0 (Roche Diagnostics, Laval, QC, Canada) were used. <italic>RPL19 </italic>was tested for variability among samples and selected as reference gene. Specific PCR conditions and primers are described in Table ##TAB##1##2##. Total RNA samples (*0502, n = 9; *0701, n = 6 and *04ns01, n = 14) were reverse transcribed using SuperScript III (Invitrogen). The qPCR was performed using LightCycler<sup>® </sup>FastStart DNA Master SYBR Green I (Roche). Relative standard curves for target and reference genes were created using dilution series with six 10-fold dilutions in triplicates. One of the dilutions was used as calibrator. Replicate determinations were performed using independent reverse transcription reactions. Results are reported as normalized ratio of target/reference concentrations. Data sets from qPCR were analyzed with a general linear model [##UREF##2##24##] using the SAS system for Windows v 9 (SAS Institute Inc., Cary NC, USA). A log transformation was used to normalize the data and directly model the ratios of transcript quantification. The ANOVA allowed analysis with unequal variances for <italic>CCL4</italic>. Statistical significance was set at p = 0.05. Results from the qPCR analysis are summarized in Table ##TAB##1##2## and Figure ##FIG##1##2##. In general, qPCR results followed the trend observed by microarray analysis, except for of <italic>SLA-DQA</italic>, which showed a pattern opposite to the one expected for the *0701/*04ns01 comparison. It appears that differences were more consistent between the *04ns01 and the two other groups, with <italic>IL1B </italic>transcript quantity reaching statistical significance (p = 0.05) and <italic>TLR2 </italic>(p = 0.0586) and <italic>CASP1 </italic>(p = 0.0651) approaching statistical significance, warranting further investigation. It is worth mentioning that lack of statistical significance does not automatically imply lack of biological significance. Analysis of variance components indicated that the individual pig (p = 0.05) was an important random effect in transcript quantification. This innate variability of individual pigs is most likely behind the lack of statistical significance in spite of fold-changes higher than 3 being observed. It also points out to the importance of including as many individuals as possible for qPCR confirmation of microarray data, especially individuals that have not been used for the microarray analysis, in order to obtain results that more closely reflect the situation in the population. In the case of <italic>CCL4</italic>, even though it appeared to be significantly differentially expressed in two of the comparisons (fold change &gt; 3), the differences got lost within the high variability showed by the gene transcript quantifications in the tested groups, indicating that the differences may not be consistent at the population level. This seems to be also the case for differences observed between the *0502 and *0701 allele groups, a fact that suggests that the gene expression profiles of these two groups are not really that distinctive.</p>", "<title>List of abbreviations</title>", "<p><italic>LTB</italic>, Lymphotoxin beta (TNF superfamily, member 3); <italic>BRD2</italic>, Bromodomain containing 2; <italic>CCL4</italic>, Chemokine (C-C motif) ligand 4; <italic>IL1B</italic>, Interleukin 1 beta; <italic>SLA-DQA</italic>, SLA class II DQ alpha; <italic>TLR2</italic>, Toll-like receptor 2; <italic>CASP1</italic>, Caspase 1; <italic>RPL19</italic>, Ribosomal protein L19, EBI IPD, European Bioinformatics Institute Immuno-Polymorphism Database; BLAST, Basic Local Alignment Search Tool; RT-PCR, Reverse transcription – polymerase chain reaction; qPCR, quantitative PCR; SLA, Swine leukocyte antigen; MHC, Major histocompatibility complex; MNCs, Blood mononuclear cells; GEO, Gene Expression Omnibus; MIAME, Minimum information about a microarray experiment; LOWESS, Locally weighted scatter plot smoothing algorithm; FDR, False Discovery Rate.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MINS participated in the conception and design of the study, carried out the qPCR confirmation, participated in the SLA-DRB1 allele characterization and drafted the manuscript. RJJ carried out the cDNA microarray hybridizations and helped draft the manuscript. BB established the SLA-defined lines of pigs and participated in the characterization of SLA-DRB1 alleles. BAM participated in the design and coordination of the project and helped to draft the manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We wish to acknowledge the financial support of Ontario Pork Producers to BAM and the Government of Iran to RJJ. The assistance of the personnel at Arkell Research Station (University of Guelph), technical support of Sophia Lim, and assistance in statistical analysis of William Sears are greatly appreciated.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Nucleotide and protein sequence alignment of SLA-DRB1 alleles</bold>. Multiple sequence alignment for (a) nucleotide and (b) protein of published SLA-DRB1*0403 alleles (designated by their GenBank accession numbers) and *04ns01 (new allele). The black arrows mark (a) the point mutation and (b) the corresponding amino acid residue substitution.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Differential transcriptional activity detected by cDNA microarray hybridization and qPCR</bold>. Mean fold changes in transcript quantification obtained by cDNA microarray hybridization () and qPCR (□). a) Microarray (n = 2 per group) and qPCR (*0502, n = 9; *04ns01, n = 14) results for the comparison between SLA-DRB1*0502 and *04ns01. b) Microarray (n = 2 per group) and qPCR (*0502, n = 9; *0701, n = 6) results for the comparison between SLA-DRB1*0502 and *0701 alleles. c) Microarray (n = 2 per group) and qPCR (*0701, n = 6; *04ns01, n = 14) results for the comparison between SLA-DRB1*0701 and *04ns01.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Summary of results from cDNA microarray and qPCR data analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"3\"><bold>MICROARRAY</bold></td><td align=\"center\" colspan=\"3\"><bold>qPCR</bold></td></tr></thead><tbody><tr><td align=\"left\">Gene</td><td align=\"left\">Fold-change <sup>a</sup></td><td align=\"left\">Ratio<sup>b</sup></td><td align=\"left\">p-value <sup>c</sup></td><td align=\"left\">Fold-change <sup>a</sup></td><td align=\"left\">Ratio</td><td align=\"left\">p-value</td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\" colspan=\"7\"><bold>Comparison A: SLA-DRB1 alleles 0502/04ns01</bold></td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\"><italic>TLR2</italic></td><td align=\"left\">2.76</td><td align=\"left\">1.468</td><td align=\"left\">&lt; 0.0001</td><td align=\"center\">3.10</td><td align=\"left\">1.65</td><td align=\"left\">0.0586</td></tr><tr><td align=\"left\"><italic>CCL4</italic></td><td align=\"left\">2.10</td><td align=\"left\">1.075</td><td align=\"left\">0.0010</td><td align=\"center\">3.27</td><td align=\"left\">1.71</td><td align=\"left\">0.2880</td></tr><tr><td align=\"left\"><italic>IL1B</italic></td><td align=\"left\">3.21</td><td align=\"left\">1.683</td><td align=\"left\">0.0025</td><td align=\"center\">14.72</td><td align=\"left\">3.88</td><td align=\"left\">0.0322</td></tr><tr><td align=\"left\" colspan=\"7\"><bold>Comparison B: SLA-DRB1 alleles 0502/0701</bold></td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\"><italic>SLA-DQA</italic></td><td align=\"left\">2.46</td><td align=\"left\">1.303</td><td align=\"left\">0.0038</td><td align=\"center\">1.8</td><td align=\"left\">0.85</td><td align=\"left\">0.3569</td></tr><tr><td align=\"left\"><italic>CCL4</italic></td><td align=\"left\">2.41</td><td align=\"left\">1.271</td><td align=\"left\">0.0158</td><td align=\"center\">3.55</td><td align=\"left\">1.83</td><td align=\"left\">0.2926</td></tr><tr><td align=\"left\"><italic>IL1B</italic></td><td align=\"left\">3.57</td><td align=\"left\">1.837</td><td align=\"left\">0.0079</td><td align=\"center\">3.1</td><td align=\"left\">1.64</td><td align=\"left\">0.4168</td></tr><tr><td align=\"left\" colspan=\"7\"><bold>Comparison C: SLA-DRB1 alleles 0701/04ns01</bold></td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\"><italic>CASP1</italic></td><td align=\"left\">1.79</td><td align=\"left\">0.840</td><td align=\"left\">0.0010</td><td align=\"center\">3.56</td><td align=\"left\">1.83</td><td align=\"left\">0.0651</td></tr><tr><td align=\"left\"><italic>SLA-DQA</italic></td><td align=\"left\">-2.52</td><td align=\"left\">-1.335</td><td align=\"left\">0.0035</td><td align=\"center\">2.02</td><td align=\"left\">1.02</td><td align=\"left\">0.4667</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Gene-specific primers and PCR conditions for relative quantification in the Light Cycler system</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Gene name</td><td align=\"left\">GenBank<sup>a</sup></td><td align=\"left\">Primers (5'-&gt; 3') <sup>b</sup></td><td align=\"left\">Prod. size (bp) <sup>c</sup></td><td align=\"left\">Ann. temp. (°C) <sup>d</sup></td><td align=\"left\">Acq. temp. (°C) <sup>e</sup></td></tr></thead><tbody><tr><td align=\"left\"><italic>TLR2</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"AB085935.1\">AB085935.1</ext-link></td><td align=\"left\">F TGCGAATCCTGAAAATAGGC</td><td align=\"left\">343</td><td align=\"left\">59</td><td align=\"left\">84</td></tr><tr><td/><td/><td align=\"left\">R CTTGCGTCAGTGATTTCTGC</td><td/><td/><td/></tr><tr><td align=\"left\"><italic>CCL4</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_001075147\">NM_001075147</ext-link></td><td align=\"left\">F GAAGCTCTGCGTGACTGTCC</td><td align=\"left\">391</td><td align=\"left\">59</td><td align=\"left\">87</td></tr><tr><td/><td/><td align=\"left\">R AGGAACAGGATCTGCTGAGG</td><td/><td/><td/></tr><tr><td align=\"left\"><italic>IL1B</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_214055.1\">NM_214055.1</ext-link></td><td align=\"left\">F GCAGATGGTGTCTGTCATCG</td><td align=\"left\">444</td><td align=\"left\">60</td><td align=\"left\">84</td></tr><tr><td/><td/><td align=\"left\">R TTCTCCATGTCCCTCTTTGG</td><td/><td/><td/></tr><tr><td align=\"left\"><italic>SLA-DQA</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"AY191777.1\">AY191777.1</ext-link></td><td align=\"left\">F TGTGGAGGTGAAGACATTGC</td><td align=\"left\">315</td><td align=\"left\">59</td><td align=\"left\">83</td></tr><tr><td/><td/><td align=\"left\">R CAGCATCACTGGAGACTTGG</td><td/><td/><td/></tr><tr><td align=\"left\"><italic>CASP1</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_214162.1\">NM_214162.1</ext-link></td><td align=\"left\">F GAGAAAATCTCACCGCTTCG</td><td align=\"left\">572</td><td align=\"left\">59</td><td align=\"left\">83</td></tr><tr><td/><td/><td align=\"left\">R AGTCACTCTTTCGGCAGTGG</td><td/><td/><td/></tr><tr><td align=\"left\"><italic>RPL19</italic></td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"AV600389\">AV600389</ext-link></td><td align=\"left\">F ATGAGACCAATGAAATCGCC</td><td align=\"left\">504</td><td align=\"left\">60</td><td align=\"left\">87</td></tr><tr><td/><td/><td align=\"left\">R CATGAGGATCCGCTTGTTTT</td><td/><td/><td/></tr></tbody></table></table-wrap>" ]
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[]
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[ "<table-wrap-foot><p><sup>a </sup>Fold change = 2 <sup>(Log-ratio) </sup>or (-1) 2 <sup>(Log-ratio)</sup>; <sup>b </sup>Median of the lowess-normalized log-ratio of intensity; <sup>c </sup>p-value with FDR correction for multiple comparisons.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>Sequenced used for primer design; <sup>b </sup>Sequence of forward (F) and reverse (R) primers in 5' to 3' orientation; <sup>c </sup>Size of the amplified PCR product; <sup>d </sup>Annealing temperature and <sup>e </sup>acquisition temperature for qPCR.</p></table-wrap-foot>" ]
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[{"surname": ["Ando", "Chardon"], "given-names": ["A", "P"], "article-title": ["Gene organization and polymorphism of the swine major histocompatibility complex"], "source": ["Anim Sci J"], "year": ["2006"], "volume": ["77"], "fpage": ["127"], "lpage": ["133"], "pub-id": ["10.1111/j.1740-0929.2006.00331.x"]}, {"surname": ["Benjamini", "Hochberg"], "given-names": ["Y", "Y"], "article-title": ["Controlling the false discovery rate: a practical and powerful approach to multiple testing."], "source": ["J Roy Statistic Soc Ser B"], "year": ["1995"], "volume": ["57"], "fpage": ["289"], "lpage": ["300"]}, {"surname": ["Littell", "Milliken", "Stroup", "Wolfinger", "Inc. SASI"], "given-names": ["RC", "GA", "WW", "RD"], "source": ["SAS System for Mixed Models"], "year": ["1996"], "publisher-name": ["Cary, NC, USA"], "lpage": ["656"]}]
{ "acronym": [], "definition": [] }
24
CC BY
no
2022-01-12 14:47:26
BMC Res Notes. 2008 Jun 23; 1:31
oa_package/67/ff/PMC2529311.tar.gz
PMC2529312
18710508
[ "<title>Background</title>", "<p>Tobacco use is a leading cause of preventable morbidity and mortality from non-communicable diseases [##REF##17765526##1##, ####REF##17132052##2##, ##REF##16162883##3####16162883##3##]. Adolescent smoking is of public health significance as many adult smokers initiated the smoking habit as adolescents. Adolescent smoking is also important as it is associated with short-term health effects such as incident and exacerbation of asthma [##REF##16973983##4##]. Smoking in adolescents may also be a marker of other unhealthy lifestyles or social problems such as alcohol use, illicit drug use, sedentary lifestyle, unprotected sex and truancy [##REF##17572955##5##, ####REF##17610717##6##, ##REF##16442742##7##, ##REF##15997674##8##, ##REF##6973285##9####6973285##9##].</p>", "<p>Globally, studies to estimate the prevalence of adolescent smoking and associated factors have been spurred by the Global Youth Tobacco Survey (GYTS) [##REF##17651482##10##,##REF##15779140##11##]. The GYTS was established by the World Health Organization (WHO) and Centers for Disease Control and Prevention's (CDC's) Office on Smoking and Health to track tobacco use among young people across countries using a standard methodology and core questionnaire. The aim of the GYTS surveillance system is to enhance the capacity of countries to design, implement, and evaluate tobacco control and public health prevention programs. Many of the published reports on Global Youth Tobacco Survey have been single year prevalence reports. However, as repeat surveys are conducted within the same settings (countries), there is opportunity to track the changes in prevalence of smoking and associated factors. In the case of Jamaica, the first round of the GYTS was conducted in 2000 and a repeat survey done in 2006. We therefore have two time points when data on adolescent smoking were obtained using nationally representative survey methods and common methodology. We therefore carried this study to estimate the prevalence of current cigarette smoking among in-school adolescents in Jamaica and associated factors using GYTS methodology in 2000 and 2006. Furthermore, we also conducted regression analysis to estimate the size of effect of association between current cigarette smoking and a list of explanatory variables. Data from the Jamaica GYTS were selected because this country has had a repeat GYTS, with the second survey having conducted just a few years following ratification of the Framework Convention on Tobacco Control (FCTC).</p>" ]
[ "<title>Methods</title>", "<p>We conducted secondary analysis of the Jamaican Global Youth Tobacco Survey (GYTS) implemented in 2000 and 2006. A full description of the data collection methods in the GYTS has been reported elsewhere [##REF##15779140##11##]. Current cigarette smoking was defined as having smoked a cigarette, even a single puff, within the last 30 days preceding the survey. We were also interested to explore if the differences in the prevalence of smoking between the 2000 and 2006 survey. Data were analysed using SUDAAN software version 9 (Research Triangle Institute, Research Triangle Park, North Carolina, United States of America). Chi-square tests were used to compare differences between proportions.</p>" ]
[ "<title>Results</title>", "<p>1752 adolescents, 48.8% male and 51.2% females participated in the 2000 survey. In 2006, 1854 participated of whom 49.5 were males and 50.5% females. Table ##TAB##0##1## indicates the prevalence of smoking among Jamaican school-going adolescents went up from 15.2% in 2000 to 16.7% in 2006, but this was not statistically significant (p = 0.22). The perception that smoking is not harmful increased 5% (from 10.9% to 15.9%). Reported parental smoking decreased from 39.4% to 35.5% in the same period of time.</p>", "<p>Table ##TAB##1##2## compares the rates of exposure to adverts between 2000 and 2006. There was a significant decrease in the prevalence of adolescents exposed to tobacco adverts on billboards (p-value = 0.037) and in newspapers/magazine (p-value &lt; 0.001). The percentage of adolescents who reported having an item with a tobacco brand logo on it increased from 13.9% to 16.4%.</p>", "<p>Table ##TAB##2##3## indicates that the perception that boys and girls who smoked had more friends increased significantly between 2000 and 2006 (p-values = 0.016 and 0.004 respectively). There was no significant difference in the perception that boys or girls who smoked were attractive.</p>", "<p>Table ##TAB##3##4## presents results from bivariate and multivariate analyses (2006). In bivariate analysis, current smoking was positively associated with male gender (OR = 2.04; 95% CI [1.56–2.68]), having smoking parents (OR = 2.38; 95% CI [1.80–3.16]), and smoking friends (OR = 21.99; 95% CI [13.99–34.57] for most or all friends smokers and OR = 5.38; 95% CI [3.80–7.61] for some friends smokers). The perception that smoking is harmful was associated with a non-significant decrease in the odds of smoking (OR = 0.83; 95% [0.57–1.23]). Results from the multivariate analysis were weaker than in bivariate analysis but with the same inference.</p>" ]
[ "<title>Discussion</title>", "<p>The prevalence of current cigarette smoking among in-school adolescents in 2000 and 2006 were 15.2% and 16.7% respectively (p = 0.22) i.e. the change was not statistically significant. The male and females GYTS prevalence estimates of current cigarette smoking among in-school adolescent were: 7.6% and 6.4% in Barbados (2002); 6.2% and 3.7% in Bahamas (2004); 18.9% and 10.4% in Belize (2002); 14.1% and 13.8% in Haiti (2005); 11.5% and 7.9% in St Lucia (2000) and 16.0% and 7.6% in Trinidad and Tobago (2000) [##UREF##0##12##]. Furthermore the prevalence of current cigarette smoking in-school adolescents had been reported at 1%–4.5% in Ethiopia, [##REF##17651482##10##], 3.0% in Blantyre-Malawi [##REF##17547101##13##], and 17.5% overall prevalence in the Americas [##UREF##1##14##].</p>", "<p>We also observed that the proportion of adolescents who reported having a parent who was a smoker reduced, so did adolescents who reported having been exposed to pro-tobacco advertisements through billboards and magazines. However the proportion of adolescents who reported owning an item with a tobacco brand logo increased. It is difficult to determine the reasons for increases in some of the characteristics while there are increased in other pro-tobacco measures.</p>", "<p>One observation regarding the Jamaican tobacco landscape is that the country signed into the World Health Organization's Framework Convention on Tobacco Control (FCTC) on 24 September 2003 and adoption followed on 7 July 2005. Currently, Jamaica has banned tobacco advertisements on local television and radio programs but advertisements from international programs have not been banned. There are also tobacco cessation programs at some health facilities with nicotine replacement therapy available. However, more still needs to be done as advertisements in local newspapers, magazines, billboards, tobacco brand logos on items not related to tobacco, public environmental tobacco smoking have not been banned. There are however, national tobacco control objectives and a dedicated agency for the control of tobacco [##UREF##0##12##].</p>", "<p>We also found that current smoking among the adolescents was associated with having closest friends who were smokers, parental smoking and male gender. Perception that smoking was harmful to health was associated with reduced likelihood of smoking.</p>", "<p>Previous studies on adolescent smoking conducted elsewhere have also reported positive relationship between peer smoking, parental smoking, male gender and smoking [##REF##17651482##10##]. Parental smoking may influence adolescent smoking by facilitating the ready availability of cigarettes in the home and moderating parental attitudes towards adolescent smoking. Peer smoking could influence adolescent attitudes and smoking in a similar way as has been described for parental smoking. It is also possible that adolescents who are smokers may be more likely to choose other smokers as closest friends. However due to the cross sectional nature of data collection over the two surveys, it is not possible to assign any of the factors as causes of adolescent smoking. Furthermore, for non-time varying factors such as sex, we cannot really say that male sex is the cause of smoking but rather that being male may be associated with other intermediate factors that may explain male predominance of smoking.</p>", "<p>We found that adolescents who perceived that smoking was harmful were less likely to smoke and those who perceived that smoking made youth attractive were less likely to smoke. According to the PRECEED/PROCEED model of health promotion individuals are likely to adopt a healthy behavior if they have the appropriate knowledge and attitude, are exposed to reinforcing and enabling factors such as friends, family and health workers [##UREF##2##15##]. We therefore suggest that intervention programs should aim to change adolescent behavior by highlighting the harmful effects of smoking. There is need to ensure that adolescents are not exposed to images that glamorize smoking. Negative attitudes towards smoking may influence eventual avoidance of smoking among adolescents.</p>", "<p>It is however interesting to note that exposure to pro-tobacco advertisement on television, magazines and newspapers and billboards declined from 2000 to 2006. However, ownership of item with tobacco brand logo increased over the period under review. Whether the decline in prevalence of exposure to advertisement was related to bans on local television programs remain to be seen. Paradoxically, the proportion of adolescents with favorable perception towards smoking increased.</p>", "<p>In a study of high school forms 4 and 5 and Jamaica, Siyobo and Lee [##REF##9494405##16##] reported prevalence of cigarette smoking at 16.6%. Males, urban residents and adolescents whose parents were skilled professionals were more likely to smoke than rural, female and children of less skilled parents. A comparison of the Soyibo and Lee report [##REF##9494405##16##] and our findings from the current study (years 2000 and 2006 surveys), suggest stability of the prevalence of cigarette smoking among adolescents in Jamaica.</p>" ]
[ "<title>Conclusion</title>", "<p>We have demonstrated that the prevalence of current cigarette smoking among in school adolescents in Jamaica has risen slightly but not statistically significant. In fact the prevalence is relatively similar to previous estimate in the late 1990's. Predictors of smoking such as male gender and smoking status of parents or friends have either decreased on remained stable over the years. The recent signing in of Jamaica to the WHO's Framework Convention on Tobacco Control may provide the socio-political environment for significant reduction of tobacco use among adolescents in Jamaica.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>We conducted this study to estimate the correlates of current cigarette smoking among in-school adolescents in Jamaica 2006 and compare prevalence of smoking and associated factors between 2000 and 2006.</p>", "<title>Results</title>", "<p>In 2006, 1854 participated of whom 49.5 were males and 50.5% females. 1752 adolescents, 48.8% male and 51.2% females participated in the 2000 survey. Between 2000 and 2006, the prevalence of smoking among Jamaican school-going adolescents went up slightly from 15.2% to 16.7% but this was not statistically significant (p = 0.22). The perception that smoking is not harmful increased from 10.9% to 15.9% while parental smoking decreased from 39.4% to 35.5%. There was a decrease in the rates of adolescents exposed to tobacco adverts on billboards (p-value = 0.037) and in newspapers/magazine (p-value &lt; 0.001). The percentage of adolescents who reported having an item with a tobacco brand logo on it increased from 13.9% to 16.4%. The perception that boys and girls who smoked had more friends increased between 2000 and 2006 (p-values = 0.016 and 0.004 respectively). Current smoking was associated with male gender (OR = 1.55; 95% CI [1.09–2.19]), having smoking parents (OR = 1.75; 95% CI [1.23–2.50]), and smoking friends (OR = 14.94; 95% CI [8.61–25.92] for most or all friends smokers and OR = 4.38; 95% CI [2.93–6.56] for some friends smokers)).</p>", "<title>Conclusion</title>", "<p>Results from this study indicate smoking was positively associated with male gender, having smoking friends or parents. We observed a slightly non significant increase in the prevalence of smoking between 2000 and 2006 among adolescents in Jamaica. Although there was a decrease in the rates of adolescents exposed to advertisement, the percentage of those who had an item with a tobacco brand logo had increased. The possible impact of the Jamaica's ratification of the Framework Convention on Tobacco control remains to be observed.</p>" ]
[ "<title>Limitations of the study</title>", "<p>The limitations of the GYTS have been reported previously elsewhere [##REF##17651482##10##]. Firstly data from the Global Youth Tobacco Survey are obtained through self-reports. As is the case with essentially all study methods where study participants self-report, there may be under-reporting as well as over-reporting. The GYTS aims to minimize such misreporting for emphasizing the collection of data through anonymous questionnaires. The possibility of mis-reporting cannot be entirely removed just by emphasizing anonymity although it can be reduced substantially.</p>", "<p>Data from the GYTS are obtained only from students who are available in school that the survey is administered in the particular school. Students who are absent from school due to any reason are not followed up. Slonim-Nevo and Mukuka have reported that out of school adolescent were more likely to engage in unhealthy behaviours compared to in-school peers [##REF##15933841##17##]. If this were also the case in Jamaica, then our results would be under-estimates. We however do not know whether this was the case or not; although it is important for the reader to keep this possibility in mind.</p>", "<p>As the GYTS only recruits students that are enrolled in school, the findings may not be applicable to all adolescents in Jamaica as out of school adolescents may have prevalence estimates substantially different from adolescents who were enrolled in school. However, primary and secondary school enrolment ratios in Jamaica are 90% and 88% with 97% and 92% attendance ratios [##UREF##3##18##]. Any biases that may result because of non-enrolment and non-attendance at school are likely to be minimal. Our results can be described as fairly representative of the adolescent population in Jamaica.</p>", "<p>Traditionally, trend analysis requires at least assessment of the prevalence at three different time points. Our study is based in two time points. However, even with the slight increase, a third reading that will show that trend is up may be more meaningful, a downward future prevalence will suggest inconclusive findings. We await future GYTS or similarly designed studies to assess whether there is a trend in prevalence of cigarette smoking among in school adolescents in Jamaica. In these future studies, it should be possible to use ARIMA (autoregressive integrated moving average) analysis to identify real changes having separated out noise from the results [##UREF##4##19##].</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>ER conducted the analysis, participated in the analysis of the data and drafting of the manuscript.</p>", "<p>ASM conceived the data analysis plan and participated in the interpretation of the findings and drafting of the manuscript.</p>", "<p>SS participated in the interpretation of the findings and drafting of manuscript.</p>", "<p>All authors read and approved the final draft of the manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors are grateful to the Centers for Disease Control and Prevention (Atlanta, Georgia, United States of America) and the World Health Organization, Division of Non-Communicable Diseases for making the data available for our analysis. Funding for the GYTS is provided by the Canadian Public Health Association, National Cancer Institute, United Nations Children Emergency Fund, and World Health Organization – Tobacco Free Initiative. The authors are not associated in any way with the funding agencies of the GYTS. We are thankful to the in-school adolescents who had participated in the survey.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Prevalence of smoking among Jamaican adolescents in 2000 and 2006</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Year 2000</td><td align=\"left\">Year 2006</td><td/></tr></thead><tbody><tr><td align=\"left\">Characteristic</td><td align=\"left\">% (95% CI)</td><td align=\"left\">% (95% CI)</td><td align=\"left\">P value</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Age in years</td><td/><td/><td align=\"left\">0.92</td></tr><tr><td align=\"left\">Total</td><td align=\"left\">15.2 (13.4–17.3)</td><td align=\"left\">16.7 (12.9–18.6)</td><td/></tr><tr><td align=\"left\"> =&lt;13</td><td align=\"left\">11.6 (9.8–14.3)</td><td align=\"left\">15.5 (12.9–18.6)</td><td/></tr><tr><td align=\"left\"> 14</td><td align=\"left\">15.8 (9.2–25.7)</td><td align=\"left\">15.2 (12.2–18.7)</td><td/></tr><tr><td align=\"left\">15</td><td align=\"left\">20.9 (16.9–25.3)</td><td align=\"left\">18.2 (14.6–22.5)</td><td/></tr><tr><td align=\"left\"> &gt; = 16</td><td align=\"left\">22.9 (18.5–26.5)</td><td align=\"left\">21.7 (16.3–28.3)</td><td/></tr><tr><td align=\"left\"> Gender (sex)</td><td/><td/><td align=\"left\">0.859</td></tr><tr><td align=\"left\">Females</td><td align=\"left\">11.7 (9.8–13.9)</td><td align=\"left\">12.0 (10.0–14.3)</td><td/></tr><tr><td align=\"left\">Males</td><td align=\"left\">19.3 (16.1–23.0)</td><td align=\"left\">21.8 (18.9–25.0)</td><td/></tr><tr><td align=\"left\">Parental smoking status</td><td/><td/><td align=\"left\">0.782</td></tr><tr><td align=\"left\"> None</td><td align=\"left\">60.8 (57.8–64.5)</td><td align=\"left\">64.4 (61.8–66.4)</td><td/></tr><tr><td align=\"left\"> One or both parents smokers</td><td align=\"left\">27.0 (22.7–31.0)</td><td align=\"left\">35.5 (33.1–38.4)</td><td/></tr><tr><td align=\"left\">Perception that smoking is harmful</td><td/><td/><td align=\"left\">0.890</td></tr><tr><td align=\"left\">Yes</td><td align=\"left\">10.9 (7.9–12.7)</td><td align=\"left\">15.9 (13.1–18.5)</td><td/></tr><tr><td align=\"left\">No</td><td align=\"left\">89.1 (86.5–91.7)</td><td align=\"left\">84.1 (81.9–86.3)</td><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Exposure to tobacco advertisements among Jamaican adolescents in 2000 and 2006</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Year 2000</td><td align=\"left\">Year 2006</td><td/></tr></thead><tbody><tr><td align=\"left\">Characteristic</td><td/><td/><td align=\"left\">P value</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Seen cigarette brand name on TV in past 30 days</td><td align=\"left\">76.9 (74.6–79.0)</td><td align=\"left\">67.4 (65.0–69.7)</td><td align=\"left\">0.080</td></tr><tr><td align=\"left\"> Males</td><td align=\"left\">76.4 (72.6–79.7)</td><td align=\"left\">66.0 (62.5–69.3)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"left\">77.4 (74.6–80.0)</td><td align=\"left\">68.7 (65.5–71.8)</td><td/></tr><tr><td align=\"left\">Has item with cigarette brand logo</td><td align=\"left\">13.9 (12.2–15.8)</td><td align=\"left\">16.4 (14.6–18.3)</td><td align=\"left\">0.296</td></tr><tr><td align=\"left\"> Males</td><td align=\"left\">15.7 (12.8–19.0)</td><td align=\"left\">19.4 (16.6–22.4)</td><td/></tr><tr><td align=\"left\">Females</td><td align=\"left\">12.2 (10.3–14.4)</td><td align=\"left\">13.5 (11.4–15.9)</td><td/></tr><tr><td align=\"left\">Seen tobacco adverts on billboards in past 30 days</td><td align=\"left\">66.0 (66.5–68.4)</td><td align=\"left\">62.9 (60.5–65.2)</td><td align=\"left\">0.037</td></tr><tr><td align=\"left\">Males</td><td align=\"left\">64.9 (60.8–68.7)</td><td align=\"left\">63.0 (59.5–66.3)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"left\">67.0 (64.0–69.9)</td><td align=\"left\">62.8 (59.5–66.0)</td><td/></tr><tr><td align=\"left\">Seen tobacco adverts in newspapers/magazines in past 30 days</td><td align=\"left\">61.4 (58.8–63.8)</td><td align=\"left\">56.5 (54.1–58.9)</td><td align=\"left\">&lt;0.001</td></tr><tr><td align=\"left\">Males</td><td align=\"left\">61.7 (57.6–65.6)</td><td align=\"left\">56.2 (52.7–59.8)</td><td/></tr><tr><td align=\"left\">Females</td><td align=\"left\">61.1 (58.0–64.1)</td><td align=\"left\">56.8 (53.4–60.1)</td><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Attitudes towards tobacco smoking distributed among Jamaican adolescents in 2000 and 2006</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">Year 2000</td><td align=\"center\">Year 2006</td><td/></tr></thead><tbody><tr><td align=\"left\">Characteristic</td><td align=\"center\">% (n)</td><td align=\"center\">% (n)</td><td align=\"left\">P value</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Felt that boys who smoke have more friends</td><td align=\"center\">49.4 (45.4–51.5)</td><td align=\"center\">69.6 (66.7–72.3)</td><td align=\"left\">0.016</td></tr><tr><td align=\"left\"> Males</td><td align=\"center\">44.8 (39.5–48.0)</td><td align=\"center\">64.4 (60.0–68.6)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"center\">54.3 (48.6–57.1)</td><td align=\"center\">75.0 (71.2–78.5)</td><td/></tr><tr><td align=\"left\">Felt like girls who smoke had more friends</td><td align=\"center\">25.2 (22.6–27.9)</td><td align=\"center\">35.9 (33.2–38.8)</td><td align=\"left\">0.004</td></tr><tr><td align=\"left\"> Males</td><td align=\"center\">24.9 (21.0–29.3)</td><td align=\"center\">34.9 (30.9–39.0)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"center\">25.4 (22.3–29.0)</td><td align=\"center\">37.1 (33.2–41.2)</td><td/></tr><tr><td align=\"left\">Felt that boys who smoke are attractive</td><td align=\"center\">10.6 (8.9–12.6)</td><td align=\"center\">14.8 (13.0–16.9)</td><td align=\"left\">0.175</td></tr><tr><td align=\"left\"> Males</td><td align=\"center\">13.8 (10.8–17.5)</td><td align=\"center\">17.9 (14.9–21.4)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"center\">7.8 (6.2–9.9)</td><td align=\"center\">12.2 (10.0–14.7)</td><td/></tr><tr><td align=\"left\">Felt that girls who smoke are attractive</td><td align=\"center\">8.9 (7.4–10.7)</td><td align=\"center\">11.1 (9.5–12.9)</td><td align=\"left\">0.327</td></tr><tr><td align=\"left\"> Males</td><td align=\"center\">11.7 (9.1–15.1)</td><td align=\"center\">13.6 (11.0–16.4)</td><td/></tr><tr><td align=\"left\"> Females</td><td align=\"center\">6.3 (4.8–8.2)</td><td align=\"center\">8.9 (7.0–11.2)</td><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Factors associated with current smoking among Jamaican adolescents in 2006</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Characteristics</td><td align=\"left\">Unadjusted odds ratios [95% CI]</td><td align=\"left\">Adjusted odds ratios [95% CI]</td></tr></thead><tbody><tr><td align=\"left\">Age (years)</td><td/><td/></tr><tr><td align=\"left\"> =&lt;13</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"> 14</td><td align=\"left\">0.97 [0.69–1.37]</td><td align=\"left\">0.79 [0.50–1.25]</td></tr><tr><td align=\"left\"> 15</td><td align=\"left\">1.21 [0.86–1.71]</td><td align=\"left\">1.08 [0.68–1.73]</td></tr><tr><td align=\"left\"> &gt; = 16</td><td align=\"left\">1.51 [1.00–2.29]</td><td align=\"left\">0.92 [0.53–1.62]</td></tr><tr><td align=\"left\">Gender</td><td/><td/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"> Male</td><td align=\"left\">2.04 [1.56–2.68]</td><td align=\"left\">1.55 [1.09–2.19]</td></tr><tr><td align=\"left\">Parental smoking status</td><td/><td/></tr><tr><td align=\"left\"> None</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"> One or both parents smokers</td><td align=\"left\">2.38 [1.80–3.16]</td><td align=\"left\">1.75 [1.23–2.50]</td></tr><tr><td align=\"left\">Best friend smokers</td><td/><td/></tr><tr><td align=\"left\"> None</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"> Some</td><td align=\"left\">5.38 [3.80–7.61]</td><td align=\"left\">4.38 [2.93–6.56]</td></tr><tr><td align=\"left\"> Most or all</td><td align=\"left\">21.99 [13.99–34.57]</td><td align=\"left\">14.94 [8.61–25.92]</td></tr><tr><td align=\"left\">Perception that smoking is harmful</td><td/><td/></tr><tr><td align=\"left\"> No</td><td align=\"left\">1.00</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"> Yes</td><td align=\"left\">0.83 [0.57–1.23]</td><td align=\"left\">0.95 [0.57–1.58]</td></tr></tbody></table></table-wrap>" ]
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[{"article-title": ["World Report on the Global Tobacco Epidemic 2008: World Health Organization, 2008; retrieved on 14 April 2008"]}, {"collab": ["Centers for Disease Control and Prevention (CDC)"], "article-title": ["Use of cigarettes and other tobacco products among students aged 13\u201315 years \u2013 worldwide, 1999\u20132005"], "source": ["Morb Mortal Wkly Rep"], "year": ["2006"], "volume": ["55"], "fpage": ["553"], "lpage": ["6"]}, {"surname": ["Green", "Kreuter"], "given-names": ["LW", "MW"], "source": ["Health promotion: An educational and environmental approach"], "year": ["1991"], "edition": ["2"], "publisher-name": ["Mountain View, Mayfield, California, United States of America"]}, {"collab": ["UNICEF"], "article-title": ["State of the World's Children 2008"], "comment": ["retrieved 19 April 2008"]}, {"collab": ["The United States Census Bureau"], "article-title": ["The X-12-ARIMA Seasonal Adjustment Program"], "comment": ["accessed 19 April 2008"]}]
{ "acronym": [], "definition": [] }
19
CC BY
no
2022-01-12 14:47:26
BMC Res Notes. 2008 Jul 28; 1:55
oa_package/18/7d/PMC2529312.tar.gz
PMC2529313
18715504
[ "<title>Background</title>", "<p>The <italic>elav </italic>(<italic>embryonic lethal abnormal visual system</italic>) gene of <italic>D. melanogaster </italic>was the the first identified member of a family of neuronal RNA binding proteins that is conserved in metazoans [##REF##8331337##1##,##REF##17928954##2##]. The proteins in this family contain three RNA Recognition Motifs (RRM), with a hinge region separating the second and third RRMs and an optional non-conserved N-terminal region. The hinge includes signals essential for nuclear export and subcellular localization [##UREF##0##3##].</p>", "<p>RRM are common protein domains found in all life kingdoms. In humans, there are 497 genes encoding RRM containing proteins, which represent 2% of the human gene products. Proteins containing one or several of these domains are capable of interacting in a sequence specific manner with single stranded RNA molecules and of directing the assembly of multiprotein complexes [##REF##17473849##4##,##REF##15853797##5##]. In spite of the remarkable sequence conservation of the RRM domains, RRM-containing proteins perform numerous functions, intervening at all the possible steps of RNA metabolism. The RRM domain is composed of about 90 amino acids and contains a conserved octapeptide termed RNP-1 (ribonucleoprotein motif) and a conserved hexapeptide termed RNP-2. Structural studies indicate that four antiparallel beta-sheets form the RNA interaction surface, with RNP-1 and RNP-2 on the two inner sheets (beta 1 and beta 3). In RNA-RRM complexes, nucleotides establish contacts with residues in the RNPs, with regions in the RRM beyond the RNP domains also involved in RNA recognition. The plasticity of RRMs in their sequence-specific recognition of topologically diverse RNA is likely to be correlated with their presence in a variety of proteins involved in the diverse steps of post-transcriptional regulation.</p>", "<p>There are three <italic>elav</italic>-related genes in <italic>D. melanogaster</italic>. The <italic>elav </italic>gene encodes a nuclear product present in all neurons throughout development and is required for the differentiation of postmitotic neurons and their maintenance [##REF##8331337##1##]. The <italic>rbp9 (RNA binding protein 9) </italic>product is present in neuronal nuclei starting at the third larval instar and also in the cytoplasm of cystocytes during oogenesis. Although neuronal expression is predominant, <italic>rbp9 </italic>mutations reveal a role in cystocyte proliferation and differentiation, but no neuronal defects have been reported [##REF##10082516##6##,##REF##15529000##7##]. The expression of <italic>fne (found in neurons) </italic>resembles <italic>elav</italic>'s, but with a slightly delayed onset. FNE is cytoplasmic, but the <italic>elav </italic>and <italic>fne </italic>genes interact, suggesting protein shuttling [##REF##12591606##8##,##REF##16282587##9##]. The products of <italic>elav </italic>family members are essentially present in the nervous system, in all of the neurons in the case of <italic>elav </italic>itself, but more generally in subsets of neurons and/or neuroblasts and glial cells. Expression has also been detected in other tissues, in particular in testes and ovaries, or found to be ubiquitous (for instance [##REF##7753842##10##]). Diverse molecular functions in the control of RNA half life, nuclear export, RNA 3' end formation, alternative RNA processing, polyadenylation and translation have been proposed for these proteins [##REF##16282587##9##,##REF##11289308##11##, ####REF##12464637##12##, ##REF##9628881##13##, ##REF##9628880##14##, ##REF##14522950##15##, ##REF##17035636##16##, ##REF##17127772##17####17127772##17##]. Multiple functions, both cytoplasmic and nuclear have been demonstrated for HuR, an ubiquitously expressed member of the human family [##REF##11289308##11##,##REF##17035636##16##,##REF##17127772##17##].</p>", "<p>The evolutionary relationship between members of the family are complex. For instance, the four human proteins share 74–91% identity, while the three Drosophila proteins share only 59–68% identity. The goal of the work reported here was to investigate these relationships. We found that the <italic>elav </italic>family has an eventful evolutionary history, somewhat masked by the high level of amino acid conservation of the gene products, but revealed by analysis of the gene structure of the different family members (11 species, 23 proteins). We attribute the rapid functional evolution of the family members, as opposed to the high level of sequence conservation, to the plasticity of the RRM domains, where small changes in critical positions have the potential to modify interactions with RNA.</p>" ]
[ "<title>Methods</title>", "<title>cDNA sequences used for the analysis of coding sequence organization in the <italic>elav </italic>gene family of <italic>Drosophila melanogaster</italic></title>", "<p>We used the transcripts data from FlyBase [##UREF##1##18##] to assess the relationship between RNA and protein coding regions. Multiple RNA isoforms from one gene were taken into account if they were a source of polypeptide diversity. For instance, seven alternative RNA forms have been reported for <italic>rbp9</italic>, which are predicted to encode six distinct polypeptides. Only one level of variation was relevant to the present analysis, that is the alternative inclusion of a mini-exon that causes the addition of 15 nucleotides (five amino acids), hence the choice of using the <italic>rbp9-A </italic>and the <italic>rbp9-D </italic>RNA forms, that differ by the presence/absence of the mini exon. In the case of both <italic>fne </italic>and <italic>elav</italic>, several transcripts have been reported but they encode a single polypeptide.</p>", "<title>Identification of <italic>elav </italic>orthologs in completely sequenced genomes and prediction of ELAV-like protein sequences</title>", "<p>We used protein sequences from the data bases deduced from cDNA analysis whenever possible, with NCBI accession numbers as follows: in humans BAD92531 (HuB, 367 amino acids), AAH30692/Q12926-2 (HuB, 346 aa), AAA58677 (HuC, 359 AA), AAH14144/Q14576 (HuC, 367 AA), AAH36071/Q8IYD4 (HuD, 366 aa), AAK57541/AAK57541 (HuD, 380 aa), AAH03376/Q15717 (HuR, 326 aa), in <italic>D. melanogaster </italic>AAA28506 (ELAV, 483 aa), AAF43091 (FNE, 356 aa), AAF51179 (RBP9 isoform A, 647 aa) and AAN10401 (RBP9 isoform D, 642 aa), in <italic>Caenorhabditis elegans </italic>NP_496057 (EXC-7, 456 aa). UniProtKB/Swiss-Prot Accession numbers are also provided for further details on the proteins: Q12926 (HuB), Q14576 (HuC), Q8IYD4 (HuD), Q15717 (HuR), P16914 (ELAV,), Q9VYI0 (FNE), Q9VQJ0 (RBP9) and Q20084 (EXC-7).</p>", "<p>When no cDNA sequences were available, we performed searches of the entire genomes using the tblastn program [##UREF##4##40##] to identify orthologs of ELAV-related genes. We analyzed the genomic regions encoding these orthologs by performing a three frame translation of the genomic sequences, and using the gene prediction program genescan [##UREF##5##41##] as well as a splice site prediction program [##UREF##6##42##]. The predicted protein coding sequences were the result of integration and manual review of these data.</p>", "<p>Using the procedures detailed above to identify <italic>elav </italic>orthologs, we reviewed predicted protein sequences that have been proposed for <italic>Apis mellifora</italic>, <italic>Aedes aegypti </italic>and <italic>Anopheles gambiae </italic>[##UREF##2##19##]. Some of our conclusions were consistent with the automated predictions of genome projects (<italic>A. mellifora</italic>, XP_394166, 343 aa), but we edited sequences of <italic>A. aegypti</italic>, and <italic>A. gambiae </italic>ELAV orthologs. The decision of editing was based upon the identification of manifest errors in the automated predictions, such as the prediction of a four base pair intron 5'-CCCT-3', missing the consensus GT-AG sequences typically flanking introns for the Ag-3 predicted transcript (XM_309157). For those two species, as well as for those where no prediction had yet been proposed, we relied upon the above procedure to identify and propose predicted sequences of ELAV orthologs. They respectively derive from genomic sequences CH477489 (Ae-1), CH477672 (Ae-2), CH477401(Ae-3) in <italic>A. aegypti</italic>, from CM000357 (Ag-1), CM000360 (Ag-2), CM000359 (Ag-3) in <italic>A. Gambiae</italic>, DS231997 (Cp-1), DS232556 (Cp-2), DS231816 (Cp-3) in <italic>Culex pipiens</italic>, CM000276 in <italic>Tribolium castaneum</italic>, DS265619 in <italic>Nasonia vitripennis</italic>, AADK01020611 (Bm-1), CH391062 (Bm-2) in <italic>Bombyx mori </italic>and DS235033 in <italic>Pediculus humanus corporis</italic>.</p>", "<p>In our analysis we used only the approximately 325 amino acids region of the proteins including the three RRM and a hinge region that links RRM2 and RRM3, because the N-terminus, when present, is not conserved. The sequences used are listed in Additional file ##SUPPL##0##1##.</p>", "<title>Identification of <italic>arginase </italic>genes in completely sequenced genomes and prediction of arginase protein sequences</title>", "<p>Arginase sequences have been deduced from cDNA sequences for several species: human (ARG1: P05089, Arg2: P78540), <italic>D. melanogaster </italic>(Q9NHA5), <italic>C. elegans </italic>(Q22659). For the other species, we used the procedure described above to propose arginase sequences. The protein sequences derive from genomic sequences CH477248 in <italic>A. aegypti</italic>, from CM000359 in <italic>A. gambiae</italic>, DS232533 in <italic>C. pipiens</italic>, CM000280 in <italic>T. castaneum</italic>, DS265617 in <italic>N. vitripennis</italic>, CH389642 in <italic>B. mori </italic>and DS235286 in <italic>P. humanus corporis</italic>. We were not able to predict a complete <italic>P. humanus corporis </italic>arginase sequence, because of the lower level of conservation. See Additional file ##SUPPL##1##2## for the arginase sequences.</p>", "<title>Protein sequence alignments and percentages of identity</title>", "<p>Alignments were performed with the ClustalW program using default parameters [##UREF##7##43##]. In the case of arginases, we focused on the region homologous to that including intron 3 in <italic>D. melanogaster</italic>. The values for percentages of identity were extracted from the ClustalW score tables.</p>", "<title>Phylogenetic analysis</title>", "<p>We used the CLC combined workbench (CLC bio A/S) version 3.6.2 to align the 27 protein sequences with an unweighted pair group method using arithmetic averages (UPGMA) and to evaluate the reliability of the inferred tree with a bootstrap analysis (500 replicates).</p>" ]
[ "<title>Results</title>", "<title>The paralogs <italic>fne </italic>and <italic>rbp9 </italic>share a conserved organization of their coding regions but <italic>elav</italic>, the third family member, is distinct</title>", "<p>All three Drosophila paralogs <italic>elav</italic>, <italic>rbp9 </italic>and <italic>fne </italic>are essentially expressed in neurons. <italic>elav </italic>null mutants are embryonic lethal, while the <italic>rbp9 </italic>null mutation is viable, but surprisingly confers a female sterility phenotype. <italic>fne </italic>null mutants, although not fully characterized, are also viable (Zanini and Samson, in preparation). In order to understand the evolutionary mechanisms responsible for the generation of these paralogs, we examined their gene structure. Although their organizations are apparently quite distinct, we found remarkable conservation in the correspondance between exons and specific protein regions in <italic>rbp9 </italic>and <italic>fne </italic>(Fig. ##FIG##0##1A##). There are two differences (1) the presence of new mini-exons respectively specific for each of the two genes and (2) the use of a single exon in <italic>fne </italic>but two in <italic>rbp9 </italic>to encode the third RRM. Strikingly, this organization is totally different in the <italic>elav </italic>gene, whose complete ORF, except for the A of the ATG initiation codon, is encoded by a single exon.</p>", "<title>Conserved exon junctions are present in most <italic>elav </italic>orthologs</title>", "<p>We took advantage of the recent sequencing of complete genomes [##UREF##1##18##, ####UREF##2##19##, ##REF##15591204##20##, ##REF##15141943##21##, ##UREF##3##22####3##22##] to survey the gene family in 11 species by (1) identifying all the family members and (2) comparing the organization of the ORF in exons. In humans, <italic>D. melanogaster </italic>and <italic>C. elegans</italic>, we extracted from the databases protein sequences deduced from cDNA analyses, and aligned genomic DNA with cDNA to determine the exon-intron structure. In other cases we used the predicted protein sequences, either published or computed for our purpose, as detailed in the Methods. We examined species from the chordata (1 species), arthropoda (9 species) and nematoda (1 species), for a total of 23 genes (Fig. ##FIG##1##2##).</p>", "<p>First, we found that the size of <italic>elav </italic>families varies (one to four members) among the 11 species that we studied, with no clear relationship between family size and brain/animal complexity (Fig. ##FIG##1##2##). For instance, dipterans possess three <italic>elav </italic>genes, while the hymenopteran <italic>Apis mellifora</italic>, with ten times as many neurons as Drosophila, possesses only one gene. Levels of identity between the proteins encoded by the 23 genes are high, with the lowest score (47%) obtained in the comparison of <italic>D. melanogaster </italic>ELAV with the unique <italic>C. elegans </italic>protein. Between humans and Drosophila, there is 54–64% amino acid identity in the ELAV-related proteins, 38% identity for the arginase proteins (ubiquitous metabolic enzymes, see below) and 33% identity for the engrailed proteins (conserved transcription factors, not shown). The levels of ELAV-related protein identity are thus remarkable. The crystal structure of the first two RRM of human HuD associated with cfos RNA, identifies 12 amino acids whose side chain is making direct RNA contacts [##REF##11175903##23##]. These residues are conserved in all 23 ELAV-like proteins that we examined, except for the arginine in RNP1 of the second RRM, which appears to be specific to the human proteins and to one of the <italic>B. mori </italic>ELAV-like, Bm-2. In the other species there is a conserved substitution by a lysine.</p>", "<p>Second, we found remarkable conservation of exon structure. From vertebrates to invertebrates, we identified eight exon junctions in the RRMs/hinge region (Fig. ##FIG##2##3##). We named them J1 to J8, from the most upstream to the most downstream. All are present in several phyla, except for J1 and J4 which are specific for FNE and RBP9 from Drosophila and are implicated in the generation of mini-exons in the sequence coding (alternative forms of) these proteins (Fig. ##FIG##0##1## &amp;##FIG##2##3##). Overall, the J2 junctions (respectively J3, J5 and J8, Fig. ##FIG##1##2## &amp;##FIG##2##3##) are unambiguously homologous since (1) the level of protein sequence conservation is such that the amino acid positions where the junctions intervene are clearly aligned and conserved (Fig. ##FIG##2##3##) and (2) nucleotide sequence analysis shows that at a given exon junction, the splice is at the same position in the codons: specifically between the first and the second bases of the spliced codons (for J2 and J5, as well as for the species-specific J1 and J4) or exactly between codons (J3 and J8). There are two exceptions to this strict conservation. First, J5 is interrupted in <italic>rbp9 </italic>of <italic>D. melanogaster </italic>by the intronic insertion of an alternative mini-exon, without alteration of the J5 5' or 3' splice sites. Second, in <italic>fne</italic>, J2 is split by the intronic insertion of a mini-exon, the J2 donor splice site is additionally shifted downstream while the J2 acceptor splice site is maintained (Fig. ##FIG##2##3##). Interestingly, the junctions J2 and J5 occupy the same position relative to RNP-1 in RRM1 and RRM2.</p>", "<p>The junctions J6 and J7 map in a moderately conserved coding region, essential for nuclear export and proper subcellular localization (Reviewed in [##REF##17928954##2##]), including only a conserved hexamer (R-SP----). Both J6 and J7 split the spliced codons between the second and the third bases. In this region, three types of events affecting the splicing seem to have occured independently: 1) the introduction of a mini-exon (in humans), that can be alternatively spliced (HuB), (2) the shift of the 5' splice site (example: <italic>N. vitripennis </italic>vs <italic>T. castaneum</italic>) (3) the shift of the 3' splice (example: the <italic>T. castaneum </italic>vs <italic>Ae-2 </italic>genes or the alternative human forms HuD-366 and HuD-380). Noticeably, the regions close respectively to J1/J2, J4/J5 and J6/J7 as well as the entire hinge region between RRM2 and RRM3 appear more variable than the rest of the protein.</p>", "<title>Intronless <italic>elav</italic>-like genes are present in Diptera and Lepidoptera</title>", "<p>Interestingly, for six of the analyzed genes (<italic>Ag-1</italic>, <italic>Ae-1</italic>, <italic>Cp-1</italic>, <italic>elav </italic>in Diptera, and <italic>Bm-1</italic>, <italic>Bm-2 </italic>in Lepidoptera), the entire conserved region of the protein is encoded by a single exon. Based upon both their gene structure and the level of protein sequence identity, the dipteran intronless genes constitute a homogeneous <italic>elav</italic>-type group. In contrast, although intronless like <italic>elav</italic>, the <italic>B. mori </italic>genes encode proteins more similar to FNE/RBP9 than to ELAV. This observation suggests that distinct evolutionary forces shaped the <italic>B. mori </italic>genes and the dipteran <italic>elav</italic>-like intronless-genes, respectively. To evaluate this hypothesis, we performed a phylogenetic analysis of the 27 ELAV orthologs/paralogs, using the UPGMA algorithm, with bootstrap analysis (Fig. ##FIG##3##4##). This analysis shows with high confidence (bootstrap values greater or equal to 99%) that in dipterans, the proteins encoded by the intronless genes (<italic>Ag-1</italic>, <italic>Ae-1</italic>, <italic>Cp-1</italic>, <italic>elav</italic>) cluster together, while the two <italic>B. mori </italic>genes products cluster with the FNE/RBP9 sequences Similar results were obtained when performing sequence alignments using the neighbor joining method (not shown).</p>", "<p>Because the <italic>D. melanogaster elav </italic>gene is nested in the third intron of the <italic>arginase </italic>gene [##REF##10878001##24##], we probed the gene environment of the intronless <italic>elav </italic>orthologs that we report here. We found that the nested <italic>elav</italic>/<italic>arg </italic>gene organization is unique to Drosophila, specifically <italic>D. melanogaster </italic>and 11 additional Drosophila species whose genomes have recently been sequenced [##REF##17994087##25##] (Fig. ##FIG##4##5##). In the 10 other non-Drosophila species examined here, there is no close linkage between the <italic>arginase </italic>gene(s) and the <italic>elav </italic>gene family members. In particular, the mosquitos, similar to <italic>D. melanogaster</italic>, each have three <italic>elav</italic>-like genes, including one intronless version, but unlike <italic>D. melanogaster </italic>they have an intronless <italic>arg</italic>, which obviously rules out the possibility of a nested gene. <italic>In B. mori</italic>, although the two intronless <italic>Bm-1 </italic>and <italic>Bm-2 </italic>genes map at loci distinct from the <italic>arg </italic>locus, an intron putatively homologous to the third intron of the <italic>D. melanogaster arginase </italic>gene is present (Fig. ##FIG##4##5##).</p>" ]
[ "<title>Discussion</title>", "<title>The <italic>D. melanogaster </italic>gene <italic>elav </italic>is specific to the dipteran phylum and results from retrotransposition</title>", "<p>The <italic>elav </italic>gene from Drosophila was the first identified member of this family, is considered as its prototype [##REF##8331337##1##], and most of the subsequently discovered orthologs are named after it. However, the present analysis highlights unique characteristics of this gene that suggest it is of recent evolutionary origin, after the separation of dipterans and lepidopterans. Aside from <italic>elav</italic>, only the dipteran genes <italic>Ae-1</italic>, <italic>Ag-1 </italic>and <italic>Cp-1 </italic>encode proteins that are more similar to ELAV than to FNE and RBP9. In addition to the intronless <italic>elav</italic>-likes, dipteran genomes carry two genes encoding proteins of the type FNE/RBP9, also found in the seven other genomes analyzed. Thus <italic>elav</italic>, <italic>Ae-1</italic>, <italic>Ag-1 </italic>and <italic>Cp-1 </italic>represent a newly evolved gene form specific to dipterans.</p>", "<p>In addition, the <italic>elav </italic>gene structure is suggestive of retrotransposition, a process considered significant in the evolution of genomes, including Drosophila [##REF##17989252##26##]. The genes <italic>Ae-1</italic>, <italic>Ag-1 </italic>and <italic>Cp</italic>-1 from mosquitoes share with <italic>elav </italic>not only a higer level of similarity between their products, but also the property of having their ORF in a single exon. The absence of introns (restricted to dipterans and <italic>B. mori </italic>in this gene family) is atypical: we identified conserved exon junctions that are a landmark present in most of the <italic>elav</italic>-related genes. Furthermore, the <italic>elav </italic>gene of Drosophila is nested in the <italic>arginase </italic>gene. In humans, retrotransposition is an important contributor to the generation of nested genes [##REF##12853948##27##]. We thus propose that <italic>elav </italic>originated from a recent retrotransposition event. It is possible that the same retrotransposition is at the origin of both the lepidopteran intronless <italic>fne</italic>/<italic>rbp9</italic>-like genes and the dipteran <italic>elav</italic>-like genes. A duplication of the retrotransposed gene in the ancestor to <italic>B. mori </italic>and different fates for the ancestral gene copies in the two groups would bring about the present situation. Alternatively, we do not exclude that independent retrotranspositions happened in lepidopteran and dipteran ancestral lineages.</p>", "<p>Interestingly, the nested <italic>arg</italic>/<italic>elav </italic>arrangement found in <italic>D. melanogaster </italic>is not conserved in the mosquitoes, where the host gene (<italic>arginase</italic>) became intronless. This parallels the nested arrangement of the intronless <italic>sina </italic>gene in an intron of the <italic>Rh4 </italic>gene, as found in mosquitoes and nine species of the Drosophila genus. The remaining three species of the genus have an intronless <italic>Rh4</italic>, with a loss of the ancestral <italic>Rh4 </italic>copy where <italic>sina </italic>was originally embedded [##REF##1946441##28##]. These situations show the lability of nested gene arrangements.</p>", "<title><italic>elav</italic>: the genesis of a new function</title>", "<p>It was unexpected to find that the copy number of <italic>elav </italic>family members varied from species to species. Given the maintenance of this gene family in all metazoans, we assume that there is a function for at least one, if not all, of the genes in each species. Mutants have been reported in only three species. The knockout of neuronal HuD in mice causes motor and sensory defects [##REF##15764704##29##]. It is not excluded that the mild phenotype of this mutant is the consequence of gene redundancy. In <italic>C. elegans</italic>, cholinergic synaptic transmission is altered in mutants of the single <italic>elav </italic>ortholog EXC-7, which is expressed in a subset of neurons and other non-neuronal cells [##REF##12906792##30##]. In both cases, viability and apparent morphology are normal. In <italic>Drosophila melanogaster</italic>, the vital gene <italic>elav </italic>is required in all neurons [##REF##8331337##1##], whereas <italic>rbp9 </italic>is essential for female fertility [##REF##15529000##7##] but does not affect viability. We recently generated null mutations of the <italic>fne </italic>gene (Zanini and Samson, in preparation), whose preliminary analysis indicates that they are viable in adults and lead to no apparent morphological defects. Aside from <italic>elav </italic>itself, characterized mutations of the <italic>elav </italic>gene family are viable, suggesting a non-vital function of the ancestral gene.</p>", "<p>Considering that <italic>elav </italic>appears to be a new member of the family, its vital function is quite striking. This situation is reminescent of that of <italic>Sex-lethal </italic>(<italic>Sxl</italic>), a gene fundamental to sex determination in Drosophila, but which does not act as a sex determining factor in non-Drosophilids. The Drosophilid genomes indeed contain two <italic>Sxl </italic>paralogs (79% identity in <italic>D. melanogaster</italic>), while non-Drosophilids have one. It has been proposed that there was a duplication of the ancestral gene in Drosophilids and acquisition of a new function by one of the copies [##REF##16604108##31##]. We believe that a retrotransposition of the <italic>elav/fne/rbp9 </italic>ancestor gene at the time of the separation of dipterans/lepidopterans led to a gene duplication and the evolution of a new function for <italic>elav</italic>.</p>", "<title>Conserved RNA binding proteins: a reservoir for accelerated functional evolution</title>", "<p>We have pointed out that the ELAV-like proteins, including ELAV itself, have maintained a high level of sequence conservation between species, higher than that of engrailed, a conserved transcription factor with a homeodomain, or that of arginase, a ubiquitous metabolic enzyme that arose before the divergence of procaryotes and eucaryotes. This is intriguing in light of the extensively documented diversity of the properties of individual members of the family. First, although there is expression in the nervous system of at least one of the <italic>elav </italic>family members in every investigated metazoan (mammals, fishes, amphibians, birds, amphioxus, <italic>C. elegans</italic>, <italic>D. melanogaster</italic>), expression is also detected in other tissues and is even sometimes ubiquitous [##REF##17928954##2##]. Second, the functions of these proteins are multiple, whether at the cellular level, where they include cell differentiation/survival [##REF##8331337##1##,##REF##10082516##6##,##REF##15764704##29##,##REF##9096138##32##] and cell proliferation/control of the cell cycle [##REF##15529000##7##,##REF##10811625##33##] or at the biological level, with impacts on motor/sensory activity, memory, fertility or viability [##REF##8331337##1##,##REF##10082516##6##,##REF##15764704##29##,##REF##11573004##34##]. Finally, the apparent subcellular localization of these proteins is diverse (nuclear, subnuclear, cytoplasmic or both), in agreement with diverse molecular functions [##REF##17928954##2##,##UREF##0##3##].</p>", "<p>The data thus reveal a diversification of the functions and of the specificity of expression of ELAV family members and implies a diversification of the interactions with other macromolecules, most evidently the RNAs whose metabolism is regulated by the RRM containing proteins. The DNA duplications and retrotranspositions that occured in the <italic>elav </italic>gene families constitute a starting point for the diversification of gene function. Changes in cell or tissue specificity of expression are often linked to modifications of non-translated regulatory regions. However, changes affecting the sub-cellular localization, known to be dependent upon the hinge region between RRM2 and RRM3, or changes in the interactions with proteins or RNA must depend upon the protein product of the <italic>elav</italic>-like genes.</p>", "<p>Sequence alignments of the ELAV-like proteins shows that they are overall very conserved. But we were puzzeld by the fact some of the conserved exon junctions (J1/J2, J4/J5 and J6/J7) are adjacent to sequences that are among the most variable of the proteins. They include short insertions of amino acids, (alternative) exon addition and amino acid variations. The intron sequence indeed provides a potential source of sequence variability: it is conceivable that intron extremities become integrated into coding sequences by shifting of the exon boundaries. Alternatively, the intron can serve as the site of insertion of a new exon. An additional surprising point was the fact that these variable micro regions are almost directly upstream of important conserved motifs, specifically RNP-1 (in RRM1 and RRM2) and the octapeptide in the region essential for nuclear export and subcellular localization. The modification of residues outside of the RNP has the potential to alter the interactions between the RRM and an RNA [##REF##15853797##5##]. Additionally, alterations of the region responsible for nuclear export/cellular localization modify this function (reviewed in [##REF##17928954##2##]). We thus propose that the maintenance of the exon junctions is vital to the evolution of the ELAV family, in particular the generation of new functions. As a consequence, one would predict that RRM1, RRM2 and the hinge region have prominent roles in functional specificity. It may be significant in this respect that RRM3 replacements in ELAV by RRM3 from RBP9 or HUD are fully functional, while RRM1 or RRM2 replacements by corresponding RRMs from RBP9 or SXL are largely non-functional [##REF##10924474##35##].</p>", "<p>More generally, it seems that RRM-containing proteins could serve as favorable targets for the rapid evolution of gene functions. Because of the structural versatility of the RRM domain, it can be adapted for sequence specific recognition of many different nucleic acid structures and different protein partners [##REF##15853797##5##]. The SXL protein, a crucial regulator of sex determination in Drosophila contains 2 RRM, and appears to be the result of such a rapid adaptation of function. In the search for genetic changes that distinguish our brains from that of our ancestors, the focus has been on the identification of non-synonymous changes in coding regions and the modification of regulatory sequences [##REF##16733552##36##]. Our work suggests that the very conserved RRM-containing proteins may have contributed to human brain evolution, especially when considering the fundamental importance of the regulation of RNA metabolism in neurons, where alternative splicing [##REF##17895907##37##] and localized RNA translation and degradation [##REF##17660744##38##,##REF##17848965##39##] take place with impacts on cortex development, neuronal regeneration and plasticity.</p>" ]
[ "<title>Conclusion</title>", "<p>The <italic>elav </italic>gene family encodes proteins with three RNA Recognition Motifs (RRM) acting as neuronal post-transcriptional regulators in all metazoans. Since they show remarkable sequence conservation, the documented diversity of their molecular roles is unexpected. We report the occurence of <italic>elav</italic>-like gene duplications and deletions in metazoans, and show that the vital <italic>elav </italic>gene of Drosophila is newly emerged, specific to dipterans and of retrotransposed origin, challenging its status of prototype for the family. These findings, together with the plasticity of the interactions between RRM and RNA, suggests that the <italic>elav</italic>-like proteins may have played an important role in the evolution of the gene functions crucial in brain evolution.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The Drosophila gene <italic>embryonic lethal abnormal visual system </italic>(<italic>elav</italic>) is the prototype of a gene family present in all metazoans. Its members encode structurally conserved neuronal proteins with three RNA Recognition Motifs (RRM) but they paradoxically act at diverse levels of post-transcriptional regulation. In an attempt to understand the history of this family, we searched for orthologs in eleven completely sequenced genomes, including those of humans, <italic>D. melanogaster </italic>and <italic>C. elegans</italic>, for which cDNAs are available.</p>", "<title>Results</title>", "<p>We analyzed 23 orthologs/paralogs of <italic>elav</italic>, and found evidence of gain/loss of gene copy number. For one set of genes, including <italic>elav </italic>itself, the coding sequences are free of introns and their products most resemble ELAV. The remaining genes show remarkable conservation of their exon organization, and their products most resemble FNE and RBP9, proteins encoded by the two <italic>elav </italic>paralogs of Drosophila. Remarkably, three of the conserved exon junctions are both close to structural elements, involved respectively in protein-RNA interactions and in the regulation of sub-cellular localization, and in the vicinity of diverse sequence variations.</p>", "<title>Conclusion</title>", "<p>The data indicate that the essential <italic>elav </italic>gene of Drosophila is newly emerged, restricted to dipterans and of retrotransposed origin. We propose that the conserved exon junctions constitute potential sites for sequence/function modifications, and that RRM binding proteins, whose function relies upon plastic RNA-protein interactions, may have played an important role in brain evolution.</p>" ]
[ "<title>Authors' contributions</title>", "<p>The author takes full responsability for the work. She asked the question, devised the approach, performed it, analyzed the results and wrote the manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank L. Rabinow, S. Mazan and P. Capy for critical reading of the manuscript. This work was supported by funding from the Centre National de la Recherche Scientifique and the University of Paris XI.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Correspondance between exons and protein regions in the <italic>elav </italic>family of <italic>D. melanogaster</italic></bold>. <bold>A: RNA structures</bold>. RNA nomenclature as in FlyBase, with details in the Methods. Boxes represent exons. The black horizontal lines are introns, with dashes respectively replacing the 5.8 kb long intron in the <italic>rbp9-RA </italic>transcript and the 2.2 kb long intron in the <italic>elav-RA </italic>transcript. White: non coding, Vertical stripes: non-conserved, Crossed: gene-specific mini-exons, respectively a 15 nucleotide long region present in alternative forms of <italic>rbp9 </italic>and a 45 nucleotide long region present in <italic>fne</italic>. All others are color coded based upon sequence similarity and according to exon-exon boundaries. <bold>B: Schematic representation of the ELAV family protein products</bold>. The color coding corresponds to that used for the RNA representation. The regions encoded by gene specific sequences have been omitted. RRM: RNA Recogntion Motif. The pairs of white vertical bars represent conserved motifs (RNP-1 and RNP-2) diagnostic of RRMs.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Exon organisation of the <italic>elav</italic>-related genes in 11 metazoans</bold>. The analyzed species are listed on the left, with classical phylogenetic relationships represented. The number of <italic>elav</italic>-like genes is listed next to the species names. Percentages of identity between their protein products and the <italic>D. melanogaster </italic>proteins ELAV, FNE and RBP9 are listed on the right side of the figure. At the top, a typical ELAV-like protein is represented, with its three RRMs and the hinge region between RRM2 and 3. The vertical arrows below point at protein regions that are, depending upon each of the 23 analyzed proteins, either encoded by exon-junctions (Jx, x = 1 to 8, see text) or by an internal exon sequence. The presence of the junction-encoded region is indicated by a vertical bar for each protein.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Protein sequence comparison among 27 ELAV-like proteins forms</bold>. Alternative protein forms are included, specifically for <italic>Drosophila </italic>RBP9 (A and D) and three of the human proteins (HuB, HuC and HuD, where HuX-n refers to the n amino acid long form of the HuX protein). \"*\" indicate that amino acids are identical in all 27 sequences, \":\" and \".\" respectively indicate conserved and semi-conserved substitutions. The octamer RNP-1 and the hexamer RNP-2, diagnostic of RRMs, are underlined. Also underlined is a conserved octamer present in the region that is crucial for nuclear export and localization. The regions in light grey boxes have been mapped as necessary for these processes in <italic>D. melanogaster </italic>ELAV, human HuR and human HuD. We identified eight exon junctions labelled J1 to J8 (see text). Bold characters and dark grey boxes are used to identify amino acids encoded by exon junctions. When the splicing connects intact codons, two amino acids are bold (J3 and J8). The symbol//replaces 85 non-conserved amino acids in the <italic>C. elegans </italic>sequence.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Phylogenetic tree of 27 ELAV-like proteins</bold>. Sequences were aligned and bootstrapped 500 times. Numbers near the branches are the bootstrap values, and the scale indicates the number of substitutions per site.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>A unique nested gene arrangement for the <italic>elav </italic>and <italic>arginase </italic>genes in <italic>D. melanogaster</italic></bold>. A: The <italic>elav </italic>gene is nested inside the third intron of the <italic>arginase </italic>gene. Complementary strands are transcribed to generate the <italic>elav </italic>and <italic>arg </italic>RNAs with inverse polarities [##REF##1946441##28##]. B: Examination of the relative <italic>arg-elav </italic>arrangement in 11 metazoans. There are two <italic>arginase </italic>genes in humans, only one in the other examined species. Column 1 documents the status of the <italic>arginase </italic>third intron. Column 2 specifies the nested (+) or independent (-) arrangement of the <italic>arginase</italic>/<italic>elav </italic>genes. N.A.: Not applicable. The third column indicates the percentage of amino-acid sequence identity of <italic>D. melanogaster </italic>compared with other species. *: N-terminally truncated arginase sequence for <italic>P. humanus corporis</italic>. See Additional file ##SUPPL##2##3## for arginase alignments.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>Fasta sequences of the three RRMs and the hinge regions of ELAV-like proteins</bold>. 27 Fasta sequences.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold>Fasta sequences of the arginases</bold>. 12 Fasta sequences.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p><bold>Protein sequence comparison among 12 arginases from 11 metazoans</bold>. Arginase sequences alignment with legend.</p></caption></supplementary-material>" ]
[]
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[{"surname": ["Keene"], "given-names": ["JD"], "article-title": ["Why is Hu where? Shuttling of early-response-gene messenger RNA subsets"], "source": ["Proc Natl Acad USA"], "year": ["1999"], "volume": ["96"], "fpage": ["5"], "lpage": ["7"], "pub-id": ["10.1073/pnas.96.1.5"]}, {"article-title": ["Flybase, A Database of Drosophila Genes & Genomes"]}, {"article-title": ["VectorBase, An NIAID Bioinformatics Resource Center for Invertebrate Vectors of Human Pathogens"]}, {"article-title": ["National Human Genome Research Institute, Status Approved Sequencing Targets"]}, {"article-title": ["Flybase, blast"]}, {"article-title": ["The New GENSCAN Web Server at MIT"]}, {"article-title": ["Berkeley Drosophila Genome Project, Splice Site Prediction by Neural Network"]}, {"article-title": ["EMBL-EBI, ClustalW2"]}]
{ "acronym": [], "definition": [] }
43
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Aug 20; 9:392
oa_package/44/79/PMC2529313.tar.gz
PMC2529314
18673543
[ "<title>Background</title>", "<p>Peripheral arterial occlusive disease (PAD) is a major manifestation of atherosclerosis and is commonly found in elderly patients. Epidemiological studies have shown that PAD affects 8 to 10 million adults in the United States [##REF##17345122##1##]. Most patients with PAD are asymptomatic. The disease is primarily diagnosed by an ankle brachial index (ABI) &lt; 0.9. The most common symptom of mild-to-moderate PAD is intermittent claudication, which is present in about one third of symptomatic patients [##REF##17345122##1##]. In addition to leg symptoms, patients with PAD are at an increased risk for developing new coronary events and eventually death from cardiovascular disease. Although conventional procedures such as stents, arterectomies, angioplasty, and bypass surgery have been successful in improving clinical symptoms of PAD to a large extent [##REF##17046794##2##], ultimately elimination of the disease may require sophisticated protocols of pharmaceutical interventions, which may depend on better understanding of molecular mechanisms involved in the disease.</p>", "<p>Previous studies have implicated the involvement of the immune system in atherosclerosis formation and progression. Animal models have been used to test the contributions of components of the immune system [##REF##1411543##3##,##REF##8274468##4##]. Cellular involvement of macrophages was found to be important in the formation and progression of atherosclerosis in animal models [##REF##8274468##4##]. In addition, various immune-related genes have been examined in an atherosclerosis animal model, and genes such as <italic>CXCR6, CXCL10, CXCR3 </italic>and <italic>CXCL16/scavenger receptor </italic>have been shown to be involved in the progression of atherosclerosis in animal models [##REF##16682613##5##, ####REF##12600915##6##, ##REF##17909108##7##, ##REF##16880330##8####16880330##8##]. In humans, many immune cells such as macrophages, lymphocytes, mast cells, and T cells are found in atherosclerosis [##UREF##0##9##]. These findings suggest that the immune system plays important roles in atherogenesis. However, data available to date are primarily derived from studies of atherosclerosis in the coronary or/and the carotid arteries, whereas data derived from clinical samples of PAD appear to be particularly limited.</p>", "<p>In the past decade, microarray analysis using high-throughput screening technology has emerged as an important tool to study gene expression patterns and to study molecular events in complex diseases [##REF##17145989##10##, ####REF##12912960##11##, ##REF##17634102##12####17634102##12##]. In this study, Affymetrix GeneChips were used to perform gene expression profiling of femoral atherosclerotic lesions to fully characterize the peripheral arterial wall gene expression patterns associated with atherosclerosis. By statistical analysis, hundreds of known and novel genes were identified that differentially express in PAD. Genes involved in immune/inflammatory responses appeared to be significantly enriched in the set of genes up-regulated in different stages of PAD. To further examine the expression patterns of individual genes in the context of particular biological or molecular pathways, gene functional enrichment was performed using Gene Ontology and KEGG database. The results revealed that immune system-related categories and pathways were significantly overrepresented in the progression of the disease, suggesting that up-regulation of immune/inflammatory genes may be critical components of the disease progression expression signature associated with atherosclerosis. These findings may provide new insights and foster a better understanding of the mechanism of PAD.</p>" ]
[ "<title>Methods</title>", "<title>Tissue Harvest</title>", "<p>After obtaining informed consent, primary femoral artery specimens containing atherosclerotic lesions were taken from 30 patients undergoing surgical bypass or limb amputation at Shanghai Ninth People's Hospital. The specimens were immediately rinsed once with PBS and cut longitudinally by the surgeon. Three quarters of the samples were stored at once in – 80°C for subsequent total RNA extraction. The remaining samples were embedded in OCT medium and snap frozen for further morphological analysis. Clinical patient parameters were also registered. For controls, five normal femoral arteries were obtained from healthy donors during organ transplantation (male, mean 31.6 years; range 22–45 years). These five samples were without clinical or gross macroscopic signs of atherosclerotic disease. The Local Ethical Committee approved all procedures in this investigation, and proper protocol was followed throughout the entire course of the experiment.</p>", "<title>Histology</title>", "<p>For each sample, cryostat sections of 8 um were stained with hematoxylin for 10 min and eosin for 2 min, dehydrated in graded alcohol, and cover-slipped with permanent mounting solution after xylene clearing.</p>", "<title>RNA Isolation and Quantification</title>", "<p>Total RNA was isolated from the samples using a Trizol reagent (Invitrogen, Carlsbad, CA) and cleaned up using RNeasy Micro Kit (Qiagen, Valencia, CA) techniques. In brief, for each tissue, at least 100 mg sample was pulverized under liquid nitrogen. After complete disruption of the tissues, the Trizol reagent was added in the amount of 1 ml/100 mg. Total RNA was extracted using the protocol supplied with the Trizol reagent. After isolation, the RNA was cleaned up using the RNeasy Micro Kit. To remove any contaminating genomic DNA, a DNase step was included, following the manufacturer's protocol. The RNA quantity and quality were determined by an Agilent Bioanalyzer 2100 and an Eppendorf Biophotometer. Any RNA samples that showed degradation was excluded from the study.</p>", "<title>Microarray Experiment</title>", "<p>One microgram of total RNA was used for generating biotin labeled cRNA. The labeling reaction was performed according to the standard Affymetrix<sup>® </sup>protocol to generate a biotin-labeled cRNA probe. The samples were hybridized to the Affymetrix<sup>® </sup>Human Genome -U133A Genechip, stained, washed and scanned according to the standard Affymetrix<sup>® </sup>protocol. The computer data files to be used in data analysis (*.dat, *.cel, *.chp) were generated with the Affymetrix GeneChip Operating Software (GCOS) Version 1.4 (Affymetrix<sup>®</sup>), using the statistical algorithm provided. All chip samples were scanned using the same instrument and followed the same protocol. Data quality assessment was then performed following the guidance in Affymetrix data analysis fundamentals manual. All quality control results met Affymetrix recommended criteria.</p>", "<title>Data process and analysis</title>", "<p>The probe level intensity data were transferred to ArrayAssist<sup>® </sup>Software (StrataGene; La Jolla, CA) for further analysis. For comparison of differential gene expression between different stage groups, the background was removed and data were normalized in accordance to the GC-RMA method [##REF##16108723##43##]. GC-RMA takes into account the GC content of the probe sequences when comparing the expression intensities of the different probesets. Then, the processed gene expression data were transformed into log base 2 and filtered to delete the genes whose detection calls were \"absent\" in all samples.</p>", "<p>Microarray data analysis was carried out to identify individual genes that were significantly expressed between classes by the software package SAM (please see Availability &amp; requirements for more information), using Δ = 0.5. Results from the difference analysis were clustered and displayed using the Cluster3.0 and Treeview1.1.0 software (please see Availability &amp; requirements for more information). Each list of differentially expressed genes was analyzed in the context of Gene Ontology (GO) in order to identify groups of genes with similar functions, or processed using MAPPFinder (Gene MicroArray Pathway Profiler; please see Availability &amp; requirements for more information). For each gene ontology term, the probability values were computed based on a hypergeometric distribution test by comparing (a) the number of genes annotated by the gene ontology term in a given list of differentially expressed genes with (b) the expected number of such genes. <italic>Z-score&gt;0 and p</italic>-values &lt; 0.05 were considered significant categories.</p>", "<p>Similar methods were used to identify curated pathways that were significantly over-represented in the data using KEGG database by using DAVID (please see Availability &amp; requirements for more information). For each pathway, the probability values were computed based on a modified Fisher exact test. EASE <italic>p</italic>-values &lt; 0.05 were considered significant categories. The enriched pathways are not entirely separate from one another. For example, many genes involved in MAPK signaling pathway can also be involved in other pathways, such as NK pathway. The interconnectedness information was manually extracted from the pathway. Because the nature and complexity of these interactions varied from pathway to pathway, a simple line connecting two pathways was used to represent their interaction. The interaction map was generated for the interaction of enriched pathways using CytoScape software.</p>", "<p>Transcription factor enrichment analysis was also performed. The putative targets of transcription factors from TRANSFAC (v7.4) were discovered by Xie et al [##REF##15735639##44##] and downloaded from the supplementary web site (please see Availability &amp; requirements for more information). All the RefSeq IDs were converted to Entrez Gene ID according to the mapping table downloaded from NCBI web site (please see Availability &amp; requirements for more information). Enrichment of transcription factor targets was performed as described previously [##REF##15920519##45##]. The interaction map was generated for the interaction of enriched transcription factors and their putative target genes using CytoScape software</p>", "<title>Real-time QPCR Analysis</title>", "<p>One microgram of total RNA was reverse transcripted using random hexamers and superscript -II reverse transcriptase (Invitrogen, Carlsbad, CA). QPCR was performed by using ABI prism 7900 (ABI, Foster City, CA) and SYBR Green Detection (Toyobo, Japan). Primers were designed by using the Primer Express 2.0 software and verified by using a BLAST search. Sequences of the primers are listed [see Additional file ##SUPPL##11##12##]. The experimental conditions followed the manufacturer's protocol and the data were analyzed with sequence Detection Software 2.0 (ABI, Foster City, CA). Relative expression of mRNA was calculated with the comparative CT method. To standardize the amount of input RNA, the GAPDH gene was included. For each sample, the experiment was performed in triplicate.</p>", "<title>Western Blotting</title>", "<p>Proteins were extracted after RNA isolation according to the Introvigen protocol (Invitrogen, Carlsbad, CA) and measured using a Bio-Rad DC protein assay (Bio-Rad, Richmond, CA, USA). Aliquots of protein (100 μg of protein each) were resolved on a 10% SDS-PAGE gel and transferred to a polyvinylidene difluoride membrane (Millipore, Medford, MA, USA). The membrane was incubated with a primary antibody overnight at 4°C and then with a secondary antibody conjugated with alkaline phosphatase (1 h at room temperature), which was detected by a chemiluminescence method. The following polyclonal primary antibodies were used: anti-human TLR7 (1:300, IMGENEX, San Diego, CA), anti-human CTSS (1:400, Abcam Inc), anti-human beta-action (1:10000, Abcam Inc).</p>", "<title>Statistics</title>", "<p>The statistical significance of real-time results was examined with the nonparametric Mann-Whitney test, using GraphPad Prism 4. In the experiment, <italic>p </italic>values &lt; 0.05 were considered significantly different between the lesions group and the normal artery group.</p>" ]
[ "<title>Results</title>", "<title>Patient classification and outcome</title>", "<p>Histological characterization of 30 collected peripheral artery samples was conducted based on the criteria of the American Heart Association. Of these samples, 15 were classified as grade III (intermediate lesions), one as grade IV and fourteen as grade V (advanced lesions). Among them, 11 intermediate lesions samples (grade III) and 14 advanced lesions samples (grade V) had RNA of sufficient quality and quantity for hybridization. Representative images of the different stages are shown in Figure ##FIG##0##1##. Further details of these 25 samples are listed in Table ##TAB##0##1##. As shown, there was no significant difference between the intermediate lesions and the advanced lesions group except for indications of hypertension. In the intermediate lesions group, 4 patients (36.4%) presented with hypertension, while 9 hypertensive patients (64.3%) were found in the advanced lesions group.</p>", "<title>Differentially regulated genes in intermediate lesions</title>", "<p>Identifying differential expression genes was achieved for different stages by using Significance Analysis of Microarrays (SAM) with a false discovery rate (FDR) of 0.5%. Comparative analysis revealed that 366 genes were differentially expressed in intermediate lesions when compared to normal femoral arteries, of which 230 genes were up-regulated and 136 were down-regulated [see Additional file ##SUPPL##0##1##]. The 100 most differentially expressed genes between intermediate lesions and normal femoral arteries are shown in Figure ##FIG##1##2A##. Notably, in the up-regulated genes, up to 85 genes have been reported to be involved in immune response, such as <italic>HLA-DQB1, HLA-DRB1, CCR1, CXCR4, C1QB </italic>and <italic>TLR7 </italic>[see Additional file ##SUPPL##1##2##]. In addition, a large number of genes known to encode proteins crucial for proteolysis (<italic>CTSB, CTSC, CTSD and CTSS</italic>) and cell proliferation (<italic>BTG1, BTG2, CDKN1A, and MCM5</italic>) appeared to be significantly changed. Since BTG1 and BTG2 are known to be involved in anti-proliferation activities, it can be of interest to further investigate their potential roles in PAD in detail. MCM5 is heavily involved in chromosomal stability. Among the down-regulated genes, those involved in calcium signaling (<italic>CAMK2G)</italic>, transport (<italic>SLC22A3, CYP1A1 </italic>and <italic>ATP5H</italic>), metabolism (<italic>GCSH </italic>and <italic>PLA2G4A</italic>), and protein amino acid dephosphorylation (<italic>PTPN20</italic>) were found to be significantly down-regulated. The Gene Ontology functional categories in which intermediate lesions are overrepresented are illustrated in Table ##TAB##1##2## and additional data [see Additional file ##SUPPL##2##3##]. As shown in the table, the most significant biological process categories in the up-regulated genes are immune response, humoral immune response, inflammatory response, and T cell proliferation (Z-score&gt;5). For down-regulated genes, the significant ones mainly represent metabolism and catabolism-related categories.</p>", "<title>Differentially regulated genes in advanced lesions</title>", "<p>When advanced lesions were compared to normal femoral arteries, 447 genes were identified, of which 172 genes were up-regulated and 275 were down-regulated [see Additional file ##SUPPL##3##4##]. The list of the 100 most differentially expressed genes is shown in Figure ##FIG##1##2B##. Interestingly, up to 37 genes involved in the immune system response, such as <italic>CCR1, CX3CR1, TLR1 and TLR7</italic>, were found to be up-regulated in advanced lesion [see Additional file ##SUPPL##4##5##], which might suggest that these immune/inflammatory related genes could serve as expression signatures characterizing different stages of PAD. In addition, genes constituting a major portion of the vascular extracellular matrix were significantly up-regulated in advanced lesions, including <italic>COL1A1, COL3A1, COL1A2, COL5A1, COL6A1, COL6A3 and LAMB1</italic>, suggesting that these genes could be involved in the femoral artery occlusion in PAD. GO analysis further confirmed the above findings, by highlighting categories of immune response, humoral immune response, inflammatory response and I-kappaB kinase/NF-kappaB cascades (Z-score&gt;5) (Table ##TAB##2##3## and Additional file ##SUPPL##5##6##). For down-regulated genes, those involved in ion transport (<italic>GRIA2 and SLC22A3</italic>) and protein folding (<italic>DNAJB5</italic>) appeared to be the most significantly down-regulated in advanced lesions. GO analysis showed that the most significant categories for down-regulated genes were response to protein stimulus, RNA metabolism, and protein folding (Table ##TAB##2##3## and Additional file ##SUPPL##5##6##).</p>", "<p>In parallel, further data analysis revealed that many genes were over-represented in both intermediate and advanced lesions vs. normal controls. Of these genes, 68 were found to be commonly up-regulated and 48 were found to be commonly down-regulated (Figure ##FIG##2##3##). The list of commonly up-regulated genes is available [see Additional file ##SUPPL##6##7##]. Some of these overlapping genes, such as <italic>CTSB, CCR1, ALOX5, and SPP1</italic>, have been previously reported to play important roles in atherogenesis [##REF##16741146##13##, ####REF##16491201##14##, ##REF##16081317##15##, ##REF##15218333##16####15218333##16##]. Accordingly, these commonly regulated genes can therefore be important for the progression of PAD. In contrast, a much larger number of genes appear to be characteristically expressed in either intermediate lesions or advanced lesions, which may therefore serve as stage-specific signatures of PAD.</p>", "<title>Differential gene expression in disease progression</title>", "<p>Intermediate lesions and advanced lesions represent different stages in disease progression of PAD. Identification of genes that exhibit characteristic expression patterns in different stages may provide information relevant to the progression of PAD. For this reason, expression profiles of normal arteries, intermediate lesions and advanced lesions were analyzed by the SAM multiclass method. Out of this analysis, 614 genes appeared to be differentially expressed in the progression of PAD with a FDR&lt;0.5% [see Additional file ##SUPPL##7##8##]. Hierarchical clustering analysis suggested that the expression patterns of the genes could be assigned to three major groups (Figure ##FIG##3##4##). The first group represents those genes commonly expressed in both intermediate and advanced lesions (Cluster II). GO terms indicate that these genes are mainly involved in the immune response, inflammatory response, cellular defense and various signaling pathways (Table ##TAB##3##4## and Additional file ##SUPPL##8##9##). These results further support the notion that the immune system may play an important role in the progression of PAD. The second group represents specifically down-regulated genes in advanced lesions (Cluster II). GO terms indicate that these genes are primarily involved in cell cycle, apoptosis, multicellular organism development and protein folding (Table ##TAB##3##4## and Additional file ##SUPPL##8##9##). Genes in the third group are represented by those down-regulated in both intermediate lesions and advanced lesions (Cluster III). GO terms indicate that these genes are mainly involved in neurogenesis, protein modification, RNA splicing, and blood pressure regulation (Table ##TAB##3##4## and see Additional file ##SUPPL##8##9##). In addition, we have performed data analysis restricted to male subjects. Up to 85% genes identified in male subjects are the same as those identified in the total samples (data not shown), which suggests that the potential gender-biases is minimal. Taken together, genes commonly up-regulated in intermediate and advanced stages are typically represented by those involved in immune and inflammatory responses, implicating enhanced immune response activities during the progression of the disease, whereas down-regulated genes in the both disease stages are primarily represented by those involved in various aspects of cell proliferation and differentiation.</p>", "<title>Validation of gene transcription by real-time PCR</title>", "<p>Real-time PCR is still the gold standard for quantitative analysis of mRNA. In order to validate the microarray results, RT- PCR was carried out on the same set of samples that were analyzed by the microarray approach. The results were highly correlated with those from the array data. (The correlation coefficient for microarray and RT-PCR was 0.835 ± 0.076). Representative RT-PCR results of 6 genes are shown in Figure ##FIG##4##5##.</p>", "<title>Transcription factors enrichment analysis</title>", "<p>Transcription factors appear to play important roles in the development or progression of atherosclerosis [##REF##17084284##17##,##REF##16873729##18##]. To address whether specific transcription factors are involved in the regulation of genes associated with the progression of PAD, we conducted a transcription factor binding site enrichment study by analyzing cross-species conserved binding sites in promoter regions of genes differentially regulated during progression of PAD. Through the Fisher Exact test, binding sites of transcription factor AP-1 and CREB appeared to be significantly enriched (<italic>q-value </italic>&lt; 0.05). AP-1 is a transcription factor known to be involved in various cellular processes. In atherosclerosis, it has been reported in gene regulation of microphages, vascular smooth muscle cells and epithelial cells [##REF##18309110##19##,##REF##18187666##20##]. In disease progression, AP-1 was enriched to regulate expression of 72 genes (Figure ##FIG##5##6A## and Additional file ##SUPPL##9##10##). The enrichment of AP1 binding sites in regulated genes associated with PAD progression may therefore suggest an important role played by this transcription factor in the development of PAD. Through literature mining, indeed, some of the potential targets of AP1 appear to be previously reported as target genes of AP-1 [##REF##15467434##21##, ####REF##9520467##22##, ##REF##12899698##23##, ##REF##18367585##24####18367585##24##]. CREB is a member of the leucine zipper family of DNA binding proteins. This transcription factor binds as a homodimer to the cAMP-responsive element and induces transcription of genes in response to hormonal stimulation [##REF##17993258##25##]. A total of 55 genes were recognized as potential targets of CREB (Figure ##FIG##5##6B## and Additional file ##SUPPL##10##11##). Although further studies are need to elucidate detailed roles played by AP1 and CREB in PAD progression, significantly enriched binding sites and highly correlated with signature genes of PAD progression suggest that these two transcription factors may play critical roles in the development of PAD.</p>", "<title>Pathways identification by overabundant genes</title>", "<p>A pathway analysis database, KEGG, was then applied to genes differentially regulated in intermediate and advanced lesions. Several overrepresented pathways were identified, and the enriched pathways appeared not to be independent of one another, many genes involved in one pathway could be also involved in another pathway. This interaction is illustrated in Figure ##FIG##6##7##, and pathway abbreviations can be found in Table ##TAB##4##5##. As demonstrated, many immune-related pathways were significantly over-represented in intermediate and/or advanced lesions including TLR, NK, BCR, FER, APP, CCC and LTEM pathways. These findings, on the one hand, provide evidence supporting previous hypotheses that immune/inflammatory responses play important roles in the development of PAD, and on the other hand, demonstrate that particular components of immune/inflammatory systems can be crucial for the genesis and progression of PAD. For instance, TLR and NK pathways are shown to be particularly overrepresented in both intermediate lesions and advanced lesions, highlighting their functional importance in the disease. The TLR pathway is shown in Figure ##FIG##7##8## with the differentially regulated genes indicated.</p>", "<title>Protein validation of TLR7 expression</title>", "<p>Members of the Toll receptor family are key mediators of innate immunity. They respond to various pathogen-associated stimuli and transduce complex signaling responses that are required for inflammation and for the subsequent development of adaptive immunity [##REF##16917510##26##]. In atherosclerosis, TLR-mediated signaling cascades are observed in macrophages, mast cells and endothelial cells [##REF##11861602##27##,##REF##11160251##28##]. Data shown in this setting demonstrate that genes involved in TLR-mediated pathway are significantly up-regulated in intermediate or advanced lesions, including <italic>TLR1, TLR2, TLR7</italic>, and <italic>MyD88</italic>. <italic>TLR1 </italic>and <italic>TLR2 </italic>have been previously reported to be significantly regulated in atherosclerosis and their functional roles have been widely investigated in atherosclerosis [##REF##16211093##29##,##REF##16982924##30##]. However, the expression of <italic>TLR7 </italic>in atherosclerosis has not been reported before. TLR7 mediates innate responses by recognizing oligonucleotide based (RNA-) molecular patterns in endocytic compartments. Our data show that it is significantly up-regulated in both intermediate and advanced lesions. Western-blot analysis was performed to further validate its expression on the protein level (Figure ##FIG##8##9##). The function of TLR7 in atherogenesis is currently under further investigation.</p>" ]
[ "<title>Discussion</title>", "<p>In the present study, we first examined the gene expression profiles of PAD. Data analysis identified a number of genes that might be significantly correlated with different levels of PAD severity. The list of differentially expressed genes in intermediate and advanced lesions contains many genes which can be important for atherosclerosis. Most of these genes have not been reported to be related to atherosclerosis before. For example, <italic>MAP4K4 </italic>is a member of the serine/threonine protein kinase family. It has been shown to specifically activate MAPK8/JNK and mediate the TNF-alpha signaling pathway [##REF##14966141##31##,##REF##9890973##32##]. In this study, it was significantly and consistently up-regulated in both intermediate and advanced lesions.</p>", "<p>A large multidisciplinary study is currently underway to comprehensively assess PAD at multiple levels [##REF##17544028##33##], The goal of that study is to investigate 300 symptomatic patients with PAD undergoing medical management with or without vascular intervention by lower extremity angioplasty/stenting or vein graft bypass, and to test the hypothesis that the systemic inflammatory response after vascular intervention influences the local milieu responsible for vascular repair and adaptation [##REF##17544028##33##]. Identification of genes through the work may be significant in the selection of candidate genes that can be investigated through these cases-control genetic epidemiology studies. Our research supports the idea that immune responses play a key role in the development of PAD.</p>", "<p>In this report, immune related genes were shown to be significantly expressed during the development of PAD. Gene functional analysis further revealed that immune related categories and pathways were significant enriched in the different stages of PAD. In these immune related genes, several genes have been shown to modulate the development of atherosclerosis in mice models. For example, IgG Fc receptors (FcgammaRs) play a role in activating the immune system and in maintaining peripheral tolerance. Previous research suggested that <italic>Fc</italic>γ receptor deficiency protects against atherosclerosis in Apolipoprotein-E knockout mice [##REF##17053192##34##]. The results suggest that broad-range inhibitors of immune and inflammatory responses can be considered as potential targets for the treatment of PAD. However, gene expression patterns of immune related genes can be different in different stages of PAD. For example, in intermediate lesions, MHC class II molecules were significantly up-regulated including <italic>HLA-DMA, HLA-DMB, HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DRB1 and HLA-DRB5</italic>. MHC class II molecules are normally restricted to a subset of antigen presenting dendritic cells, B cells, macrophages, and thymic epithelium cells [##REF##10837054##35##]. These cells can be detected close to CD4+ T cells and present peptides to the T cells. The results suggest that there can be an ongoing immune activation in the intermediate lesions. However, MHC class II molecules were not differentially expressed in advanced lesions, even with a higher false discovery rate, which may suggest that the HLA-mediated immune activation may occur mainly in the progression stages of PAD. In addition, complement molecules were also significantly up-regulated in intermediate lesions, not in advanced lesions. Previous studies have implicated that activation of the complement system is probably associated with the initiation and progression of atherosclerosis [##REF##16516969##36##,##REF##11287977##37##]. Our data thus provide direct evidence from clinical samples demonstrating that complement system mainly play a role in the development stages of PAD. It is therefore conceivable that different and complex immune/inflammatory responses may take place at different stages of PAD.</p>", "<p>Atherosclerosis is a systemic, multifocal disease leading to various symptoms and clinical events including cardiovascular disease, cerebrovascular disease, and peripheral arterial disease. Our results reveal that many genes identified in the report are also expressed in coronary or carotid atherosclerotic lesions. For example, <italic>C3AR1 </italic>and <italic>C5R1 </italic>are receptors of C3 (C3a) and C5a respectively. A recent study shows that <italic>C3AR1 </italic>and <italic>C5R1 </italic>are expressed in human atherosclerotic coronary plaques [##REF##17234193##38##]. Double immunofluorescence staining has shown that the plaque of cells that express both <italic>C3aR </italic>and <italic>C5aR </italic>are macrophages, T cells, endothelial cells, and sub-endothelial smooth muscle cells. In addition, gene expression changes between atherosclerosis from coronary and carotid artery samples have been measured by microarray technology in recent years. One study using microarray found that 82 genes were differentially expressed in both animal model and human coronary artery atherosclerosis disease [##REF##15870398##39##]. Our data confirmed 29 genes and 18 genes had significantly different expression in intermediate lesions and advanced lesions, respectively. Moreover, these genes had expression trends similar to the ones found in our data, but our data showed higher fold-changes. In these overlapping genes, 14 were reported to be involved in immune response. Another microarray study found that 206 genes were differentially expressed in aortic atherosclerosis samples [##REF##15297278##40##]. Our data confirms 43 genes and 32 genes had significantly different expression in intermediate and advanced lesions (FDR&lt;1%), respectively. Importantly, in these overlapping genes, 15 were reported to be involved in immune response. Taken together, the results suggested that immune response is a common feature in atherosclerosis-related diseases. Our microarray study differs from prior microarray studies in the array type, sample type, sample classification, and analytical techniques. Nevertheless, the high level of overlapping genes suggests that there are similar molecular mechanisms in the development of peripheral arterial disease and other atherosclerosis-related diseases.</p>", "<p>Several limitations of our approach should be noted. First, hybridization-based microarrays, despite their immense potential, have inherent shortcomings related to deficient standardization of methods employed in normalization, statistical analysis, and so on [##REF##15904488##41##,##REF##17211383##42##]. In this study, we have attempted to limit these shortcomings by selecting subjects who were phenotypically similar to each other except for hypertension. In addition, the initial phases of data analysis, we used different normalization and statistical methods to identify differentially expressed genes. After choosing SAM, we used a rigorous false discovery rate to minimize false positive results. Expression patterns were validated by confirming mRNA expression patterns with conventional molecular techniques. We attempted, based on current literature, to suggest a potential functional role for genes whose expression was markedly altered. Second, atherosclerosis is a slow, progressive disease that may start in childhood; entirely normal arteries can only be obtained from young donors, a factor that can affect gene expression measurements. Although previous research and our data analysis suggest that age had very little effect on genes, further work is needed to identify age-related genes. Third, the relatively small number of patients did not allow us to assess serial changes in the disease development in more detail as would have been possible in animal models [##REF##16516969##36##]. Furthermore, we do not know to what extent the observed changes in gene expression translate into protein synthesis and function, and which genes cause atherosclerosis. Future studies are needed to address these issues.</p>" ]
[ "<title>Conclusion</title>", "<p>We first examined the gene expression profiles of PAD; the results from this analysis provide an initial step towards a better understanding of molecular mechanisms underlying PAD development. Differences in immune-related responses were observable at the gene expression level. These findings may be significant for understanding the molecular basis of PAD and investigating pharmacological approaches for the prevention and amelioration of atherosclerosis in PAD.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Peripheral arterial disease (PAD), a major manifestation of atherosclerosis, is associated with significant cardiovascular morbidity, limb loss and death. However, mechanisms underlying the genesis and progression of the disease are far from clear. Genome-wide gene expression profiling of clinical samples may represent an effective approach to gain relevant information.</p>", "<title>Results</title>", "<p>After histological classification, a total of 30 femoral artery samples, including 11 intermediate lesions, 14 advanced lesions and 5 normal femoral arteries, were profiled using Affymetrix microarray platform. Following real-time RT-PCR validation, different algorithms of gene selection and clustering were applied to identify differentially expressed genes. Under a stringent cutoff, i.e., a false discovery rate (FDR) &lt;0.5%, we found 366 genes were differentially regulated in intermediate lesions and 447 in advanced lesions. Of these, 116 genes were overlapped between intermediate and advanced lesions, including 68 up-regulated genes and 48 down-regulated ones. In these differentially regulated genes, immune/inflammatory genes were significantly up-regulated in different stages of PAD, (85/230 in intermediate lesions, 37/172 in advanced lesions). Through literature mining and pathway analysis using different databases such as Gene Ontology (GO), and the Kyoto Encyclopedia of Gene and Genomics (KEGG), genes involved in immune/inflammatory responses were significantly enriched in up-regulated genes at different stages of PAD(p &lt; 0.05), revealing a significant correlation between immune/inflammatory responses and disease progression. Moreover, immune-related pathways such as Toll-like receptor signaling and natural killer cell mediated cytotoxicity were particularly enriched in intermediate and advanced lesions (P &lt; 0.05), highlighting their pathogenic significance during disease progression.</p>", "<title>Conclusion</title>", "<p>Lines of evidence revealed in this study not only support previous hypotheses, primarily based on studies of animal models and other types of arterial disease, that inflammatory responses may influence the development of PAD, but also permit the recognition of a wide spectrum of immune/inflammatory genes that can serve as signatures for disease progression in PAD. Further studies of these signature molecules may eventually allow us to develop more sophisticated protocols for pharmaceutical interventions.</p>" ]
[ "<title>Availability &amp; requirements</title>", "<p>SAM software package: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www-stat.stanford.edu/~tibs/SAM/\"/></p>", "<p>Cluster3.0 and Treeview1.1.0 software: <ext-link ext-link-type=\"uri\" xlink:href=\"http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/software.htm\"/></p>", "<p>MAPPFinder (Gene MicroArray Pathway Profiler): <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.genmapp.org\"/></p>", "<p>DAVID: <ext-link ext-link-type=\"uri\" xlink:href=\"http://david.abcc.ncifcrf.gov/\"/></p>", "<p>Xie et al Supplementary Information: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.broad.mit.edu/seq/HumanMotifs/\"/></p>", "<p>NCBI web site: <ext-link ext-link-type=\"ftp\" xlink:href=\"ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2refseq.gz\"/></p>", "<title>Authors' contributions</title>", "<p>SJF, experiment design and conduction, and manuscript drafting; HGZ, clinical sample processing; JTS, transcription factor enrichment analysis; AA, WPK, KC and LO–M, data analysis and manuscript revising; JQZ, data analysis; YZD, data analysis and literature support; JZ and MEJ, experimental design and manuscript revising; JGJ, experiment design, data analysis and manuscript revising. All the authors read and approved this version of the manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported in part by grants from Science and Technology Commission of Shanghai Municipality (project 044319209, 04JC14084 and 06QA14059), Chinese National Key Program for Basic Research (973: 2006CB910405), Chinese National High Tech Program (863: 2007AA02Z335 and 863: 2006AA02Z332), 100-Talent and Knowledge Innovation Programs of Chinese Academy of Science (J. Z), and the Komen Foundation (FAS0703850, WPK and LOM).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Histological characteristics of various severities of femoral atherosclerotic lesions in PAD patients</bold>. HE stain analysis of histological characteristics of collected femoral non-atherosclerotic arteries and atherosclerotic arteries. 8 um cryostat sections were stained with hematoxylin and eosin, dehydrated in graded alcohol, and cover-slipped with permanent mounting solution after xylene clearing. Three representative samples are listed: normal artery (A, B, C), intermediate lesions (D, E, F), and advanced lesions (G, H, I).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Heatmap of the 100 most differentially expressed genes in intermediate lesions and advanced lesions, respectively</bold>. SAM analysis reveals genes with differential expression in PAD. This analysis compared plaques from within arteries of either intermediate (n = 11) or advanced lesions (n = 14) to normal control group, respectively. Heatmap representation of the 100 most differentially expressed in intermediate lesions (A) and advanced lesions (B). Samples are displayed in columns and genes in rows. Gene expression is represented as a color, normalized across each row, with brighter red for higher values and brighter green for lower values. Gene symbols are listed to the right. N (Normal control group), Int (intermediate lesions group), Ad(advanced lesions group). The list of differentially expressed genes in intermediate lesions and advanced lesions is provided [see Additional file ##SUPPL##0##1## and Additional file ##SUPPL##3##4##].</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Over-represented genes in both intermediate lesions and advanced lesions</bold>. A. The genes whose expressions were significantly changed in intermediate lesions and advanced lesions, respectively, are shown in a Venn diagram. B, C. Heatmap representation of commonly up-regulated genes (B) and commonly down-regulated genes (C) in overlapping genes, respectively. Samples are displayed in columns and genes in rows. Gene expression is represented as a color, normalized across each row, with brighter red for higher values and brighter green for lower values. Gene symbols are listed to the right. N (Normal control group), Int (intermediate lesions group), Ad (advanced lesions group).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Hierarchical clustering analysis of the differentially expressed genes in disease progression</bold>. The differentially expressed genes analyzed by hierarchical clustering method in disease progression. The genes were classified into three major clusters by visual inspection. Clustering method: Average linking; Similarity measure: Euclidean distance. Samples are displayed in columns and genes in rows. Gene expression is represented as a color, normalized across each row, with brighter red for higher values and brighter green for lower values. N (Normal control group), Int (intermediate lesions group), Ad (advanced lesions group). The list of differentially expressed genes in disease progression is provided [see Additional file ##SUPPL##7##8##].</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Real-time PCR and the relative expression level of six genes</bold>. Six genes mRNA in normal femoral artery (N, black round), intermediate lesions (Int, ascending triangle) and advanced lesions (Ad, descending triangle) were determined by real-time PCR and presented as a ratio to GAPDH mRNA. mRNA abundance in intermediate lesions or advanced lesions was differentially expressed (*P &lt; 0.05, and **P &lt; 0.01, respectively) when normal samples were used for comparison.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Enriched transcription factors and their putative target genes in disease progression</bold>. Putative targets of transcription factors were curated based on results by Xiaohui Xie. Fisher Exact test showed that two transcription factors (AP-1 and CREB) were significantly enriched in disease progression (q-value &lt;0.05). AP-1 and CREB were enriched to regulate 72 and 55 genes expression, respectively. A, B. The top 20 putative target genes of AP-1 and CREB were listed, respectively. A list of the enriched transcription factor and their putative targets is provided [see Additional file ##SUPPL##9##10## and Additional file ##SUPPL##10##11##].</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Interactions of KEGG pathways for differentially expressed genes in different stages of PAD</bold>. Pathways are enriched in intermediate lesions (A) and advanced lesions (B), respectively. Many genes involved in one pathway could also be involved in another pathway. A, B. Networking displayed the interaction of pathways in intermediate lesions and advanced lesions, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Over-representation of Toll-like receptor signaling pathway genes</bold>. Analysis of over-representation of differentially expressed genes in pathway from KEGG. The Toll-like receptor signaling pathway is illustrated with significantly regulated genes highlighted.</p></caption></fig>", "<fig position=\"float\" id=\"F9\"><label>Figure 9</label><caption><p><bold>Significant expression of TLR7 in femoral atherosclerotic lesions</bold>. Western blot analysis of TLR7 in atherosclerotic femoral arteries (As, n = 3) and normal femoral arteries (N, n = 3). The protein level of Cathepsin S, whose expression was previously validated in atherosclerosis, was also examined in femoral atherosclerotic lesions. Beta – actin served as a loading control.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Patient characteristics for the 25 samples in the microarray analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\"><bold>Intermediate lesions group (n = 11)</bold></td><td align=\"left\"><bold>Advanced lesions group (n = 14)</bold></td></tr></thead><tbody><tr><td align=\"left\">Age*</td><td align=\"left\">76.1 ± 4.68</td><td align=\"left\">76.6 ± 3.79</td></tr><tr><td align=\"left\">Male, n (%)</td><td align=\"left\">8(72.7)</td><td align=\"left\">12(85.7)</td></tr><tr><td align=\"left\">Hypertension<sup>†</sup>, n (%)</td><td/><td/></tr><tr><td align=\"left\">Yes</td><td align=\"left\">4(36.4)</td><td align=\"left\">9(64.3)</td></tr><tr><td align=\"left\">no</td><td align=\"left\">7(63.6)</td><td align=\"left\">5(35.7)</td></tr><tr><td align=\"left\">Hypercholesterolemia, n (%)</td><td/><td/></tr><tr><td align=\"left\">Yes</td><td align=\"left\">9(81.8)</td><td align=\"left\">11(78.6)</td></tr><tr><td align=\"left\">no</td><td align=\"left\">2(18.2)</td><td align=\"left\">3(21.4)</td></tr><tr><td align=\"left\">Diabetes, n (%)</td><td/><td/></tr><tr><td align=\"left\">Yes</td><td align=\"left\">7(63.6)</td><td align=\"left\">10(71.4)</td></tr><tr><td align=\"left\">no</td><td align=\"left\">4(36.4)</td><td align=\"left\">4(28.6)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Biological process categories overrepresented in intermediate lesions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>GO Name</bold></td><td align=\"right\"><bold>Z-score</bold></td><td align=\"right\"><bold><italic>P</italic>-value</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>For up-regulated genes </bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">immune response</td><td align=\"right\">14.491</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">antigen processing, endogenous antigen via MHC class I</td><td align=\"right\">3.821</td><td align=\"right\">0.014</td></tr><tr><td align=\"left\">antigen processing, exogenous antigen via MHC class II</td><td align=\"right\">5.968</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">cellular defense response</td><td align=\"right\">5.669</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell-mediated immune response</td><td align=\"right\">3.093</td><td align=\"right\">0.038</td></tr><tr><td align=\"left\">T-helper 1 type immune response</td><td align=\"right\">3.214</td><td align=\"right\">0.035</td></tr><tr><td align=\"left\">humoral immune response</td><td align=\"right\">9.451</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">complement activation</td><td align=\"right\">3.608</td><td align=\"right\">0.016</td></tr><tr><td align=\"left\">inflammatory response</td><td align=\"right\">8.515</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">neutrophil chemotaxis</td><td align=\"right\">6.629</td><td align=\"right\">0.004</td></tr><tr><td align=\"left\">leukocyte adhesion</td><td align=\"right\">4.767</td><td align=\"right\">0.013</td></tr><tr><td align=\"left\">immune cell activation</td><td align=\"right\">3.268</td><td align=\"right\">0.011</td></tr><tr><td align=\"left\">immune cell migration</td><td align=\"right\">6.25</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell surface receptor linked signal transduction</td><td align=\"right\">2.082</td><td align=\"right\">0.042</td></tr><tr><td align=\"left\">integrin-mediated signaling pathway</td><td align=\"right\">2.633</td><td align=\"right\">0.035</td></tr><tr><td align=\"left\">intracellular signaling cascade</td><td align=\"right\">4.367</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">MAPKKK cascade</td><td align=\"right\">2.413</td><td align=\"right\">0.037</td></tr><tr><td align=\"left\">myeloid cell differentiation</td><td align=\"right\">2.876</td><td align=\"right\">0.042</td></tr><tr><td align=\"left\">lipid transport</td><td align=\"right\">3.255</td><td align=\"right\">0.011</td></tr><tr><td align=\"left\">Phagocytosis</td><td align=\"right\">4.757</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">Apoptosis</td><td align=\"right\">2.091</td><td align=\"right\">0.049</td></tr><tr><td align=\"left\">cell proliferation</td><td align=\"right\">4.257</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">T cell proliferation</td><td align=\"right\">5.489</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">oxygen transport</td><td align=\"right\">3.255</td><td align=\"right\">0.016</td></tr><tr><td align=\"left\">icosanoid metabolism</td><td align=\"right\">3.524</td><td align=\"right\">0.010</td></tr><tr><td align=\"left\">lipoprotein metabolism</td><td align=\"right\">3.291</td><td align=\"right\">0.017</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\"><bold>For down-regulated genes</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">cellular catabolism</td><td align=\"right\">2.708</td><td align=\"right\">0.014</td></tr><tr><td align=\"left\">amine catabolism</td><td align=\"right\">3.599</td><td align=\"right\">0.011</td></tr><tr><td align=\"left\">glycine catabolism</td><td align=\"right\">8.457</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">glycine metabolism</td><td align=\"right\">5.817</td><td align=\"right\">0.008</td></tr><tr><td align=\"left\">ubiquitin cycle</td><td align=\"right\">2.092</td><td align=\"right\">0.041</td></tr><tr><td align=\"left\">protein folding</td><td align=\"right\">3.088</td><td align=\"right\">0.008</td></tr><tr><td align=\"left\">Endocytosis</td><td align=\"right\">2.521</td><td align=\"right\">0.032</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Biological process categories overrepresented in advanced lesions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>GO Name</bold></td><td align=\"right\"><bold>Z-score</bold></td><td align=\"right\"><bold><italic>P</italic>-value</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>For up-regulated genes </bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">immune response</td><td align=\"right\">6.305</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cellular defense response</td><td align=\"right\">2.94</td><td align=\"right\">0.026</td></tr><tr><td align=\"left\">humoral immune response</td><td align=\"right\">5.392</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">inflammatory response</td><td align=\"right\">6.179</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">immune cell migration</td><td align=\"right\">4.778</td><td align=\"right\">0.014</td></tr><tr><td align=\"left\">intracellular signaling cascade</td><td align=\"right\">4.297</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">small GTPase mediated signal transduction</td><td align=\"right\">2.372</td><td align=\"right\">0.024</td></tr><tr><td align=\"left\">I-kappaB kinase/NF-kappaB cascade</td><td align=\"right\">6.408</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">T cell proliferation</td><td align=\"right\">4.186</td><td align=\"right\">0.016</td></tr><tr><td align=\"left\">induction of apoptosis</td><td align=\"right\">3.709</td><td align=\"right\">0.007</td></tr><tr><td align=\"left\">oxygen transport</td><td align=\"right\">4.778</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">amine transport</td><td align=\"right\">3.051</td><td align=\"right\">0.024</td></tr><tr><td align=\"left\">anion transport</td><td align=\"right\">3.373</td><td align=\"right\">0.006</td></tr><tr><td align=\"left\">phosphate transport</td><td align=\"right\">6.139</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">organic acid transport</td><td align=\"right\">2.634</td><td align=\"right\">0.039</td></tr><tr><td align=\"left\">nucleoside monophosphate metabolism</td><td align=\"right\">5.853</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">cell adhesion</td><td align=\"right\">3.958</td><td align=\"right\">0.000</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\"><bold>For down-regulated genes</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">response to protein stimulus</td><td align=\"right\">6.79</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">response to unfolded protein</td><td align=\"right\">6.79</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">integrin-mediated signaling pathway</td><td align=\"right\">4.36</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">Apoptosis</td><td align=\"right\">2.838</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">muscle cell differentiation</td><td align=\"right\">3.771</td><td align=\"right\">0.032</td></tr><tr><td align=\"left\">vasculature development</td><td align=\"right\">3.795</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">neuron differentiation</td><td align=\"right\">3.993</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">vitamin transport</td><td align=\"right\">3.771</td><td align=\"right\">0.023</td></tr><tr><td align=\"left\">RNA metabolism</td><td align=\"right\">3.476</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">RNA splicing</td><td align=\"right\">3.497</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">glutamine metabolism</td><td align=\"right\">4.901</td><td align=\"right\">0.004</td></tr><tr><td align=\"left\">macromolecule metabolism</td><td align=\"right\">2.214</td><td align=\"right\">0.023</td></tr><tr><td align=\"left\">protein folding</td><td align=\"right\">5.643</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell-matrix adhesion</td><td align=\"right\">2.605</td><td align=\"right\">0.036</td></tr><tr><td align=\"left\">peptide hormone secretion</td><td align=\"right\">5.401</td><td align=\"right\">0.004</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>GO discovered categories for disease progression manner analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>GO Name</bold></td><td align=\"right\"><bold>Z-Score</bold></td><td align=\"right\"><bold><italic>P</italic>-value</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>For Cluster I</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">immune response</td><td align=\"right\">8.908</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">inflammatory response</td><td align=\"right\">7.643</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cellular defense response</td><td align=\"right\">5.938</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">chemotaxis</td><td align=\"right\">7.611</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell surface receptor linked signal transduction</td><td align=\"right\">2.937</td><td align=\"right\">0.009</td></tr><tr><td align=\"left\">cytokine and chemokine mediated signaling pathway</td><td align=\"right\">4.378</td><td align=\"right\">0.004</td></tr><tr><td align=\"left\">JAK-STAT cascade</td><td align=\"right\">3.254</td><td align=\"right\">0.020</td></tr><tr><td align=\"left\">MAPKKK cascade</td><td align=\"right\">3.062</td><td align=\"right\">0.006</td></tr><tr><td align=\"left\">endocytosis</td><td align=\"right\">1.956</td><td align=\"right\">0.049</td></tr><tr><td align=\"left\">angiogenesis</td><td align=\"right\">4.452</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell adhesion</td><td align=\"right\">4.580</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">ion homeostasis</td><td align=\"right\">3.090</td><td align=\"right\">0.010</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\"><bold>For Cluster II</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">apoptosis</td><td align=\"right\">3.871</td><td align=\"right\">0.002</td></tr><tr><td align=\"left\">cell cycle</td><td align=\"right\">4.108</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">cell growth</td><td align=\"right\">2.285</td><td align=\"right\">0.037</td></tr><tr><td align=\"left\">epidermal cell differentiation</td><td align=\"right\">3.075</td><td align=\"right\">0.044</td></tr><tr><td align=\"left\">protein folding</td><td align=\"right\">3.677</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">macromolecule metabolic process</td><td align=\"right\">3.022</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">RNA metabolic process</td><td align=\"right\">5.378</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">transcription</td><td align=\"right\">4.593</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">peripheral nervous system development</td><td align=\"right\">2.950</td><td align=\"right\">0.046</td></tr><tr><td align=\"left\">multicellular organismal development</td><td align=\"right\">3.927</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">vitamin transport</td><td align=\"right\">4.426</td><td align=\"right\">0.008</td></tr><tr><td align=\"left\">response to stimulus</td><td align=\"right\">4.579</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">circadian rhythm</td><td align=\"right\">5.090</td><td align=\"right\">0.001</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\"><bold>For Cluster III</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">neurogenesis</td><td align=\"right\">2.612</td><td align=\"right\">0.014</td></tr><tr><td align=\"left\">neuron recognition</td><td align=\"right\">3.315</td><td align=\"right\">0.046</td></tr><tr><td align=\"left\">cell motility</td><td align=\"right\">2.093</td><td align=\"right\">0.042</td></tr><tr><td align=\"left\">glycogen metabolic process</td><td align=\"right\">2.999</td><td align=\"right\">0.025</td></tr><tr><td align=\"left\">protein modification process</td><td align=\"right\">3.166</td><td align=\"right\">0.001</td></tr><tr><td align=\"left\">RNA processing</td><td align=\"right\">2.183</td><td align=\"right\">0.042</td></tr><tr><td align=\"left\">RNA splicing</td><td align=\"right\">2.591</td><td align=\"right\">0.021</td></tr><tr><td align=\"left\">phosphate metabolic process</td><td align=\"right\">2.346</td><td align=\"right\">0.024</td></tr><tr><td align=\"left\">blood pressure regulation</td><td align=\"right\">4.028</td><td align=\"right\">0.008</td></tr><tr><td align=\"left\">heart contraction</td><td align=\"right\">2.901</td><td align=\"right\">0.022</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>KEGG biological pathways for differentially expressed genes in different stages</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>KEGG Pathway Name</bold></td><td align=\"left\"><bold>Pathway ID</bold></td><td align=\"right\"><bold>Genes involved </bold></td><td align=\"right\"><bold><italic>P</italic>-Value</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>For intermediate lesions</bold></td><td/><td/><td/></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Type I Diabetes Mellitus(TIDM)</td><td align=\"left\">Hsa04940</td><td align=\"right\">9</td><td align=\"right\">0.002</td></tr><tr><td align=\"left\">Antigen Processing and Presentation(APP)</td><td align=\"left\">Hsa04612</td><td align=\"right\">13</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">Complement and Coagulation Cascades(CCC)</td><td align=\"left\">Hsa04610</td><td align=\"right\">12</td><td align=\"right\">0.003</td></tr><tr><td align=\"left\">Cell Adhesion Molecules(CAM)</td><td align=\"left\">Hsa04514</td><td align=\"right\">17</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">Toll-Like Receptor Signaling Pathway(TLR)</td><td align=\"left\">Hsa04620</td><td align=\"right\">13</td><td align=\"right\">0.013</td></tr><tr><td align=\"left\">Natural Killer Cell Mediated Cytotoxicity(NK)</td><td align=\"left\">Hsa04650</td><td align=\"right\">15</td><td align=\"right\">0.029</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\"><bold>For advanced lesions</bold></td><td/><td/><td/></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Focal Adhesion(FA)</td><td align=\"left\">Hsa04510</td><td align=\"right\">25</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">ECM-Receptor Interaction(ECM)</td><td align=\"left\">Hsa04512</td><td align=\"right\">15</td><td align=\"right\">0.000</td></tr><tr><td align=\"left\">Toll-Like Receptor Signaling Pathway(TLR)</td><td align=\"left\">Hsa04620</td><td align=\"right\">13</td><td align=\"right\">0.002</td></tr><tr><td align=\"left\">Regulation of Actin Cytoskeleton(RAC)</td><td align=\"left\">Hsa04810</td><td align=\"right\">20</td><td align=\"right\">0.005</td></tr><tr><td align=\"left\">Fc Epsilon RI Signaling Pathway(FER)</td><td align=\"left\">Hsa04664</td><td align=\"right\">10</td><td align=\"right\">0.012</td></tr><tr><td align=\"left\">MAPK Signaling Pathway(MAPK)</td><td align=\"left\">Hsa04010</td><td align=\"right\">22</td><td align=\"right\">0.019</td></tr><tr><td align=\"left\">Natural Killer Cell Mediated Cytotoxicity(NK)</td><td align=\"left\">Hsa04650</td><td align=\"right\">13</td><td align=\"right\">0.021</td></tr><tr><td align=\"left\">Long-Term Potentiation(LTP)</td><td align=\"left\">Hsa04720</td><td align=\"right\">8</td><td align=\"right\">0.030</td></tr><tr><td align=\"left\">Cell Communication(CC)</td><td align=\"left\">Hsa01430</td><td align=\"right\">12</td><td align=\"right\">0.032</td></tr><tr><td align=\"left\">B Cell Receptor Signaling Pathway(BCR)</td><td align=\"left\">Hsa04662</td><td align=\"right\">8</td><td align=\"right\">0.042</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional File 1</title><p><bold>Table 1</bold>. 366 differentially expressed genes in intermediate lesions relative to normal femoral arteries. Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Fold Change and q-value(%) which is the lowest FDR are listed in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional File 2</title><p><bold>Table 2</bold>. Immune-related genes in intermediate lesions relative to normal femoral arteries, Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Fold Change and q-value(%) which is the lowest FDR are listed in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional File 3</title><p><bold>Table 3</bold>. Cell component and molecular function categories overrepresented in intermediate lesions, the first half of the table indicates categories highly significant for up-regulated genes; the second half of the table shows categories highly significant for down-regulated genes. The calculated <italic>p</italic>-values and Z-scores for each category are shown.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional File 4</title><p><bold>Table 4</bold>. 447 differentially expressed genes in advanced lesions relative to normal femoral arteries. Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Fold Change and q-value(%) which is the lowest FDR are listed in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional File 5</title><p><bold>Table 5</bold>. Immune-related genes in advanced lesions relative to normal femoral arteries, Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Fold Change and q-value(%) which is the lowest FDR are listed in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional File 6</title><p><bold>Table 6</bold>. Cell component and molecular function categories overrepresented in advanced lesions, the first half of the table indicates categories highly significant for up-regulated genes; the second half of the table shows categories highly significant for down-regulated genes. The calculated <italic>p</italic>-values and Z-scores for each category are shown.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional File 7</title><p><bold>Table 7</bold>. 68 commonly up-regulated genes in intermediate lesions and advanced lesions, Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Fold Change and q-value(%) which is the lowest FDR are listed in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional File 8</title><p><bold>Table 8</bold>. 614 differentially expressed genes in disease progression. Affymetrix Probe Set ID, Cluster ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Contrast which is the standardized mean difference between the gene's expression in that class versus its overall mean expression, and q-value(%)which is the lowest FDR are listed. In the table, contrast1, 2 and 3 represent the standardized mean difference in normal femoral arteries, intermediate lesions, and advanced lesions, respectively.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional File 9</title><p><bold>Table 9</bold>. The detail GO overrepresented categories for disease progression in each cluster, the calculated <italic>p</italic>-values and Z-scores for each category are shown in the table.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional File 10</title><p><bold>Table 10</bold>. 72 putative AP-1 target genes in disease progression. Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Contrast, and q-value(%) are listed.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S11\"><caption><title>Additional File 11</title><p><bold>Table 11</bold>. 55 putative CREB target genes in disease progression. Affymetrix Probe Set ID, Gene Title, Gene Symbol, GO Biological Process, GO Molecular Function, GO Cellular Component, Unigene, Entrez Gene, Ensembl, Chromosome Number, Socre(d), Contrast, and q-value(%) were listed.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S12\"><caption><title>Additional File 12</title><p><bold>Table 12</bold>. The primer sequences of selected genes for real-time PCR.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*as mean ± SD.</p><p>† P &lt; 0.05 (Student t test)</p></table-wrap-foot>", "<table-wrap-foot><p>GO analysis was applied to differentially expressed genes in intermediate lesions compared with normal controls. Similar significant categories were not included to reduce redundancy. The first half of the table indicates categories highly significant for up-regulated genes; the second half of the table shows categories highly significant for down-regulated genes. Calculated <italic>p</italic>-values and Z-scores for each category are shown. The corresponding cell component categories and molecular function categories are available in additional data [see Additional file ##SUPPL##2##3##]</p></table-wrap-foot>", "<table-wrap-foot><p>GO analysis was applied to differentially expressed genes in advanced lesions compared with normal controls. Some other selected categories are shown. The first half of the table indicates categories highly significant for up-regulated genes; the second half of the table shows categories highly significant for down-regulated genes. Calculated <italic>p</italic>-values and Z-scores for each category are shown. The corresponding cell component categories and molecular function categories are available in additional data [see Additional file ##SUPPL##5##6##]</p></table-wrap-foot>", "<table-wrap-foot><p>GO analysis was applied to differentially expressed genes in each cluster group. Some selected categories are shown. The Calculated <italic>p</italic>-values and Z-scores for each category are shown. The detailed GO results are provided in the additional data [see Additional file ##SUPPL##8##9##].</p></table-wrap-foot>", "<table-wrap-foot><p>Genes identified from SAM under FDR&lt;1% were tested for overrepresentation within the pathways for the KEGG under the modified Fisher Exact test assumption; <italic>p</italic>-Value and genes involved in the pathway are provided for each pathway</p></table-wrap-foot>" ]
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[{"surname": ["Hansson", "Robertson", "S\u00f6derberg-Naucl\u00e9r"], "given-names": ["G\u00f6ranK", "Anna-KarinL", "Cecilia"], "article-title": ["INFLAMMATION AND ATHEROSCLEROSIS"], "source": ["Annual Review of Pathology: Mechanisms of Disease"], "year": ["2006"], "volume": ["1"], "fpage": ["297"], "lpage": ["329"], "pub-id": ["10.1146/annurev.pathol.1.110304.100100"]}]
{ "acronym": [], "definition": [] }
45
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Aug 1; 9:369
oa_package/96/7c/PMC2529314.tar.gz
PMC2529315
18671879
[ "<title>Background</title>", "<p>Prolonged exercises may lead to damage of muscle fibres with leakage of water, ions and proteins, in particular muscle specific enzymes, whose serum levels are diagnostic for muscle injury [##REF##15126716##1##]. However the mechanisms that underlie the development of cellular muscle damage after exercise are not yet clarified. The extend of physical burden as well as nutritional and metabolic aspects, genetics and temperature affect the degree of damage, that is due to an imbalance of energy and oxygen demands and supply of the muscle cells [##REF##8904017##2##, ####REF##1773817##3##, ##REF##2393546##4##, ##REF##11484686##5####11484686##5##]. Similarly, at <italic>post mortem </italic>termination of nutrient and energy supply and anoxia occurs. <italic>Post mortem </italic>anaerobe energy production by glycolysis stops at low pH and finally energy supply collapses marked by increased cytoplasmatic Ca<sup>2+ </sup>levels and activation of Ca<sup>2+ </sup>dependent intracellular processes. The conversion of muscle to meat is thus marked by the activity of proteins of the anaerobic energy and calcium metabolism, lactacidosis, assembly of actin-myosin-complexes and proteolysis. This is accompanied by leakage of the muscle cells and loss of water, ion and proteins. There is considerable variation in the amount of fluid released from the muscle during maturation to meat that might reflect difference in the sensitivity to metabolic imbalance and physical stressors of various genotypes. These biochemical processes play an important role not only in muscle injury but also in meat quality in pork industry. Water holding capacity (WHC) of pork is an important aspect of palatability that affects overall quality and acceptability of meat and is a consistent problem in the pork industry for many years [##UREF##0##6##,##UREF##1##7##]. Water-holding capacity can be measured in form of drip loss. Drip development during storage of meat is principally caused by shrinkage of myofibrils due to changes of energy reserves, pH and temperature <italic>post mortem </italic>[##UREF##2##8##]. Heritability estimates for drip loss vary from 0.08–0.30 depending on the method of drip measurement or breed [##UREF##3##9##, ####REF##15644503##10##, ##REF##16100060##11##, ##UREF##4##12##, ##UREF##5##13####5##13##]. Biological mechanisms and the genetic background underlying variation in drip are not fully understood.</p>", "<p>A genome scan is the most general approach to identify genomic regions exhibiting quantitative trait loci (QTL), classically for complex phenotypic characteristics that vary in degree and can be attributed to effects of many gene (subsequently termed pheneQTL = pQTL). QTL for WHC were mapped in many regions of porcine chromosomes [##REF##11471059##14##, ####REF##11768109##15##, ##UREF##6##16##, ##REF##16543555##17####16543555##17##]. QTL regions are generally large and contain several putative causal genes. Combining microarray data with quantitative trait loci (pQTL) linkage studies offers new options of understanding the biology at a global level and the genetic factors affecting the trait of interest. Integration of positional and functional information facilitates focussing on most relevant candidate genes in each pQTL region [##REF##12415114##18##]. QTL analysis of expression levels of a gene identifies genomic regions, which are likely to contain at least one causal gene with regulatory effect on the expression level, termed expression QTL, eQTL [##REF##12646919##19##,##UREF##7##20##]. In order to identify genes and pathways with multiple evidence of their role in the biology of a trait, it is proposed here to combine (1) information on pQTL with analysis of (2) trait correlated expression and with (3) mapping of eQTL for the corresponding trait dependent regulated genes. Under the assumption that genes with trait correlated expression levels belong to pathways or networks relevant for the control of the trait, correlation analysis of microarray expression data and records of WHC, measured as drip loss, reveals a list of functional candidate genes. Functional annotation allows identification of biological pathways and offers an insight into the biological processes causing variation in the genetically based trait, WHC. Information of the expression study merged with results of a pQTL study for the trait drip loss performed in the same population already down scales the list of primary candidate genes. Further, adding eQTL analyses for transcripts showing trait dependent expression enables addressing genes, which show trait associated expression, map to pQTL regions, and exhibit <italic>cis </italic>regulation. These genes are functional positional candidate genes likely to exhibit polymorphisms affecting their own expression and by this the phenotypic trait drip loss, i.e. they are likely to be causal genes in the pQTL of that trait.</p>" ]
[ "<title>Methods</title>", "<title>Animals and tissue collection</title>", "<p>This study was based on data originating from the three-generation resource family structure, trait measurements, genotyping procedures and linkage analysis as described in detail by Liu <italic>et al</italic>. [##REF##17459017##21##]. For these experiments a total of 585 F2 progeny were used comprising 31 full-sib families. The F0 animals used were animals of two commercial breeds, the Duroc and Pietrain breed. Grandparental purebred F0 animals were reciprocal mated and 32 F1 animals were used for producing the F2. The total population was further denoted as \"DuPi population\". All animals were free of the mutation at the ryanodine receptor locus, RYR1, which is responsible for malignant hyperthermia syndrome. Genotypes of 116 microsatellite markers were used. For linkage mapping, twopoint and multipoint procedures of the CRI-MAP package version 2.4 were employed [##UREF##19##69##]. Expression profiling and eQTL analysis were conducted on 74 F2 animals of the resource population with previously identified pQTL for drip [##REF##17459017##21##]. These 74 animals represented a subset of the population covering 25 full-sib families derived from all five F1 boars of the population and 18 out of 27 F1 sows. Animals were selected that represented the genotype combinations at the major pQTL on SSC5 and 18 at equal proportions with equal numbers of male and female [##REF##16918915##22##]. Genotypes at the remaining QTL were considered as to avoid overrepresentation of any homozygote QTL genotype. As expected, when assuming mainly additive genetic effects of the QTL, the phenotype of drip loss of these selected animals had a normal distribution as shown in Figure ##FIG##3##4##.</p>", "<title>Drip loss phenotype</title>", "<p>Drip loss was scored based on a bag-method using a size-standardized sample from the <italic>longissimus dorsi </italic>that was collected at 24 hours <italic>post mortem</italic>. A sample was weighed, suspended in a plastic bag, held at 4°C and re-weighed 48 hours later for water loss [##UREF##20##70##,##UREF##21##71##]. Drip loss was calculated as a percentage of lost weight based on the starting weight of a sample.</p>", "<title>Whole genome expression profiling</title>", "<p>Immediately <italic>post mortem </italic>tissue samples were collected and snap frozen that were taken between the 13th and 14th rib from the center of <italic>M. longissimus dorsi </italic>of 74 animals. Total RNA was isolated using TRI Reagent (Sigma, Taufkirchen, Germany) according to the manufacturer's protocol. After DNaseI treatment the RNA was cleaned up using the RNeasy Kit (Qiagen, Hilden, Germany). The quantity of RNA was established using the NanoDrop ND-1000 spectrophotometer (Peqlab, Erlangen, Germany) and the integrity was checked by running 1 μg of RNA on 1% agarose gel. In addition absence of DNA contamination was checked using the RNA as a template in standard PCR amplifying fragments of PRL32 and HPRT. Muscle expression pattern were assessed using 74 Porcine Genome Array which contains 23,937 probe sets that interrogate approximately 23,256 transcripts from 20,201 S. scrofa genes. Preparation of target products, hybridization and scanning using the GeneChip scanner 3000 were performed according to Affymetrix protocols using 5 μg of total RNA to prepare antisense biotinylated RNA. The quality of hybridization was assessed in all samples following the manufacturer's recommendations. Data were analysed with Affymetrix GCOS 1.1.1 software using global scaling to a target signal of 500. Data were then imported into Arrays Assist software (Stratagene Europe, Amsterdam, The Netherlands) for subsequent analysis. The data were processed with MAS5.0 to generate cell intensity files (present or absent). Quantitative expression levels of the present transcripts were estimated using PLIER (Probe Logarithmic Intensity Error) for normalization. The microarray data related to all samples have been deposited in the Gene Expression Omnibus (GEO, [##UREF##22##72##]) public repository (GEO accession number: GSE10204).</p>", "<title>Correlation between drip loss phenotype and gene expression</title>", "<p>Phenotypic data, i.e. expression levels and drip loss, were adjusted for systematic effects by analysis of variance performed with the procedure 'Mixed' of the SAS software package (SAS System for Windows, Release 8.02) before analysing their correlation and performing eQTL analysis. Full-sib family and sex were used as fixed effects, carcass weight as a covariate and slaughter date as random effects. Pearson correlation coefficients were calculated between the predicted values of the log2 transformed expression intensities of all 11,453 probes and the predicted values of drip loss of the 74 animals used. For each pair of transcript and drip loss, Pearson correlation together with the P-value was computed. The corresponding q-values were calculated to determine the FDR [##REF##12883005##73##]. Genes that showed correlation at r ≥ 0.37 with p ≤ 0.001, corresponding to q ≤ 0.004, were analyzed further.</p>", "<title>Functional annotation clustering</title>", "<p>Based on BLAST comparison of EnsEMBL human cDNA and genomic sequences with the Affymetrix porcine target sequences, which were extended with porcine sequence information of the Pig Gene Index (Institute for Genome Research, TIGR), 19,675 of 24,123 transcripts on the Affymetrix Porcine microarray, representing 11,265 unique genes, were annotated [##REF##16879364##23##]. This source of annotation list was used in this study. In addition, probe sets showing trait dependent expression with bit scores below 50 were rechecked for their identity by blasting Affymetrix core sequences of these probe sets before functional annotation analysis. The list of significantly trait correlated transcripts was analyzed according to predefined pathways and functional categories annotated by KEGG [##REF##10592173##28##], and GO [##REF##10802651##74##] using the DAVID bioinformatic resource [##UREF##23##75##]. Therefore, the Affymetrix IDs of the human probe sets corresponding to the porcine probes sets were used as reported by Tsai et al., [##REF##16879364##23##]. By this, differentially regulated genes were functionally annotated to large amounts of physiological pathway information that are of general relevance. However, physiology of some cellular processes may differ between species. Pig physiology closely resembles human physiology, thus given the lack of porcine-specific pathways the use the human pathways information and extrapolation of these pathways for the pig can be expected to provide biological meaningful results [##REF##17567520##76##].</p>", "<title>eQTL analysis</title>", "<p>In order to map eQTL adjusted expression values of 1279 unique probe sets showing significant correlation to drip loss were regressed onto the additive and dominance coefficients in intervals of 1 cM using the F2 option of QTL express [##REF##11847090##77##]. Chromosome-wide and genome-wide significance levels were estimated by permutation tests [##REF##7851788##29##]. The analysis identified 897 eQTL with chromosome-wide and genome-wide significance. Mapping of eQTL to the gene itself indicates that <italic>cis </italic>changes are responsible for the different expression levels, whereas mapping positions of eQTL different from the position of the corresponding genes indicate <italic>trans </italic>regulation. Correspondingly, <italic>cis </italic>acting regulation of transcription was considered for genes where available published experimental mapping data or comparative mapping data indicated their position within the corresponding interval of flanking markers of the eQTL peak; genes mapping outside the flanking marker interval of their corresponding eQTL were considered having <italic>trans </italic>acting regulation of transcription.</p>", "<title>Mapping of <italic>AHNAK</italic></title>", "<p>Mapping of <italic>AHNAK </italic>was achieved by screening of the Radiation Hybrid mapping panel of INRA, France, and Minnesota University, USA, IMpRH, and analysis of the data vector using the two-point and the multi-point analysis option of the IMpRH mapping tool [##REF##10980153##78##].</p>", "<title>Quantitative Real-time PCR (qRT-PCR)</title>", "<p>Transcripts of <italic>AHNAK </italic>and <italic>SLC3A2 </italic>were quantified by real-time reverse transcription PCR (RT-PCR) using the iCycler apparatus (Bio-Rad Laboratories GmbH, Munich, Germany) and the iQ SYBR Green Supermix (Bio-Rad). Real time RT-PCR were performed in duplicate using 56 animals of 22 full-sib families out of 74 individuals of 25 full-sib families that were previously used for microarray analysis. RNA was isolated as described above. Two microgram RNA were reverse transcribed to cDNA using SuperScriptIII MMLV reverse transcriptase (Invitrogen, Karlsruhe, Germany) in a reaction containing 500 ng oligo (dT)<sub>11</sub>VN primer 500 ng random hexamer primer according to the manufacturer's protocol. Templates were amplified using the gene specific primers (AHNAKup 3'-tgtcactggctcaccagaag-5', AHNAKdw 3'-gtcgctgaaggaatttgagc-5' and SLC3A2up 3'-ctgtggctgccaagatgaag-5', SLC3A2dw 3'-atctgctgtaggtcggagga-5') by 45 cycles of 95°C for 15 seconds denaturation, 60°C for 30 seconds annealing, and 72°C for 30 seconds extension preceded by initial denaturation of 95°C for 10 minutes as a universal thermal cycling parameter. Based on the analysis of melting curves of the PCR products a high temperature fluorescence acquisition point was estimated and included to the amplification cycle program. For all assays a standard curve was generated by amplifying serial dilutions of specific PCR products. After completion of the qPCR melting curve analysis and afterwards agarose gel electrophoresis were performed to confirm specificity of the amplification. Normalisation of variation in RT-PCR efficiency and initial RNA input was performed by using <italic>RPL32 </italic>(RPL32up 3'-agcccaagatcgtcaaaaag-5'; RPL32dw 3'-tgttgctcccataaccaatg-5') and <italic>HPRT</italic><bold>(</bold>HPRTup 3'-acactggcaaaacaatgcaa-5'; HPRTdw 3'-tcaagggcatagcctaccac-5') gene as internal standard and by dividing calculated <italic>AHNAK </italic>and <italic>SLC3A2 </italic>mRNA copy numbers with a mean normalization factor derived from the expression of the reference genes. Real time RT-PCR and microarray data were compared by Pearson correlation (SAS version 9.1 SAS Institute, Cary, NC) and eQTL were estimated as described above.</p>" ]
[ "<title>Results</title>", "<p>Expression profiling and eQTL analysis were conducted on 74 F2 animals of a resource population with previously identified pQTL for drip [##REF##17459017##21##]. The 74 animals were chosen to give a good representation of the population in terms of families and genotypes at the major pQTL [##REF##16918915##22##]. Using Affymetrix Porcine Genome Arrays, 23,256 expression measurements were obtained from each M. <italic>longissimus dorsi </italic>RNA samples of these animals representing 11,265 unique genes according to the annotation reported by Tsai et al. [##REF##16879364##23##]. After processing the Affymetrix CEL files with MAS5, where a 'present call' is assigned, the pre-selected data set was further analyzed with the more sophisticated hybrid algorithm PLIER [##UREF##8##24##, ####REF##12490442##25##, ##UREF##9##26##, ##REF##16942624##27####16942624##27##]. This revealed 11,453 probe sets for further. The overall strategy to identify functional positional candidate genes is shown on Figure ##FIG##0##1##.</p>", "<title>Correlation of transcript abundance and drip loss</title>", "<p>The normalized expression data and drip loss phenotypes were pre-adjusted for systematic effects of family and treatment/environment using a general linear model. Pearson correlation was calculated between each of the 11,453 gene expression values and drip loss phenotypes. A histogram of pair wise correlation coefficients of expression value and drip loss is shown on Figure ##FIG##1##2##. A total of 1,279 genes were significantly associated at <italic>p </italic>≤ 0.001 corresponding to <italic>q </italic>≤ 0.004, with 601 genes showing negative correlation and 678 genes showing positive correlation of their transcript abundance with drip loss. The lists of coefficients of correlation (r) between drip loss and expression levels, p-values, as well as corresponding q-values are shown in supplementary table 1 [see Additional file ##SUPPL##0##1##]. The correlations ranged between l0.37-0.67l.</p>", "<title>Biological pathway associated with drip loss</title>", "<p>We tested the list of significantly positive and negative correlated genes for enrichment in functional annotation groups as defined in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases [##REF##10592173##28##]. Out of 1279 probes sets 1076 had records in the GO database. GO categories of genes with expression levels negatively correlated with WHC, i.e. positively correlated to drip loss, are shown in Table ##TAB##0##1##. Five functional groups were found significant (median of EASE Score ≤ 0.05) including genes related to receptor and signal transducer activity, non-membrane-bound organelles, cytoskeleton, plasma membrane, and cell communication and signalling. Two KEGG pathways were also significant (EASE Score ≤ 0.05) 'extracellular matrix receptor interaction' and 'calcium signalling pathway'. The lists of genes of both pathways as well as the correlation coefficient with drip loss are shown in Table ##TAB##1##2## and ##TAB##2##3##. Among the genes with negative correlation of expression level with drip loss, functional groups represented were mitochondrial genes, electron and ion transporter activity, and protein metabolism (Table ##TAB##3##4##). The KEGG pathway 'oxidative phosphorylation', which belongs to the functional group of transporters activity, was also significant (EASE Score = 1.26E-06). Twenty genes in this pathway were found significantly correlated with drip loss (Table ##TAB##4##5##).</p>", "<title>Coincidence of eQTL and pQTL for drip loss</title>", "<p>In order to scale down the list of candidate genes that can be derived from the previously done pQTL study and from the global analysis of trait correlated expression presented here we aimed to combine these approaches with an analysis of eQTL.</p>", "<p>Classically, QTL analysis is applied for the identification of genes responsible for complex traits such as meat quality or growth traits (pQTL). Similarly, when the expression levels of genes are defined as a quantitative trait, QTL analysis can map the genetic determinants responsible for their transcriptional levels (eQTL). As described by Liu [##REF##17459017##21##], four pQTL for drip loss were identified with line cross models on SSC2, SSC3, SSC5 and SSC18 between <italic>SW2623-S0141</italic>, <italic>SW72-S0164</italic>, <italic>SW491-SW1482 </italic>and <italic>S0062-SWR414</italic>, respectively. For half sib models, pQTL for drip were detected on SSC6 and SSC18 at position <italic>S0035-S0087 </italic>and <italic>SWR414-SY31</italic>. Combined line cross and half sib models revealed an additional drip loss pQTL on SSC4 in position <italic>S0214-S0097 </italic>(Liu et al., accepted).</p>", "<p>A total of 1279 genes with significant correlation of transcript abundance to drip loss were selected for eQTL linkage mapping. Significance thresholds were determined by 10,000 permutations according to Churchill and Doerge [##REF##7851788##29##] revealing 5% and 1% chromosome-wide significance levels as well as 5% and 1% genome-wide significance levels after Bonferroni correction for 18 autosomes of the haploid porcine genome. The 5% chromosome-wide threshold corresponds approximately to the suggestive linkage threshold proposed by Lander and Kruglyak [##REF##7581446##30##]. In total the analysis revealed 897 eQTL with chromosome-wide significance at the p ≤ 0.05 level including 156 eQTL significant at the p ≤ 0.01 chromosome-wide level and 48 and 12 eQTL significant at genome-wide p ≤ 0.05 and p ≤ 0.01 levels, respectively [see Additional file ##SUPPL##1##2##]. The eQTL distribution on all chromosomes is shown in Figure ##FIG##2##3##. The F-value of eQTL ranged from 4.4 to 18.2 corresponding to LOD scores of 1.8 to 6.4 for different chromosomes. In total 104 significant eQTL were detected in the pQTL target regions for drip loss on SSC2, 3, 4, 5, 6, and 18 [see Additional file ##SUPPL##2##3##]. Additional 66 candidate genes mapping within the pQTL regions for drip loss on SSC 2, 3, 4, 5, 6 and 18 showed 119 eQTL in other genomic regions, thus indicating <italic>trans </italic>mode of regulation [see Additional file ##SUPPL##3##4##].</p>", "<p>Mapping of eQTL to the gene itself indicates that <italic>cis </italic>changes are responsible for the different expression levels, whereas mapping positions of eQTL different from the position of the corresponding genes indicate <italic>trans </italic>regulation. By these definitions of <italic>cis </italic>and <italic>trans </italic>acting regulation out of 104 eQTL that coincided with pQTL for drip, 96 belong to genes that had <italic>trans </italic>acting regulation of transcription, 8 genes had <italic>cis </italic>acting transcriptional regulation (Table ##TAB##5##6##). For 7 out of these 8 <italic>cis </italic>regulated genes additional <italic>trans </italic>acting regulatory regions were found.</p>", "<p>According to mapping information of Affymetrix probe sets accessible via the PigQTLdb about 300 porcine Affymetrix elements are located at the pQTL for drip loss on SSC2 between markers <italic>SW2623-S0141 </italic>[[##UREF##10##31##], <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.animalgenome.org/QTLdb/pig.html\"/>]. In this region 14 eQTL were mapped. Out of these 2 corresponded to genes that were under <italic>cis </italic>acting transcriptional control, Ssc.16645.1.S1 (<italic>AHNAK</italic>) and Ssc.2330.1.S1 (<italic>SLC3A2</italic>), while the other corresponded to genes that mapped elsewhere, thus indicating <italic>trans </italic>acting control. Because of the interesting functional links of <italic>AHNAK </italic>to WHC its position was confirmed by RH mapping. <italic>AHNAK </italic>was assigned to the SSC2 close to marker <italic>SWR783 </italic>(LOD score 17.8) in the interval of <italic>SW2623 </italic>and <italic>S0141</italic>. Quantification of transcripts of AHNAK and SLC3A2 by real time reverse transcription PCR (RT-PCR) revealed significant correlation with expression data obtained from microarray analysis (r = 0.6; p &lt; 0.0001 and r = 0.4; p = 0.002, respectively). Moreover, trait correlated expression of AHNAK and SLC3A2 was confirmed (r = 0.3; p = 0.02 and r = -0.4; p = 0.004, respectively). Mapping of eQTL for AHNAK and SLC3A2 based on real time RT-PCR data fit results based on microarray data (marker interval <italic>SW2623 </italic>and <italic>S0141; </italic>F = 7 and F = 5, respectively; chromosome-wide significant at p ≤ 0.5). On SSC3, 545 probes sets were found between markers <italic>SW72 </italic>and <italic>S0164</italic>. 38 eQTL were detected in the drip pQTL region with three having <italic>cis </italic>acting transcriptional regulation: Ssc_3574_1_A1_at (<italic>MAP4K4</italic>), Ssc.20772.1.S1_at (<italic>USP39</italic>) and Ssc_10360_1_S1_at (hypothetical protein (<italic>LOC162073</italic>)). 14 eQTL were detected in the SSC4 pQTL region for drip and only one <italic>cis </italic>acting eQTL (Ssc_12110_1_S1_at (<italic>PRCC)</italic>) was found. No eQTL were detected in the SSC5 drip pQTL between markers <italic>SW491 </italic>and <italic>SW1482</italic>. 305 probes sets were found in the drip QTL region of SSC6. Out of 24 eQTL in that region, two eQTL indicated <italic>cis </italic>mode of regulation (Ssc_4843_1_A1_at (<italic>BBS2</italic>), Ssc_5334_1_S1_at (<italic>COQ9</italic>)). 110 probes sets were detected in the pQTL region of SSC18. All of 11 eQTL, which were detected in this region, showed <italic>trans </italic>acting regulation of expression.</p>" ]
[ "<title>Discussion</title>", "<p>WHC is influenced by many factors including genetic and environmental effects. We addressed the problem to dissect the genetic background of this complex trait by using the strategy of combining (1) the correlation of expression of many thousands of genes measured simultaneously by microarray technology with quantitative phenotypic data of drip loss, (2) mapping of QTL for the trait drip loss, and (3) mapping of QTL for the expression levels of genes with trait associated expression (Figure ##FIG##0##1##). QTL analyses provide information suitable to address positional candidate genes whereas trait associated expression studies reveal functional candidate genes. Taking both together, i.e. taking into account the localisation of functional candidate genes in QTL regions, enables to define functional positional candidate genes. Additional insight from eQTL analysis derives from three cases. (1) eQTL are detected within the pQTL but the functional candidate genes itself are located elsewhere, i.e. they are under <italic>trans </italic>control. These are genes that are likely to be trait dependent expressed due to hierarchically superior genes located in the pQTL that actually represent candidate genes (positional candidate genes). Here the eQTL analysis provides a link between functional and positional candidate genes. In this study 90 functional candidate genes were found with their corresponding 96 eQTL being situated within the previously detected pQTL. These genes point to biological pathways, which are relevant to the trait, and perhaps to causal genes underlying the QTL. However these genes themselves may either be not polymorphic, or the power of the pQTL analysis was not sufficient to detect them, or trait associated expression of these genes is rather an effect than a cause of variation. (2) For functional positional candidate genes being under <italic>trans </italic>control it can be speculated that the nature of variation affecting the phenotype is differential expression due to polymorphisms in hierarchically superior genes and different responsiveness of the candidate genes to regulatory mechanisms. Here the eQTL combined with pQTL and trait associated expression directs to biological pathways and genes relevant for the trait of interest. In total 66 positional functional candidate genes which corresponded to 119 eQTL with <italic>trans </italic>mode of expression regulation were found. (3) For genes categorized positional functional candidate genes, mapping of their corresponding eQTL in <italic>cis </italic>highlights them as genes showing variation with impact on the trait of interest and the expression level, indicating that the nature of the variation is likely a polymorphism in regulatory regions of the gene. Eight genes of this category were identified in this study. These genes are regarded as primary targets for further analysis.</p>", "<p>However, it is important to mention that phenotypic variation may be due to genetic variation causing differential expression and/or structural variation of the gene products. The later are not addressed by eQTL analyses. Further, there are functional candidate genes that are under <italic>cis </italic>mode of transcriptional regulation, where there is no link between eQTL and pQTL analyses.</p>", "<title>Trait dependent expression analysis</title>", "<p>The association between a quantitative phenotype and gene expression can be examined pair wise using a Pearson correlation coefficient between the expression of a single gene and a continuous phenotype. The approach of trait correlated expression analysis already proofs to be useful by many studies [##REF##11389458##32##, ####REF##12687497##33##, ##REF##14769913##34####14769913##34##]. Kraft [##REF##12687497##33##] used the within-family correlation analysis to remove the effect of family stratification. Here we used general linear models to account for systematic effects of family and environment on both drip phenotypes and expression levels in the correlation analysis. The pre-adjustment of individual phenotypes and expression levels increased the power to identify genetic effects compared to analyses conducted with raw data and revealed biologically meaningful relationship among the traits. Blalock et al. [##REF##14769913##34##], considered correlation significant at <italic>p </italic>≤ 0.05 corresponding to false discovery rates of 20%. In this study, genes were considered for further analysis showing correlation coefficients between gene expression and drip loss of r ≥ 0.37, with <italic>p </italic>≤ 0.001 and corresponding <italic>q </italic>≤ 0.004.</p>", "<title>Biological categories and/or pathways of positively correlated genes</title>", "<p>Currently, we do not completely understand the specific biochemical and/or biophysical mechanisms underlying differences in meat water holding capacity. The processes of muscle conversion to meat occurred in <italic>post mortem </italic>stage. One possible explanation for some of the variation that exists resides in the structure of the muscle cell itself. Most studies concentrated on <italic>post mortem </italic>process of drip [##REF##15080343##35##,##UREF##11##36##]. In this study, transcript levels of muscle at slaughter were correlated with drip loss at <italic>post mortem </italic>meat stage in order to reveal insight into the biological processes that are initiated during life and thus are under genetic control and finally determine the liability to develop elevated drip loss. The mechanism underlying this liability trait may also be valid for the (patho-) physiological processes that take place during muscle damage due to biochemical and physical burden at prolonged exercises. Functional annotation analysis is essentially based on the extrapolation of pathway information and gene ontology data of human to the pig. Thus general cellular physiological processes are taken into account, whereas any pig-specific functional annotation data and in particular information on the physiology of the trait drip loss are not addressed during the automated bioinformatics analyses. However, the relevant knowledge has been taken into account in the biological interpretation of the results. The study revealed changes in genomic regulation of multiple cellular pathways that correlate with drip loss. The genes with positive correlation of transcript abundance and drip loss were genes of the group of receptor activity, non-membrane-bound organelle, cytoskeleton, plasma membrane and cell signal. Recently, many studies have shown that degradation of cytoskeleton and other structural proteins plays an important role in drip loss at <italic>post mortem </italic>[##REF##15080343##35##,##UREF##12##37##, ####UREF##13##38##, ##UREF##14##39####14##39##]. As shown in this study the transcript abundance of genes of the cytoskeleton and other structural proteins increased with increasing drip loss. Extra cellular matrix proteins binding integrins and interacting with the cell cytoskeleton are important in controlling many steps in cell membrane-cytoskeleton attachments and in signalling pathways [##REF##1555235##40##]. The degradation of integrin has been suggested to increase the drip channel formation between the cell and cell membrane and thus to be associated with drip loss during <italic>post mortem </italic>storage on pork [##UREF##12##37##,##UREF##15##41##]. The degradation of integrin may be due to the activity of the calpain system which requires high concentration of calcium for activation [##UREF##15##41##]. In this study, the enrichment of transcripts of extra cellular matrix receptor pathways among the positively drip correlated genes suggested that WHC may be involved with a breakdown of this extracellular matrix that activate the proteolytic system and thereby promote enzymatic degradation [##UREF##16##42##]. Calcium signalling pathways are very peculiar in nature. When there is an extracellular change, cells get the message either by introduction of calcium ions into cytoplasm or by evacuation to outside through ion channels. Increase in intranuclear Ca<sup>2+ </sup>initiates gene expression and cell cycle procession, but also can activate degradative processes in programmed cell death or apoptosis [##REF##7820847##43##]. Gene sets associated with calcium signalling pathways were enriched with decreasing water holding capacity. For example, epidermal growth factor receptor (EGFR) showed highest positive correlation with drip loss (r = 0.67, p &lt; 0.0001). An early signal generated by the activation of EGFR upon ligand binding is a transient increase in the cytosolic concentration of free calcium ion ([Ca<sup>2+</sup>]<sub>cyt</sub>) [##UREF##17##44##]. Entry of extracellular Ca<sup>2+</sup>, and Ca<sup>2+ </sup>release from intracellular stores, both appear to contribute to the generation of the EGF-mediated [Ca<sup>2+</sup>]<sub>cyt </sub>spike [##REF##1875911##45##, ####REF##1314702##46##, ##REF##7649158##47####7649158##47##]. Early <italic>post mortem </italic>higher Ca<sup>2+ </sup>concentration causes rapid contraction, an increase in the rate of muscle metabolism, and accelerated pH decline with resulting higher drip [##UREF##13##38##]. Another hypothesis is that higher Ca<sup>2+ </sup>concentration present in muscle fibres early <italic>post mortem </italic>is a source for the activation of Ca<sup>2+ </sup>dependent protease, phosphatases and phospholipases like the calpain system which influences drip production. Increased cytoplasmic Ca<sup>2+ </sup>levels are also observed due to excessive exercises. This may initiate vicious cycles of cell degradation because of the Ca<sup>2+ </sup>dependent activation of proteolytic enzymes such as calpain that by themselves digest structural elements of the muscle fibres leading to membrane damage, leakage of intracellular water and proteins and further accumulation of Ca<sup>2+ </sup>[##REF##2205778##48##]. Together, increase in transcript levels of genes involved in cytoskeleton, and extracellular matrix receptor pathways as well as calcium signalling pathways in muscle play an important role in final meat quality.</p>", "<title>Biological categories and/or pathways of negatively correlated gene</title>", "<p>Though the energy metabolism is crucial for muscles, the biochemical processes involved in the change from aerobic metabolism <italic>ante mortem </italic>to anaerobic metabolism <italic>post mortem</italic>, which associates to drip loss, is not much investigated. The negatively correlated transcripts were enriched in mitochondrion, transporter activity and protein metabolism GO categories as well as oxidative phosphorylation pathway. A dominant role of mitochondria is the production of ATP by several different biochemical routes, i.e. via aerobe glycolysis and via oxidative phosphorylation. At the pre-slaughter stage in living animals with the presence of oxygen, aerobic processes take place. When oxygen is limited (<italic>post mortem</italic>) the glycolytic products will be metabolised by anaerobic respiration, a process that is independent of the mitochondria. A shift from aerobic to anaerobic metabolism – favouring the production of lactic acid – results in a pH decline <italic>post mortem </italic>and thereby influence the water holding capacity in muscle [##UREF##18##49##]. In our study, 63 transcripts belong to mitochondrion GO category and 20 transcripts belong to the oxidative phosphorylation pathway. The negative correlations with drip loss may indicate reduced activity of biochemical processes of ATP production via oxidative pathways in mitochondria of animals with high drip loss, reduced number of mitochondria in their muscle, i.e. higher content of glycolytic fibers, or reduced ATP reserves in the muscle.</p>", "<p>Together, analysis of trait correlated expression revealed that the complex relationships between biological processes taking place in live skeletal muscle and meat quality are driven on the one hand by the energy reserves in the muscle and their metabolisation as well as on the other hand by the muscle structure itself.</p>", "<title><italic>cis </italic>and <italic>trans </italic>mode of regulation of gene expression in QTL regions for WHC</title>", "<p>Expression-QTL for genes showing high correlation with the phenotype may provide the necessary information required for identifying genes that control quantitative phenotypes. Categorizing eQTL has the potential to enable reverse genetics approaches for the identification of genes controlling quantitative traits, and may also help to enhance the rate of QTL cloning [##REF##15803197##50##]. In particular, if the pQTL for drip loss were caused by interstrain differences of gene expression, the genetic determinants responsible for the pQTL would be restricted to the genes that were encoded inside the pQTL region and provide variations of gene expression under <italic>cis </italic>acting transcriptional fashion in the F2 population. In this case, their eQTL were found to reside at the same chromosomal positions at which they were encoded and the lod score curves with the peak of eQTL should coincide with those of the pQTL. Local eQTL where expression phenotypes map to the genes themselves, are of great interest, because they are direct candidates for previously mapped pQTL.</p>", "<p>Many investigations have reported the successful mapping of quantitative trait loci for gene expression phenotypes (eQTL) in rat or mice [##REF##15711544##51##, ####REF##16205357##52##, ##REF##15837804##53####15837804##53##]. Such genetical genomics analyses in livestock are still scarce. Among livestock species, poultry is well placed to embrace this technology. De Koning et al. [##REF##17575201##54##] identified the <italic>cis </italic>and <italic>trans </italic>effects for a functional body weight QTL on chicken chromosome 4 in breast tissue samples from chickens with contrasting QTL genotypes. Kadarmideen and Janss [##REF##17132818##55##] presented a comparative systems genetic analysis on the physiology of cortisol levels in mice and pigs with the aim to show the potential of a comprehensive computational approach to quickly identify candidate genes. Here, the first expression QTL study is presented performed in a segregating pig population with focus on the trait drip loss. In a first step we analysed the correlation between trait dependent gene expression and the phenotype drip loss, which revealed biologically meaningful relationship. In the second step, eQTL were identified for transcripts that showed trait correlated expression, which supplies us with information about the genomic location of putative regulatory loci. This strategy reduces the number of several thousand eQTLs which were not associated with drip loss. The <italic>trans </italic>acting eQTL represent transcripts whose abundance is regulated by loci remote from the genomic locus of each of these genes. In our study the proportion of <italic>trans </italic>eQTL is higher (92%) than in other studies (60%–65%) [##REF##15711544##51##,##REF##16079240##56##]. Here eQTL analysis was focussed on functional positional candidate genes for a trait that varies in degree, i.e. the study was driven by transcriptional and positional restrictions on the genes analysed. A network of genes relevant to the traits was addressed representing additive and pleiotropic as well as non-additive epistatic effects on the trait. This may lead to higher proportion of <italic>trans </italic>regulated genes compared to studies were eQTL were identified independent from any positional restrictions on the corresponding genes. <italic>Cis </italic>acting eQTL serve as an important new resource for the identification of positional candidates in QTL studies. We detected 8 out of 104 genes acting in <italic>cis</italic>, whereas Yashimita et al., [##REF##16079240##56##] and Dumas et al., [##REF##10826556##57##] reported 9 out of 13 genes and 1 out of 5 genes, respectively, acting in <italic>cis</italic>.</p>", "<title>Candidate genes for WHC</title>", "<p>The candidacy of <italic>cis </italic>regulated functional positional candidate genes has three-fold experimental evidence. In particular for <italic>AHNAK </italic>a number of reasons for its candidacy for drip loss have been put forward: (i) This gene is located in the SSC2 QTL region for drip loss as confirmed by RH-mapping. The pQTL for drip in this region was also found in other studies [##REF##11471059##14##,##UREF##6##16##,##REF##16543555##17##]. (ii) The correlation between drip loss and <italic>AHNAK </italic>is high (r = 0.53; p &lt; 0.0001). (iii) The eQTL for <italic>AHNAK </italic>indicates a <italic>cis </italic>acting mode of regulation with genome wide significance (lod score = 6.4; F = 18.2). Real time RT-PCR performed for AHNAK support the microarray data in terms of trait correlated expression. Also significant correlation was observed of expression values obtained from microarrays and real time RT-PCR, respectively. Further, eQTL analysis of real time RT-PCR data matches those of microarray data. <italic>AHNAK </italic>is a functional candidate gene due to its role in muscle contraction, cell adhesion and proliferation as well as its interaction with calcium. <italic>AHNAK</italic>, a nuclear phosphoprotein with the estimated molecular mass of 700 Da, is expressed in all muscular cells [##REF##10593863##58##,##REF##12588962##59##]. <italic>AHNAK </italic>is implicated in calcium flux regulation. At low calcium concentrations, <italic>AHNAK </italic>proteins are mainly localized in the nucleus, but the increase in intracellular calcium levels leads the protein to translocate to the plasma membrane [##REF##7698224##60##]. <italic>AHNAK </italic>relocates from the cytosol to the cytosolic surface of the plasma membrane during the formation of cell-cell contacts [##REF##14699089##61##]. The main localization of <italic>AHNAK </italic>is at the plasma membrane in adult muscle cells [##REF##12588962##59##]. <italic>AHNAK </italic>contains three distinct structural regions: the NH<sub>2</sub>-terminal 251-amino acid region, a large central region of about 4300 amino acids with 36 repeated units, and the COOH-terminal 1002 amino acids region. The carboxyl-terminal region of <italic>AHNAK </italic>proteins mediates cellular localization and interaction with L-type Ca<sup>2+ </sup>channels, calcium-binding S100B protein, as well as actin of thin filaments for muscle contraction [##REF##12153988##62##, ####REF##11312263##63##, ##REF##15001564##64####15001564##64##].</p>", "<p><italic>MAP4K4 </italic>a member of the serine/threonine protein kinase family is involved in MAPK signalling for cell proliferation and differentiation as response to stressors and in cell adhesion via integrin beta 1 [##REF##12612079##65##,##REF##11967148##66##]. Here <italic>MAP4K4 </italic>appeared as a prominent candidate for drip loss. <italic>MAP4K4 </italic>expression is induced by TNF-alpha and promotes insulin resistance [##REF##16461467##67##], whereas silencing of <italic>MAP4K4 </italic>prevents insulin resistance in human skeletal muscle and enhances glucose uptake [##REF##17227768##68##]. This evidence promotes our finding of a positive correlation of <italic>MAP4K4 </italic>with drip loss. Reduced <italic>MAP4K4 </italic>expression, promotes glucose uptake, therefore increasing glucose content in muscle cells. By increasing energy depots in the muscle prior to slaughter, the anaerobic production of lactate <italic>post mortem </italic>may be delayed, thereby delaying of decline in pH and reducing drip loss.</p>", "<p>Candidacy of <italic>SLC3A2 </italic>was confirmed by real time PCR. <italic>SLC3A2 </italic>is a member of the solute carrier family and encodes a cell surface, transmembrane protein. It associates with integrins and mediates integrin-dependent signalling related to normal cell growth. Information about function of <italic>BBC2</italic>, <italic>PRCC</italic>, <italic>USP39</italic>, <italic>LOC162073 </italic>and <italic>COQ9 </italic>are too limited to allow deducing functional links to the trait drip loss or other candidate genes for this trait.</p>" ]
[ "<title>Conclusion</title>", "<p>Analysis of trait dependent expression showed a global picture on the biological networks active <italic>ante mortem </italic>that affect <italic>post mortem </italic>processes important for final establishment of meat properties. Functional annotation of differentially expressed genes revealed the general trend of genes of cytoskeleton, cell-cell contacts and signalling including calcium signalling pathways being positively correlated whereas genes of biological networks of oxidative metabolism were negatively correlated with drip loss. Physiological studies indicated that biological processes affecting meat development are driven by the <italic>post mortem </italic>anoxia. Abundance and activity of enzymes and proteins of energy and calcium metabolism and proteolysis of muscle structural proteins have been shown to be major determinants with regard to the trait drip loss. The meat quality phenotype established later after slaughter depends on the transcriptome of skeletal muscle prior to slaughter and thus is already determined in living cells under genetic control. Integrating expression data with QTL analysis for the trait of interest (phene QTL, pQTL) and for gene expression levels (expression QTL, eQTL) facilitates creating a priority list of genes out of the orchestra of genes of biological networks relevant to drip for further analysis and subsequent cloning of causal genes. By combining map-based and function-driven data functional positional candidate genes could be identified. By adding data derived from eQTL analysis and matching these to the gene map and pQTL map allowed addressing genes with <italic>trans </italic>and <italic>cis </italic>mode of transcriptional control. In particular functional positional candidate genes under <italic>cis </italic>acting regulation are of high priority for further analysis. The first porcine eQTL-map of drip correlated transcripts in pQTL regions will facilitate cloning causal genes.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Leakage of water and ions and soluble proteins from muscle cells occurs during prolonged exercise due to ischemia causing muscle damage. Also <italic>post mortem </italic>anoxia during conversion of muscle to meat is marked by loss of water and soluble components from the muscle cell. There is considerable variation in the water holding capacity of meat affecting economy of meat production. Water holding capacity depends on numerous genetic and environmental factors relevant to structural and biochemical muscle fibre properties a well as <italic>ante </italic>and <italic>post </italic>slaughter metabolic processes.</p>", "<title>Results</title>", "<p>Expression microarray analysis of M. <italic>longissimus dorsi </italic>RNAs of 74 F2 animals of a resource population showed 1,279 transcripts with trait correlated expression to water holding capacity. Negatively correlated transcripts were enriched in functional categories and pathways like extracellular matrix receptor interaction and calcium signalling. Transcripts with positive correlation dominantly represented biochemical processes including oxidative phosphorylation, mitochondrial pathways, as well as transporter activity. A linkage analysis of abundance of trait correlated transcripts revealed 897 expression QTL (eQTL) with 104 eQTL coinciding with QTL regions for water holding capacity; 96 transcripts had <italic>trans </italic>acting and 8 had <italic>cis </italic>acting regulation.</p>", "<title>Conclusion</title>", "<p>The complex relationships between biological processes taking place in live skeletal muscle and meat quality are driven on the one hand by the energy reserves and their utilisation in the muscle and on the other hand by the muscle structure itself and calcium signalling. Holistic expression profiling was integrated with QTL analysis for the trait of interest and for gene expression levels for creation of a priority list of genes out of the orchestra of genes of biological networks relevant to the liability to develop elevated drip loss.</p>" ]
[ "<title>Abbreviations</title>", "<p>QTL: quantitative trait loci; eQTL: expression quantitative trait loci; pQTL: phene quantitative trait loci; SSC: Sus scrofa chromosome; PLIER: Probe Logarithmic Error Intensity Estimate; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; FDR: false discovery ratio;</p>", "<title>Authors' contributions</title>", "<p>SP analyzed the microarray data and wrote the paper; EJ and CP collected the material and analyzed the linkage map; EM, MS, TS and CW aided in data analysis and helped in drafting the manuscript; KS and KW conceived and designed the study, contributed to data interpretation and helped in drafting the manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The authors thank Annette Jugert and Joana Bittner for excellent technical help. This research was supported by German Research Foundation (Deutsche Forschungs-gemeinschaft, DFG; Forschergruppe 'DRIP', FOR 753)</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Strategy of identification of candidate genes for WHC</bold>. Genes rated present after normalization were included in the statistical analysis. Both expression and phenotype data were adjusted using the general linear model before performing Pearson correlation analysis. Genes with trait correlated expression were included in the eQTL analysis. Genes with significant eQTL were assigned <italic>cis </italic>regulated if the genes' position matches the position of its eQTL, others were considered <italic>trans </italic>regulated. Also the position of the genes relative to QTL for drip was taken into account.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Histogram of the distribution of pair wise correlation coefficients of expression value and drip loss.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold><italic>F</italic>-statistic of a total of 897 eQTL on all 18 porcine chromosomes</bold>. The horizontal lines represent the respective significance thresholds.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Histogram of the distribution of drip loss phenotypes among a subset of 74 animals of the DuPi population selected for chip hybridization.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>GO categories of genes with positively correlated expression with drip loss</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Functional Group 1</td><td align=\"left\">Median: 0.005</td><td align=\"center\">Number of genes</td><td align=\"center\">% of genes in pathways</td><td align=\"center\">EASE Score</td></tr></thead><tbody><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">receptor activity</td><td align=\"center\">55</td><td align=\"center\">8.91</td><td align=\"center\">0.000</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">signal transducer activity</td><td align=\"center\">84</td><td align=\"center\">13.61</td><td align=\"center\">0.006</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">transmembrane receptor activity</td><td align=\"center\">28</td><td align=\"center\">4.54</td><td align=\"center\">0.009</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 2</td><td align=\"left\">Median: 0.006</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">intracellular non-membrane-bound organelle</td><td align=\"center\">76</td><td align=\"center\">12.32</td><td align=\"center\">0.006</td></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">non-membrane-bound organelle</td><td align=\"center\">76</td><td align=\"center\">12.32</td><td align=\"center\">0.006</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 3</td><td align=\"left\">Median: 0.011</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cytoskeleton organization and biogenesis</td><td align=\"center\">28</td><td align=\"center\">4.54</td><td align=\"center\">0.003</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">actin binding</td><td align=\"center\">22</td><td align=\"center\">3.57</td><td align=\"center\">0.003</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">cytoskeletal protein binding</td><td align=\"center\">28</td><td align=\"center\">4.54</td><td align=\"center\">0.005</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">actin filament-based process</td><td align=\"center\">16</td><td align=\"center\">2.59</td><td align=\"center\">0.011</td></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">cytoskeleton</td><td align=\"center\">44</td><td align=\"center\">7.13</td><td align=\"center\">0.013</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">actin cytoskeleton organization and biogenesis</td><td align=\"center\">15</td><td align=\"center\">2.43</td><td align=\"center\">0.014</td></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">actin cytoskeleton</td><td align=\"center\">17</td><td align=\"center\">2.76</td><td align=\"center\">0.061</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 4</td><td align=\"left\">Median: 0.018</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">plasma membrane</td><td align=\"center\">65</td><td align=\"center\">10.53</td><td align=\"center\">0.006</td></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">integral to plasma membrane</td><td align=\"center\">43</td><td align=\"center\">6.97</td><td align=\"center\">0.018</td></tr><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">intrinsic to plasma membrane</td><td align=\"center\">43</td><td align=\"center\">6.97</td><td align=\"center\">0.020</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 5</td><td align=\"left\">Median: 0.011</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cell communication</td><td align=\"center\">114</td><td align=\"center\">18.48</td><td align=\"center\">0.011</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">signal transduction</td><td align=\"center\">107</td><td align=\"center\">17.34</td><td align=\"center\">0.011</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">intracellular signaling cascade</td><td align=\"center\">52</td><td align=\"center\">8.43</td><td align=\"center\">0.046</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Genes of extracellular matrix receptor pathway positively correlated with drip loss</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">AFFY_ID</td><td align=\"center\">r</td><td align=\"center\">p-value</td><td align=\"center\">q-value</td><td align=\"left\">gene name (gene symbol)</td></tr></thead><tbody><tr><td align=\"left\">Ssc_24909_1_S1_at</td><td align=\"center\">0.413</td><td align=\"center\">0.0003</td><td align=\"center\">0.0017</td><td align=\"left\">laminin, alpha 4 (LAMA4)</td></tr><tr><td align=\"left\">Ssc_8843_1_A1_at</td><td align=\"center\">0.397</td><td align=\"center\">0.0005</td><td align=\"center\">0.0023</td><td align=\"left\">fibronectin 1 (FN1)</td></tr><tr><td align=\"left\">Ssc_3902_1_S1_at</td><td align=\"center\">0.476</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">septin 5 (PNUTL1)</td></tr><tr><td align=\"left\">Ssc_4345_1_S2_at</td><td align=\"center\">0.386</td><td align=\"center\">0.0007</td><td align=\"center\">0.0028</td><td align=\"left\">collagen, type IV, alpha 1 (COL4A1)</td></tr><tr><td align=\"left\">Ssc_16589_1_S1_at</td><td align=\"center\">0.380</td><td align=\"center\">0.0008</td><td align=\"center\">0.0031</td><td align=\"left\">collagen, type VI, alpha 3 (COL6A3)</td></tr><tr><td align=\"left\">Ssc_1099_1_S1_at</td><td align=\"center\">0.398</td><td align=\"center\">0.0004</td><td align=\"center\">0.0020</td><td align=\"left\">laminin, gamma 1 (LAMC1)</td></tr><tr><td align=\"left\">Ssc_1091_3_A1_at</td><td align=\"center\">0.472</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">collagen, type I, alpha 1 (COL1A1)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Genes of calcium signalling pathway positively correlated with drip loss</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">AFFY_ID</td><td align=\"center\">r</td><td align=\"center\">p-value</td><td align=\"center\">q-value</td><td align=\"left\">gene name (gene symbol)</td></tr></thead><tbody><tr><td align=\"left\">Ssc_22248_1_A1_at</td><td align=\"center\">0.478</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">guanine nucleotide binding protein, alpha stimulating complex locus (GNAS)</td></tr><tr><td align=\"left\">Ssc_17453_1_S1_at</td><td align=\"center\">0.389</td><td align=\"center\">0.0006</td><td align=\"center\">0.0025</td><td align=\"left\">ATPase, Ca++ transporting, plasma membrane 4 (ATP2B4)</td></tr><tr><td align=\"left\">Ssc_4203_1_S1_at</td><td align=\"center\">0.379</td><td align=\"center\">0.0009</td><td align=\"center\">0.0033</td><td align=\"left\">v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (ERBB3)</td></tr><tr><td align=\"left\">Ssc_7883_1_A1_at</td><td align=\"center\">0.377</td><td align=\"center\">0.0009</td><td align=\"center\">0.0033</td><td align=\"left\">oxytocin receptor (OXTR)</td></tr><tr><td align=\"left\">Ssc_25651_1_S1_at</td><td align=\"center\">0.446</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">protein phosphatase 3 (formerly 2B), catalytic subunit, beta isoform (PPP3CB)</td></tr><tr><td align=\"left\">Ssc_22641_1_S1_at</td><td align=\"center\">0.489</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">ATPase, Ca++ transporting, cardiac muscle, slow twitch 2 (ATP2A2)</td></tr><tr><td align=\"left\">Ssc_55_1_S1_at</td><td align=\"center\">0.664</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">epidermal growth factor receptor (EGFR)</td></tr><tr><td align=\"left\">Ssc_8_1_S1_at</td><td align=\"center\">0.395</td><td align=\"center\">0.0005</td><td align=\"center\">0.0023</td><td align=\"left\">ryanodine receptor 1 (RYR1)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>GO categories of genes with negatively correlated expression with drip loss</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Functional Group 1</td><td align=\"left\">Median: 1.69E-4</td><td align=\"center\">Number of genes</td><td align=\"center\">% of genes in pathways</td><td align=\"center\">EASE Score</td></tr></thead><tbody><tr><td align=\"left\">GOTERM_CC_ALL</td><td align=\"left\">mitochondrion</td><td align=\"center\">63</td><td align=\"center\">11.89</td><td align=\"center\">8.28E-09</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 2</td><td align=\"left\">Median: 2.35E-4</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">electron carrier activity</td><td align=\"center\">17</td><td align=\"center\">3.21</td><td align=\"center\">9.21E-07</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">NADH dehydrogenase activity</td><td align=\"center\">13</td><td align=\"center\">2.45</td><td align=\"center\">2.26E-06</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">carrier activity</td><td align=\"center\">35</td><td align=\"center\">6.60</td><td align=\"center\">3.57E-06</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">sodium ion transporter activity</td><td align=\"center\">13</td><td align=\"center\">2.45</td><td align=\"center\">4.24E-06</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">oxidoreductase activity</td><td align=\"center\">15</td><td align=\"center\">2.83</td><td align=\"center\">5.05E-06</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">hydrogen ion transporter activity</td><td align=\"center\">20</td><td align=\"center\">3.77</td><td align=\"center\">4.90E-05</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">inorganic cation transporter activity</td><td align=\"center\">20</td><td align=\"center\">3.77</td><td align=\"center\">8.29E-05</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">primary active transporter activity</td><td align=\"center\">22</td><td align=\"center\">4.15</td><td align=\"center\">9.72E-05</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">electron transporter activity</td><td align=\"center\">25</td><td align=\"center\">4.72</td><td align=\"center\">1.29E-04</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">metal ion transporter activity</td><td align=\"center\">14</td><td align=\"center\">2.64</td><td align=\"center\">3.41E-04</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">cation transporter activity</td><td align=\"center\">30</td><td align=\"center\">5.66</td><td align=\"center\">6.26E-04</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">ion transporter activity</td><td align=\"center\">33</td><td align=\"center\">6.23</td><td align=\"center\">0.002</td></tr><tr><td align=\"left\">GOTERM_MF_ALL</td><td align=\"left\">transporter activity</td><td align=\"center\">60</td><td align=\"center\">11.32</td><td align=\"center\">0.007</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">generation of precursor metabolites and energy</td><td align=\"center\">32</td><td align=\"center\">6.04</td><td align=\"center\">0.032</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Functional Group 3</td><td align=\"left\">Median: 0.050</td><td/><td/><td/></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">protein metabolism</td><td align=\"center\">131</td><td align=\"center\">24.72</td><td align=\"center\">0.002</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cellular protein metabolism</td><td align=\"center\">121</td><td align=\"center\">22.83</td><td align=\"center\">0.003</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cellular metabolism</td><td align=\"center\">244</td><td align=\"center\">46.04</td><td align=\"center\">0.005</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cellular macromolecule metabolism</td><td align=\"center\">121</td><td align=\"center\">22.83</td><td align=\"center\">0.005</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">macromolecule metabolism</td><td align=\"center\">168</td><td align=\"center\">31.70</td><td align=\"center\">0.007</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">metabolism</td><td align=\"center\">255</td><td align=\"center\">48.11</td><td align=\"center\">0.013</td></tr><tr><td align=\"left\">GOTERM_BP_ALL</td><td align=\"left\">cellular process</td><td align=\"center\">338</td><td align=\"center\">63.77</td><td align=\"center\">0.051</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Genes of with oxidative phosphorylation pathway negatively correlated with drip loss</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">AFFY_ID</td><td align=\"center\">r</td><td align=\"center\">p-value</td><td align=\"center\">q-value</td><td align=\"left\">gene name (symbol)</td></tr></thead><tbody><tr><td align=\"left\">Ssc_886_1_S1_at</td><td align=\"center\">-0.381</td><td align=\"center\">0.0008</td><td align=\"center\">0.0035</td><td align=\"left\">cytochrome c-1 (CYC1)</td></tr><tr><td align=\"left\">Ssc_2028_1_S1_at</td><td align=\"center\">-0.400</td><td align=\"center\">0.0004</td><td align=\"center\">0.0022</td><td align=\"left\">ATPase, H+ transporting, lysosomal 14 kDa, V1 subunit F (ATP6V1F)</td></tr><tr><td align=\"left\">Ssc_26100_1_S1_at</td><td align=\"center\">-0.438</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 7 (NDUFB7)</td></tr><tr><td align=\"left\">Ssc_922_2_S1_at</td><td align=\"center\">-0.449</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">NADH dehydrogenase (ubiquinone) flavoprotein 2 (NDUFV2)</td></tr><tr><td align=\"left\">Ssc_3869_1_A1_at</td><td align=\"center\">-0.451</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 1 (NDUFC1)</td></tr><tr><td align=\"left\">Ssc_15103_1_S1_at</td><td align=\"center\">-0.543</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">NADH dehydrogenase (ubiquinone) Fe-S protein 6 (NDUFS6)</td></tr><tr><td align=\"left\">Ssc_22694_1_S1_at</td><td align=\"center\">-0.413</td><td align=\"center\">0.0003</td><td align=\"center\">0.0019</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 6 (NDUFB6)</td></tr><tr><td align=\"left\">Ssc_21308_2_S1_at</td><td align=\"center\">-0.387</td><td align=\"center\">0.0007</td><td align=\"center\">0.0033</td><td align=\"left\">cytochrome c oxidase assembly protein (COX10)</td></tr><tr><td align=\"left\">Ssc_2184_1_S1_at</td><td align=\"center\">-0.415</td><td align=\"center\">0.0002</td><td align=\"center\">0.0015</td><td align=\"left\">cytochrome c oxidase subunit VIa polypeptide 2 (COX6A2)</td></tr><tr><td align=\"left\">Ssc_2957_1_S1_at</td><td align=\"center\">-0.500</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">ATP synthase, H+ transporting, mitochondrial F0 complex, subunit G (ATP5L)</td></tr><tr><td align=\"left\">Ssc_1219_1_S1_at</td><td align=\"center\">-0.439</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">ATP synthase, H+ transporting, mitochondrial F0 complex, subunit d (ATP5H)</td></tr><tr><td align=\"left\">Ssc_17183_1_S1_at</td><td align=\"center\">-0.445</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">ATP synthase, H+ transporting, mitochondrial F1 complex, delta subunit (ATP5D)</td></tr><tr><td align=\"left\">Ssc_1108_1_S1_at</td><td align=\"center\">-0.398</td><td align=\"center\">0.0004</td><td align=\"center\">0.0022</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 8 (NDUFA8)</td></tr><tr><td align=\"left\">Ssc_6891_1_S1_at</td><td align=\"center\">-0.392</td><td align=\"center\">0.0006</td><td align=\"center\">0.0029</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 4 (NDUFB4)</td></tr><tr><td align=\"left\">Ssc_23542_1_A1_at</td><td align=\"center\">-0.406</td><td align=\"center\">0.0003</td><td align=\"center\">0.0019</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 11 (NDUFB11)</td></tr><tr><td align=\"left\">Ssc_20956_1_S1_at</td><td align=\"center\">-0.401</td><td align=\"center\">0.0004</td><td align=\"center\">0.0022</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 3 (NDUFA3)</td></tr><tr><td align=\"left\">Ssc_1287_1_S1_at</td><td align=\"center\">-0.382</td><td align=\"center\">0.0008</td><td align=\"center\">0.0035</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 9 (NDUFB9)</td></tr><tr><td align=\"left\">Ssc_3708_1_S1_at</td><td align=\"center\">-0.430</td><td align=\"center\">0.0001</td><td align=\"center\">0.0009</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 alpha subcomplex (NDUFA12)</td></tr><tr><td align=\"left\">Ssc_1687_1_S1_at</td><td align=\"center\">-0.433</td><td align=\"center\">0.0001</td><td align=\"center\">0.0009</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 10 (NDUFB10)</td></tr><tr><td align=\"left\">Ssc_24943_1_S1_at</td><td align=\"center\">-0.466</td><td align=\"center\">&lt;.0001</td><td align=\"center\">0.0001</td><td align=\"left\">NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 11(NDUFA11)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T6\"><label>Table 6</label><caption><p>Eight candidate genes with <italic>cis </italic>eQTL in the region of drip loss QTL of SSC2, 3, 4 and 6 and trait correlated expression</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"3\">eQTL</td><td align=\"center\" colspan=\"3\">trait correlated expression</td><td/></tr><tr><td/><td colspan=\"3\"><hr/></td><td colspan=\"3\"><hr/></td><td/></tr><tr><td align=\"left\">Probe_Set_ID</td><td align=\"left\">SSC</td><td align=\"left\">POS [cM]</td><td align=\"left\">F</td><td align=\"left\">r</td><td align=\"left\">p-value</td><td align=\"left\">q-value</td><td align=\"left\">gene name (symbol)</td></tr></thead><tbody><tr><td align=\"left\">Ssc.16645.1.S1_at</td><td align=\"left\">2</td><td align=\"left\">20</td><td align=\"left\">18.2</td><td align=\"left\">0.53</td><td align=\"left\">&lt;.0001</td><td align=\"left\">9.44E-05</td><td align=\"left\">AHNAK nucleoprotein (AHNAK)</td></tr><tr><td align=\"left\">Ssc.2330.1.S1_at</td><td align=\"left\">2</td><td align=\"left\">35</td><td align=\"left\">6.5</td><td align=\"left\">-0.43</td><td align=\"left\">&lt;.0001</td><td align=\"left\">8.77E-04</td><td align=\"left\">solute carrier family 3 (SLC3A2)</td></tr><tr><td align=\"left\">Ssc_3574_1_A1_at</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">9.4</td><td align=\"left\">0.56</td><td align=\"left\">&lt;.0001</td><td align=\"left\">9.44E-05</td><td align=\"left\">mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4)</td></tr><tr><td align=\"left\">Ssc_10360_1_S1_at</td><td align=\"left\">3</td><td align=\"left\">0</td><td align=\"left\">5.1</td><td align=\"left\">0.51</td><td align=\"left\">&lt;.0001</td><td align=\"left\">9.44E-05</td><td align=\"left\">hypothetical protein (LOC162073)</td></tr><tr><td align=\"left\">Ssc.20772.1.S1_at</td><td align=\"left\">3</td><td align=\"left\">37</td><td align=\"left\">5.2</td><td align=\"left\">-0.49</td><td align=\"left\">&lt;.0001</td><td align=\"left\">1.03E-04</td><td align=\"left\">ubiquitin specific peptidase 39 (USP39)</td></tr><tr><td align=\"left\">Ssc_12110_1_S1_at</td><td align=\"left\">4</td><td align=\"left\">66</td><td align=\"left\">7.0</td><td align=\"left\">-0.58</td><td align=\"left\">&lt;.0001</td><td align=\"left\">1.03E-04</td><td align=\"left\">papillary renal cell carcinoma (PRCC)</td></tr><tr><td align=\"left\">Ssc_5334_1_S1_at</td><td align=\"left\">6</td><td align=\"left\">42</td><td align=\"left\">7.5</td><td align=\"left\">-0.47</td><td align=\"left\">&lt;.0001</td><td align=\"left\">1.03E-04</td><td align=\"left\">coenzyme Q9 homolog (CoQ9)</td></tr><tr><td align=\"left\">Ssc_4843_1_A1_at</td><td align=\"left\">6</td><td align=\"left\">46</td><td align=\"left\">5.1</td><td align=\"left\">0.45</td><td align=\"left\">&lt;.0001</td><td align=\"left\">9.44E-05</td><td align=\"left\">Bardet-Biedl syndrome 2 (BBS2)</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional File 1</title><p>Coefficients of correlation (r) between drip loss and expression level, p values and q value. the table lists the Affymetrix probe set IDs with coefficients of correlation (r) of expression level and the trait drip loss as well as corresponding p-values and q-values.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional File 2</title><p>897 eQTL with chromosome-wide significance at p ≤ 0.05 including 68 eQTL significant at p ≤ 0.01 chromosome-wide level and 48 and 12 eQTL significant at genome-wide p ≤ 0.05 and p ≤ 0.01 significance levels, coefficients of correlation (r) between drip loss and expression level, p values and q values. the table lists the subset of Affymetrix probe set IDs with information about eQTL detected at different levels of significance.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional File 3</title><p>104 significant eQTL, which were detected in QTL regions for drip loss on SSC 2, 3, 4, 5,6 and 18; coefficients of correlation (r) between drip loss and expression level, p values and q values. the table lists the subset of Affymetrix probe set IDs with eQTL detected within QTL regions for the trait drip loss.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional File 4</title><p>66 candidate genes mapping within the pQTL regions for drip loss on SSC 2, 3, 4, 5, 6 and 18 but showing 119 eQTL in other genomic region; coefficients of correlation (r) between drip loss and expression level, p values and q values. the table lists the subset of Affymetrix probe set IDs that map within QTL regions for the trait drip loss but show eQTL outside any QTL region for drip loss.</p></caption></supplementary-material>" ]
[]
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"pub-id": ["10.1016/S0309-1740(02)00186-9"]}, {"surname": ["Green", "Falls", "Crooks"], "given-names": ["P", "K", "S"], "source": ["Documentation for CRI-MAP (Version 2.4)"], "year": ["1990"], "publisher-name": ["Washington University School of Medicine, St. Louis, MO"]}, {"surname": ["Honikel"], "given-names": ["KO"], "article-title": ["Wasserbindungsverm\u00f6gen von Fleisch"], "source": ["Mitteilungsblatt der BAFF"], "year": ["1986"], "volume": ["94"], "fpage": ["7150"], "lpage": ["7154"]}, {"surname": ["Kauffman", "Eikelenboom", "Wal", "Merkus", "Zaar"], "given-names": ["RG", "G", "PG van der", "G", "M"], "article-title": ["The use of filter paper to estimate drip loss of porcine musculature"], "source": ["Meat Science"], "year": ["1986"], "volume": ["18"], "fpage": ["191"], "lpage": ["200"], "pub-id": ["10.1016/0309-1740(86)90033-1"]}, {"article-title": ["Gene Expression Omnibus"]}, {"surname": ["Glynn Dennis", "Sherman", "Hosack", "Yang", "Wei Gao", "Lane", "Lempicki"], 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{ "acronym": [], "definition": [] }
78
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Jul 31; 9:367
oa_package/de/aa/PMC2529315.tar.gz
PMC2529316
18673565
[ "<title>Background</title>", "<p>Studies over the last two decades have revealed that <italic>cis</italic>-regulatory elements, i.e. enhancers, contain multiple DNA-binding sites for different transcription factors (TFs) that cooperatively function to direct the tissue specific expression of their associated genes [##UREF##0##1##]. DNA sequence comparisons of different co-regulating enhancers suggest that many of these enhancers rely on different combinations of TFs to achieve coordinate gene regulation [##REF##15788537##2##]. For example, during early <italic>Drosophila </italic>neural development, combinatorial interaction of proneural basic helix-loop-helix (bHLH) TFs with homeodomain proteins, regulate commitment and patterning of neural precursors [##REF##15975935##3##, ####REF##15668164##4##, ##REF##16059914##5##, ##REF##17294361##6##, ##REF##7576311##7##, ##REF##10679430##8####10679430##8##].</p>", "<p>Cross-species analysis of individual <italic>Drosophila </italic>enhancers, using <italic>EvoPrinter </italic>or conventional alignment based phylogenetic comparative analysis [##REF##15345045##9##,##REF##16203978##10##] and the twelve sequenced <italic>Drosophila </italic>genomes, representing over 160 million years of collective evolutionary divergence, reveals that these enhancers are made up of clusters of highly conserved sequence blocks (CSBs), separated by less conserved sequences of variable length [##REF##17490485##11##]. CSBs that are longer than 8–10 bp are likely to be made up of adjacent or overlapping DNA-binding sites for different TFs. For example, the <italic>Drosophila Krüppel </italic>central domain enhancer contains overlapping highly conserved binding sites for its known regulators [##REF##2114978##12##, ####REF##2065664##13##, ##REF##1348871##14####1348871##14##,##REF##16203978##10##]. Specifically, work from the Jäckle laboratory [##REF##1348871##14##] has shown that one CSB of the central domain enhancer, 16 base pairs in length, contains overlapping binding sites for the antagonistic Bicoid activator and the Knirps repressor TFs.</p>", "<p>In order to initiate the functional dissection of CSBs that make up neural precursor gene enhancers and to gain a better understanding of their architecture in terms of the substructure of their constituent sequence elements, we have developed a multi-step protocol (collectively known as <italic>cis</italic>-Decoder) that allows for the rapid identification of short 6 to 14 bp DNA elements, termed <italic>cis</italic>-Decoder tags (<italic>c</italic>DTs), within enhancer CSBs; these <italic>c</italic>DTs are shared between CSBs of two or more enhancers with either related or divergent functions [##REF##17490485##11##]. To discover enhancer type-specific elements that regulate gene expression in neural precursor cells – including genes expressed in early delaminating CNS neuroblasts (NBs) and the proneural clusters and sensory organ precursors of the PNS – we have performed <italic>cis</italic>-Decoder analysis of CSBs from <italic>in vivo </italic>characterized enhancers. For early CNS development, we have selected the previously described enhancers of six genes that activate expression in early delaminating CNS NBs: <italic>deadpan </italic>(<italic>dpn</italic>), <italic>hunchback </italic>(<italic>hb</italic>), <italic>nerfin-1</italic>, <italic>scratch </italic>(<italic>scrt</italic>; the SA enhancer), <italic>snail </italic>(<italic>sna</italic>) and <italic>worniu </italic>(<italic>wor</italic>) (Table ##TAB##0##1##) [##REF##8582269##15##, ####REF##2846287##16##, ##UREF##1##17##, ##REF##8119127##18####8119127##18##]. For the <italic>cis</italic>-regulatory regions that drive expression in the proneural clusters (PNCs) and sensory organ precursors (SOPs) of the PNS we selected the <italic>in vivo </italic>characterized enhancers for <italic>bearded </italic>(<italic>brd</italic>), <italic>deadpan </italic>(<italic>dpn</italic>), <italic>rhomboid </italic>(<italic>rho</italic>), <italic>scrt </italic>and <italic>sna </italic>(Table ##TAB##0##1##) [##REF##10409499##19##, ####REF##17094800##20##, ##REF##9716538##21##, ##REF##7958878##22##, ##REF##15737936##23##, ##REF##9649507##24####9649507##24##].</p>", "<p>Our analysis of the CSBs from these characterized enhancers has identified known TF DNA-binding sites and novel sequences of as yet unknown function. Enhancer type-specific sequence elements within CSBs appear in different combinations and contexts in enhancers of co-regulated genes. The information gained from <italic>cis</italic>-Decoder analysis of the neural precursor cell enhancer CSBs was used to discover a novel co-regulating enhancer that directs <italic>Drosophila nervy </italic>expression. Our studies indicate that although specific core DNA-binding sites (such as those for bHLH and homeodomain TFs) are enriched in enhancers of co-regulated genes, enhancer-binding specificity is most likely conferred through sequences that flank the consensus core docking sites. The fact that shared sequence elements of co-regulated enhancers reside in different combinations and positional ordering within each of the enhancers, suggests that their combined presence but not necessarily their relative positions is required for <italic>cis</italic>-regulatory function.</p>" ]
[ "<title>Methods</title>", "<title>Generation of <italic>EvoPrints </italic>and CSB-libraries</title>", "<p><italic>EvoPrinter </italic>analysis was performed as described [##REF##16203978##10##,##REF##18307801##61##]. This analysis used <italic>EvoPrinterHD </italic>(please see Availability &amp; requirements for more information) a second-generation <italic>EvoPrinter </italic>program that uses an <italic>enhanced</italic>-BLAT algorithm for increased resolution of conserved sequences [##REF##18307801##61##]. Detailed instructions are provided at the <italic>EvoPrinter </italic>web site.</p>", "<p>When possible, all twelve <italic>Drosophila </italic>species were used for the <italic>EvoPrint </italic>analysis, while species that exhibited sequencing gaps were excluded. CSBs within enhancers were curated from either an <italic>EvoPrint</italic>, which reveals bases conserved in all species, or a relaxed print (also known as an <italic>EvoDifference </italic>profile) that identifies base pairs that are conserved in all but one of the species. The collective evolutionary divergence for all of the <italic>EvoPrints </italic>was greater than 140 My and in most cases, when all twelve species were included in the analysis, <italic>EvoPrints </italic>represented over ~200 My of additive divergence. With the exception of two NB enhancers, <italic>scrt </italic>and <italic>wor</italic>, the size of each curated sequence was less than 1800 bases (Table ##TAB##0##1##). CSBs of 6 bp or longer were extracted from the EvoPrints using EvoPrint parser to generate CSB libraries. The number of CSBs in each enhancer, enhancer length, and relation of the enhancer with respect to the transcriptional start site is shown in Table ##TAB##0##1##. Lists of CSBs for each library are given at the <italic>cis</italic>-Decoder web site (please see Availability &amp; requirements for more information).</p>", "<title>Generation of <italic>cis</italic>-Decoder Tag libraries</title>", "<p>In order to focus the analysis on neural-specific and neural-enriched <italic>c</italic>DTs, those cDTs that were found at high frequency in non-neural (mesodermal) enhancers were placed in a shared/common <italic>c</italic>DT-library. To identify neural specific <italic>c</italic>DT elements, the frequency of <italic>c</italic>DTs was scored against an out-group of mesodermal CSBs [##REF##17490485##11##], and subsequently the common elements were removed. Prior to removal of mesodermal <italic>c</italic>DTs, the number of NB <italic>c</italic>DTs was 856, whereas after removal of shared <italic>c</italic>DTs, the number dropped to 272, indicating that the majority of <italic>c</italic>DTs shared by NB enhancers were also present in mesodermal enhancers.</p>", "<p>Three <italic>c</italic>DT-libraries were generated by alignment of NB, PNS and E(spl) CSBs and are provided at the cis-Decoder web site (please see Availability &amp; requirements for more information). The number of <italic>c</italic>DTs in each library was 272, 333 and 226 respectively. Of the 272 NB <italic>c</italic>DTs, less than half (120) aligned exclusively with NB CSBs, and did not align with PNS or E(spl) CSB sequences. Only 21% of the NB <italic>c</italic>DTs corresponded to PNS tags – in other words only 21% of the NB tags aligned two times or more with PNS CSBs.</p>", "<title>Cytoscape analysis</title>", "<p>We have adapted the biomolecular interaction network software Cytoscape [##REF##14597658##62##] in order to display shared <italic>c</italic>DTs from different enhancer CSBs. The following data structure was used: node1 xx node2, where node1 is the name of an enhancer, xx refers to any designator and node2 is the <italic>c</italic>DT sequence. This data structure facilitates the display of enhancer identity and shared sequence elements in an interactive pattern. Cytoscape analysis requires elimination of the reverse complements of <italic>c</italic>DTs in order to avoid duplicate representation. To eliminate duplicate reverse-complement <italic>c</italic>DTs, we used the program <italic>c</italic>DT-Uncomplementer (please see Availability &amp; requirements for more information). After removing duplicates, <italic>c</italic>DT-cataloger was used to name each node according to the enhancer aligning with that <italic>c</italic>DT.</p>", "<title>Identification of novel neural precursor cell enhancers</title>", "<p>To identify novel enhancers that direct gene expression in neural precursor cells, we curated <italic>c</italic>DTs that were shared by multiple identified NB enhancers and submitted them to the web-based genomic search tool <italic>FlyEnhancer </italic>[##REF##11752406##55##], to discover other genomic regions with similar densities of <italic>c</italic>DTs. Candidate sequences that contained densities of <italic>c</italic>DTs alignments were subject to <italic>EvoPrinterHD </italic>analysis to determine the extent of conservation. Candidate enhancer regions were selected for enhancer/reporter studies.</p>", "<title>Generation and analysis of <italic>nervy </italic>enhancer/reporter transformant lines</title>", "<p>Genomic DNA containing the putative <italic>nervy </italic>enhancer (734 bp) was amplified by PCR using standard methods. Primers for the <italic>nervy </italic>upstream region including BglII and Nhe1 sites (bold) were respectively <bold>AGATCT</bold>CTAAAGCCCTCGATGTGCCC (5') and <bold>GCTAGC</bold>TCCGACCAGTCGTAAGTGGCG (3'). Fragments were gel purified and cloned into the pCRII-TOPO double promoter vector. Sequencing verified the fidelity of the PCR and cloning. After cutting with Bgl and Nhe1, gel purification was performed and fragments were cloned into pH-Stinger [##REF##15038159##63##]. Details of our procedure are available upon request. The generation of transformant lines and embryo immunohistochemistry were carried out as described previously [##REF##17714701##64##].</p>" ]
[ "<title>Results and discussion</title>", "<title>Neural precursor cell enhancers share highly conserved core sequence elements</title>", "<p>To determine the extent to which neural precursor cell enhancers share highly conserved sequence elements, we performed <italic>cis</italic>-Decoder analysis of <italic>in vivo </italic>characterized enhancers (Table ##TAB##0##1##) [##REF##8582269##15##, ####REF##2846287##16##, ##UREF##1##17##, ##REF##8119127##18##, ##REF##10409499##19##, ##REF##17094800##20##, ##REF##9716538##21##, ##REF##7958878##22##, ##REF##15737936##23##, ##REF##9649507##24##, ##REF##10452845##25##, ##REF##10903170##26##, ##REF##7590239##27##, ##REF##7590238##28####7590238##28##]. Our analysis revealed the presence of both novel elements and sequences that contained consensus DNA-binding sites for known regulators of early neurogenesis. Table ##TAB##1##2## lists <italic>c</italic>DTs shared by multiple CNS or PNS neural precursor cell enhancers. None of the elements shown were present in our collection of 819 CSBs from <italic>in vivo </italic>characterized mesodermal enhancers, thus ensuring their enrichment in neural enhancers. Highlighted are consensus binding sites for known TFs; basic Helix-Loop Helix (bHLH) factors and Suppressor of Hairless [Su(H)], respectively acting in proneural and neurogenic pathways [##REF##7576311##7##]; Antennapedia class homeodomain proteins [##REF##16075387##29##], identified by their core ATTA binding sequence, and the ubiquitously expressed Pbx- (Pre-B Cell Leukemia TF) class homeodomain protein Extradenticle, a cofactor of many TFs [##REF##15177017##30##], identified by the core binding sequence of ATCA. More than half the conserved <italic>c</italic>DTs were novel, without identified interacting proteins. Many of the CSBs consisted of 8 or more bp, and often contained core sequences identical to binding sites for known factors as well as other core sequences that aligned with shorter novel <italic>c</italic>DTs, suggesting that the longer <italic>c</italic>DTs may contain core recognition sequences for two or more TFs.</p>", "<p>Most <italic>c</italic>DTs discovered in this analysis represent elements that are shared pairwise, i.e., by only two of the NB enhancers examined (see the website for a list of cDTs that are shared by only two of the enhancers examined). The fact that the majority of <italic>c</italic>DTs are shared two ways, with only a small subset of sequences being shared three or more ways, suggests that the <italic>cis</italic>-regulation of early neural precursor genes is carried out by a large number of factors acting combinatorially and/or that many of the identified <italic>c</italic>DTs may in fact represent interlocking sites for multiple factors, and the exact orientation and spacing of these sites may differ among enhancers.</p>", "<title>Neural specific cDTs that contain bHLH TF DNA-binding sites</title>", "<p>During <italic>Drosophila </italic>neurogenesis, bHLH proteins function as proneural TFs to initiate neurogenesis in both the central and peripheral nervous system. TFs encoded by the <italic>achaete-scute </italic>complex function in both systems, while the related Atonal bHLH protein functions exclusively in the PNS [##REF##12094208##31##]. Different proneural bHLH TFs, acting together with the ubiquitous dimerization partner Daughterless, bind to distinct E-boxes that contain different core sequences [##REF##15485919##32##]. In addition to the core recognition sequence, flanking bases are important to the DNA binding specificity of bHLH factors [##REF##10373509##33##].</p>", "<p>One of the principle observations of this study was that the core central two bases of the hexameric E-box DNA-binding site (CA<bold>NN</bold>TG; core bases are bold throughout) were conserved in all the species used to generate the <italic>EvoPrint</italic>. All of the enhancers included in this study contained one or more conserved bHLH-binding sites (Table ##TAB##2##3##), with NB and PNS enhancers averaging 3.9 and 4.1 binding sites respectively. More than a third of the core bases in NB bHLH sites contained a core <bold>GC </bold>sequence, and more than a third of the core bases in PNS bHLH sites contained either a core <bold>GC </bold>or a <bold>GG </bold>sequence. The most common E-box among the NB CSBs was CA<bold>GC</bold>TG with 14 sites in four of the six enhancers. The CA<bold>GC</bold>TG and CA<bold>GG</bold>TG E-boxes are high-affinity sites for Achaete/Scute bHLH proteins [##REF##7958878##22##,##REF##10605108##34##]. However the CA<bold>GC</bold>TG site itself is not specific to NB enhancers, as evidenced by its presence in four of the mesodermal enhancer CSBs characterized previously [##REF##17490485##11##]. The most common bHLH-binding site among PNS enhancers was also the CA<bold>GC</bold>TG E-box with 11 occurrences in six of the 13 enhancers. In contrast, the most common bHLH motif in enhancers of the E(spl)-complex [##REF##10452845##25##, ####REF##10903170##26##, ##REF##7590239##27##, ##REF##7590238##28####7590238##28##] was CA<bold>AG</bold>TG (data not shown), with 16 occurrences in 8 of the 11 enhancers. CA<bold>GG</bold>TG, previously shown to be an Atonal DNA-binding site [##REF##15485919##32##], was also common in E(spl) enhancers, with 9 occurrences in 8 of the 13 enhancers, but was less prevalent among NB enhancers. The CA<bold>GG</bold>TG box was also overrepresented in PNS and E(spl) enhancers relative to its appearance in NB enhancers, and it was also present in four of the characterized mesodermal enhancer CSBs. The CA<bold>GA</bold>TG box was present six times among PNS enhancers but not at all among NB enhancers. Thus there appears to be some specificity of E-boxes in the different enhancer types. The fact that each of these E-boxes is conserved in all the species in the analysis, suggests that there is a high degree of specificity conferred by the E-box core sequence.</p>", "<p>Our analysis also reveals that not only are the core bases of E-boxes shared between similarly regulated enhancers, but bases flanking the E-box were also found to be highly conserved and are also frequently shared by these enhancers. Among the E-boxes found in CSBs of NB enhancers (many are illustrated in Table ##TAB##1##2##) aaCA<bold>GC</bold>TG (core bases of E-box are bold, flanking bases lower case) is repeated three times in <italic>nerfin-1 </italic>and once in <italic>scrt</italic>; gCA<bold>CT</bold>TG is repeated three times in <italic>scrt</italic>; CA<bold>GC</bold>TGCA is repeated twice in <italic>wor</italic>, and CA<bold>GC</bold>TGctg is repeated twice in <italic>scrt </italic>(see Fig ##FIG##0##1##). In the <italic>dpn </italic>CNS NB enhancer, the E-box CAGCTG is found twice, separated by a single base (CA<bold>GC</bold>TGaCA<bold>GC</bold>TG). None of these sequences were present in mesodermal enhancers examined, but each is found in PNS enhancers; CA<bold>GC</bold>TGCA is repeated multiple times among PNS enhancers. Among the conserved PNS enhancer E-boxes (CA<bold>AA</bold>TGca, gcCA<bold>AA</bold>TG, cacCA<bold>AA</bold>TGg, CA<bold>CA</bold>TGttg, gCA<bold>CG</bold>TGtgc, ttgCA<bold>CG</bold>TG, agCA<bold>CG</bold>TGcc, aCA<bold>GA</bold>TG, ggCA<bold>GA</bold>TGt, CA<bold>GC</bold>TGccg, CA<bold>GC</bold>TGcaattt, gCA<bold>GG</bold>TGta and cCA<bold>GG</bold>TGa) each, including flanking bases, is found in two or three PNS enhancers, and these are distributed among all 13 enhancers. Of these, only agCA<bold>CG</bold>TGcc, CA<bold>GC</bold>TGccg, cCA<bold>GG</bold>TGa were found once in our sample of neuroblast enhancers and none were found in our sample of mesodermal enhancers. The sequence aaCA<bold>AG</bold>TG is found in 4 E(spl) complex enhancers, those for <italic>E(spl)m8, mγ, HLHmδ </italic>and <italic>m6</italic>, and the sequence aCA<bold>GC</bold>TGc is found twice in <italic>E(spl)m8 </italic>and once in <italic>m4 </italic>and <italic>m6; </italic>neither sequence was found in our mesodermal enhancers. Therefore, although a given hexameric sequence may often be shared by all three types of enhancers, NB, PNS and E(spl), when flanking bases are taken into account there appears to be enhancer type-specific enrichment for different E-boxes.</p>", "<title>Neural specific cDTs that contain Antennapedia class homeodomain DNA-binding sites</title>", "<p>Antennapedia class homeodomain proteins play essential roles in multiple aspects of neural development including cell proliferation and cell identity [##REF##12492146##35##]. The segmental identity of <italic>Drosophila </italic>NBs is conferred by input from TFs encoded by homeotic loci of the Antennapedia and bithorax complexes [##REF##9651493##36##, ####REF##16049114##37##, ##REF##15580266##38####15580266##38##]. For example, ectopic expression of <italic>abd-A</italic>, which specifies the NB6-4a lineage, down-regulates levels of the G1 cyclin, <italic>CycE </italic>[##REF##15580266##38##]. Loss of Polycomb group factors has been shown to lead to aberrant derepression of posterior Hox gene expression in postembryonic NBs, which causes NB death and termination of proliferation in the mutant clones [##REF##17287254##39##].</p>", "<p>We have examined the enhancer-type specificity of sequences flanking the Antennapedia class core DNA-binding sequence, ATTA [##REF##8044836##40##]. Nearly 25% of the NB and PNS CSBs examined in this study contain this core recognition sequence. ATTA-containing sites were found multiple times in selected NB and PNS enhancers (Figure ##FIG##0##1##). The <italic>cis</italic>-Decoder analysis identified 18 different neural specific ATTA containing <italic>c</italic>DTs that were exclusively shared by two or more PNS enhancers or CNS enhancers and 10 were found to be shared between PNS and CNS. The most common <italic>c</italic>DT, <bold>ATTA</bold>gca, was shared by two CNS and two PNS enhancers (Figure ##FIG##0##1##; consensus homeodomain-binding sites are bold, flanking sequence lower case). In addition, 6 homeodomain-binding site <italic>c</italic>DTs were found twice in <italic>wor </italic>CSBs, a<bold>ATTA</bold>ccg, tttga<bold>ATTA</bold>, aatca<bold>ATTA</bold>, <bold>ATTAAT</bold>ctt and aaacaa<bold>ATTA</bold>g, but not in other CNS or PNS enhancer CSBs. In some cases these <italic>c</italic>DTs were found repeated in given enhancer CSBs. Only one of these <italic>c</italic>DTs aligned with CSBs of enhancers of the E(spl) complex. Given that 2/3 of the occurrences of HOX sites in these promoters can be accounted for by <italic>c</italic>DTs whose flanking sequences are shared between enhancers, it is unlikely that the appearance of these shared sequences occurs by chance.</p>", "<p>In summary, the appearance of Hox sites in the context of conserved sequences shared by functionally related enhancers suggests that the specificity of consensus homeodomain-binding sites is conferred by adjacent bases, either through recognition of adjacent bases by the TF itself or in conjunction with one or more co-factors.</p>", "<title>Neural specific cDTs that contain Pbx/Extradenticle sites</title>", "<p>Examination of the <italic>c</italic>DTs from <italic>Drosophila </italic>NB and PNS enhancers revealed that many contained the core Pbx/Extradenticle docking site ATGA [##REF##8327485##41##,##REF##7910944##42##]. In <italic>Drosophila</italic>, Extradenticle has been shown to have Hox-dependent and independent functions [##REF##16515781##43##]. Studies have also shown that Pbx factors provide DNA-binding specificity for homeodomain TFs, facilitating specification of distinct structures along the body axis [##REF##16515781##43##]. In the CNS enhancers of <italic>Drosophila</italic>, most predicted Pbx/Extradenticle sites are not, however, found adjacent to Hox sites.</p>", "<p>Our analysis revealed that 8 of the Pbx motifs were shared between CNS and PNS enhancer types, and 16 were shared between similarly expressed enhancers (Figure ##FIG##1##2##), thus indicating that there appears to be some degree of specificity to Pbx site function when flanking bases are taken into account. Three of the Pbx binding-site containing elements also exhibit ATTA Hox sites: 1) the dodecamer GATG<bold>ATTAAT</bold>CT (Pbx site is ATGA, Hox sites in bold) shared by the PNS enhancers <italic>edl </italic>and <italic>amos </italic>(references in Table ##TAB##0##1##), contains a homeodomain ATTA site that overlaps the Pbx site by a single base, and 2) the smaller heptamer ATG<bold>ATTA</bold>, shared by <italic>pfe </italic>and <italic>ato</italic>, likewise contains a homeodomain ATTA site (bold) that overlaps ATGA Pbx site by a single base. Adjacent Hox and Pbx sites have been documented to facilitate synergy between the two factors [##REF##8710498##44##]. Taken together our findings suggest that, as with homeodomain-binding sites, the conserved bases flanking putative Pbx sites are functionally important. These flanking bases are likely to confer different DNA-binding affinities for Pbx factors or are required for binding of other TFs.</p>", "<title>Neural specific cDTs that contain Suppressor of Hairless binding sites</title>", "<p>Also indicating a degree of biological specificity of enhancer types is the distribution of Suppressor of Hairless Su(H) binding sites among neural enhancers. Su(H) is the Notch pathway effector TF of <italic>Drosophila </italic>[##REF##11301266##45##]. The members of the E(spl) complex, both the multiple basic helix-loop-helix (bHLH) repressor genes and the Bearded family members, have been shown to be Su(H) dependent [##REF##15737936##23##,##REF##10903170##26##]. The consensus <italic>in vitro </italic>DNA binding site for Su(H) is RTGRGAR (where R = A or G) [##REF##10452845##25##]. Notch signaling via Su(H) occurs through conserved single or paired sites [##REF##17284587##46##] and the presence of conserved sites for other transcription regulators associated with CSBs containing Su(H) binding sites has been documented [##REF##18039873##47##].</p>", "<p>Within the CSBs of the six NB enhancers examined, only two, <italic>dpn </italic>and <italic>wor</italic>, contained conserved putative Su(H)-binding sites; two <italic>dpn </italic>sites matched one of the Su(H) consensus sites (GTGGGAA) and two <italic>wor </italic>sites match the sequence ATGGGAA. Only one of the two <italic>dpn </italic>sites contained flanking bases conforming to the widely distributed CGTGGGAA site of E(spl) Su(H) binding sites and none of the NB enhancers contained paired Su(H) sites typical of the E(spl) enhancers [##REF##10452845##25##,##REF##17284587##46##]. Of the 13 PNS cis-regulatory regions examined, only four enhancers contained putative Su(H)-binding sites [<italic>sna </italic>and <italic>ato </italic>(ATGGGAA), <italic>brd </italic>(GTGGGAG)] and <italic>dpn </italic>(GTGGGAA). <italic>dpn </italic>also contained a pair of sites that conforms to the SPS configuration frequently found in Su(H) enhancers (CSB sequence: AAT<bold>GTGAGAA</bold>AAAAACT<bold>TTCTCAC</bold>GATCACCTT, Su(H) sites in bold, Pbx site is ATCA). The lack of Su(H) sites in PNS enhancers was noted by Reeves and Posakony [##REF##15737936##23##], who suggested that these enhancers are directly regulated by the proneural proteins but not activated in response to Notch-mediated lateral inhibitory signaling. Among the conserved sequences of E(spl) gene enhancers there is an average of 3.4 consensus Su(H) binding sites per enhancer, with most enhancers containing both types of sites, i.e., those with either A or G in the central position (data not shown).</p>", "<p>We offer three insights with respect to Su(H) binding sites. First, although <italic>in vitro </italic>DNA-binding studies suggest there is a flexibility in the Su(H) binding site, like the bHLH E-box, comparative analysis shows that within any one the Su(H) sites there is no sequence flexibility. Except for the pair of Su(H) sites in the <italic>dpn </italic>PNS enhancer, none of the CNS or PNS sites contained a central A; less that a quarter of the E(spl) sites consisted of a central A, and all these were conserved across all species examined. In light of the high conservation in these regions the invariant core and flanking sequences are important for the unique Su(H) function at any particular site.</p>", "<p>A second finding was the extensive conservation of bases flanking the consensus Su(H) sequence in the E(spl) complex genes (data not shown). For example, the <italic>c</italic>DT <bold>GTGGGAA</bold>ACACACGAC [Su(H) site bold] was present in <italic>HLHm3 </italic>and <italic>HLHm5 </italic>enhancer CSBs, and ACC<bold>GTGGGAA</bold>AC was conserved in <italic>HLHm3 </italic>and <italic>HLHmβ </italic>enhancers. The conservation of bases flanking the consensus Su(H) binding site suggests that the Su(H) site may be flanked by additional binding sites for co-operative or competitive factors, or else, that Su(H) contacts additional bases besides the consensus heptamer.</p>", "<p>A third observation is that in most cases Su(H) binding sites are imbedded in larger CSBs, suggesting that CSB function is regulated by the integrated function of multiple TFs. For example the <italic>dpn </italic>NB enhancer Su(H) site is imbedded in a CSB of 24 bases, and the <italic>atonal </italic>PNS enhancer Su(H) site is imbedded in a CSB of 45 bases. In the E(spl) complex, CSB #6 of HLHmγ, consisting of 30 bases and CSB#13 of m8, consisting of 31 bases (each contains a GTGGGAA Su(H) site, a CACGAG element, conforming to a Hairy N-box consensus CACNAG [##REF##2540957##48##,##REF##1631102##49##], and an AGGA Tramtrack (Ttk) DNA-binding core recognition sequence [##REF##8247159##50##], but the order and context of these three sites is different for each enhancer). Although Su(H) binding sites were present in only a minority of NB and PNS enhancers, the conservation of core bases, as well as the complexity of their flanking conserved sequences points to a diversity of Su(H) function and interaction with other factors.</p>", "<title>Neural specific cDTs that contain core DNA-binding sites for other known TFs</title>", "<p>Two of these elements, one exclusively present in NB enhancers (C<bold>AGGA</bold>TA) and a second exclusively present in PNS enhancers (GT<bold>AGGA</bold>), contained consensus core AGGA DNA-binding sites for Ttk [##REF##8247159##50##], a BTB domain TF that has been shown to regulate pair rule genes during segmentation and to repress neural cell fates [##REF##7748559##51##, ####REF##9199357##52##, ##REF##12204250##53####12204250##53##]. Another site (C<bold>ACCCCA</bold>), shared by both NB and PNS enhancers, conforms to the consensus binding site of IA-1 (ACCCCA), the vertebrate homolog of <italic>nerfin-1 </italic>[##REF##11842116##54##]. Most of the cDTs of Table ##TAB##1##2## do not contain sequences corresponding to consensus binding-sites of known regulators of NB expression. The fact that they are represented multiple times in NB CSB sequences suggests that they contain binding sites for unknown regulators of neurogenesis in <italic>Drosophila</italic>.</p>", "<title>Neural-enriched cDTs</title>", "<p>Neural enriched <italic>c</italic>DTs that are shared between multiple NB enhancers and also exhibit a low frequency in the sample of mesodermal enhancers examined in this study serve as a resource for understanding enhancer elements that may not have an exclusive neural function [see Additional file ##SUPPL##0##1##]. Notable here is the presence of CAGCTG bHLH DNA binding sites (all with flanking A, CC and TC) and Antennapedia class homeobox (Hox) core DNA binding site ATTA [##REF##8044836##40##], as well as additional Ttk and Pbx/Extradenticle sites. Present in this list are portions of sequences conforming to Su(H) binding sites described above. Of particular interest in this table are sequences that are also enriched in the PNS (p); these sites may bind factors that play similar developmental roles in different tissues. For example, the presumptive Ttk site, AA<bold>AGGA </bold>(core sequence in bold) is highly enriched in segmental enhancers. Thus, some of these sites can be identified as targets of known TFs, but the identity of most are as yet unknown. These elements shared by multiple enhancers may be useful in identifying other enhancers driving expression in NBs.</p>", "<title><italic>cis</italic>-Decoder analysis reveals a complex sub-structure of enhancer CSBs</title>", "<p><italic>EvoPrint </italic>analysis revealed that all of the enhancer regions examined in this study contained multiple CSBs that were greater that 15 to 20 bases in length. The occurrence of overlapping DNA-binding sites for different TFs is currently the best explanation for the maintenance of intact CSB sequences across ~160 millions of years of collective species divergence. Our analysis has revealed that the sequence context, order and orientation of shared <italic>c</italic>DTs can differ between co-regulating enhancers.</p>", "<p>Two examples are given here of the complex contextual appearance of <italic>c</italic>DTs that appear frequently in CNS and PNS enhancers (Figure ##FIG##2##3##). Each of the eight CSBs shown was nearly fully 'covered' by <italic>c</italic>DTs of the NB library (data not shown), suggesting that each contains multiple overlapping binding sites for a number of TFs. First, examination of the distribution of <italic>c</italic>DT GCTGCA reveals that it overlaps, by one and two bases, adjacent but different consensus bHLH sites in <italic>scrt </italic>CSB<sup>#</sup>32, while in <italic>scrt </italic>CSB<sup>#</sup>23 it overlaps a third consensus bHLH sequence by two bases. In the PNS enhancer <italic>char</italic>, in CSB<sup>#</sup>17, GCTGCA overlaps a bHLH site, but in a different configuration (overlapping four bases) than found in the two CNS enhancers illustrated in Figure ##FIG##2##3A##. In <italic>amos </italic>CSB<sup>#</sup>26, GCTGCA appears adjacent to a HOX site and does not overlap a bHLH site. Second, examination of the distribution of the <italic>c</italic>DT GGCACG reveals that it overlaps different consensus bHLH sites in <italic>scrt </italic>CSB<sup>#</sup>32 and <italic>wor </italic>CSB<sup>#</sup>106, overlapping the bHLH site in the former by one base and in the latter by four bases. GGCACG overlaps a CAGCTG bHLH-binding site in <italic>rho </italic>CSB<sup>#</sup>18, but in a different configuration than the overlap with CAGCTG in the <italic>wor </italic>CSB. In the PNS enhancer <italic>scrt</italic>, GGCACG in CSB<sup>#</sup>5 overlaps a Hairy site N-box (consensus CACNAG) [##REF##2540957##48##,##REF##1631102##49##]. N-boxes were most common in E(spl) CSBs, but were also present in NB and PNS enhancer CSBs. In these two examples, and others we have examined, there is no consistent spatial constraints to the association of known TF-binding sites (i.e., bHLH-binding E-box sites) with novel <italic>c</italic>DTs; a picture that emerges is one of combinatorial complexity, in which known or novel <italic>c</italic>DTs are associated with each other in different contexts on different CSBs.</p>", "<p>As an initial step toward determining if different TFs interacted with one another or competed for flanking DNA-binding sites, we examined the proximity of known binding sites to one another in CSBs for bHLH, Hox, Pbx and Su(H). The results of this analysis for NB CSBs are shown in Table ##TAB##3##4##; data for other enhancer types is summarized here. Most striking was the presence of multiple adjacent Hox ATTA sites (10 instances on NB CSBs) and combinations of Hox and Pbx sites (9 instances NB CSBs). A typical example is the association of one Pbx site, a bHLH site and two Hox sites on a <italic>wor </italic>NB enhancer CSB (AAT<bold>CATTTG</bold>TAATAATTAG; Pbx site is ATCA, Hox sites are TAAT and ATTA, and bHLH site is bold). Associations of Hox and Pbx sites was also apparent in PNS enhancer CSBs, and in addition there was a high level of combined Hox and bHLH sites (11 instances on PNS CSBs), but in E(spl) enhancers only a higher level of the combination of Hox and Pbx sites (8 instances) was apparent. An example of the association of Hox and bHLH sites in a PNS enhancer is found in an <italic>achaete-scute </italic>dorso-central enhancer CSB (CAAAACAA<bold>CACTTG</bold>CTCTATTAAC; bHLH site in bold and Hox site is ATTA). There was also a distinctly higher level of Pbx sites on the same CSBs as bHLH sites in NBs CSBs (6 instances), but this combination was not apparent for PNS or E(spl) CSBs. Association of bHLH sites with Su(H) binding sites was apparent in E(spl) enhancer CSBs, especially when presence on adjacent CSBs (14 instances) was taken into account. Only in one of the 7 instances of paired Su(H) sites on E(spl) enhancers were these sites on the same CSBs, while in four other instances they were on adjacent CSBs. Although we often find sites in close proximity, both known and functionally uncharacterized sites are, with a few exceptions, not present in fixed uniform orientation in similarly regulated enhancers. This highlights the complex combinatorial arrangement and position flexibility of TF-binding sites within enhancer CSBs.</p>", "<title>The use of <italic>cis-Decoder</italic>, <italic>FlyEnhancer </italic>and <italic>EvoPrinter </italic>to identify novel enhancers</title>", "<p>We have used the information derived from <italic>cis-Decoder </italic>analysis of neural precursor cell enhancers to search for other genomic sequences with similar <italic>cis</italic>-regulatory properties. Having identified <italic>c</italic>DTs found multiple times among NB enhancers, we used the genomic search tool <italic>FlyEnhancer </italic>[##REF##11752406##55##] to identify <italic>Drosophila melanogaster </italic>genomic sequences that contained clusters of the following <italic>c</italic>DTs (number in parenthesis is the total number of each <italic>c</italic>DT in our sample of six NB enhancers): GGCACG (6), GGAATC (4), TGACAG (6), TGGGGT (4), CAGCTG (14), TGATTT (9) CAAGTG (7), CATATTT (5), TGATCC (7) and CTAAGC (6). As a lower limit, a minimum of three CAGCTG bHLH sites was set for this search, because of the prevalence of this site in <italic>nerfin-1 </italic>and <italic>deadpan </italic>NB enhancers. Each sequence detected by this search was subjected to <italic>EvoPrinter </italic>analysis to determine the extent of its sequence conservation. Among the <italic>c</italic>DT clusters identified, our search identified a 5' region adjacent to the <italic>nervy </italic>gene ([] that contained three conserved CAGCTG sites as well five other sites identical to TGACAG, GGAATC, TGGGGT, GGCACG and CATATTT (see below). <italic>nervy</italic>, originally identified as a target of homeotic gene regulation, is expressed in a subset of early CNS NBs, as well as in PNS SOP cells [##REF##7498738##56##]. Later studies have implicated <italic>nervy</italic>, along with cyclic adenosine monophosphate (cAMP)-dependent protein kinase (PKA) in antagonizing Sema-1a-PlexA-mediated axonal repulsion [##REF##14976319##57##], and <italic>nervy </italic>has been shown to promote mechanosensory organ development by enhancing Notch signaling [##REF##16168983##58##].</p>", "<p><italic>EvoPrinter </italic>analysis revealed that the cluster of neural precursor cell enhancer <italic>c</italic>DTs positioned 90 bp upstream from the <italic>nervy </italic>transcribed sequence contains highly conserved sequences (Figure ##FIG##3##4A##; chr2R:20,162,556-20,163,290). This region contains 10 CSBs that include six conserved E-boxes, three of which conform to the CAGCTG sequence that was prominent in <italic>nerfin-1 </italic>and <italic>deadpan </italic>promoters. To determine if this region functions as a neural precursor cell enhancer, we generated transformant lines containing the <italic>nervy </italic>CSB cluster linked to a minimal promoter/GFP reporter transgene (see methods section). Our analysis of the reporter expression driven by the <italic>nervy </italic>upstream fragment revealed a pattern indistinguishable from early <italic>nervy </italic>mRNA expression [##REF##7498738##56##] (Figure ##FIG##4##5##). Specifically, we detected expression in a large subset of early delaminating NBs and in SOPs and secondary precursor cells of the PNS. Significantly, the <italic>nervy </italic>enhancer, unlike <italic>nerfin-1 </italic>and <italic>deadpan </italic>NB enhancers, activates reporter expression in then PNS and not just in early NBs.</p>", "<p>A new <italic>c</italic>DT-library was generated combining the <italic>nervy </italic>enhancer CSBs and the NB and PNS enhancer CSBs used to generate the libraries described above. The new <italic>c</italic>DTs, along with the previously defined <italic>c</italic>DTs were aligned back to <italic>nervy </italic>CSBs (Figure ##FIG##3##4b##). Most <italic>c</italic>DTs were found only once in previously examined NB or PNS CSBs, but 21 cDTs appeared in our original analysis, described above, that did not include the <italic>nervy </italic>enhancer. The addition of this new enhancer to our analysis resulted in the discovery of a significant number of <italic>c</italic>DTs that had not been found previously. Three <italic>c</italic>DTs that were identified in the previous analysis, tCA<bold>GC</bold>TGc, cagCA<bold>GC</bold>TG and aaCA<bold>GC</bold>TG, contain bHLH DNA-binding sites (central bases of E-box in bold, flanking sequence are lower case). Aligning <italic>c</italic>DTs that are specific to the CNS or PNS may indicate sequences required to specifically drive expression in either the CNS or PNS.</p>" ]
[ "<title>Results and discussion</title>", "<title>Neural precursor cell enhancers share highly conserved core sequence elements</title>", "<p>To determine the extent to which neural precursor cell enhancers share highly conserved sequence elements, we performed <italic>cis</italic>-Decoder analysis of <italic>in vivo </italic>characterized enhancers (Table ##TAB##0##1##) [##REF##8582269##15##, ####REF##2846287##16##, ##UREF##1##17##, ##REF##8119127##18##, ##REF##10409499##19##, ##REF##17094800##20##, ##REF##9716538##21##, ##REF##7958878##22##, ##REF##15737936##23##, ##REF##9649507##24##, ##REF##10452845##25##, ##REF##10903170##26##, ##REF##7590239##27##, ##REF##7590238##28####7590238##28##]. Our analysis revealed the presence of both novel elements and sequences that contained consensus DNA-binding sites for known regulators of early neurogenesis. Table ##TAB##1##2## lists <italic>c</italic>DTs shared by multiple CNS or PNS neural precursor cell enhancers. None of the elements shown were present in our collection of 819 CSBs from <italic>in vivo </italic>characterized mesodermal enhancers, thus ensuring their enrichment in neural enhancers. Highlighted are consensus binding sites for known TFs; basic Helix-Loop Helix (bHLH) factors and Suppressor of Hairless [Su(H)], respectively acting in proneural and neurogenic pathways [##REF##7576311##7##]; Antennapedia class homeodomain proteins [##REF##16075387##29##], identified by their core ATTA binding sequence, and the ubiquitously expressed Pbx- (Pre-B Cell Leukemia TF) class homeodomain protein Extradenticle, a cofactor of many TFs [##REF##15177017##30##], identified by the core binding sequence of ATCA. More than half the conserved <italic>c</italic>DTs were novel, without identified interacting proteins. Many of the CSBs consisted of 8 or more bp, and often contained core sequences identical to binding sites for known factors as well as other core sequences that aligned with shorter novel <italic>c</italic>DTs, suggesting that the longer <italic>c</italic>DTs may contain core recognition sequences for two or more TFs.</p>", "<p>Most <italic>c</italic>DTs discovered in this analysis represent elements that are shared pairwise, i.e., by only two of the NB enhancers examined (see the website for a list of cDTs that are shared by only two of the enhancers examined). The fact that the majority of <italic>c</italic>DTs are shared two ways, with only a small subset of sequences being shared three or more ways, suggests that the <italic>cis</italic>-regulation of early neural precursor genes is carried out by a large number of factors acting combinatorially and/or that many of the identified <italic>c</italic>DTs may in fact represent interlocking sites for multiple factors, and the exact orientation and spacing of these sites may differ among enhancers.</p>", "<title>Neural specific cDTs that contain bHLH TF DNA-binding sites</title>", "<p>During <italic>Drosophila </italic>neurogenesis, bHLH proteins function as proneural TFs to initiate neurogenesis in both the central and peripheral nervous system. TFs encoded by the <italic>achaete-scute </italic>complex function in both systems, while the related Atonal bHLH protein functions exclusively in the PNS [##REF##12094208##31##]. Different proneural bHLH TFs, acting together with the ubiquitous dimerization partner Daughterless, bind to distinct E-boxes that contain different core sequences [##REF##15485919##32##]. In addition to the core recognition sequence, flanking bases are important to the DNA binding specificity of bHLH factors [##REF##10373509##33##].</p>", "<p>One of the principle observations of this study was that the core central two bases of the hexameric E-box DNA-binding site (CA<bold>NN</bold>TG; core bases are bold throughout) were conserved in all the species used to generate the <italic>EvoPrint</italic>. All of the enhancers included in this study contained one or more conserved bHLH-binding sites (Table ##TAB##2##3##), with NB and PNS enhancers averaging 3.9 and 4.1 binding sites respectively. More than a third of the core bases in NB bHLH sites contained a core <bold>GC </bold>sequence, and more than a third of the core bases in PNS bHLH sites contained either a core <bold>GC </bold>or a <bold>GG </bold>sequence. The most common E-box among the NB CSBs was CA<bold>GC</bold>TG with 14 sites in four of the six enhancers. The CA<bold>GC</bold>TG and CA<bold>GG</bold>TG E-boxes are high-affinity sites for Achaete/Scute bHLH proteins [##REF##7958878##22##,##REF##10605108##34##]. However the CA<bold>GC</bold>TG site itself is not specific to NB enhancers, as evidenced by its presence in four of the mesodermal enhancer CSBs characterized previously [##REF##17490485##11##]. The most common bHLH-binding site among PNS enhancers was also the CA<bold>GC</bold>TG E-box with 11 occurrences in six of the 13 enhancers. In contrast, the most common bHLH motif in enhancers of the E(spl)-complex [##REF##10452845##25##, ####REF##10903170##26##, ##REF##7590239##27##, ##REF##7590238##28####7590238##28##] was CA<bold>AG</bold>TG (data not shown), with 16 occurrences in 8 of the 11 enhancers. CA<bold>GG</bold>TG, previously shown to be an Atonal DNA-binding site [##REF##15485919##32##], was also common in E(spl) enhancers, with 9 occurrences in 8 of the 13 enhancers, but was less prevalent among NB enhancers. The CA<bold>GG</bold>TG box was also overrepresented in PNS and E(spl) enhancers relative to its appearance in NB enhancers, and it was also present in four of the characterized mesodermal enhancer CSBs. The CA<bold>GA</bold>TG box was present six times among PNS enhancers but not at all among NB enhancers. Thus there appears to be some specificity of E-boxes in the different enhancer types. The fact that each of these E-boxes is conserved in all the species in the analysis, suggests that there is a high degree of specificity conferred by the E-box core sequence.</p>", "<p>Our analysis also reveals that not only are the core bases of E-boxes shared between similarly regulated enhancers, but bases flanking the E-box were also found to be highly conserved and are also frequently shared by these enhancers. Among the E-boxes found in CSBs of NB enhancers (many are illustrated in Table ##TAB##1##2##) aaCA<bold>GC</bold>TG (core bases of E-box are bold, flanking bases lower case) is repeated three times in <italic>nerfin-1 </italic>and once in <italic>scrt</italic>; gCA<bold>CT</bold>TG is repeated three times in <italic>scrt</italic>; CA<bold>GC</bold>TGCA is repeated twice in <italic>wor</italic>, and CA<bold>GC</bold>TGctg is repeated twice in <italic>scrt </italic>(see Fig ##FIG##0##1##). In the <italic>dpn </italic>CNS NB enhancer, the E-box CAGCTG is found twice, separated by a single base (CA<bold>GC</bold>TGaCA<bold>GC</bold>TG). None of these sequences were present in mesodermal enhancers examined, but each is found in PNS enhancers; CA<bold>GC</bold>TGCA is repeated multiple times among PNS enhancers. Among the conserved PNS enhancer E-boxes (CA<bold>AA</bold>TGca, gcCA<bold>AA</bold>TG, cacCA<bold>AA</bold>TGg, CA<bold>CA</bold>TGttg, gCA<bold>CG</bold>TGtgc, ttgCA<bold>CG</bold>TG, agCA<bold>CG</bold>TGcc, aCA<bold>GA</bold>TG, ggCA<bold>GA</bold>TGt, CA<bold>GC</bold>TGccg, CA<bold>GC</bold>TGcaattt, gCA<bold>GG</bold>TGta and cCA<bold>GG</bold>TGa) each, including flanking bases, is found in two or three PNS enhancers, and these are distributed among all 13 enhancers. Of these, only agCA<bold>CG</bold>TGcc, CA<bold>GC</bold>TGccg, cCA<bold>GG</bold>TGa were found once in our sample of neuroblast enhancers and none were found in our sample of mesodermal enhancers. The sequence aaCA<bold>AG</bold>TG is found in 4 E(spl) complex enhancers, those for <italic>E(spl)m8, mγ, HLHmδ </italic>and <italic>m6</italic>, and the sequence aCA<bold>GC</bold>TGc is found twice in <italic>E(spl)m8 </italic>and once in <italic>m4 </italic>and <italic>m6; </italic>neither sequence was found in our mesodermal enhancers. Therefore, although a given hexameric sequence may often be shared by all three types of enhancers, NB, PNS and E(spl), when flanking bases are taken into account there appears to be enhancer type-specific enrichment for different E-boxes.</p>", "<title>Neural specific cDTs that contain Antennapedia class homeodomain DNA-binding sites</title>", "<p>Antennapedia class homeodomain proteins play essential roles in multiple aspects of neural development including cell proliferation and cell identity [##REF##12492146##35##]. The segmental identity of <italic>Drosophila </italic>NBs is conferred by input from TFs encoded by homeotic loci of the Antennapedia and bithorax complexes [##REF##9651493##36##, ####REF##16049114##37##, ##REF##15580266##38####15580266##38##]. For example, ectopic expression of <italic>abd-A</italic>, which specifies the NB6-4a lineage, down-regulates levels of the G1 cyclin, <italic>CycE </italic>[##REF##15580266##38##]. Loss of Polycomb group factors has been shown to lead to aberrant derepression of posterior Hox gene expression in postembryonic NBs, which causes NB death and termination of proliferation in the mutant clones [##REF##17287254##39##].</p>", "<p>We have examined the enhancer-type specificity of sequences flanking the Antennapedia class core DNA-binding sequence, ATTA [##REF##8044836##40##]. Nearly 25% of the NB and PNS CSBs examined in this study contain this core recognition sequence. ATTA-containing sites were found multiple times in selected NB and PNS enhancers (Figure ##FIG##0##1##). The <italic>cis</italic>-Decoder analysis identified 18 different neural specific ATTA containing <italic>c</italic>DTs that were exclusively shared by two or more PNS enhancers or CNS enhancers and 10 were found to be shared between PNS and CNS. The most common <italic>c</italic>DT, <bold>ATTA</bold>gca, was shared by two CNS and two PNS enhancers (Figure ##FIG##0##1##; consensus homeodomain-binding sites are bold, flanking sequence lower case). In addition, 6 homeodomain-binding site <italic>c</italic>DTs were found twice in <italic>wor </italic>CSBs, a<bold>ATTA</bold>ccg, tttga<bold>ATTA</bold>, aatca<bold>ATTA</bold>, <bold>ATTAAT</bold>ctt and aaacaa<bold>ATTA</bold>g, but not in other CNS or PNS enhancer CSBs. In some cases these <italic>c</italic>DTs were found repeated in given enhancer CSBs. Only one of these <italic>c</italic>DTs aligned with CSBs of enhancers of the E(spl) complex. Given that 2/3 of the occurrences of HOX sites in these promoters can be accounted for by <italic>c</italic>DTs whose flanking sequences are shared between enhancers, it is unlikely that the appearance of these shared sequences occurs by chance.</p>", "<p>In summary, the appearance of Hox sites in the context of conserved sequences shared by functionally related enhancers suggests that the specificity of consensus homeodomain-binding sites is conferred by adjacent bases, either through recognition of adjacent bases by the TF itself or in conjunction with one or more co-factors.</p>", "<title>Neural specific cDTs that contain Pbx/Extradenticle sites</title>", "<p>Examination of the <italic>c</italic>DTs from <italic>Drosophila </italic>NB and PNS enhancers revealed that many contained the core Pbx/Extradenticle docking site ATGA [##REF##8327485##41##,##REF##7910944##42##]. In <italic>Drosophila</italic>, Extradenticle has been shown to have Hox-dependent and independent functions [##REF##16515781##43##]. Studies have also shown that Pbx factors provide DNA-binding specificity for homeodomain TFs, facilitating specification of distinct structures along the body axis [##REF##16515781##43##]. In the CNS enhancers of <italic>Drosophila</italic>, most predicted Pbx/Extradenticle sites are not, however, found adjacent to Hox sites.</p>", "<p>Our analysis revealed that 8 of the Pbx motifs were shared between CNS and PNS enhancer types, and 16 were shared between similarly expressed enhancers (Figure ##FIG##1##2##), thus indicating that there appears to be some degree of specificity to Pbx site function when flanking bases are taken into account. Three of the Pbx binding-site containing elements also exhibit ATTA Hox sites: 1) the dodecamer GATG<bold>ATTAAT</bold>CT (Pbx site is ATGA, Hox sites in bold) shared by the PNS enhancers <italic>edl </italic>and <italic>amos </italic>(references in Table ##TAB##0##1##), contains a homeodomain ATTA site that overlaps the Pbx site by a single base, and 2) the smaller heptamer ATG<bold>ATTA</bold>, shared by <italic>pfe </italic>and <italic>ato</italic>, likewise contains a homeodomain ATTA site (bold) that overlaps ATGA Pbx site by a single base. Adjacent Hox and Pbx sites have been documented to facilitate synergy between the two factors [##REF##8710498##44##]. Taken together our findings suggest that, as with homeodomain-binding sites, the conserved bases flanking putative Pbx sites are functionally important. These flanking bases are likely to confer different DNA-binding affinities for Pbx factors or are required for binding of other TFs.</p>", "<title>Neural specific cDTs that contain Suppressor of Hairless binding sites</title>", "<p>Also indicating a degree of biological specificity of enhancer types is the distribution of Suppressor of Hairless Su(H) binding sites among neural enhancers. Su(H) is the Notch pathway effector TF of <italic>Drosophila </italic>[##REF##11301266##45##]. The members of the E(spl) complex, both the multiple basic helix-loop-helix (bHLH) repressor genes and the Bearded family members, have been shown to be Su(H) dependent [##REF##15737936##23##,##REF##10903170##26##]. The consensus <italic>in vitro </italic>DNA binding site for Su(H) is RTGRGAR (where R = A or G) [##REF##10452845##25##]. Notch signaling via Su(H) occurs through conserved single or paired sites [##REF##17284587##46##] and the presence of conserved sites for other transcription regulators associated with CSBs containing Su(H) binding sites has been documented [##REF##18039873##47##].</p>", "<p>Within the CSBs of the six NB enhancers examined, only two, <italic>dpn </italic>and <italic>wor</italic>, contained conserved putative Su(H)-binding sites; two <italic>dpn </italic>sites matched one of the Su(H) consensus sites (GTGGGAA) and two <italic>wor </italic>sites match the sequence ATGGGAA. Only one of the two <italic>dpn </italic>sites contained flanking bases conforming to the widely distributed CGTGGGAA site of E(spl) Su(H) binding sites and none of the NB enhancers contained paired Su(H) sites typical of the E(spl) enhancers [##REF##10452845##25##,##REF##17284587##46##]. Of the 13 PNS cis-regulatory regions examined, only four enhancers contained putative Su(H)-binding sites [<italic>sna </italic>and <italic>ato </italic>(ATGGGAA), <italic>brd </italic>(GTGGGAG)] and <italic>dpn </italic>(GTGGGAA). <italic>dpn </italic>also contained a pair of sites that conforms to the SPS configuration frequently found in Su(H) enhancers (CSB sequence: AAT<bold>GTGAGAA</bold>AAAAACT<bold>TTCTCAC</bold>GATCACCTT, Su(H) sites in bold, Pbx site is ATCA). The lack of Su(H) sites in PNS enhancers was noted by Reeves and Posakony [##REF##15737936##23##], who suggested that these enhancers are directly regulated by the proneural proteins but not activated in response to Notch-mediated lateral inhibitory signaling. Among the conserved sequences of E(spl) gene enhancers there is an average of 3.4 consensus Su(H) binding sites per enhancer, with most enhancers containing both types of sites, i.e., those with either A or G in the central position (data not shown).</p>", "<p>We offer three insights with respect to Su(H) binding sites. First, although <italic>in vitro </italic>DNA-binding studies suggest there is a flexibility in the Su(H) binding site, like the bHLH E-box, comparative analysis shows that within any one the Su(H) sites there is no sequence flexibility. Except for the pair of Su(H) sites in the <italic>dpn </italic>PNS enhancer, none of the CNS or PNS sites contained a central A; less that a quarter of the E(spl) sites consisted of a central A, and all these were conserved across all species examined. In light of the high conservation in these regions the invariant core and flanking sequences are important for the unique Su(H) function at any particular site.</p>", "<p>A second finding was the extensive conservation of bases flanking the consensus Su(H) sequence in the E(spl) complex genes (data not shown). For example, the <italic>c</italic>DT <bold>GTGGGAA</bold>ACACACGAC [Su(H) site bold] was present in <italic>HLHm3 </italic>and <italic>HLHm5 </italic>enhancer CSBs, and ACC<bold>GTGGGAA</bold>AC was conserved in <italic>HLHm3 </italic>and <italic>HLHmβ </italic>enhancers. The conservation of bases flanking the consensus Su(H) binding site suggests that the Su(H) site may be flanked by additional binding sites for co-operative or competitive factors, or else, that Su(H) contacts additional bases besides the consensus heptamer.</p>", "<p>A third observation is that in most cases Su(H) binding sites are imbedded in larger CSBs, suggesting that CSB function is regulated by the integrated function of multiple TFs. For example the <italic>dpn </italic>NB enhancer Su(H) site is imbedded in a CSB of 24 bases, and the <italic>atonal </italic>PNS enhancer Su(H) site is imbedded in a CSB of 45 bases. In the E(spl) complex, CSB #6 of HLHmγ, consisting of 30 bases and CSB#13 of m8, consisting of 31 bases (each contains a GTGGGAA Su(H) site, a CACGAG element, conforming to a Hairy N-box consensus CACNAG [##REF##2540957##48##,##REF##1631102##49##], and an AGGA Tramtrack (Ttk) DNA-binding core recognition sequence [##REF##8247159##50##], but the order and context of these three sites is different for each enhancer). Although Su(H) binding sites were present in only a minority of NB and PNS enhancers, the conservation of core bases, as well as the complexity of their flanking conserved sequences points to a diversity of Su(H) function and interaction with other factors.</p>", "<title>Neural specific cDTs that contain core DNA-binding sites for other known TFs</title>", "<p>Two of these elements, one exclusively present in NB enhancers (C<bold>AGGA</bold>TA) and a second exclusively present in PNS enhancers (GT<bold>AGGA</bold>), contained consensus core AGGA DNA-binding sites for Ttk [##REF##8247159##50##], a BTB domain TF that has been shown to regulate pair rule genes during segmentation and to repress neural cell fates [##REF##7748559##51##, ####REF##9199357##52##, ##REF##12204250##53####12204250##53##]. Another site (C<bold>ACCCCA</bold>), shared by both NB and PNS enhancers, conforms to the consensus binding site of IA-1 (ACCCCA), the vertebrate homolog of <italic>nerfin-1 </italic>[##REF##11842116##54##]. Most of the cDTs of Table ##TAB##1##2## do not contain sequences corresponding to consensus binding-sites of known regulators of NB expression. The fact that they are represented multiple times in NB CSB sequences suggests that they contain binding sites for unknown regulators of neurogenesis in <italic>Drosophila</italic>.</p>", "<title>Neural-enriched cDTs</title>", "<p>Neural enriched <italic>c</italic>DTs that are shared between multiple NB enhancers and also exhibit a low frequency in the sample of mesodermal enhancers examined in this study serve as a resource for understanding enhancer elements that may not have an exclusive neural function [see Additional file ##SUPPL##0##1##]. Notable here is the presence of CAGCTG bHLH DNA binding sites (all with flanking A, CC and TC) and Antennapedia class homeobox (Hox) core DNA binding site ATTA [##REF##8044836##40##], as well as additional Ttk and Pbx/Extradenticle sites. Present in this list are portions of sequences conforming to Su(H) binding sites described above. Of particular interest in this table are sequences that are also enriched in the PNS (p); these sites may bind factors that play similar developmental roles in different tissues. For example, the presumptive Ttk site, AA<bold>AGGA </bold>(core sequence in bold) is highly enriched in segmental enhancers. Thus, some of these sites can be identified as targets of known TFs, but the identity of most are as yet unknown. These elements shared by multiple enhancers may be useful in identifying other enhancers driving expression in NBs.</p>", "<title><italic>cis</italic>-Decoder analysis reveals a complex sub-structure of enhancer CSBs</title>", "<p><italic>EvoPrint </italic>analysis revealed that all of the enhancer regions examined in this study contained multiple CSBs that were greater that 15 to 20 bases in length. The occurrence of overlapping DNA-binding sites for different TFs is currently the best explanation for the maintenance of intact CSB sequences across ~160 millions of years of collective species divergence. Our analysis has revealed that the sequence context, order and orientation of shared <italic>c</italic>DTs can differ between co-regulating enhancers.</p>", "<p>Two examples are given here of the complex contextual appearance of <italic>c</italic>DTs that appear frequently in CNS and PNS enhancers (Figure ##FIG##2##3##). Each of the eight CSBs shown was nearly fully 'covered' by <italic>c</italic>DTs of the NB library (data not shown), suggesting that each contains multiple overlapping binding sites for a number of TFs. First, examination of the distribution of <italic>c</italic>DT GCTGCA reveals that it overlaps, by one and two bases, adjacent but different consensus bHLH sites in <italic>scrt </italic>CSB<sup>#</sup>32, while in <italic>scrt </italic>CSB<sup>#</sup>23 it overlaps a third consensus bHLH sequence by two bases. In the PNS enhancer <italic>char</italic>, in CSB<sup>#</sup>17, GCTGCA overlaps a bHLH site, but in a different configuration (overlapping four bases) than found in the two CNS enhancers illustrated in Figure ##FIG##2##3A##. In <italic>amos </italic>CSB<sup>#</sup>26, GCTGCA appears adjacent to a HOX site and does not overlap a bHLH site. Second, examination of the distribution of the <italic>c</italic>DT GGCACG reveals that it overlaps different consensus bHLH sites in <italic>scrt </italic>CSB<sup>#</sup>32 and <italic>wor </italic>CSB<sup>#</sup>106, overlapping the bHLH site in the former by one base and in the latter by four bases. GGCACG overlaps a CAGCTG bHLH-binding site in <italic>rho </italic>CSB<sup>#</sup>18, but in a different configuration than the overlap with CAGCTG in the <italic>wor </italic>CSB. In the PNS enhancer <italic>scrt</italic>, GGCACG in CSB<sup>#</sup>5 overlaps a Hairy site N-box (consensus CACNAG) [##REF##2540957##48##,##REF##1631102##49##]. N-boxes were most common in E(spl) CSBs, but were also present in NB and PNS enhancer CSBs. In these two examples, and others we have examined, there is no consistent spatial constraints to the association of known TF-binding sites (i.e., bHLH-binding E-box sites) with novel <italic>c</italic>DTs; a picture that emerges is one of combinatorial complexity, in which known or novel <italic>c</italic>DTs are associated with each other in different contexts on different CSBs.</p>", "<p>As an initial step toward determining if different TFs interacted with one another or competed for flanking DNA-binding sites, we examined the proximity of known binding sites to one another in CSBs for bHLH, Hox, Pbx and Su(H). The results of this analysis for NB CSBs are shown in Table ##TAB##3##4##; data for other enhancer types is summarized here. Most striking was the presence of multiple adjacent Hox ATTA sites (10 instances on NB CSBs) and combinations of Hox and Pbx sites (9 instances NB CSBs). A typical example is the association of one Pbx site, a bHLH site and two Hox sites on a <italic>wor </italic>NB enhancer CSB (AAT<bold>CATTTG</bold>TAATAATTAG; Pbx site is ATCA, Hox sites are TAAT and ATTA, and bHLH site is bold). Associations of Hox and Pbx sites was also apparent in PNS enhancer CSBs, and in addition there was a high level of combined Hox and bHLH sites (11 instances on PNS CSBs), but in E(spl) enhancers only a higher level of the combination of Hox and Pbx sites (8 instances) was apparent. An example of the association of Hox and bHLH sites in a PNS enhancer is found in an <italic>achaete-scute </italic>dorso-central enhancer CSB (CAAAACAA<bold>CACTTG</bold>CTCTATTAAC; bHLH site in bold and Hox site is ATTA). There was also a distinctly higher level of Pbx sites on the same CSBs as bHLH sites in NBs CSBs (6 instances), but this combination was not apparent for PNS or E(spl) CSBs. Association of bHLH sites with Su(H) binding sites was apparent in E(spl) enhancer CSBs, especially when presence on adjacent CSBs (14 instances) was taken into account. Only in one of the 7 instances of paired Su(H) sites on E(spl) enhancers were these sites on the same CSBs, while in four other instances they were on adjacent CSBs. Although we often find sites in close proximity, both known and functionally uncharacterized sites are, with a few exceptions, not present in fixed uniform orientation in similarly regulated enhancers. This highlights the complex combinatorial arrangement and position flexibility of TF-binding sites within enhancer CSBs.</p>", "<title>The use of <italic>cis-Decoder</italic>, <italic>FlyEnhancer </italic>and <italic>EvoPrinter </italic>to identify novel enhancers</title>", "<p>We have used the information derived from <italic>cis-Decoder </italic>analysis of neural precursor cell enhancers to search for other genomic sequences with similar <italic>cis</italic>-regulatory properties. Having identified <italic>c</italic>DTs found multiple times among NB enhancers, we used the genomic search tool <italic>FlyEnhancer </italic>[##REF##11752406##55##] to identify <italic>Drosophila melanogaster </italic>genomic sequences that contained clusters of the following <italic>c</italic>DTs (number in parenthesis is the total number of each <italic>c</italic>DT in our sample of six NB enhancers): GGCACG (6), GGAATC (4), TGACAG (6), TGGGGT (4), CAGCTG (14), TGATTT (9) CAAGTG (7), CATATTT (5), TGATCC (7) and CTAAGC (6). As a lower limit, a minimum of three CAGCTG bHLH sites was set for this search, because of the prevalence of this site in <italic>nerfin-1 </italic>and <italic>deadpan </italic>NB enhancers. Each sequence detected by this search was subjected to <italic>EvoPrinter </italic>analysis to determine the extent of its sequence conservation. Among the <italic>c</italic>DT clusters identified, our search identified a 5' region adjacent to the <italic>nervy </italic>gene ([] that contained three conserved CAGCTG sites as well five other sites identical to TGACAG, GGAATC, TGGGGT, GGCACG and CATATTT (see below). <italic>nervy</italic>, originally identified as a target of homeotic gene regulation, is expressed in a subset of early CNS NBs, as well as in PNS SOP cells [##REF##7498738##56##]. Later studies have implicated <italic>nervy</italic>, along with cyclic adenosine monophosphate (cAMP)-dependent protein kinase (PKA) in antagonizing Sema-1a-PlexA-mediated axonal repulsion [##REF##14976319##57##], and <italic>nervy </italic>has been shown to promote mechanosensory organ development by enhancing Notch signaling [##REF##16168983##58##].</p>", "<p><italic>EvoPrinter </italic>analysis revealed that the cluster of neural precursor cell enhancer <italic>c</italic>DTs positioned 90 bp upstream from the <italic>nervy </italic>transcribed sequence contains highly conserved sequences (Figure ##FIG##3##4A##; chr2R:20,162,556-20,163,290). This region contains 10 CSBs that include six conserved E-boxes, three of which conform to the CAGCTG sequence that was prominent in <italic>nerfin-1 </italic>and <italic>deadpan </italic>promoters. To determine if this region functions as a neural precursor cell enhancer, we generated transformant lines containing the <italic>nervy </italic>CSB cluster linked to a minimal promoter/GFP reporter transgene (see methods section). Our analysis of the reporter expression driven by the <italic>nervy </italic>upstream fragment revealed a pattern indistinguishable from early <italic>nervy </italic>mRNA expression [##REF##7498738##56##] (Figure ##FIG##4##5##). Specifically, we detected expression in a large subset of early delaminating NBs and in SOPs and secondary precursor cells of the PNS. Significantly, the <italic>nervy </italic>enhancer, unlike <italic>nerfin-1 </italic>and <italic>deadpan </italic>NB enhancers, activates reporter expression in then PNS and not just in early NBs.</p>", "<p>A new <italic>c</italic>DT-library was generated combining the <italic>nervy </italic>enhancer CSBs and the NB and PNS enhancer CSBs used to generate the libraries described above. The new <italic>c</italic>DTs, along with the previously defined <italic>c</italic>DTs were aligned back to <italic>nervy </italic>CSBs (Figure ##FIG##3##4b##). Most <italic>c</italic>DTs were found only once in previously examined NB or PNS CSBs, but 21 cDTs appeared in our original analysis, described above, that did not include the <italic>nervy </italic>enhancer. The addition of this new enhancer to our analysis resulted in the discovery of a significant number of <italic>c</italic>DTs that had not been found previously. Three <italic>c</italic>DTs that were identified in the previous analysis, tCA<bold>GC</bold>TGc, cagCA<bold>GC</bold>TG and aaCA<bold>GC</bold>TG, contain bHLH DNA-binding sites (central bases of E-box in bold, flanking sequence are lower case). Aligning <italic>c</italic>DTs that are specific to the CNS or PNS may indicate sequences required to specifically drive expression in either the CNS or PNS.</p>" ]
[ "<title>Conclusion</title>", "<p>The major finding of this study is that enhancers of co-regulated genes in neural precursor cells possess complex combinatorial arrangements of highly conserved <italic>c</italic>DT elements. Comparisons between NB and PNS enhancers identified CNS and PNS type-specific <italic>c</italic>DTs and <italic>c</italic>DTs that were enriched in one or another enhancer type. <italic>cis</italic>-Decoder analysis also revealed that many of the conserved sequences contain DNA-binding sites for classical regulators of neurogenesis, including bHLH, Hox, Pbx, and Su(H) factors. Although <italic>in vitro </italic>DNA-binding studies have shown that many of these factors have a certain degree of flexibility in the sequences to which they bind, defined in terms of a position weight matrix [##REF##17238282##60##], our studies show that for any given appearance these sites are actually highly conserved across all species of the <italic>Drosophila </italic>genus. The genus invariant conservation in many of these characterized binding sites indicates that there are distinct constraints to that sequence in terms of its function.</p>", "<p>The high degree of conservation displayed in the enhancer CSBs could derive from unique sequence requirements of individual TFs, or the intertwined nature of multiple DNA-binding sites for different TFs. Thus there is a higher degree of biological specificity to these sites than the flexibility that is detected using <italic>in vitro </italic>DNA-binding studies. As an example, the requirement for a specific core for the bHLH binding site, i.e., for a CAGCTG E-box for <italic>nerfin-1</italic>, <italic>deadpan </italic>and <italic>nervy</italic>, suggests that it is the TF itself that demands sequence conservation; however, the requirement for conserved flanking sequences suggests that additional specific factors may be involved. Although the inter-species conservation of core and flanking sites has been noted by others [##REF##10452845##25##], the extent of this conservation is rather surprising. To what extent and how evolutionary changes in enhancer function take place, given the conservation of core enhancer sequences, remains a question for future investigation.</p>", "<p>In addition to classic regulators of neurogenesis, <italic>cis</italic>-Decoder reveals additional conserved novel elements that are widely distributed or only detected in pairs of enhancers. Many of these novel elements flank known transcription binding motifs in one CSB, but appear independent of known motifs in another. The appearance of novel elements in multiple contexts suggests that they may represent DNA-binding sites for additional factors that are essential for enhancer function. Only through discovery of the factors binding these sequences will it become clear what role they play in enhancer function.</p>", "<p>Preliminary functional analysis of CSBs within the <italic>nerfin-1 </italic>neuroblast enhancer reveals that CSBs carry out different regulatory roles (Alexander Kuzin, unpublished results). Altering <italic>c</italic>DT sequences within the <italic>nerfin-1 </italic>CSBs reveals that most are required for cell-specific activation or repression or for normal enhancer expression levels. CSB swapping studies reveals that, for the most part, the order and arrangement of a number of tested CSBs was not important for enhancer function in reporter studies. The discovery of the <italic>nervy </italic>neural enhancer by searching the genome with commonly occurring NB <italic>c</italic>DTs underscores the potential use of <italic>EvoPrinter </italic>and <italic>cis</italic>-Decoder analysis for the identification of additional neural enhancers. By starting with known enhancers and building <italic>c</italic>DT libraries from their CSBs, one now has the ability to search for other genes expressed during any biological event.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The presence of highly conserved sequences within <italic>cis</italic>-regulatory regions can serve as a valuable starting point for elucidating the basis of enhancer function. This study focuses on regulation of gene expression during the early events of <italic>Drosophila </italic>neural development. We describe the use of <italic>EvoPrinter </italic>and <italic>cis</italic>-Decoder, a suite of interrelated phylogenetic footprinting and alignment programs, to characterize highly conserved sequences that are shared among co-regulating enhancers.</p>", "<title>Results</title>", "<p>Analysis of <italic>in vivo </italic>characterized enhancers that drive neural precursor gene expression has revealed that they contain clusters of highly conserved sequence blocks (CSBs) made up of shorter shared sequence elements which are present in different combinations and orientations within the different co-regulating enhancers; these elements contain either known consensus transcription factor binding sites or consist of novel sequences that have not been functionally characterized. The CSBs of co-regulated enhancers share a large number of sequence elements, suggesting that a diverse repertoire of transcription factors may interact in a highly combinatorial fashion to coordinately regulate gene expression. We have used information gained from our comparative analysis to discover an enhancer that directs expression of the <italic>nervy </italic>gene in neural precursor cells of the CNS and PNS.</p>", "<title>Conclusion</title>", "<p>The combined use <italic>EvoPrinter </italic>and <italic>cis</italic>-Decoder has yielded important insights into the combinatorial appearance of fundamental sequence elements required for neural enhancer function. Each of the 30 enhancers examined conformed to a pattern of highly conserved blocks of sequences containing shared constituent elements. These data establish a basis for further analysis and understanding of neural enhancer function.</p>" ]
[ "<title>Availability &amp; requirements</title>", "<p><italic>EvoPrinterHD</italic>: <ext-link ext-link-type=\"uri\" xlink:href=\"http://evoprinter.ninds.nih.gov/\"/></p>", "<p><italic>cis</italic>-Decoder, CSB-libraries: <ext-link ext-link-type=\"uri\" xlink:href=\"http://evoprinter.ninds.nih.gov/cisdecoder/csblibraries.htm\"/></p>", "<p><italic>cis</italic>-Decoder, cDT-libraries: <ext-link ext-link-type=\"uri\" xlink:href=\"http://evoprinter.ninds.nih.gov/cisdecoder/cdtlibraries.htm\"/></p>", "<p><italic>c</italic>DT-Uncomplementer: <ext-link ext-link-type=\"uri\" xlink:href=\"http://evoprinter.ninds.nih.gov/cisdecoder/uncomplementer.htm\"/></p>", "<title>Authors' contributions</title>", "<p>WR and KB participated in the design and implementation of the algorithms. AK and MK participated in the cloning of enhancers. TB and WFO conceived of the study, participated in the design and coordination of the algorithms and prepared the manuscript. All authors have read and approved the final draft of the manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Jermaine Ross and Antonios Ekatomatis for their technical assistance and Judith Brody for editorial expertise. This research was supported by the Intramural Research Program of the NIH, NINDS.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Shared <italic>c</italic>DTs that contain Antennapedia class homeodomain protein DNA-binding sites within CNS and PNS neural precursor cell enhancers</bold>. Shown is a Cytoscape display of CNS and PNS neural precursor cell enhancer cDTs that contain core ATTA homeodomain DNA-binding sites. <italic>c</italic>DTs flanking the enhancer names are shared by CSBs of a single enhancer type, and <italic>c</italic>DTs positioned between the enhancer names are shared in common by CSBs of the two different enhancer types. Only <italic>c</italic>DTs of 7 or more bases shared by two or more enhancers are portrayed.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Shared <italic>c</italic>DTs that contain Pbx/Extradenticle core DNA-binding sites</bold>. Cytoscape analysis of shared Pbx/Extradenticle DNA-binding site (TGAT) containing <italic>c</italic>DT elements present in CNS NB and/or PNS enhancers. <italic>c</italic>DTs flanking the enhancer names are shared by CSBs of a single enhancer type, and <italic>c</italic>DTs positioned between the enhancer names are shared in common by CSBs of the two different enhancer types.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Shared sequence elements are found in different orientations and patterns within CSBs of neural precursor cell enhancers</bold>. Shown are CSBs from CNS (A) and PNS (B) enhancers aligned to three different frequently found neural specific or enriched <italic>c</italic>DTs. Shown in parentheses is the number of appearances of each <italic>c</italic>DT among CNS NB enhancers (nb), and PNS enhancers (p). Putative bHLH, Hox and Hairy TFDNA-binding sites are over-lined black, red or blue, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold><italic>EvoPrinter </italic>and <italic>cis</italic>-Decoder analysis of the <italic>Drosophila nervy </italic>neural precursor cell enhancer region</bold>. <italic>EvoPrinter </italic>(A) and <italic>cis</italic>-Decoder (B) analysis of a <italic>nervy </italic>neural precursor cell enhancer positioned 90 bp upstream from the <italic>nervy </italic>transcriptional start site. A) Shown is an <italic>EvoPrint </italic>of the <italic>Drosophila melanogaster nervy </italic>gene 5' flanking DNA (487 bp). The predicted <italic>nervy </italic>transcriptional start site is denoted with an arrow. Test species included in the comparative analysis were <italic>D. simulans, D. sechellia, D. erecta, D. yakuba, D. ananassae, D. persimilis, D. pseudoobscura, D. willistoni </italic>and <italic>D. grimshawi</italic>. Black capital letters represent bases conserved in all, or all but one, species. Putative TFDNA-binding sites within the conserved sequences are highlighted (bHLH E-box sites, yellow; an Pbx/Extradenticle site, blue; and an Antennapedia class homeodomain binding site, green). B) Conserved sequence blocks identified in the <italic>nervy EvoPrint </italic>(A) were extracted and scanned for the presence of neural precursor cell enhancer <italic>c</italic>DTs. <italic>c</italic>DTs were generated using NB and PNS enhancer CSBs listed in Table ##TAB##0##1##. <italic>c</italic>DTs generated by the inclusion of the <italic>nervy </italic>CSBs in the <italic>c</italic>DT library construction are also shown. CNS neuroblast specific cDTs are highlighted in red typeface, PNS precursor cell specific are noted with blue typeface and those present in both are indicated with black typeface (the number of enhancers that contain a <italic>c</italic>DT is also indicated).</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Expression pattern of the <italic>nervy </italic>enhancer-GFP reporter transgene during embryonic CNS and PNS development</bold>. Shown are GFP immunostains of stage 10 (A) and stage 13 (B) embryos from a transformant line that contains the <italic>nervy </italic>upstream genomic sequence (shown in Figure 4A) adjacent to a minimal promoter/GFP reporter transgene (anterior is up). A) During early nervous system development, GFP reporter expression is detected in CNS neuroblasts and in PNS sensory organ precursor cells. The letter S indicates the PNS sensory organ precursor column and letters L, I, and M mark the lateral, intermediate and medial CNS neuroblast columns, respectively. Arrow indicates the ventral cord midline. B) GFP reporter expression is also detected in the secondary precursor cells of the developing PNS. Shown is the right half of the thoracic and abdominal segments. The letters V, V', L and D indicate the ventral, lateral and dorsal PNS neuronal cell clusters [##REF##7555719##59##].</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p><italic>Drosophila </italic>enhancers included in the <italic>cis</italic>-Decoder analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Enhancer</bold></td><td align=\"left\"><bold># CSBs</bold></td><td align=\"left\"><bold>Location*/Size in bp</bold></td><td align=\"left\"><bold>References</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>CNS Neuroblast</bold></td><td/><td/><td/></tr><tr><td align=\"left\"><italic>deadpan</italic></td><td align=\"left\">32</td><td align=\"left\">-1405 to -2772/1367</td><td align=\"left\">[##REF##8582269##15##]</td></tr><tr><td align=\"left\"><italic>hunchback</italic></td><td align=\"left\">26</td><td align=\"left\">-36 to -1270/1224</td><td align=\"left\">[##REF##2846287##16##]</td></tr><tr><td align=\"left\"><italic>nerfin-1</italic></td><td align=\"left\">29</td><td align=\"left\">-1529 to -160/1369</td><td align=\"left\">A. Kuzin (personal com.)</td></tr><tr><td align=\"left\"><italic>scratch</italic></td><td align=\"left\">48</td><td align=\"left\">-10867 to -4796/6069</td><td align=\"left\">[##REF##8582269##15##]</td></tr><tr><td align=\"left\"><italic>worniu</italic></td><td align=\"left\">106</td><td align=\"left\">-51 to -6940/5989</td><td align=\"left\">[##UREF##1##17##]</td></tr><tr><td align=\"left\"><italic>snail</italic></td><td align=\"left\">26</td><td align=\"left\">-244- to -1138/869</td><td align=\"left\">[##REF##8119127##18##]</td></tr><tr><td align=\"left\"><bold>PNS Precursor Cell</bold></td><td/><td/><td/></tr><tr><td align=\"left\"><italic>achaete </italic>(DC)</td><td align=\"left\">28</td><td align=\"left\">-5584 to -7725/2141</td><td align=\"left\">[##REF##10409499##19##]</td></tr><tr><td align=\"left\"><italic>amos </italic>(3.5)</td><td align=\"left\">56</td><td align=\"left\">-2397 to +102/2499</td><td align=\"left\">[##REF##17094800##20##]</td></tr><tr><td align=\"left\"><italic>atonal </italic>(F:2.6)</td><td align=\"left\">60</td><td align=\"left\">-2807 to +175/2982</td><td align=\"left\">[##REF##9716538##21##]</td></tr><tr><td align=\"left\"><italic>bearded</italic></td><td align=\"left\">20</td><td align=\"left\">-589 to +25/614</td><td align=\"left\">[##REF##7958878##22##]</td></tr><tr><td align=\"left\"><italic>Pray For Elves</italic></td><td align=\"left\">27</td><td align=\"left\">+2236 to +2711/475</td><td align=\"left\">[##REF##15737936##23##]</td></tr><tr><td align=\"left\"><italic>charlatan</italic></td><td align=\"left\">18</td><td align=\"left\">+15440 to +17758/2318</td><td align=\"left\">[##REF##15737936##23##]</td></tr><tr><td align=\"left\"><italic>deadpan</italic></td><td align=\"left\">18</td><td align=\"left\">-4166 to -4677/511</td><td align=\"left\">[##REF##8582269##15##]</td></tr><tr><td align=\"left\"><italic>ETS-domain lacking</italic></td><td align=\"left\">15</td><td align=\"left\">+998 to +2174/1176</td><td align=\"left\">[##REF##15737936##23##]</td></tr><tr><td align=\"left\"><italic>rhomboid</italic></td><td align=\"left\">21</td><td align=\"left\">-6669 to -8419/1720</td><td align=\"left\">[##REF##15737936##23##]</td></tr><tr><td align=\"left\"><italic>schizo</italic></td><td align=\"left\">20</td><td align=\"left\">+31166 to + 32661/1595</td><td align=\"left\">[##REF##15737936##23##]</td></tr><tr><td align=\"left\"><italic>scute</italic></td><td align=\"left\">7</td><td align=\"left\">-2562 to -3015/453</td><td align=\"left\">[##REF##9649507##24##]</td></tr><tr><td align=\"left\"><italic>scratch</italic></td><td align=\"left\">21</td><td align=\"left\">-3898 to -2000/1998</td><td align=\"left\">[##REF##8582269##15##]</td></tr><tr><td align=\"left\"><italic>Snail</italic></td><td align=\"left\">17</td><td align=\"left\">-543 to +40/583</td><td align=\"left\">[##REF##8119127##18##]</td></tr><tr><td align=\"left\"><bold>E(spl) enhancers</bold></td><td/><td/><td/></tr><tr><td align=\"left\"><italic>HLHm3</italic></td><td align=\"left\">32</td><td align=\"left\">-2176 to +177/2353</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>HLH m5</italic></td><td align=\"left\">38</td><td align=\"left\">-1743 to +21/1767</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>HLH m7</italic></td><td align=\"left\">20</td><td align=\"left\">-770 to -26/744</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>E(spl)m8</italic></td><td align=\"left\">22</td><td align=\"left\">-773 to +188/961</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>HLHmβ</italic></td><td align=\"left\">20</td><td align=\"left\">-1012 to +81/1093</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>HLH mγ</italic></td><td align=\"left\">25</td><td align=\"left\">-49 to -853/814</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>HLHmδ</italic></td><td align=\"left\">20</td><td align=\"left\">-1634 to +25/1659</td><td align=\"left\">[##REF##10452845##25##]</td></tr><tr><td align=\"left\"><italic>E(spl)m2</italic></td><td align=\"left\">36</td><td align=\"left\">-1659 to +48/1707</td><td align=\"left\">[##REF##10903170##26##]</td></tr><tr><td align=\"left\"><italic>E(spl)m4</italic></td><td align=\"left\">24</td><td align=\"left\">-157 to -1056/899</td><td align=\"left\">[##REF##7590239##27##,##REF##7590238##28##]</td></tr><tr><td align=\"left\"><italic>E(spl)m6</italic></td><td align=\"left\">26</td><td align=\"left\">-880 to +7/887</td><td align=\"left\">[##REF##10903170##26##]</td></tr><tr><td align=\"left\"><italic>E(spl)mγ</italic></td><td align=\"left\">26</td><td align=\"left\">-1718 to -40/1678</td><td align=\"left\">[##REF##10903170##26##]</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Conserved neural specific sequence elements within two or more neural precursor cell enhancers</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">CDTs</td><td align=\"center\" colspan=\"6\">CNS Neural Precursor Cell Enhancers</td><td align=\"center\" colspan=\"13\">PNS Neural Precursor Cell Enhancers</td></tr><tr><td colspan=\"1\"><hr/></td><td colspan=\"6\"><hr/></td><td colspan=\"13\"><hr/></td></tr><tr><td align=\"left\"><italic>Gene-&gt;</italic></td><td align=\"center\"><italic>dpn</italic></td><td align=\"center\"><italic>hb</italic></td><td align=\"center\"><italic>nf-1</italic></td><td align=\"center\"><italic>scrt</italic></td><td align=\"center\"><italic>sna</italic></td><td align=\"center\"><italic>wor</italic></td><td align=\"center\"><italic>ac</italic></td><td align=\"center\"><italic>amos</italic></td><td align=\"center\"><italic>ato</italic></td><td align=\"center\"><italic>brd</italic></td><td align=\"center\"><italic>char</italic></td><td align=\"center\"><italic>dpn</italic></td><td align=\"center\"><italic>edl</italic></td><td align=\"center\"><italic>pfe</italic></td><td align=\"center\"><italic>rho</italic></td><td align=\"center\"><italic>sc</italic></td><td align=\"center\"><italic>scrt</italic></td><td align=\"center\"><italic>siz</italic></td><td align=\"center\"><italic>sna</italic></td></tr></thead><tbody><tr><td align=\"left\">ACT<bold>TGAT</bold>T</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">TTTGA<bold>ATTA</bold></td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\"><bold>TAATTGAT</bold></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\"><bold>TGAT</bold>TTCT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AA<bold>ATTA</bold>GT</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AAGTGCAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AA<bold>ATTA</bold>GT</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">A<bold>CAGCTG</bold>T</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">TACGTGT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GATTTAC</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CGGCGTC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CAGGATA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CACTTCA</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AATGTGT</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AATGCAC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AACATAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AAAATGC</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\"><bold>TGAT</bold>CCA</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GCACGA</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GATTCC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GAGTGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td/><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">ATGGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CTAAGC</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AATCCC</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CACCCG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AGATAT</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td 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align=\"center\">-</td></tr><tr><td align=\"left\">GCTTCC</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GTTTGA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">TCACCT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AAAAACT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AACACGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CAAACAA</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td 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align=\"center\">-</td></tr><tr><td align=\"left\"><bold>CACGTG</bold>CT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CAAACG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CCTACT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CCTGTC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">TGAGAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CGCGAG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CGCGTGGCA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GCTTTCA<bold>ATTA</bold></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\"><bold>CAGCTG</bold>CAATT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">G<bold>CACGTG</bold>TGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">CAC<bold>CAAATG</bold>G</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\"><bold>CACGTG</bold>CAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">G<bold>CAGGTG</bold>TA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">TGGTGGTGG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">TTGAAAA<bold>A</bold></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">ATTGCAGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">ATTGAAAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GACAACA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GAATTGA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CTTTCAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GTGAGAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">ACGTGTG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"left\">AACCACC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">ACCCCTA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">ACGGAAG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AG<bold>ATTA</bold>T</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">AGCGTCA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\"><bold>CATCTG</bold>T</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CAGCAC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GT<bold>AGGA</bold></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CCGTGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">CGCCTC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GAAAGC</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td></tr><tr><td align=\"left\">GAGTCA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">TAGCCA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">TCTATT</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"left\">ATCTAA</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Conserved bHLH binding sites in NB and PNS enhancer <italic>cis-</italic>Decoder tags</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>CANNTG E-box</bold></td><td align=\"center\" colspan=\"6\"><bold>CNS Neural Precursor Cell Enhancers</bold></td><td align=\"center\" colspan=\"13\"><bold>PNS Neural Precursor Cell Enhancers</bold></td></tr><tr><td colspan=\"1\"><hr/></td><td colspan=\"6\"><hr/></td><td colspan=\"13\"><hr/></td></tr><tr><td/><td align=\"center\"><bold><italic>dpn</italic></bold></td><td align=\"center\"><bold><italic>hb</italic></bold></td><td align=\"center\"><bold><italic>nf-1</italic></bold></td><td align=\"center\"><bold><italic>scrt</italic></bold></td><td align=\"center\"><bold><italic>sna</italic></bold></td><td align=\"center\"><bold><italic>wor</italic></bold></td><td align=\"center\"><bold><italic>ac</italic></bold></td><td align=\"center\"><bold><italic>amos</italic></bold></td><td align=\"center\"><bold><italic>ato</italic></bold></td><td align=\"center\"><bold><italic>brd</italic></bold></td><td align=\"center\"><bold><italic>char</italic></bold></td><td align=\"center\"><bold><italic>dpn</italic></bold></td><td align=\"center\"><bold><italic>edl</italic></bold></td><td align=\"center\"><bold><italic>pfe</italic></bold></td><td align=\"center\"><bold><italic>rho</italic></bold></td><td align=\"center\"><bold><italic>sc</italic></bold></td><td align=\"center\"><bold><italic>scrt</italic></bold></td><td align=\"center\"><bold><italic>siz</italic></bold></td><td align=\"center\"><bold><italic>sna</italic></bold></td></tr></thead><tbody><tr><td align=\"center\">CAGCTG</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">4</td><td align=\"center\">-</td><td align=\"center\">5</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CAGGTG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CAGATG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">1</td></tr><tr><td align=\"center\">CAAATG</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">4</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr><tr><td align=\"center\">CAATTG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CAACTG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CAAGTG</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\">1</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CATGTG</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CATATG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">CACGTG</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">1</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Co-appearance of TF binding sites within CSBs and in adjacent CSBs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Pbx</td><td align=\"left\">bHLH</td><td align=\"left\">Hox</td><td align=\"left\">Su(H)</td></tr></thead><tbody><tr><td align=\"left\">Pbx</td><td align=\"left\">5/6*</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\">bHLH</td><td align=\"left\">6/12</td><td align=\"left\">2/4</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Hox</td><td align=\"left\">9/14</td><td align=\"left\">3/12</td><td align=\"left\">10/23</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Su(H)</td><td align=\"left\">0/2</td><td align=\"left\">0/0</td><td align=\"left\">1/3</td><td align=\"left\">0/0</td></tr><tr><td align=\"left\">Total</td><td align=\"left\">47</td><td align=\"left\">36</td><td align=\"left\">57</td><td align=\"left\">3</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><italic>cis</italic>-Decoder tags with multiple hits on two or more NB enhancers. All are NB enriched with a low level of hits on mesoderm CSBs.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*Relative to transcription start site</p></table-wrap-foot>", "<table-wrap-foot><p>Consensus binding sites for bHLH transcription factors (CANNTG), Antp class homeodomain proteins (ATTA and TAAT), Pbx/Extradenticle (TGAT and ATCA), Tramtrack (AGGA) and Su(H) (TTCCCAC) are bold.</p></table-wrap-foot>", "<table-wrap-foot><p>*First and second numbers represent appearance on same and adjacent CSBs, respectively.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2164-9-371-1\"/>", "<graphic xlink:href=\"1471-2164-9-371-2\"/>", "<graphic xlink:href=\"1471-2164-9-371-3\"/>", "<graphic xlink:href=\"1471-2164-9-371-4\"/>", "<graphic xlink:href=\"1471-2164-9-371-5\"/>" ]
[ "<media xlink:href=\"1471-2164-9-371-S1.doc\" mimetype=\"application\" mime-subtype=\"msword\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Davidson"], "given-names": ["EH"], "source": ["The regulatory genome; Gene regulatory networks in development and evolution"], "year": ["2006"], "publisher-name": ["Burlington MA, Academic Press (Elsevier)"]}, {"surname": ["Ashraf", "Ganguly", "Roote", "Ip"], "given-names": ["SI", "A", "J", "YT"], "article-title": ["Worniu, a snail family zinc-finger protein, is required for brain development in Drosophila"], "source": ["Dev Dynamics"], "year": ["2004"], "volume": ["231"], "fpage": ["379"], "lpage": ["386"], "pub-id": ["10.1002/dvdy.20130"]}]
{ "acronym": [], "definition": [] }
64
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Aug 1; 9:371
oa_package/51/b4/PMC2529316.tar.gz
PMC2529317
18700039
[ "<title>Background</title>", "<p><italic>Phenylobacterium zucineum </italic>strain HLK1<sup>T </sup>is a facultative intracellular microbe recently identified by us [##REF##16908113##1##]. It is a rod-shaped Gram-negative bacterium 0.3–0.5 × 0.5–2 μm in size. It belongs to the genus <italic>Phenylobacterium </italic>[##UREF##0##2##], which presently comprises 5 species, <italic>P. lituiforme </italic>(FaiI3T) [##REF##15545448##3##], <italic>P. falsum </italic>(AC49T) [##REF##15997701##4##], <italic>P. immobile </italic>(ET) [##UREF##0##2##], <italic>P. koreense </italic>(Slu-01T) [##REF##16166702##5##], and <italic>P. zucineum </italic>(HLK1<sup>T</sup>) [##REF##16908113##1##]. They were isolated from subsurface aquifer, alkaline groundwater, soil, activated sludge from a wastewater treatment plant, and the human leukemia cell line K562, respectively. Except for <italic>P. zucineum</italic>, they are environmental bacteria, and there is no evidence that these microbes are associated with eukaryotic cells. The HLK1<sup>T </sup>strain, therefore, represents the only species so far in the genus <italic>Phenylobacterium </italic>that can infect and survive in human cells. Since most, if not all, of the known microbes that can invade human cells are pathogenic, we proposed that HLK1<sup>T </sup>may have pathogenic relevance to humans [##REF##16908113##1##]. Unlike the known intracellular pathogens that undergo a cycle involving invasion, overgrowth, and disruption of the host cells, and repeating the cycle by invading new cells, HLK1<sup>T </sup>is able to establish a stable parasitic association with its host, i.e., the strain does not overgrow intracellularly to kill the host, and the host cells carry them to their progeny. One cell line (SW480) infected with <italic>P. zucineum </italic>has been stably maintained for nearly three years in our lab (data not shown).</p>", "<p>In this report, we present the complete genome sequence of <italic>P. zucineum</italic>.</p>" ]
[ "<title>Methods</title>", "<title>Bacterial growth and genomic library construction</title>", "<p><italic>P. zucineum </italic>strain HLK1<sup>T</sup>was grown in LB (Luria-Bertani) broth at 37°C and then harvested for the preparation of genomic DNA[##REF##16908113##1##]. Genomic DNA was prepared using a bacterial genomic DNA purification kit (V-Gene Biotech., Hangzhou, China) according to the manufacturer's instructions. Sheared DNA samples were fractionated to construct three different genomic libraries, containing average insert sizes of 2.0–2.5 kb, 2.5–3.0 kb and 3.5–4.0 kb. The resulting pUC18-derived library plasmids were extracted using the alkaline lysis method and subjected to direct DNA sequencing with automated capillary DNA sequencers (ABI3730 or MegaBACE1000).</p>", "<title>Sequencing and finishing</title>", "<p>The genome of <italic>P. zucineum </italic>was sequenced by means of the whole genome shotgun method with the phred/phrap/consed software packages [##REF##9521922##24##, ####REF##9521921##25##, ##REF##9521923##26##, ##REF##7542800##27####7542800##27##]. Sequencing and subsequent gene identification was carried out as described in our earlier publications [##REF##11935017##28##, ####REF##17375201##29##, ##REF##11997336##30####11997336##30##]. Briefly, during the shotgun sequence phase, clones were picked randomly from three shotgun libraries and then sequenced from both ends. 44,667 successful sequence reads (&gt;100 bp at Phred value Q13), accounting for 5.47× sequence coverage of the genome, were assembled into 563 sequence contigs representing 60 scaffolds connected by end-pairing information.</p>", "<p>The finishing phase involved iterative cycles of laboratory work and computational analysis. To reduce the numbers of scaffolds, reads were added into initial contig assembly by using failed universal primers as primers and by using plasmid clones that extended outwards from the scaffolds as sequence reaction templates. To resolve the low-quality regions, resequencing of the involved reads in low quality regions with universal primers and primer walking the plasmid clones were the first choice, otherwise, resequencing with alternate temperature conditions resolved the remaining low-quality regions. New sequence reads obtained from the above laboratory work were assembled into existing contigs, which yielded new contigs and new scaffolds connected by end-pairing information. Then, consed interface helped us to do nest round of laboratory work based on new arisen contig assembly. After about four iterative cycles of the above \"finish\" procedures to close gaps and to resolve the low-quality regions, the PCR product obtained by using total genomic DNA as template was sequenced from both ends to close the last physical gap. In addition, the overall sequence quality of the genome was further improved by using the following criteria: (1) two independent high-quality reads as minimal coverage, and (2) Phred quality value = Q40 for each given base. Collectively, 3,542 successful reads were incorporated into initial assembles during the finishing phase. The final assembly was composed of two circular \"contigs\", of which a smaller one with a protein cluster (including <italic>repA</italic>, <italic>repB</italic>, <italic>parA </italic>and <italic>parB</italic>) related to plasmid replication was assigned as the plasmid, and the larger one was the chromosome.</p>", "<title>Annotation</title>", "<p>tRNA genes were predicted with tRNAscan-SE [##REF##9023104##31##]. Repetitive sequences were detected by REPuter [##REF##11713313##32##,##REF##10366664##33##], coupled with intensive manual alignment. We identified and annotated the protein profiles of chromosome and plasmid with the same workstream. For the chromosome, the first set of potential CDSs in the chromosome was established with Glimmer 2.0 trained with a set of ORFs longer than 500 bp from its genomic sequence at default settings [##REF##10556321##34##]. The resulting 5,029 predicted CDSs were BLAST searched against the NCBI non-redundant protein database to determine their homology [##REF##9254694##35##]. 1,174 annotated proteins without the word \"hypothetical\" or \"unknown\" in their function description, and without frameshifts or in-frame stop codons, were selected as the second training set. The resulting second set of 4,018 predicted CDSs (assigned as \"predicted CDSs\") were searched against the NCBI non-redundant protein database. Predicted CDSs that accorded with the following BLAST search criteria were considered \"true proteins\": (1) 80% of the query sequence was aligned and (2) E-value ≤ 1e<sup>-10</sup>. Then, the ORFs extracted from the chromosome region among \"true proteins\" were searched against the NCBI non-redundant protein database. The ORFs satisfying the same criteria as true proteins were considered \"true ORFs\". Overlapping proteins were manually inspected and resolved, according to the principle we described previously [##REF##11997336##30##]. The final version of the protein profile comprised three parts: true proteins, true ORFs, and predicted CDSs located in the rest of the genome. The translational start codon of each protein was identified by the widely used RBS script [##REF##11751220##36##] and then refined by comparison with homologous proteins [##REF##11997336##30##].</p>", "<p>To further investigate the function of each protein, we used InterProScan to search against the InterPro protein family database [##REF##12520011##37##]. The up-to-date KEGG pathway database was used for pathway analysis [##REF##16381885##38##]. All proteins were searched against the COG database which included 66 completed genomes [##REF##9381173##39##,##REF##12969510##40##]. The final annotation was manually inspected by comprehensively integrating the results from searching against the databases of nr, COG, KEGG, and InterPro.</p>", "<title>Phylogenetic tree construction</title>", "<p>16S rRNA genes were retrieved from 63 alphaproteobacteria, <italic>P. zucineum </italic>and <italic>Escherichia coli </italic>O157:H7 EDL933. A neighbor-joining tree with bootstrapping was built using MEGA [##REF##17488738##41##]. The gammaproteobacterium <italic>E. coli </italic>was used as the outgroup to root the tree. To illustrate the evolutionary history of heat shock related proteins (RpoH, DnaK and GrpE), neighbor-joining trees based on the 16S rRNA genes and the above three proteins of 5 representative alphaproteobacteria (<italic>Sinorhizobium meliloti </italic>1021, <italic>Brucella suis </italic>1330, <italic>C. crescentus </italic>CB15, <italic>Rickettsia conorii </italic>str. Malish 7, <italic>Gluconobacter oxydans </italic>621H), <italic>P. zucineum </italic>and <italic>E. coli </italic>O157:H7 EDL933 were constructed.</p>", "<title>Comparative genomics</title>", "<p>Sequence data for comparative analyses were obtained from the NCBI database <ext-link ext-link-type=\"ftp\" xlink:href=\"ftp://ftp.ncbi.nlm.nih.gov/genbank/genomes/Bacteria/\"/>. The database has 520 completely sequenced bacterial genomes (sequences downloaded on 2007/06/05). All <italic>P. zucineum </italic>ORFs were searched against the ORFs from all other bacterial genomes with BLASTP. The number of <italic>P. zucineum </italic>ORFs matched to each genome with significance (E value = 1e<sup>-10</sup>) was calculated.</p>", "<p>To illustrate the contribution of transcriptional regulators and two-component signal transduction proteins to environmental adaptation, we compared the mean fraction of these two types of proteins in bacteria living in 6 different habitats, as described by Merav Parter [##REF##17888177##42##]. These are: (1) obligate bacteria that are necessarily associated with a host, (2) specialized bacteria that live in specific environments, such as marine thermal vents, (3) aquatic bacteria that live in fresh or seawater, (4) facultative bacteria, free-living bacteria that are often associated with a host, (5) multiple bacteria that live in many different environments, and (6) terrestrial bacteria that live in the soil. For bacteria with more than one sequenced strain, we chose only one strain for the comparative study. The numbers of bacterial species in each group were: 26 obligate, 5 specialized, 4 aquatic, 28 facultative, 27 multiple, and 3 terrestrial. We annotated the proteins of these 93 species with the same workflow used for <italic>P. zucineum </italic>and calculated the mean fraction of transcriptional regulators and two-component signal transduction proteins.</p>", "<p>In addition, we annotated the ORFs of 5 representative alphaproteobacteria with different habitats (multiple bacteria <italic>S. meliloti </italic>1021 and <italic>G. oxydan</italic>s 621H, facultative bacterium <italic>B. suis </italic>1330, aquatic bacterium <italic>C. crescentus </italic>CB15, and obligate bacterium <italic>R. conorii </italic>str. Malish 7) using the same workflow and computed the distributions of proteins involved in environmental adaptation.</p>", "<title>Ortholog identification</title>", "<p>All proteins encoded by one genome were BLASTP searched against a database of proteins encoded by another genome [##REF##9254694##35##], and <italic>vice versa</italic>. The threshold used in these comparisons was 1e<sup>-10</sup>. Orthology was identified if two proteins were each other's best BLASTP hit (best reciprocal match).</p>", "<title>Data accessibility</title>", "<p>The sequences reported in this paper have been deposited in the GenBank database. The accession numbers for chromosome and plasmid are <ext-link ext-link-type=\"gen\" xlink:href=\"CP000747\">CP000747</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"CP000748\">CP000748</ext-link>, respectively.</p>" ]
[ "<title>Results</title>", "<title>Genome anatomy</title>", "<p>The genome is composed of a circular chromosome (3,996,255 bp) and a circular plasmid (382,976 bp) (Figure ##FIG##0##1##; Table ##TAB##0##1##). The G + C contents of chromosome and plasmid are 71.35% and 68.5%, respectively. There are 3,861 putative protein-coding genes (3,534 in the chromosome and 327 in the plasmid), of which 3,180 have significant matches in the non-redundant protein database. Of the matches, 585 are conserved hypothetical proteins and 2,595 are proteins with known or predicted functions. Forty-two tRNA genes and one 16S-23S-5S rRNA operon were identified in the chromosome.</p>", "<p>There are 7 families of protein-coding repetitive sequences and a family of noncoding repeats in the genome (Table ##TAB##1##2##). Notably, identical copies of repeats 02–04 were found in both the chromosome and the plasmid, suggesting their potential involvement in homologous recombination.</p>", "<p>On the basis of COG (Cluster of Orthologous Groups) classification, the chromosome is enriched in genes for basic metabolism, such as categories E (amino acid transport and metabolism) and I (lipid transport and metabolism), accounting for 8.29% and 6.09% of the total genes in the chromosome, respectively. On the other hand, the plasmid is enriched for genes in categories O (posttranslational modification, protein turnover, chaperones) and T (signal transduction mechanisms), constituting 12.96% and 9.72% of the total genes in the plasmid, respectively.</p>", "<p>As to genes in the plasmid that cope with environmental stimuli, about half of the genes in category O are molecular chaperones (17/32), including 2 <italic>dnaJ</italic>-like molecular chaperones, 2 clusters of <italic>dnaK </italic>and its co-chaperonin <italic>grpE </italic>(PHZ_p0053-0054 and PHZ_p0121-122), a cluster of <italic>groEL </italic>and its co-chaperonin <italic>groES </italic>(PHZ_p0095-0096), and 9 heat shock proteins Hsp20. Of 23 genes in category T, there is one cluster (FixLJ, PHZ_p0187-0188), which is essential for the growth of <italic>C. crescentus </italic>under hypoxic conditions [##REF##15911751##6##].</p>", "<title>General metabolism</title>", "<p>The enzyme sets of glycolysis and the Entner-Doudoroff pathway are complete in the genome. All genes comprising the pentose phosphate pathway except gluconate kinase were identified, consistent with our previous experimental result that the strain cannot utilize gluconate [##REF##16908113##1##]. The genome lacks two enzymes (<italic>kdh</italic>, alpha ketoglutarate dehydrogenase and <italic>kgd</italic>, alpha ketoglutarate decarboxylase), making the oxidative and reductive branches of the tricarboxylic acid cycle operate separately. The genome has all the genes for the synthesis of fatty acids, 20 amino acids, and corresponding tRNAs. Although full sets of genes for the biosynthesis of purine and pyrimidine were identified, enzymes for the salvage pathways of purine (<italic>apt</italic>, adenine phosphoribosyltransferase; <italic>ade</italic>, adenine deaminase) and pyrimidine (<italic>cdd</italic>, cytidine deaminase; <italic>codA</italic>, cytosine deaminase; <italic>tdk</italic>, thymidine kinase; <italic>deoA</italic>, thymidine phosphorylase; <italic>upp</italic>, uracil phosphoribosyltransferase; <italic>udk</italic>, uridine kinase; and <italic>udp</italic>, uridine phosphorylase) were absent. The plasmid encodes some metabolic enzymes, such as those participating in glycolysis, the pentose phosphate pathway, and the citric acid cycle. However, it is worth noting that the plasmid has a gene (6-phosphogluconate dehydrogenase) that is the only copy in the genome (PHZ_p0183).</p>", "<p>Like most other species in the genus <italic>Phenylobacterium</italic>, the strain is able to use L-phenylalanine as a sole carbon source under aerobic conditions [##REF##16908113##1##]. A recent study revealed that phenylalanine can be completely degraded through the homogentisate pathway in <italic>Pseudomonas putida </italic>U [##REF##15262943##7##]. <italic>P. zucineum </italic>may use the same strategy to utilize phenylalanine, because all the enzymes for the conversion of phenylalanine through intermediate homogentisate to the final products fumarate and acetoacetate are present in the chromosome (Table ##TAB##2##3##).</p>", "<title>Functional elements responding to environmental transition</title>", "<p>HLK1<sup>T </sup>is able to survive intracellularly and extracellularly. Consistently, the genome contains the fundamental elements to support the life cycle in different environments. The genome contains abundant two-component signal transduction proteins, transcriptional regulators, and heat shock response proteins, enabling the strain to respond to extra- and intra-cellular stimuli at transcriptional and post-translational levels. Among the total of 102 two-component signal transduction proteins (91 in the chromosome and 11 in the plasmid), there are 36 histidine kinases, 48 response regulators, and 18 hybrid proteins fused with histidine kinase and response regulator. Sixteen pairs of histidine kinase and response regulator (1 in the plasmid) are adjacently aligned and may act as functional operons. These tightly linked modules make two-component signal transduction systems respond to environmental changes efficiently. The genome encodes 170 transcriptional regulators (16 in the plasmid) (Table ##TAB##3##4##). Notably, we annotated the proteins of 93 bacteria (see methods – comparative genomics) with the same annotation criteria used for <italic>P. zucineum </italic>and found that the fraction of two-component signal transduction proteins and transcriptional regulators was positively correlated with the capacity for environmental adaptation (Figure ##FIG##1##2##). The genome contains 17 extracytoplasmic function (ECF) sigma factors (3 in the plasmid) (Table ##TAB##4##5##). ECFs are suggested to play a role in environmental adaptation for <italic>Pseudomonas putida </italic>KT2440, whose genome contains 19 ECFs [##REF##12534467##8##]. <italic>P. zucineum </italic>has 3 heat shock sigma factors <italic>rpoH </italic>(2 in the plasmid) and 33 heat shock molecular chaperons (17 in the plasmid) (Table ##TAB##5##6##), which can cope with a variety of stresses, including cellular energy depletion, extreme concentrations of heavy metals, and various toxic substances. [##REF##9680198##9##].</p>", "<p>The genes for cell motility include 3 chemotaxis operons, 7 MCP (methyl-accepting chemotaxis) genes, 15 other genes related to chemotaxis (Table ##TAB##6##7##), and 43 genes for the biogenesis of the flagellum (Table ##TAB##7##8##).</p>", "<p>The genome contains sec-dependent, sec-independent, typical type II (Table ##TAB##8##9##) and IV secretion systems (Table ##TAB##9##10##), which are known to play important roles in adapting to diverse conditions [##REF##14572546##10##,##REF##16322447##11##].</p>", "<p>To better understand the roles of proteins responsible for environmental transition, we computed the distributions of those proteins in 5 representative alphaproteobacteria with typical habitats (see methods – comparative genomics). Like other multiple bacteria and facultative bacteria, which can survive in multiple niches, <italic>P. zucineum </italic>encodes a higher fraction of ECFs, transcriptional regulators and two-component signal transduction proteins than obligate bacteria (Table ##TAB##8##9##). Notably, <italic>P. zucineum </italic>has the largest number of heat shock related proteins (Table ##TAB##5##6##), in comparison to the 5 representative alphaproteobacteria and 93 bacteria (data not shown). Among the plasmid-encoded heat shock related proteins are 2 RpoH (PHZ_p0049 and PHZ_p0288) and 2 DnaK-GrpE clusters (PHZ_p0053-0054 and PHZ_p0121-0122). Further phylogenetic analysis suggested that the plasmid-encoded DnaK-GrpE clusters may have undergone a genus-specific gene duplication event (Figure ##FIG##2##3C## &amp;##FIG##2##3D##).</p>", "<title>Adaptation to an intracellular life cycle</title>", "<p>To survive intracellularly, <italic>P. zucineum </italic>must succeed in adhering to and subsequently invading the host cell [##REF##16497583##12##], defending against a hostile intracellular environment [##REF##15101970##13##, ####REF##8993856##14##, ##REF##12368447##15##, ##REF##10922044##16####10922044##16##], and capturing iron at very low concentration [##REF##11018148##17##].</p>", "<p>It is well known that the pilus takes part in adhering to and invading a host cell [##REF##16497583##12##]. We identified one pili biosynthesis gene (<italic>pilA</italic>) and 2 operons for pili biosynthesis (Table ##TAB##10##11##).</p>", "<p>The genes involved in defense against oxidative stress include superoxide dismutase (PHZ_c0927, PHZ_c1092), catalase (PHZ_c2899), peroxiredoxin (PHZ_c1548), hydroperoxide reductase (<italic>ahpF</italic>, alkyl hydroperoxide reductase, subunit f, PHZ_c2725, <italic>ahpC</italic>, alkyl hydroperoxide reductase, subunit c, PHZ_c2724), and the glutathione redox cycle system (glutathione reductase [PHZ_c1740, PHZ_c1981], glutathione synthetase [PHZ_c3479], and γ-glutamylcysteine synthetase [PHZ_c0446, PHZ_c0523]).</p>", "<p>Since intracellular free Fe is not sufficient to support the life of bacteria, to survive intracellularly, they must use protein-bound iron, such as heme and transferrin, via transporters and/or the siderophore system. The <italic>P. zucineum </italic>genome has one ABC type siderophore transporter system (PHZ_c1893-1895), one ABC type heme transporter system (PHZ_c0136, PHZ_c0139, PHZ_c0140), and 60 TonB-dependent receptors which may uptake the iron-siderophore complex (Table ##TAB##11##12##).</p>", "<title>Comparative genomics between <italic>P. zucineum </italic>and <italic>C. crescentus</italic></title>", "<p>Comparative genomic analysis demonstrated that <italic>P. zucineum </italic>is phylogenetically the closest to <italic>C. crescentus </italic>[##REF##11259647##18##] (Figure ##FIG##3##4##), consistent with the phylogenetic analysis based on 16S RNA gene sequences (Figure ##FIG##4##5##).</p>", "<p>Though the genome size and protein number of <italic>P. zucineum </italic>(4.37 Mb, 3,861 proteins) are similar to those of <italic>C. crescentus </italic>(4.01 Mb, 3,767 proteins), no large-scale synteny was found between the genomes. The largest synteny region is only about 30 kb that encodes 24 proteins. The conservation region with the largest number of proteins is the operon encoding 27 ribosomal proteins. In addition, the species share only 57.8% (2,231/3,861) of orthologous proteins. Categories J (translation, ribosomal structure and biogenesis), F (nucleotide transport and metabolism), and L (replication, recombination and repair) are the top 3 conservative COG categories between the species, sharing 88.01%, 81.67%, and 80.65% of the orthologs, respectively.</p>", "<title>Comparison of cell cycle genes between <italic>P. zucineum </italic>and <italic>C. crescentus</italic></title>", "<p>Since <italic>P. zucineum </italic>is phylogenetically closest to <italic>C. crescentus</italic>, and since the latter is a model organism for studies of the prokaryotic cell cycle [##REF##11930012##19##,##REF##15031731##20##], we compared the genes regulating the cell cycle between these species.</p>", "<p>The cell cycle of <italic>C. crescentus </italic>is controlled to a large extent by the master regulator CtrA, which controls the transcription of 95 genes involved in the cycle [##REF##11930012##19##,##REF##15031731##20##]. On the other hand, <italic>ctrA </italic>is regulated at the levels of transcription, phosphorylation, and proteolytic degradation by its target genes, e.g., DNA methyltransferase (CcrM) regulates the transcription of <italic>ctrA</italic>, histidine kinases (CckA, PleC, DivJ, DivL) regulate its activity, and ClpXP degrades it. These regulatory 'loops' enable CtrA to precisely control the progression of the cell cycle.</p>", "<p><italic>P. zucineum </italic>has most of the orthologs mentioned above (Table ##TAB##12##13##). Among the 95 CtrA-regulated genes in <italic>C. crescentus</italic>, 75 have orthologs in the <italic>P. zucineum </italic>genome (Additional file ##SUPPL##0##1##). The fraction of CtrA-regulated genes with orthologs in <italic>P. zucineum </italic>(76.9%, 73/95) is significantly greater than the mean level of the whole genome (57.8%, 2,231/3,861), indicating that the CtrA regulatory system is highly conserved. Genes participating in regulating central events of the cell cycle, such as CcrM (CC0378), Clp protease (CC1963) and 14 regulatory proteins, except for one response regulator (CC3286), are present in the <italic>P. zucineum </italic>genome. The genes without counterparts in <italic>P. zucineum </italic>are mostly for functionally unknown proteins.</p>", "<p>Notably, the sequence of CtrA is strikingly similar between <italic>P. zucineum </italic>and <italic>C. crescentus</italic>, with 93.07% identity of amino acid sequence and 89.88% identity of nucleotide sequence. In addition, they share identical promoters (p1 and p2) [##REF##10359766##21##] and the motif (GAnTC) recognized by DNA methyltransferase (CcrM) (Figure ##FIG##5##6##) [##REF##10464180##22##], suggesting that they probably share a similar regulatory loop of CtrA.</p>", "<p>Consistent with the results from <italic>in silico </italic>sequence analysis, the CtrA of <italic>P. zucineum </italic>can restore the growth of temperature-sensitive strain LC2195 (a CtrA mutant) of <italic>C. crescentus </italic>[##REF##8548829##23##] at 37°C, indicating that the CtrA of <italic>P. zucineum </italic>can functionally compliment that of <italic>C. crescentus </italic>in our experimental conditions (data not shown).</p>", "<p>Taken together, the comparative genomics of <italic>P. zucineum </italic>and <italic>C. crescentus </italic>suggests that the cell cycle of the former is likely to be regulated similarly to that of the latter.</p>", "<title>Presence of ESTs of the strain in human</title>", "<p>Since <italic>P. zucineum </italic>strain HLK1<sup>T </sup>can invade and persistently live in several human cell lines [##REF##16908113##1##], we were curious about whether this microbe can infect humans. By blasting against the human EST database (dbEST release 041307 with 7,974,440 human ESTs) with the whole genome sequence of <italic>P. zucineum</italic>, we found 9 matched ESTs (Table ##TAB##13##14##), of which 3 were from a library constructed from tissue adjacent to a breast cancer, and 6 were from a library constructed from a cell line of lymphatic origin. The preliminary data suggest that <italic>P. zucineum </italic>may invade humans.</p>" ]
[]
[ "<title>Conclusion</title>", "<p>This work presents the first complete bacterial genome in the genus <italic>Phenylobacterium</italic>. Genome analysis reveals the fundamental basis for this strain to invade and persistently survive in human cells. <italic>P. zucineum </italic>is phylogenetically closest to <italic>C. crescentus </italic>based on comparative genome analysis.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p><italic>Phenylobacterium zucineum </italic>is a recently identified facultative intracellular species isolated from the human leukemia cell line K562. Unlike the known intracellular pathogens, <italic>P. zucineum </italic>maintains a stable association with its host cell without affecting the growth and morphology of the latter.</p>", "<title>Results</title>", "<p>Here, we report the whole genome sequence of the type strain HLK1<sup>T</sup>. The genome consists of a circular chromosome (3,996,255 bp) and a circular plasmid (382,976 bp). It encodes 3,861 putative proteins, 42 tRNAs, and a 16S-23S-5S rRNA operon. Comparative genomic analysis revealed that it is phylogenetically closest to <italic>Caulobacter crescentus</italic>, a model species for cell cycle research. Notably, <italic>P. zucineum </italic>has a gene that is strikingly similar, both structurally and functionally, to the cell cycle master regulator CtrA of <italic>C. crescentus</italic>, and most of the genes directly regulated by CtrA in the latter have orthologs in the former.</p>", "<title>Conclusion</title>", "<p>This work presents the first complete bacterial genome in the genus <italic>Phenylobacterium</italic>. Comparative genomic analysis indicated that the CtrA regulon is well conserved between <italic>C. crescentus </italic>and <italic>P. zucineum</italic>.</p>" ]
[ "<title>Abbreviations</title>", "<p>EST: Expressed Sequence Tag; KEGG: Kyoto Encyclopedia of Genes and Genomes.</p>", "<title>Authors' contributions</title>", "<p>XH and SH designed the project; YL, XX, ZD, ZL, ZY and JS performed the research; SH and BZ contributed new reagents\\analytical tools; YL, XX, and ZD analyzed the data; and XH, YL, and SH wrote the paper. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported in part by the Cheung Kong Scholars Programme (National Ministry of Education, China, and the Li Ka Shing Foundation, Hong Kong) to XH, a Natural Science Foundation of China grant (30672382) to XH, and a Zhejiang Natural Science Foundation, China, grant (R204204) to XH. We thank Dr. Lucy Shapiro (Department of Developmental Biology, Stanford University) for the gifts of the <italic>C. crescentus </italic>temperature sensitive strain LC2195 and the plasmid pSAL14. We are grateful to Dr. Iain Bruce (Department of Physiology, Zhejiang University School of Medicine) for English editing.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Circular representation of the <italic>P. zucineum </italic>strain HLK1<sup>T </sup>chromosome and plasmid (smaller circle)</bold>. Circles indicate (from the outside): (1) Physical map scaled in megabases from base 1, the start of the putative replication origin. (2) Coding sequences transcribed in the clockwise direction are color-coded according to COG functional category. (3) Coding sequences transcribed in the counterclockwise direction are color-coded according to COG functional category. (4) Proteins involved in establishment of intracellular niche are TonB-dependent receptors (orange) and pilus genes (sienna). (5) Functional elements responsible for environmental transition are extracytoplasmic function sigma factors (royal blue), transcriptional regulators (violet red), two-component signal transduction proteins (deep sky blue), heat shock molecular chaperons (spring green), type IV secretion systems (plum), chemotaxis systems (green yellow) and flagellum proteins (gray). (6) G + C percent content (10-kb window and 1-kb incremental shift for chromosome; 300 bp window and 150 bp for incremental shift for plasmid); values larger than average (71.35% in chromosome and 68.5% in plasmid) are in red and smaller in medium blue. (7) GC skew (10-kb window and 1-kb incremental shift for chromosome; 300 bp window and 150 bp for incremental shift for plasmid); values greater than zero are in gold and smaller in purple. (8) Repeat families, repeats 01-08 are in dark salmon, dark red, wheat, tomato, light green, salmon, dark blue and gold, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Comparative analysis of transcriptional regulators and two-component signal transduction proteins in 6 groups of bacteria classified according to their habitats</bold>. (A): The mean number of transcriptional regulators in each megabase pair of the genomes. (B): The mean number of two-component signal transduction proteins in each megabase pair of the genomes. The fraction of transcriptional regulators and two-component signal transduction proteins (solid black circle) of <italic>P. zucineum </italic>were 41.56 genes/Mb and 23.30 genes/Mb, respectively. Error bars represent standard errors. O: Obligate (26 species), S: Specialized (5 species), AQ: Aquatic (4 species), F: Facultative (28 species), M: Multiple (27 species), T: Terrestrial (3 species).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Neighbor-joining trees of 5 representative alphaproteobacteria and <italic>P. zucineum</italic>, inferred from (A) 16S rRNA genes, (B) RpoH proteins, (C) DnaK proteins and (D) GrpE proteins</bold>. The node labels are bootstrap values (100 replicates). The plasmid-encoded DnaK and GrpE of <italic>P. zucineum </italic>may have undergone a genus-specific gene duplication event (C &amp;</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>List of top 10 complete sequenced bacteria closest to <italic>P. zucineum</italic></bold>. All 10 are alphaproteobacteria. Among all the sequenced bacterial genomes, <italic>C. crescentus </italic>shares the greatest number of similar ORFs with <italic>P. zucineum</italic></p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Neighbor-joining tree of the alphaproteobacteria, inferred from 16S rRNA genes</bold>. The node labels are bootstrap values (100 replicates). <italic>C. crescentus </italic>is phylogenetically the closest to <italic>P. zucineum</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Nucleotide acid sequence alignment of the <italic>ctrA </italic>promoter regions (-200 to +21) of <italic>C. crescentus </italic>and <italic>P. zucineum</italic></bold>. Blue background: identical nucleotides; \"-\": gaps; red and black box: P1 and P2 promoter; black underline: motif recognized by CcrM; red underline: first 21 nucleotides starting with initial codon \"ATG.\".</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Genome summary of <italic>P. zucineum </italic>Strain <italic>HLK1</italic><sup>T</sup></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Genomic Element</td><td/><td align=\"center\">Chromosome</td><td align=\"center\">plasmid</td></tr></thead><tbody><tr><td align=\"left\">Length (bp)</td><td/><td align=\"center\">3,996,255</td><td align=\"center\">382,976</td></tr><tr><td align=\"left\">GC content (%)</td><td/><td align=\"center\">71.35</td><td align=\"center\">68.54</td></tr><tr><td align=\"left\">Proteins</td><td/><td align=\"center\">3, 534</td><td align=\"center\">327</td></tr><tr><td/><td align=\"left\">Coding region of genome (%)</td><td align=\"center\">88.85%</td><td align=\"center\">81.94%</td></tr><tr><td/><td align=\"left\">Proteins with known or predicted function</td><td align=\"center\">2,394(67.75%)</td><td align=\"center\">201(61.47%)</td></tr><tr><td/><td align=\"left\">Conserved hypothetical proteins</td><td align=\"center\">560(15.84%)</td><td align=\"center\">25(7.65%)</td></tr><tr><td/><td align=\"left\">Hypothetical proteins</td><td align=\"center\">580(16.41%)</td><td align=\"center\">101(30.88%)</td></tr><tr><td align=\"left\">rRNA operon</td><td/><td align=\"center\">1</td><td align=\"center\">0</td></tr><tr><td align=\"left\">tRNAs</td><td/><td align=\"center\">42</td><td align=\"center\">0</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Proteins in each</td><td align=\"left\">[J] Translation, ribosomal structure and biogenesis</td><td align=\"center\">185 (5.24%)</td><td align=\"center\">3 (1.21%)</td></tr><tr><td align=\"left\">COG category</td><td align=\"left\">[K] Transcription</td><td align=\"center\">210 (5.94%)</td><td align=\"center\">22 (8.91%)</td></tr><tr><td/><td align=\"left\">[L] Replication, recombination and repair</td><td align=\"center\">139 (3.93%)</td><td align=\"center\">23 (9.31%)</td></tr><tr><td/><td align=\"left\">[D] Cell cycle control, cell division, chromosome partitioning</td><td align=\"center\">27 (0.76%)</td><td align=\"center\">0</td></tr><tr><td/><td align=\"left\">[V] Defense mechanisms</td><td align=\"center\">51 (1.44%)</td><td align=\"center\">3 (1.21%)</td></tr><tr><td/><td align=\"left\">[T] Signal transduction mechanisms</td><td align=\"center\">166 (4.7%)</td><td align=\"center\">24 (9.72%)</td></tr><tr><td/><td align=\"left\">[M] Cell wall/membrane/envelope biogenesis</td><td align=\"center\">195 (5.52%)</td><td align=\"center\">15 (6.07%)</td></tr><tr><td/><td align=\"left\">[N] Cell motility</td><td align=\"center\">62 (1.75%)</td><td align=\"center\">4 (1.62%)</td></tr><tr><td/><td align=\"left\">[U] Intracellular trafficking, secretion, and vesicular transport</td><td align=\"center\">96 (2.72%)</td><td align=\"center\">13 (5.26%)</td></tr><tr><td/><td align=\"left\">[O] Posttranslational modification, protein turnover, chaperones</td><td align=\"center\">151 (4.27%)</td><td align=\"center\">32 (12.96%)</td></tr><tr><td/><td align=\"left\">[C] Energy production and conversion</td><td align=\"center\">188 (5.32%)</td><td align=\"center\">16 (6.48%)</td></tr><tr><td/><td align=\"left\">[G] Carbohydrate transport and metabolism</td><td align=\"center\">161 (4.56%)</td><td align=\"center\">15 (6.07%)</td></tr><tr><td/><td align=\"left\">[E] Amino acid transport and metabolism</td><td align=\"center\">293 (8.29%)</td><td align=\"center\">5 (2.02%)</td></tr><tr><td/><td align=\"left\">[F] Nucleotide transport and metabolism</td><td align=\"center\">58 (1.64%)</td><td align=\"center\">3 (1.21%)</td></tr><tr><td/><td align=\"left\">[H] Coenzyme transport and metabolism</td><td align=\"center\">116 (3.28%)</td><td align=\"center\">3 (1.21%)</td></tr><tr><td/><td align=\"left\">[I] Lipid transport and metabolism</td><td align=\"center\">215 (6.09%)</td><td align=\"center\">12 (4.86%)</td></tr><tr><td/><td align=\"left\">[P] Inorganic ion transport and metabolism</td><td align=\"center\">223 (6.31%)</td><td align=\"center\">24 (9.72%)</td></tr><tr><td/><td align=\"left\">[Q] Secondary metabolites biosynthesis, transport and catabolism</td><td align=\"center\">152(4.3%)</td><td align=\"center\">9 (3.64%)</td></tr><tr><td/><td align=\"left\">[R] General function prediction only</td><td align=\"center\">444 (12.57%)</td><td align=\"center\">28 (11.34%)</td></tr><tr><td/><td align=\"left\">[S] Function unknown</td><td align=\"center\">307 (8.69%)</td><td align=\"center\">20 (8.10%)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Repetitive elements in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Repeat ID</td><td align=\"center\">Length bp</td><td align=\"center\">DR<sup>1</sup></td><td align=\"center\" colspan=\"2\">Number of copies</td><td align=\"center\" colspan=\"2\">Position of insertion</td><td align=\"left\">Identity (%)</td><td align=\"left\">Coding information</td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td><td/><td/></tr><tr><td/><td/><td/><td align=\"center\">Complete<sup>2</sup></td><td align=\"left\">Partial</td><td align=\"center\">Chromosome</td><td align=\"center\">Plasmid</td><td/><td/></tr></thead><tbody><tr><td align=\"left\">Repeat01<sup>3</sup></td><td align=\"center\">2,587</td><td align=\"center\">7</td><td align=\"center\">3</td><td align=\"left\">1</td><td align=\"center\">0</td><td align=\"center\">4</td><td align=\"left\">&gt;99</td><td align=\"left\">Transposase</td></tr><tr><td align=\"left\">Repeat02<sup>4</sup></td><td align=\"center\">1,262</td><td align=\"center\">3</td><td align=\"center\">3</td><td align=\"left\">1</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"left\">100</td><td align=\"left\">Transposase</td></tr><tr><td align=\"left\">Repeat03<sup>5</sup></td><td align=\"center\">1,392</td><td align=\"center\">NA</td><td align=\"center\">4</td><td align=\"left\">2</td><td align=\"center\">4</td><td align=\"center\">2</td><td align=\"left\">100</td><td align=\"left\">Transposase</td></tr><tr><td align=\"left\">Repeat04<sup>6</sup></td><td align=\"center\">1,257</td><td align=\"center\">NA</td><td align=\"center\">10</td><td align=\"left\">0</td><td align=\"center\">7</td><td align=\"center\">3</td><td align=\"left\">100</td><td align=\"left\">Transposase</td></tr><tr><td align=\"left\">Repeat05</td><td align=\"center\">1,554</td><td align=\"center\">NA</td><td align=\"center\">2</td><td align=\"left\">0</td><td align=\"center\">2</td><td align=\"center\">0</td><td align=\"left\">&gt;98</td><td align=\"left\">Hypothetical protein</td></tr><tr><td align=\"left\">Repeat06</td><td align=\"center\">1,136</td><td align=\"center\">NA</td><td align=\"center\">2</td><td align=\"left\">0</td><td align=\"center\">2</td><td align=\"center\">0</td><td align=\"left\">&gt;90</td><td align=\"left\">Isovaleryl-CoA dehydrogenase</td></tr><tr><td align=\"left\">Repeat07</td><td align=\"center\">1,077</td><td align=\"center\">NA</td><td align=\"center\">2</td><td align=\"left\">0</td><td align=\"center\">2</td><td align=\"center\">0</td><td align=\"left\">&gt;98</td><td align=\"left\">2-nitropropane dioxygenase</td></tr><tr><td align=\"left\">Repeat08</td><td align=\"center\">≈130</td><td align=\"center\">NA</td><td align=\"center\">13</td><td align=\"left\">0</td><td align=\"center\">13</td><td align=\"center\">0</td><td align=\"left\">&gt;90</td><td align=\"left\">Noncoding repeats</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Phenylalanine-degrading enzymes in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Gene</td><td align=\"left\"><italic>P. zucineum </italic>Locus</td><td align=\"center\" colspan=\"2\">Length (bp)</td><td align=\"center\" colspan=\"2\">Alignment coverage (%)</td><td align=\"center\">Score</td><td align=\"center\">Amino acid Identity (%)</td><td align=\"left\">Gene name</td></tr><tr><td/><td/><td colspan=\"4\"><hr/></td><td/><td/><td/></tr><tr><td/><td/><td align=\"center\"><italic>P. putida</italic></td><td align=\"center\"><italic>P. zucineum</italic></td><td align=\"center\"><italic>P. putida</italic></td><td align=\"center\"><italic>P. zucineum</italic></td><td/><td/><td/></tr></thead><tbody><tr><td align=\"center\"><italic>phhA</italic></td><td align=\"left\">PHZ_c1409</td><td align=\"center\">262</td><td align=\"center\">308</td><td align=\"center\">83.59</td><td align=\"center\">71.75</td><td align=\"center\">219</td><td align=\"center\">48.65</td><td align=\"left\">phenylalanine-4-hydroxylase</td></tr><tr><td align=\"center\"><italic>phhB</italic></td><td align=\"left\">PHZ_c0077</td><td align=\"center\">118</td><td align=\"center\">97</td><td align=\"center\">79.66</td><td align=\"center\">93.81</td><td align=\"center\">38.5</td><td align=\"center\">26.32</td><td align=\"left\">carbinolamine dehydratase</td></tr><tr><td align=\"center\"><italic>tryB</italic></td><td align=\"left\">PHZ_c1644</td><td align=\"center\">398</td><td align=\"center\">406</td><td align=\"center\">60.05</td><td align=\"center\">57.39</td><td align=\"center\">33.9</td><td align=\"center\">21.86</td><td align=\"left\">tyrosine aminotransferase</td></tr><tr><td align=\"center\"><italic>hpd</italic></td><td align=\"left\">PHZ_c2833</td><td align=\"center\">358</td><td align=\"center\">374</td><td align=\"center\">98.32</td><td align=\"center\">93.58</td><td align=\"center\">398</td><td align=\"center\">57.98</td><td align=\"left\">4-hydroxyphenylpyruvate dioxygenase</td></tr><tr><td align=\"center\"><italic>hmgA</italic></td><td align=\"left\">PHZ_c2831</td><td align=\"center\">433</td><td align=\"center\">377</td><td align=\"center\">60.28</td><td align=\"center\">67.64</td><td align=\"center\">53.5</td><td align=\"center\">22.3</td><td align=\"left\">homogentisate 1,2-dioxygenase</td></tr><tr><td align=\"center\"><italic>hmgB</italic></td><td align=\"left\">PHZ_c0313</td><td align=\"center\">430</td><td align=\"center\">226</td><td align=\"center\">9.77</td><td align=\"center\">18.14</td><td align=\"center\">27.7</td><td align=\"center\">39.53</td><td align=\"left\">fumarylacetoacetate hydrolase</td></tr><tr><td align=\"center\"><italic>hmgC</italic></td><td align=\"left\">PHZ_c0314</td><td align=\"center\">210</td><td align=\"center\">212</td><td align=\"center\">98.1</td><td align=\"center\">98.11</td><td align=\"center\">213</td><td align=\"center\">51.67</td><td align=\"left\">maleylacetoacetate isomerase</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Transcriptional regulators in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Family name</td><td align=\"left\">Action type</td><td align=\"center\">Chromosome</td><td align=\"center\">Plasmid</td><td align=\"left\">Proposed roles</td></tr></thead><tbody><tr><td align=\"left\">AsnC family</td><td align=\"left\">Activator/repressor</td><td align=\"center\">8</td><td align=\"center\">0</td><td align=\"left\">Amino acid biosynthesis</td></tr><tr><td align=\"left\">AraC family</td><td align=\"left\">Activator</td><td align=\"center\">10</td><td align=\"center\">1</td><td align=\"left\">Carbon metabolism, stress response and pathogenesis</td></tr><tr><td align=\"left\">ArsR family</td><td align=\"left\">Repressor</td><td align=\"center\">8</td><td align=\"center\">0</td><td align=\"left\">Metal resistance</td></tr><tr><td align=\"left\">BlaI family</td><td align=\"left\">Repressor</td><td align=\"center\">2</td><td align=\"center\">0</td><td align=\"left\">Penicillin resistance</td></tr><tr><td align=\"left\">Cold shock family</td><td align=\"left\">Activator</td><td align=\"center\">6</td><td align=\"center\">0</td><td align=\"left\">Low-temperature resistance</td></tr><tr><td align=\"left\">Cro/CI family</td><td align=\"left\">Repressor</td><td align=\"center\">9</td><td align=\"center\">2</td><td align=\"left\">Unknown<sup>2</sup></td></tr><tr><td align=\"left\">Crp/Fnr family</td><td align=\"left\">Activator/repressor</td><td align=\"center\">7</td><td align=\"center\">2</td><td align=\"left\">Global responses, catabolite repression and anaerobiosis</td></tr><tr><td align=\"left\">GntR family</td><td align=\"left\">Repressor</td><td align=\"center\">7</td><td align=\"center\">0</td><td align=\"left\">General metabolism</td></tr><tr><td align=\"left\">LacI family</td><td align=\"left\">Repressor</td><td align=\"center\">4</td><td align=\"center\">0</td><td align=\"left\">Carbon source utilization</td></tr><tr><td align=\"left\">LuxR family</td><td align=\"left\">Activator</td><td align=\"center\">5</td><td align=\"center\">1</td><td align=\"left\">Quorum sensing, biosynthesis and metabolism, etc.</td></tr><tr><td align=\"left\">LysR family</td><td align=\"left\">Activator/repressor</td><td align=\"center\">15</td><td align=\"center\">1</td><td align=\"left\">Carbon and nitrogen metabolism</td></tr><tr><td align=\"left\">MarR family</td><td align=\"left\">Activator/repressor</td><td align=\"center\">6</td><td align=\"center\">0</td><td align=\"left\">Multiple antibiotic resistance</td></tr><tr><td align=\"left\">MerR family</td><td align=\"left\">Repressor</td><td align=\"center\">9</td><td align=\"center\">2</td><td align=\"left\">Resistance and detoxification</td></tr><tr><td align=\"left\">TetR family</td><td align=\"left\">Repressor</td><td align=\"center\">22</td><td align=\"center\">0</td><td align=\"left\">Biosynthesis of antibiotics, efflux pumps, osmotic stress, etc.</td></tr><tr><td align=\"left\">XRE family</td><td align=\"left\">Repressor</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"left\">Unknown (initial function is lysogeny maintenance)</td></tr><tr><td align=\"left\">Other types<sup>2</sup></td><td align=\"left\">-</td><td align=\"center\">34</td><td align=\"center\">5</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Total</td><td align=\"left\">-</td><td align=\"center\">154</td><td align=\"center\">16</td><td align=\"left\">-</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Extracytoplasmic function (ECF) sigma factors in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\" colspan=\"3\">Location of proteins</td><td align=\"center\">COG category</td></tr><tr><td/><td colspan=\"3\"><hr/></td><td/></tr><tr><td/><td align=\"center\">Genomic element</td><td align=\"center\">5'-end</td><td align=\"center\">3'-end</td><td/></tr></thead><tbody><tr><td align=\"left\">PHZ_p0151</td><td align=\"center\">Plasmid</td><td align=\"center\">171,032</td><td align=\"center\">170,316</td><td align=\"center\">COG1595<sup>1</sup></td></tr><tr><td align=\"left\">PHZ_p0174</td><td align=\"center\">Plasmid</td><td align=\"center\">208,703</td><td align=\"center\">208,053</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_p0192</td><td align=\"center\">Plasmid</td><td align=\"center\">229,133</td><td align=\"center\">228,516</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c0249</td><td align=\"center\">Chromosome</td><td align=\"center\">249,840</td><td align=\"center\">250,553</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c0301</td><td align=\"center\">Chromosome</td><td align=\"center\">296,299</td><td align=\"center\">295,706</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c1475</td><td align=\"center\">Chromosome</td><td align=\"center\">1,676,920</td><td align=\"center\">1,677,492</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c1529</td><td align=\"center\">Chromosome</td><td align=\"center\">1,730,783</td><td align=\"center\">1,731,403</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c1531</td><td align=\"center\">Chromosome</td><td align=\"center\">1,732,219</td><td align=\"center\">1,732,800</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c1907</td><td align=\"center\">Chromosome</td><td align=\"center\">2,134,971</td><td align=\"center\">2,135,507</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2171</td><td align=\"center\">Chromosome</td><td align=\"center\">2,447,581</td><td align=\"center\">2,448,396</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2233</td><td align=\"center\">Chromosome</td><td align=\"center\">2,526,836</td><td align=\"center\">2,527,369</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2394</td><td align=\"center\">Chromosome</td><td align=\"center\">2,724,759</td><td align=\"center\">2,725,307</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2577</td><td align=\"center\">Chromosome</td><td align=\"center\">2,965,250</td><td align=\"center\">2,964,390</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2585</td><td align=\"center\">Chromosome</td><td align=\"center\">2,970,368</td><td align=\"center\">2,969,811</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c2684</td><td align=\"center\">Chromosome</td><td align=\"center\">3,077,272</td><td align=\"center\">3,076,727</td><td align=\"center\">COG1595</td></tr><tr><td align=\"left\">PHZ_c0569</td><td align=\"center\">Chromosome</td><td align=\"center\">605,441</td><td align=\"center\">604,233</td><td align=\"center\">COG4941<sup>2</sup></td></tr><tr><td align=\"left\">PHZ_c3154</td><td align=\"center\">Chromosome</td><td align=\"center\">3,582,010</td><td align=\"center\">3,583,269</td><td align=\"center\">COG4941</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T6\"><label>Table 6</label><caption><p>Distribution of heat shock related proteins in <italic>P. zucineum </italic>and representative alphaproteobacteria with different living habitats</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Content\\Species</td><td align=\"center\"><italic>S. meliloti</italic></td><td align=\"center\"><italic>B. suis</italic></td><td align=\"center\"><italic>C. crescentus</italic></td><td align=\"center\" colspan=\"2\"><italic>P. zucineum</italic></td><td align=\"center\"><italic>R. conorii</italic></td><td align=\"center\"><italic>G. oxydans</italic></td></tr><tr><td/><td/><td/><td/><td colspan=\"2\"><hr/></td><td/><td/></tr><tr><td/><td/><td/><td/><td align=\"center\">Chromosome</td><td align=\"center\">Plasmid</td><td/><td/></tr></thead><tbody><tr><td align=\"center\"><italic>rpoH</italic>, heat shock sigma factor<sup>1</sup></td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\"><italic>dnaK</italic>, molecular chaperone<sup>2 </sup>(Hsp70)</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\"><italic>grpE</italic>, molecular chaperone (co-chaperonin of Hsp70)</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\">dnaK-like molecular chaperone</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">0</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\">dnaJ, molecular chaperone</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">0</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\">dnaJ-like molecular chaperone</td><td align=\"center\">4</td><td align=\"center\">3</td><td align=\"center\">3</td><td align=\"center\">6</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\">3</td></tr><tr><td align=\"center\"><italic>groEL</italic>, molecular chaperone (hsp60)</td><td align=\"center\">5</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\"><italic>groES</italic>, molecular chaperone (Hsp10, co-chaperonin of Hsp60)</td><td align=\"center\">3</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td></tr><tr><td align=\"center\">molecular chaperone Hsp20</td><td align=\"center\">5</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">3</td><td align=\"center\">9</td><td align=\"center\">0</td><td align=\"center\">3</td></tr><tr><td align=\"center\">molecular chaperone Hsp33</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">0</td><td align=\"center\">0</td><td align=\"center\">1</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T7\"><label>Table 7</label><caption><p>Chemotaxis proteins in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus <italic>P. zucineum</italic></td><td align=\"center\">5'-end</td><td align=\"center\">3'-end</td><td align=\"left\">Name</td><td align=\"center\">Orthologs <italic>C. crescentus</italic></td><td align=\"center\">Operon</td><td align=\"center\">Best BLAST match</td></tr></thead><tbody><tr><td align=\"left\">PHZ_c0690</td><td align=\"center\">753,270</td><td align=\"center\">753,812</td><td align=\"left\">chemotaxis protein CheW</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>M. magneticum AMB-1</italic></td></tr><tr><td align=\"left\">PHZ_c0691</td><td align=\"center\">753,812</td><td align=\"center\">755,218</td><td align=\"left\">chemotaxis protein methyltransferase CheR</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>M. magnetotacticum MS-1</italic></td></tr><tr><td align=\"left\">PHZ_c0692</td><td align=\"center\">755,240</td><td align=\"center\">755,836</td><td align=\"left\">chemotaxis signal transduction protein</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>Rhodospirillum centenum</italic></td></tr><tr><td align=\"left\">PHZ_c0693</td><td align=\"center\">755,836</td><td align=\"center\">757,488</td><td align=\"left\">methyl-accepting chemotaxis protein</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>M. magneticum AMB-1</italic></td></tr><tr><td align=\"left\">PHZ_c0694</td><td align=\"center\">757,501</td><td align=\"center\">759,642</td><td align=\"left\">chemotaxis histidine kinase CheA</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>M. magnetotacticum MS-1</italic></td></tr><tr><td align=\"left\">PHZ_c0695</td><td align=\"center\">759,642</td><td align=\"center\">760,709</td><td align=\"left\">chemotaxis response regulator CheB</td><td align=\"center\">-</td><td align=\"center\">1</td><td align=\"center\"><italic>Rhodospirillum centenum</italic></td></tr><tr><td align=\"left\">PHZ_c3230</td><td align=\"center\">3,661,514</td><td align=\"center\">3,661,050</td><td align=\"left\">CheE protein</td><td align=\"center\">-</td><td align=\"center\">2</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3231</td><td align=\"center\">3,662,099</td><td align=\"center\">3,661,527</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">CC0440</td><td align=\"center\">2</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3233</td><td align=\"center\">3,662,860</td><td align=\"center\">3,662,477</td><td align=\"left\">chemotaxis protein CheYII</td><td align=\"center\">CC0591</td><td align=\"center\">2</td><td align=\"center\"><italic>R. palustris CGA009</italic></td></tr><tr><td align=\"left\">PHZ_c3234</td><td align=\"center\">3,663,186</td><td align=\"center\">3,666,188</td><td align=\"left\">chemotaxis histidine kinase CheA</td><td align=\"center\">CC0594</td><td align=\"center\">2</td><td align=\"center\"><italic>Azospirillum brasilense</italic></td></tr><tr><td align=\"left\">PHZ_c3235</td><td align=\"center\">3,666,188</td><td align=\"center\">3,666,733</td><td align=\"left\">chemotaxis protein CheW</td><td align=\"center\">CC0595</td><td align=\"center\">2</td><td align=\"center\"><italic>Rhodospirillum centenum</italic></td></tr><tr><td align=\"left\">PHZ_c3236</td><td align=\"center\">3,666,786</td><td align=\"center\">3,669,191</td><td align=\"left\">methyl-accepting chemotaxis protein McpH</td><td align=\"center\">CC3349</td><td align=\"center\">2</td><td align=\"center\"><italic>R. palustris CGA009</italic></td></tr><tr><td align=\"left\">PHZ_c3237</td><td align=\"center\">3,670,166</td><td align=\"center\">3,669,336</td><td align=\"left\">chemotaxis protein methyltransferase CheR</td><td align=\"center\">CC0598</td><td align=\"center\">2</td><td align=\"center\"><italic>R. palustris HaA2</italic></td></tr><tr><td align=\"left\">PHZ_c3238</td><td align=\"center\">3,671,242</td><td align=\"center\">3,670,166</td><td align=\"left\">chemotaxis response regulator CheB</td><td align=\"center\">CC0597</td><td align=\"center\">2</td><td align=\"center\"><italic>M. magneticum AMB-1</italic></td></tr><tr><td align=\"left\">PHZ_c3371</td><td align=\"center\">3,820,121</td><td align=\"center\">3,819,669</td><td align=\"left\">CheE protein</td><td align=\"center\">CC0441</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3372</td><td align=\"center\">3,820,729</td><td align=\"center\">3,820,124</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">-</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3373</td><td align=\"center\">3,821,034</td><td align=\"center\">3,820,729</td><td align=\"left\">CheU protein</td><td align=\"center\">CC0439</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3374</td><td align=\"center\">3,821,651</td><td align=\"center\">3,821,082</td><td align=\"left\">chemotaxis protein CheD</td><td align=\"center\">CC0438</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3375</td><td align=\"center\">3,822,037</td><td align=\"center\">3,821,651</td><td align=\"left\">chemotaxis protein CheYII</td><td align=\"center\">CC0437</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3376</td><td align=\"center\">3,823,068</td><td align=\"center\">3,822,040</td><td align=\"left\">chemotaxis response regulator CheB</td><td align=\"center\">CC0436</td><td align=\"center\">3</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3377</td><td align=\"center\">3,823,955</td><td align=\"center\">3,823,068</td><td align=\"left\">chemotaxis protein methyltransferase CheR</td><td align=\"center\">CC0435</td><td align=\"center\">3</td><td align=\"center\"><italic>A. cryptum JF-5</italic></td></tr><tr><td align=\"left\">PHZ_c3378</td><td align=\"center\">3,824,410</td><td align=\"center\">3,823,946</td><td align=\"left\">chemotaxis protein CheW</td><td align=\"center\">CC0434</td><td align=\"center\">3</td><td align=\"center\"><italic>Rhizobium etli CFN 42</italic></td></tr><tr><td align=\"left\">PHZ_c3379</td><td align=\"center\">3,826,614</td><td align=\"center\">3,824,422</td><td align=\"left\">chemotaxis histidine kinase CheA</td><td align=\"center\">CC0433</td><td align=\"center\">3</td><td align=\"center\"><italic>A. cryptum JF-5</italic></td></tr><tr><td align=\"left\">PHZ_c3380</td><td align=\"center\">3,826,997</td><td align=\"center\">3,826,635</td><td align=\"left\">chemotaxis protein CheYI</td><td align=\"center\">CC0432</td><td align=\"center\">3</td><td align=\"center\"><italic>Caulobacter vibrioides</italic></td></tr><tr><td align=\"left\">PHZ_c3381</td><td align=\"center\">3,827,299</td><td align=\"center\">3,826,997</td><td align=\"left\">CheX protein</td><td align=\"center\">CC0431</td><td align=\"center\">3</td><td align=\"center\"><italic>Sinorhizobium meliloti</italic></td></tr><tr><td align=\"left\">PHZ_c3382</td><td align=\"center\">3,829,234</td><td align=\"center\">3,827,306</td><td align=\"left\">methyl-accepting chemotaxis protein McpA</td><td align=\"center\">CC0430</td><td align=\"center\">3</td><td align=\"center\"><italic>A. cryptum JF-5</italic></td></tr><tr><td align=\"left\">PHZ_c0101</td><td align=\"center\">94,220</td><td align=\"center\">93,750</td><td align=\"left\">CheE protein</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c0102</td><td align=\"center\">94,795</td><td align=\"center\">94,220</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c0297</td><td align=\"center\">292,469</td><td align=\"center\">292,864</td><td align=\"left\">chemotaxis protein CheYIV</td><td align=\"center\">CC3471</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c0298</td><td align=\"center\">292,867</td><td align=\"center\">293,679</td><td align=\"left\">chemotaxis protein methyltransferase CheR</td><td align=\"center\">CC3472</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c0732</td><td align=\"center\">803,383</td><td align=\"center\">804,876</td><td align=\"left\">methyl-accepting chemotaxis protein McpB</td><td align=\"center\">CC0428</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c0961</td><td align=\"center\">1,057,134</td><td align=\"center\">1,058,720</td><td align=\"left\">methyl-accepting chemotaxis protein McpI</td><td align=\"center\">CC2847</td><td align=\"center\">scatted</td><td align=\"center\"><italic>R. palustris CGA009</italic></td></tr><tr><td align=\"left\">PHZ_c1198</td><td align=\"center\">1,380,883</td><td align=\"center\">1,383,294</td><td align=\"left\">methyl-accepting chemotaxis protein McpU</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>A. cryptum JF-5</italic></td></tr><tr><td align=\"left\">PHZ_c1199</td><td align=\"center\">1,383,297</td><td align=\"center\">1,383,758</td><td align=\"left\">chemotaxis protein CheW1</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>Sinorhizobium meliloti</italic></td></tr><tr><td align=\"left\">PHZ_c1687</td><td align=\"center\">1,890,274</td><td align=\"center\">1,891,176</td><td align=\"left\">chemotaxis MotB protein</td><td align=\"center\">CC1573</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c1936</td><td align=\"center\">2,169,634</td><td align=\"center\">2,169,939</td><td align=\"left\">chemotactic signal response protein CheL</td><td align=\"center\">CC2583</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c2211</td><td align=\"center\">2,499,744</td><td align=\"center\">2,499,274</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>O. alexandrii HTCC2633</italic></td></tr><tr><td align=\"left\">PHZ_c2392</td><td align=\"center\">2,720,611</td><td align=\"center\">2,720,144</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c2741</td><td align=\"center\">3,142,750</td><td align=\"center\">3,143,238</td><td align=\"left\">chemotaxis protein CheYIII</td><td align=\"center\">CC3155</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3123</td><td align=\"center\">3,549,150</td><td align=\"center\">3,550,016</td><td align=\"left\">chemotaxis MotA protein</td><td align=\"center\">CC0750</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. crescentus CB15</italic></td></tr><tr><td align=\"left\">PHZ_c3401</td><td align=\"center\">3,848,811</td><td align=\"center\">3,850,766</td><td align=\"left\">methyl-accepting chemotaxis protein McpA</td><td align=\"center\">-</td><td align=\"center\">scatted</td><td align=\"center\"><italic>C. vibrioides</italic></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T8\"><label>Table 8</label><caption><p>Flagella genes in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\">5'-end</td><td align=\"center\">3'-end</td><td align=\"left\">Name</td><td align=\"center\">Gene symbol</td><td align=\"center\">Proposed role</td></tr></thead><tbody><tr><td align=\"left\">PHZ_c0080</td><td align=\"center\">75,413</td><td align=\"center\">76,462</td><td align=\"left\">flagellin modification protein FlmA</td><td align=\"center\"><italic>flmA</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c0081</td><td align=\"center\">76,467</td><td align=\"center\">77,621</td><td align=\"left\">flagellin modification protein FlmB</td><td align=\"center\"><italic>flmB</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c0745</td><td align=\"center\">816,772</td><td align=\"center\">818,034</td><td align=\"left\">flagellar hook-length control protein FliK</td><td align=\"center\"><italic>fliK</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0787</td><td align=\"center\">868,051</td><td align=\"center\">866,696</td><td align=\"left\">flagellar hook protein FlgE</td><td align=\"center\"><italic>flgE</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0788</td><td align=\"center\">868,860</td><td align=\"center\">868,171</td><td align=\"left\">flagellar hook assembly protein FlgD</td><td align=\"center\"><italic>flgD</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0789</td><td align=\"center\">870,604</td><td align=\"center\">868,865</td><td align=\"left\">flagellar hook length determination protein</td><td align=\"center\"><italic>flage</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c0790</td><td align=\"center\">870,819</td><td align=\"center\">872,918</td><td align=\"left\">flagellar hook-associated protein</td><td align=\"center\"><italic>flaN</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0791</td><td align=\"center\">872,933</td><td align=\"center\">873,862</td><td align=\"left\">flagellin and related hook-associated proteins</td><td align=\"center\">-</td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0853</td><td align=\"center\">945,008</td><td align=\"center\">946,354</td><td align=\"left\">flagellum-specific ATP synthase FliI</td><td align=\"center\"><italic>fliI</italic></td><td align=\"center\">protein export ATPase</td></tr><tr><td align=\"left\">PHZ_c0854</td><td align=\"center\">946,354</td><td align=\"center\">946,758</td><td align=\"left\">fliJ protein</td><td align=\"center\"><italic>fliJ</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0857</td><td align=\"center\">950,714</td><td align=\"center\">948,621</td><td align=\"left\">flagellar biosynthesis protein FlhA</td><td align=\"center\"><italic>flhA</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c0859</td><td align=\"center\">952,470</td><td align=\"center\">952,138</td><td align=\"left\">flagellar motor switch protein FliN</td><td align=\"center\"><italic>fliN</italic></td><td align=\"center\">motor</td></tr><tr><td align=\"left\">PHZ_c0860</td><td align=\"center\">953,126</td><td align=\"center\">952,479</td><td align=\"left\">flbE protein</td><td align=\"center\"><italic>flbE</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c0861</td><td align=\"center\">954,151</td><td align=\"center\">953,126</td><td align=\"left\">flagellar motor switch protein FliG</td><td align=\"center\"><italic>fliG</italic></td><td align=\"center\">motor</td></tr><tr><td align=\"left\">PHZ_c0862</td><td align=\"center\">955,794</td><td align=\"center\">954,151</td><td align=\"left\">flagellar M-ring protein FliF</td><td align=\"center\"><italic>fliF</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0913</td><td align=\"center\">1,007,753</td><td align=\"center\">1,006,992</td><td align=\"left\">flagellar L-ring protein FlgH</td><td align=\"center\"><italic>flgH</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0914</td><td align=\"center\">1,008,508</td><td align=\"center\">1,007,753</td><td align=\"left\">distal basal-body ring component protein FlaD</td><td align=\"center\"><italic>flaD</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0915</td><td align=\"center\">1,009,300</td><td align=\"center\">1,008,515</td><td align=\"left\">flagellar basal-body rod protein FlgG</td><td align=\"center\"><italic>flgG</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0916</td><td align=\"center\">1,010,052</td><td align=\"center\">1,009,318</td><td align=\"left\">flagellar basal-body rod protein FlgF</td><td align=\"center\"><italic>flgF</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0917</td><td align=\"center\">1,010,272</td><td align=\"center\">1,010,874</td><td align=\"left\">flagellar basal body-associated protein FliL</td><td align=\"center\"><italic>fliL</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0918</td><td align=\"center\">1,010,910</td><td align=\"center\">1,011,983</td><td align=\"left\">flagellar motor switch protein FliM</td><td align=\"center\"><italic>fliM</italic></td><td align=\"center\">motor</td></tr><tr><td align=\"left\">PHZ_c0922</td><td align=\"center\">1,017,085</td><td align=\"center\">1,016,351</td><td align=\"left\">flagellar biosynthesis protein FliP</td><td align=\"center\"><italic>fliP</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c0923</td><td align=\"center\">1,017,420</td><td align=\"center\">1,017,151</td><td align=\"left\">flagellar protein FliO</td><td align=\"center\"><italic>fliO</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c0924</td><td align=\"center\">1,017,502</td><td align=\"center\">1,017,918</td><td align=\"left\">flagellar basal-body rod protein FlgB</td><td align=\"center\"><italic>flgB</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0925</td><td align=\"center\">1,017,942</td><td align=\"center\">1,018,355</td><td align=\"left\">flagellar basal-body rod protein FlgC</td><td align=\"center\"><italic>flgC</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0926</td><td align=\"center\">1,018,370</td><td align=\"center\">1,018,678</td><td align=\"left\">flagellar hook-basal body complex protein FliE</td><td align=\"center\"><italic>fliE</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c0930</td><td align=\"center\">1,021,796</td><td align=\"center\">1,022,056</td><td align=\"left\">flagellar biosynthesis protein FliQ</td><td align=\"center\"><italic>fliQ</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c0931</td><td align=\"center\">1,022,079</td><td align=\"center\">1,022,837</td><td align=\"left\">flagellar biosynthesis protein FliR</td><td align=\"center\"><italic>fliR</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c0932</td><td align=\"center\">1,022,837</td><td align=\"center\">1,023,913</td><td align=\"left\">flagellar biosynthesis protein FlhB</td><td align=\"center\"><italic>flhB</italic></td><td align=\"center\">export apparatus</td></tr><tr><td align=\"left\">PHZ_c1380</td><td align=\"center\">1,563,281</td><td align=\"center\">1,562,745</td><td align=\"left\">putative flagella accessory protein FlaCE</td><td align=\"center\"><italic>flaCE</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1381</td><td align=\"center\">1,565,145</td><td align=\"center\">1,563,358</td><td align=\"left\">flagellin modification protein FlmG</td><td align=\"center\"><italic>flmG</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c1382</td><td align=\"center\">1,565,343</td><td align=\"center\">1,565,765</td><td align=\"left\">flagellar repressor protein FlbT</td><td align=\"center\"><italic>flbT</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c1383</td><td align=\"center\">1,565,782</td><td align=\"center\">1,566,093</td><td align=\"left\">flagellar biosynthesis regulator FlaF</td><td align=\"center\"><italic>flaF</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c1384</td><td align=\"center\">1,566,375</td><td align=\"center\">1,567,202</td><td align=\"left\">flagellin FljM</td><td align=\"center\"><italic>fljM</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1385</td><td align=\"center\">1,567,469</td><td align=\"center\">1,568,314</td><td align=\"left\">flagellin FljM</td><td align=\"center\"><italic>fljM</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1386</td><td align=\"center\">1,568,434</td><td align=\"center\">1,568,724</td><td align=\"left\">flagellin FlaG</td><td align=\"center\"><italic>flaG</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1387</td><td align=\"center\">1,568,887</td><td align=\"center\">1,569,720</td><td align=\"left\">flagellin FljL</td><td align=\"center\"><italic>fljL</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1935</td><td align=\"center\">2,168,522</td><td align=\"center\">2,169,634</td><td align=\"left\">flagellar P-ring protein FglI</td><td align=\"center\"><italic>fglI</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c1937</td><td align=\"center\">2,169,942</td><td align=\"center\">2,170,382</td><td align=\"left\">flagellar basal-body protein FlbY</td><td align=\"center\"><italic>flbY</italic></td><td align=\"center\">flagellar structure</td></tr><tr><td align=\"left\">PHZ_c2595</td><td align=\"center\">2,982,550</td><td align=\"center\">2,983,593</td><td align=\"left\">flagellin modification protein FlmD</td><td align=\"center\"><italic>flmD</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c2597</td><td align=\"center\">2,984,874</td><td align=\"center\">2,986,508</td><td align=\"left\">flagellin modification protein FlmG</td><td align=\"center\"><italic>flmG</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c2599</td><td align=\"center\">2,989,315</td><td align=\"center\">2,989,974</td><td align=\"left\">flmC; flagellin modification protein FlmC</td><td align=\"center\"><italic>flmC</italic></td><td align=\"center\">regulator</td></tr><tr><td align=\"left\">PHZ_c2600</td><td align=\"center\">2,990,549</td><td align=\"center\">2,989,977</td><td align=\"left\">flagellin modification protein FlmH</td><td align=\"center\"><italic>flmH</italic></td><td align=\"center\">regulator</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T9\"><label>Table 9</label><caption><p>Distributions of proteins involved in environmental adaptation in <italic>P. zucineum </italic>and representative alphaproteobacteria with different living habitats</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Species</td><td align=\"center\"><italic>S. meliloti</italic></td><td align=\"center\"><italic>B. suis</italic></td><td align=\"center\"><italic>C. crescentus</italic></td><td align=\"center\"><italic>P. zucineum</italic></td><td align=\"center\"><italic>R. conorii</italic></td><td align=\"center\"><italic>G. oxydans</italic></td></tr></thead><tbody><tr><td align=\"center\">Genome size (Mb)</td><td align=\"center\">6.69</td><td align=\"center\">3.32</td><td align=\"center\">4.02</td><td align=\"center\">4.38</td><td align=\"center\">1.27</td><td align=\"center\">2.92</td></tr><tr><td align=\"center\">GC content (%)</td><td align=\"center\">62.2</td><td align=\"center\">57.3</td><td align=\"center\">67.2</td><td align=\"center\">71.1</td><td align=\"center\">32.4</td><td align=\"center\">60.8</td></tr><tr><td align=\"center\">Habitat</td><td align=\"center\">Multiple<sup>1</sup></td><td align=\"center\">Facultative<sup>1</sup></td><td align=\"center\">Aquatic<sup>1</sup></td><td align=\"center\">Facultative<sup>2</sup></td><td align=\"center\">Obligate<sup>1</sup></td><td align=\"center\">Multiple<sup>3</sup></td></tr><tr><td align=\"center\">ECF, extracytoplasmic function sigma factor (/Mb)</td><td align=\"center\">11 (1.6)</td><td align=\"center\">2 (0.6)</td><td align=\"center\">15 (3.7)</td><td align=\"center\">17 (3.9)</td><td align=\"center\">0 (0)</td><td align=\"center\">2 (0.7)</td></tr><tr><td align=\"center\">Transcriptional regulator (/Mb)</td><td align=\"center\">433 (64.7)</td><td align=\"center\">149(44.9)</td><td align=\"center\">183 (45.5)</td><td align=\"center\">170 (38.8)</td><td align=\"center\">11 (8.7)</td><td align=\"center\">89 (30.1)</td></tr><tr><td align=\"center\">Two-component signal transduction protein (/Mb)</td><td align=\"center\">113 (16.9)</td><td align=\"center\">44 (13.3)</td><td align=\"center\">111 (27.6)</td><td align=\"center\">102 (23.3)</td><td align=\"center\">7 (5.5)</td><td align=\"center\">41 (14.1)</td></tr><tr><td align=\"center\">molecular chaperone</td><td align=\"center\">23</td><td align=\"center\">12</td><td align=\"center\">14</td><td align=\"center\">33</td><td align=\"center\">8</td><td align=\"center\">14</td></tr><tr><td align=\"center\">Flagellar protein</td><td align=\"center\">41</td><td align=\"center\">37</td><td align=\"center\">42</td><td align=\"center\">43</td><td align=\"center\">10</td><td align=\"center\">40</td></tr><tr><td align=\"center\">Chemotaxis protein</td><td align=\"center\">42</td><td align=\"center\">4</td><td align=\"center\">48</td><td align=\"center\">41</td><td align=\"center\">0</td><td align=\"center\">11</td></tr><tr><td align=\"center\">Pilus protein</td><td align=\"center\">13</td><td align=\"center\">4</td><td align=\"center\">9</td><td align=\"center\">16</td><td align=\"center\">2</td><td align=\"center\">4</td></tr><tr><td align=\"center\">Sec-dependent secretion system</td><td align=\"center\">11</td><td align=\"center\">11</td><td align=\"center\">11</td><td align=\"center\">11</td><td align=\"center\">11</td><td align=\"center\">12</td></tr><tr><td align=\"center\">Sec-independent secretion system</td><td align=\"center\">4</td><td align=\"center\">4</td><td align=\"center\">4</td><td align=\"center\">4</td><td align=\"center\">3</td><td align=\"center\">4</td></tr><tr><td align=\"center\">Type II secretory protein</td><td align=\"center\">2</td><td align=\"center\">0</td><td align=\"center\">8</td><td align=\"center\">13</td><td align=\"center\">0</td><td align=\"center\">3</td></tr><tr><td align=\"center\">Type IV secretory protein</td><td align=\"center\">9</td><td align=\"center\">8</td><td align=\"center\">9</td><td align=\"center\">31</td><td align=\"center\">15</td><td align=\"center\">1</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T10\"><label>Table 10</label><caption><p>Type IV secretion systems in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\" colspan=\"3\">Location of protein</td><td align=\"left\">Name</td></tr><tr><td/><td colspan=\"3\"><hr/></td><td/></tr><tr><td/><td align=\"center\">Genomic element</td><td align=\"center\">5'-end</td><td align=\"center\">3'-end</td><td/></tr></thead><tbody><tr><td align=\"left\">PHZ_p0007</td><td align=\"center\">Plasmid</td><td align=\"center\">6,786</td><td align=\"center\">7,445</td><td align=\"left\">type IV secretion protein, VirB1</td></tr><tr><td align=\"left\">PHZ_p0008</td><td align=\"center\">Plasmid</td><td align=\"center\">7,483</td><td align=\"center\">7,800</td><td align=\"left\">type IV secretion protein, VirB2</td></tr><tr><td align=\"left\">PHZ_p0009</td><td align=\"center\">Plasmid</td><td align=\"center\">7,816</td><td align=\"center\">8,148</td><td align=\"left\">type IV secretion protein, VirB3</td></tr><tr><td align=\"left\">PHZ_p0010</td><td align=\"center\">Plasmid</td><td align=\"center\">8,144</td><td align=\"center\">10,546</td><td align=\"left\">type IV secretion protein, VirB4</td></tr><tr><td align=\"left\">PHZ_p0011</td><td align=\"center\">Plasmid</td><td align=\"center\">10,546</td><td align=\"center\">11,298</td><td align=\"left\">type IV secretion protein, VirB5</td></tr><tr><td align=\"left\">PHZ_p0012</td><td align=\"center\">Plasmid</td><td align=\"center\">11,553</td><td align=\"center\">12,488</td><td align=\"left\">type IV secretion protein, VirB6</td></tr><tr><td align=\"left\">PHZ_p0013</td><td align=\"center\">Plasmid</td><td align=\"center\">12,816</td><td align=\"center\">13,493</td><td align=\"left\">type IV secretion protein, VirB8</td></tr><tr><td align=\"left\">PHZ_p0014</td><td align=\"center\">Plasmid</td><td align=\"center\">13,493</td><td align=\"center\">14,320</td><td align=\"left\">type IV secretion protein, VirB9</td></tr><tr><td align=\"left\">PHZ_p0015</td><td align=\"center\">Plasmid</td><td align=\"center\">14,320</td><td align=\"center\">15,543</td><td align=\"left\">type IV secretion protein, VirB10</td></tr><tr><td align=\"left\">PHZ_p0016</td><td align=\"center\">Plasmid</td><td align=\"center\">15,543</td><td align=\"center\">16,538</td><td align=\"left\">type IV secretion protein, VirB11</td></tr><tr><td align=\"left\">PHZ_c1506</td><td align=\"center\">Chromosome</td><td align=\"center\">1,709,481</td><td align=\"center\">1,709,999</td><td align=\"left\">type IV secretion protein, TraF</td></tr><tr><td align=\"left\">PHZ_c1508</td><td align=\"center\">Chromosome</td><td align=\"center\">1,711,058</td><td align=\"center\">1,712,773</td><td align=\"left\">type IV secretion protein, VirD2</td></tr><tr><td align=\"left\">PHZ_c1509</td><td align=\"center\">Chromosome</td><td align=\"center\">1,712,790</td><td align=\"center\">1,714,763</td><td align=\"left\">type IV secretion protein, VirD4</td></tr><tr><td align=\"left\">PHZ_c1512</td><td align=\"center\">Chromosome</td><td align=\"center\">1,716,262</td><td align=\"center\">1,717,242</td><td align=\"left\">conjugal transfer protein, TrbB</td></tr><tr><td align=\"left\">PHZ_c1513</td><td align=\"center\">Chromosome</td><td align=\"center\">1,717,242</td><td align=\"center\">1,717,559</td><td align=\"left\">conjugal transfer protein, TrbC</td></tr><tr><td align=\"left\">PHZ_c1514</td><td align=\"center\">Chromosome</td><td align=\"center\">1,717,562</td><td align=\"center\">1,717,828</td><td align=\"left\">conjugal transfer protein, TrbD</td></tr><tr><td align=\"left\">PHZ_c1515</td><td align=\"center\">Chromosome</td><td align=\"center\">1,717,836</td><td align=\"center\">1,720,283</td><td align=\"left\">conjugal transfer protein, TrbE</td></tr><tr><td align=\"left\">PHZ_c1516</td><td align=\"center\">Chromosome</td><td align=\"center\">1,720,283</td><td align=\"center\">1,721,014</td><td align=\"left\">conjugal transfer protein, TrbJ</td></tr><tr><td align=\"left\">PHZ_c1517</td><td align=\"center\">Chromosome</td><td align=\"center\">1,721,238</td><td align=\"center\">1,722,398</td><td align=\"left\">conjugal transfer protein, TrbL</td></tr><tr><td align=\"left\">PHZ_c1518</td><td align=\"center\">Chromosome</td><td align=\"center\">1,722,401</td><td align=\"center\">1,723,084</td><td align=\"left\">conjugal transfer protein, TrbF</td></tr><tr><td align=\"left\">PHZ_c1519</td><td align=\"center\">Chromosome</td><td align=\"center\">1,723,087</td><td align=\"center\">1,724,064</td><td align=\"left\">conjugal transfer protein, TrbG</td></tr><tr><td align=\"left\">PHZ_c1520</td><td align=\"center\">Chromosome</td><td align=\"center\">1,724,070</td><td align=\"center\">1,725,212</td><td align=\"left\">conjugal transfer protein, TrbI</td></tr><tr><td align=\"left\">PHZ_c2348</td><td align=\"center\">Chromosome</td><td align=\"center\">2,660,517</td><td align=\"center\">2,660,813</td><td align=\"left\">type IV secretion protein, VirB2</td></tr><tr><td align=\"left\">PHZ_c2349</td><td align=\"center\">Chromosome</td><td align=\"center\">2,660,809</td><td align=\"center\">2,661,144</td><td align=\"left\">type IV secretion protein, VirB3</td></tr><tr><td align=\"left\">PHZ_c2350</td><td align=\"center\">Chromosome</td><td align=\"center\">2,661,119</td><td align=\"center\">2,663,497</td><td align=\"left\">type IV secretion protein, VirB4</td></tr><tr><td align=\"left\">PHZ_c2352</td><td align=\"center\">Chromosome</td><td align=\"center\">2,664,374</td><td align=\"center\">2,665,309</td><td align=\"left\">type IV secretion protein, VirB6</td></tr><tr><td align=\"left\">PHZ_c2353</td><td align=\"center\">Chromosome</td><td align=\"center\">2,665,482</td><td align=\"center\">2,666,159</td><td align=\"left\">type IV secretion protein, VirB8</td></tr><tr><td align=\"left\">PHZ_c2354</td><td align=\"center\">Chromosome</td><td align=\"center\">2,666,159</td><td align=\"center\">2,667,004</td><td align=\"left\">type IV secretion protein, VirB9</td></tr><tr><td align=\"left\">PHZ_c2355</td><td align=\"center\">Chromosome</td><td align=\"center\">2,667,004</td><td align=\"center\">2,668,041</td><td align=\"left\">type IV secretion protein, VirB10</td></tr><tr><td align=\"left\">PHZ_c2356</td><td align=\"center\">Chromosome</td><td align=\"center\">2,668,046</td><td align=\"center\">2,669,035</td><td align=\"left\">type IV secretion protein, VirB11</td></tr><tr><td align=\"left\">PHZ_c2357</td><td align=\"center\">Chromosome</td><td align=\"center\">2,669,091</td><td align=\"center\">2,670,872</td><td align=\"left\">type IV secretion protein, VirD4</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T11\"><label>Table 11</label><caption><p>Pilus proteins in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\">5'-end</td><td align=\"center\">3'-end</td><td align=\"left\">Name</td><td align=\"center\">Gene symbol</td></tr></thead><tbody><tr><td align=\"left\">PHZ_c0356</td><td align=\"center\">362,116</td><td align=\"center\">362,289</td><td align=\"left\">pilus subunit protein PilA</td><td align=\"center\"><italic>pilA</italic></td></tr><tr><td align=\"left\">PHZ_c2992</td><td align=\"center\">3,412,800</td><td align=\"center\">3,413,318</td><td align=\"left\">Flp pilus assembly protein TadG</td><td align=\"center\"><italic>tadG</italic></td></tr><tr><td align=\"left\">PHZ_c2995</td><td align=\"center\">3,415,220</td><td align=\"center\">3,415,468</td><td align=\"left\">Flp pilus assembly protein, pilin Flp</td><td align=\"center\">-</td></tr><tr><td align=\"left\">PHZ_c2996</td><td align=\"center\">3,415,532</td><td align=\"center\">3,416,023</td><td align=\"left\">Flp pilus assembly protein, protease CpaA</td><td align=\"center\"><italic>cpaA</italic></td></tr><tr><td align=\"left\">PHZ_c2997</td><td align=\"center\">3,416,039</td><td align=\"center\">3,416,899</td><td align=\"left\">pilus assembly protein CpaB</td><td align=\"center\"><italic>cpaB</italic></td></tr><tr><td align=\"left\">PHZ_c2998</td><td align=\"center\">3,416,899</td><td align=\"center\">3,418,350</td><td align=\"left\">pilus assembly protein CpaC</td><td align=\"center\"><italic>cpaC</italic></td></tr><tr><td align=\"left\">PHZ_c2999</td><td align=\"center\">3,418,355</td><td align=\"center\">3,419,587</td><td align=\"left\">pilus assembly protein CpaE</td><td align=\"center\"><italic>cpaE</italic></td></tr><tr><td align=\"left\">PHZ_c3000</td><td align=\"center\">3,419,594</td><td align=\"center\">3,420,991</td><td align=\"left\">pilus assembly protein CpaF</td><td align=\"center\"><italic>cpaF</italic></td></tr><tr><td align=\"left\">PHZ_c3001</td><td align=\"center\">3,421,030</td><td align=\"center\">3,421,944</td><td align=\"left\">Flp pilus assembly protein TadB</td><td align=\"center\"><italic>tadB</italic></td></tr><tr><td align=\"left\">PHZ_c3002</td><td align=\"center\">3,421,944</td><td align=\"center\">3,422,903</td><td align=\"left\">Flp pilus assembly protein TadC</td><td align=\"center\"><italic>tadC</italic></td></tr><tr><td align=\"left\">PHZ_c3027</td><td align=\"center\">3,451,637</td><td align=\"center\">3,452,566</td><td align=\"left\">Flp pilus assembly protein CpaB</td><td align=\"center\"><italic>cpaB</italic></td></tr><tr><td align=\"left\">PHZ_c3028</td><td align=\"center\">3,452,580</td><td align=\"center\">3,453,893</td><td align=\"left\">Flp pilus assembly protein, secretin CpaC</td><td align=\"center\"><italic>cpaC</italic></td></tr><tr><td align=\"left\">PHZ_c3029</td><td align=\"center\">3,453,893</td><td align=\"center\">3,455,056</td><td align=\"left\">Flp pilus assembly protein, ATPase CpaE</td><td align=\"center\"><italic>cpaE</italic></td></tr><tr><td align=\"left\">PHZ_c3030</td><td align=\"center\">3,455,059</td><td align=\"center\">3,456,489</td><td align=\"left\">Flp pilus assembly protein ATPase CpaF</td><td align=\"center\"><italic>cpaF</italic></td></tr><tr><td align=\"left\">PHZ_c3031</td><td align=\"center\">3,456,489</td><td align=\"center\">3,457,445</td><td align=\"left\">Flp pilus assembly protein TadB</td><td align=\"center\"><italic>tadB</italic></td></tr><tr><td align=\"left\">PHZ_c3032</td><td align=\"center\">3,457,492</td><td align=\"center\">3,458,391</td><td align=\"left\">Flp pilus assembly protein TadC</td><td align=\"center\"><italic>tadC</italic></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T12\"><label>Table 12</label><caption><p>TonB-dependent receptors in the <italic>P. zucineum </italic>genome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Annotation</td><td align=\"center\">Chromosome</td><td align=\"center\">Plasmid</td><td align=\"center\">COG category</td></tr></thead><tbody><tr><td align=\"left\">TonB-dependent receptor</td><td align=\"center\">51</td><td align=\"center\">2</td><td align=\"center\">COG1629<sup>1</sup></td></tr><tr><td align=\"left\">TonB-dependent receptor vitamin B12</td><td align=\"center\">3</td><td align=\"center\">0</td><td align=\"center\">COG4206<sup>2</sup></td></tr><tr><td align=\"left\">TonB-dependent receptor</td><td align=\"center\">4</td><td align=\"center\">0</td><td align=\"center\">COG4771<sup>3</sup></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T13\"><label>Table 13</label><caption><p>Comparison of the signal transduction pathways regulating CtrA between the <italic>P. zucineum </italic>and the <italic>C. crescentus</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Locus</td><td align=\"center\" colspan=\"2\">Length</td><td align=\"center\">Amino acid Identity (%)</td><td align=\"center\">Annotation</td></tr><tr><td align=\"center\"><italic>C. crescentus</italic></td><td align=\"left\"><italic>P. zucineum</italic></td><td align=\"center\"><italic>C. crescentus</italic></td><td align=\"center\"><italic>P. zucineum</italic></td><td/><td/></tr></thead><tbody><tr><td align=\"center\">CC0378</td><td align=\"left\">PHZ_c0577</td><td align=\"center\">355</td><td align=\"center\">359</td><td align=\"center\">80.00</td><td align=\"center\">modification methylase CcrM</td></tr><tr><td align=\"center\">CC1078</td><td align=\"left\">PHZ_c0933</td><td align=\"center\">691</td><td align=\"center\">663</td><td align=\"center\">67.22</td><td align=\"center\">cell cycle histidine kinase CckA</td></tr><tr><td align=\"center\">CC2482</td><td align=\"left\">PHZ_c2681</td><td align=\"center\">842</td><td align=\"center\">606</td><td align=\"center\">63.78</td><td align=\"center\">sensor histidine kinase PleC</td></tr><tr><td align=\"center\">CC1063</td><td align=\"left\">PHZ_c2712</td><td align=\"center\">597</td><td align=\"center\">504</td><td align=\"center\">53.83</td><td align=\"center\">sensor histidine kinase DivJ</td></tr><tr><td align=\"center\">CC3484</td><td align=\"left\">PHZ_c0218</td><td align=\"center\">769</td><td align=\"center\">769</td><td align=\"center\">67.66</td><td align=\"center\">tyrosine kinase DivL</td></tr><tr><td align=\"center\">CC2463</td><td align=\"left\">PHZ_c1309</td><td align=\"center\">130</td><td align=\"center\">121</td><td align=\"center\">89.26</td><td align=\"center\">polar differentiation response regulator DivK</td></tr><tr><td align=\"center\">CC1963</td><td align=\"left\">PHZ_c1817</td><td align=\"center\">202</td><td align=\"center\">205</td><td align=\"center\">80.19</td><td align=\"center\">ATP-dependent protease, ClpP subunit</td></tr><tr><td align=\"center\">CC1961</td><td align=\"left\">PHZ_c1814</td><td align=\"center\">420</td><td align=\"center\">420</td><td align=\"center\">90.47</td><td align=\"center\">ATP-dependent protease, ClpX subunit</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T14\"><label>Table 14</label><caption><p>Human ESTs matching the genome sequences of <italic>P. zucineum</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Query GI</td><td align=\"center\">Sample origin</td><td align=\"center\">Query Length</td><td align=\"center\" colspan=\"2\">Query Position</td><td align=\"center\" colspan=\"2\">Chromosome Position</td><td align=\"center\">Score</td><td align=\"center\">E Value</td><td align=\"center\">Similarity (%)</td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td><td/><td/><td/></tr><tr><td/><td/><td/><td align=\"center\">Begin</td><td align=\"center\">End</td><td align=\"center\">Begin</td><td align=\"center\">End</td><td/><td/><td/></tr></thead><tbody><tr><td align=\"center\">14251638</td><td align=\"center\">Breast tissue<sup>1</sup></td><td align=\"center\">226</td><td align=\"center\">41</td><td align=\"center\">175</td><td align=\"center\">1,276,914</td><td align=\"center\">1,277,048</td><td align=\"center\">204</td><td align=\"center\">2.00E-53</td><td align=\"center\">94.07</td></tr><tr><td align=\"center\">8261474</td><td align=\"center\">Breast tissue</td><td align=\"center\">116</td><td align=\"center\">1</td><td align=\"center\">108</td><td align=\"center\">1,277,042</td><td align=\"center\">1,276,937</td><td align=\"center\">167</td><td align=\"center\">2.00E-42</td><td align=\"center\">96.31</td></tr><tr><td align=\"center\">14251634</td><td align=\"center\">Breast tissue</td><td align=\"center\">142</td><td align=\"center\">19</td><td align=\"center\">134</td><td align=\"center\">1,277,054</td><td align=\"center\">1,276,937</td><td align=\"center\">204</td><td align=\"center\">1.00E-53</td><td align=\"center\">97.46</td></tr><tr><td align=\"center\">33194938</td><td align=\"center\">Lymphatic cell line<sup>2</sup></td><td align=\"center\">441</td><td align=\"center\">8</td><td align=\"center\">441</td><td align=\"center\">1,029,575</td><td align=\"center\">1,029,142</td><td align=\"center\">749</td><td align=\"center\">0</td><td align=\"center\">96.77</td></tr><tr><td align=\"center\">33194696</td><td align=\"center\">Lymphatic cell line</td><td align=\"center\">652</td><td align=\"center\">8</td><td align=\"center\">652</td><td align=\"center\">1,029,575</td><td align=\"center\">1,028,931</td><td align=\"center\">1,166</td><td align=\"center\">0</td><td align=\"center\">97.67</td></tr><tr><td align=\"center\">33193754</td><td align=\"center\">Lymphatic cell line</td><td align=\"center\">654</td><td align=\"center\">8</td><td align=\"center\">654</td><td align=\"center\">1,029,575</td><td align=\"center\">1,028,929</td><td align=\"center\">1,191</td><td align=\"center\">0</td><td align=\"center\">98.15</td></tr><tr><td align=\"center\">7117824</td><td align=\"center\">Lymphatic cell line</td><td align=\"center\">405</td><td align=\"center\">7</td><td align=\"center\">405</td><td align=\"center\">1,558,831</td><td align=\"center\">1,558,433</td><td align=\"center\">735</td><td align=\"center\">0</td><td align=\"center\">98.25</td></tr><tr><td align=\"center\">33194587</td><td align=\"center\">Lymphatic cell line</td><td align=\"center\">638</td><td align=\"center\">7</td><td align=\"center\">638</td><td align=\"center\">2,864,470</td><td align=\"center\">2,863,838</td><td align=\"center\">1,191</td><td align=\"center\">0</td><td align=\"center\">98.89</td></tr><tr><td align=\"center\">7114909</td><td align=\"center\">Lymphatic cell line</td><td align=\"center\">347</td><td align=\"center\">6</td><td align=\"center\">347</td><td align=\"center\">3,498,624</td><td align=\"center\">3,498,283</td><td align=\"center\">654</td><td align=\"center\">0</td><td align=\"center\">99.12</td></tr></tbody></table></table-wrap>" ]
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[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>Supplemental Table 1 </bold>Comparison of genes directly regulated by CtrA between <italic>P. zucineum </italic>and <italic>C. crescentus</italic>.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><sup>1</sup>Size in base pairs of the consensus and the direct repeat (DR) generated by insertion into the genome target site.</p><p><sup>2</sup>A copy is complete if the length of the repeat is ≥ 90% of the consensus, otherwise, the copy is partial.</p><p><sup>3</sup>One complete copy which harbors a 7 bp direct repeat (TCCTAAC) that disrupts the VirD4.</p><p><sup>4</sup>The partial copy is located in the plasmid.</p><p><sup>5</sup>Two partial copies are located in the chromosome, of which a \"partial\" copy with full length is inserted by a copy of repeat04.</p><p><sup>6</sup>repeats 01–04 are IS elements</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>Initial function is related to controlling the expression of phage gene</p><p><sup>2</sup>\"Other types\" include the transcriptional regulators with only one member in the <italic>P. zucineum </italic>genome or transcriptional regulators that could not be classified into any known family.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>COG1595, DNA-directed RNA polymerase specialized sigma subunit, sigma24 homolog;</p><p><sup>2</sup>COG4941, predicted RNA polymerase sigma factor containing a TPR repeat domain</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>rpoH may be responsible for the expression of some or all heat shock proteins</p><p><sup>2</sup>The function of molecular chaperones is to protect unfolded proteins induced by stress factors through renaturation or degradation in cooperation with protease.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>The habitats of <italic>S. meliloti, B. suis, and R. conorii </italic>were indicated in a recent publication [##REF##17888177##42##].</p><p><sup>2</sup>According to our recent publication [##REF##16908113##1##], <italic>P. zucineum </italic>was classified as \"facultative\". <sup>3</sup>Given that <italic>G. oxydans </italic>is often isolated from sugary niches (such as flowers and fruits) and associated soil (such as garden soil and baker's soil) [##REF##11361077##43##], we classified <italic>G. oxydans </italic>as \"multiple\".</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>COG1629, Outer membrane receptor proteins, mostly Fe transport</p><p><sup>2</sup>COG4206, Outer membrane cobalamin receptor protein</p><p><sup>3</sup>COG4774, Outer membrane receptor for monomeric catechols</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>All of three sequences come from the library BN0075 containing 182 ESTs; the original dataset was produced by a modification of the EST sequencing strategy ORESTES (open reading frame expressed sequences tags)[##REF##11593022##44##,##REF##10737800##45##]</p><p><sup>2</sup>All six sequences come from the library NIH_MGC_51 containing 2,381 ESTs; the original dataset was produced and released by the \"Mammalian Gene Collection\" project [##REF##15489334##46##].</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2164-9-386-1\"/>", "<graphic xlink:href=\"1471-2164-9-386-2\"/>", "<graphic xlink:href=\"1471-2164-9-386-3\"/>", "<graphic xlink:href=\"1471-2164-9-386-4\"/>", "<graphic xlink:href=\"1471-2164-9-386-5\"/>", "<graphic xlink:href=\"1471-2164-9-386-6\"/>" ]
[ "<media xlink:href=\"1471-2164-9-386-S1.xls\" mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Lingens", "Blecher", "Blecher", "Blobel", "Eberspacher", "Frohner", "Gorisch", "Gorisch", "Layh"], "given-names": ["F", "R", "H", "F", "J", "C", "H", "H", "G"], "article-title": ["Phenylobacterium immobile gen. nov., sp. nov., a gram-negative bacterium that degrades the herbicide chloridazon"], "source": ["Int J Syst Bacteriol"], "year": ["1985"], "volume": ["35"], "fpage": ["26"], "lpage": ["39"]}]
{ "acronym": [], "definition": [] }
46
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Aug 13; 9:386
oa_package/b4/cb/PMC2529317.tar.gz
PMC2529318
18715506
[ "<title>Background</title>", "<p>Protein phosphatase 2A (PP2A), one of the major serine/threonine protein phosphatases in the cell, is involved in the control of a large number of cellular events including cell growth, intracellular signalling, DNA replication, transcription, translation, cell differentiation and cell transformation [##REF##11171037##1##,##REF##15661531##2##]. The key to understand how PP2A is capable of regulating such diverse, and sometimes even opposite functions, is its structure. The core of PP2A consists of a structural PR65/A subunit and a catalytic C subunit, both existing in two isoforms, α and β. To this PP2A dimer (PP2A<sub>D</sub>), a third regulatory B-type subunit can bind. It is generally believed that the regulatory B-type subunits target the phosphatase to distinct substrates and intracellular localisations. At present approximately 20 regulatory B-type subunits have been described. Based on their primary structure, they can be divided into three families: PR55/B, PR61/B' (also called B56) and PR72/B\" [##REF##11171037##1##]. They share two conserved A subunit binding domains (ASBD) [##REF##11856313##3##]. In theory, about 80 different combinations of trimeric ABC holoenzymes can be formed. How many actually exist in the cell, is unknown and most probably differs in different tissues due to the tissue-specific expression of some PP2A subunits [##REF##11171037##1##]. Furthermore, phosphorylation and methylation of the catalytic C subunit play an important role in the assembly of specific trimeric holoenzymes [##REF##18291659##4##,##REF##17635907##5##].</p>", "<p>In the present study, we focus on the regulatory PR72/B\" subunit family named after the molecular weight of the first identified member [##REF##8392071##6##]. In mammals, sofar 6 members have been described: PR72 [##REF##8392071##6##], PR130 [##REF##8392071##6##], PR70 [##REF##12605688##7##], PPP2R3L product [##REF##11173861##8##], G5PR [##REF##12167160##9##] and mPR59 [##REF##9927208##10##], all sharing a conserved region with two ASBDs important for binding to PP2A<sub>D</sub>. Characteristically for this family, are – in addition to both ASBDs – two Ca<sup>2+</sup>-binding EF-hand motifs [##REF##12524438##11##]. Mutation analysis of these EF-hand motifs together with several binding and activity studies indicate that Ca<sup>2+ </sup>can influence the heterotrimeric assembly and catalytic activity of the B\"-containing PP2A [##REF##12524438##11##, ####REF##17535922##12##, ##REF##17991896##13##, ##REF##18397887##14####18397887##14##].</p>", "<p>PR72 and PR130, the founding members of the B\" family, are two N-terminal splice variants with a different tissue distribution pattern. PR72 is highly abundant in heart and skeletal muscle and barely detectable in other tissues. PR130, on the other hand, has a more widespread distribution [##REF##8392071##6##]. Both splice variants have a role in Wnt signalling since they both regulate Naked Cuticle (Nkd) function, yet apparently in opposite ways [##REF##15687260##15##,##REF##16567647##16##]. Furthermore, addition of IQ-1, a compound which disrupts binding of PR72 and PR130 to both PP2A<sub>D </sub>and Nkd, results in prevention of embryonic stem cell differentiation due to a change of co-activators associating with β-catenin [##REF##17372190##17##]. In addition, PR72-containing PP2A (PP2A<sub>T72</sub>) is also responsible for the glutamate-dependent dephosphorylation of Thr75 in dopamine- and cAMP-regulated phosphoprotein of 32 kDa (DARPP-32) in dopaminoceptive neuronal cells of the striatum [##REF##17535922##12##]. PR130-containing PP2A (PP2A<sub>T130</sub>) has been described as an interacting protein of CG-NAP (centrosome and Golgi localised PKN-associated protein), a scaffolding protein that assembles several protein kinases (PKA, PKN) and protein phosphatases (PP1, PP2A<sub>T130</sub>) on centrosome and Golgi apparatus [##REF##10358086##18##]. PP2A<sub>T130 </sub>is also suggested to be involved in the calcium release from the sarcoplasmic reticulum of heart cells as it can interact with the ryanodine receptor type 2, a heart-specific Ca<sup>2+ </sup>channel found to be hyperphosphorylated in some patients with heart failure [##REF##11352932##19##]. In <italic>Xenopus laevis</italic>, an additional splice variant, named XN73, has been found. This protein contains the specific N-terminus of PR130 followed by a short tail of 7 amino acids and thus lacks the ASBD necessary for PP2A<sub>D</sub>-binding. Consequently, this protein is not a regulatory PR72/B\" subunit <italic>strictu senso </italic>[##REF##12605688##7##] but-based on its sequence-might compete in binding to other cellular partners of PR130.</p>", "<p>After identification of PR48 as a B\" subunit family member which can bind Cdc6 [##REF##10629059##20##] and therefore involved in regulation of the cell cycle, it was found that PR48 represents a partial clone of a larger human B\" subunit, PR70 [##REF##12605688##7##]. Currently, two PR48-containing isoforms have been described with different N-termini: PR70 and PPP2R3L product. PPP2R3L product is mainly expressed in heart and skeletal muscle [##REF##11173861##8##], whereas PR70 is ubiquitously expressed [##REF##12605688##7##]. Recently, various links between PR70 and cell cycle progression have been discovered. PR70 can bind retinoblastoma protein (pRb), thereby regulating its phosphorylation status following oxidative stress [##REF##17991896##13##] and it can associate with Cdc6 [##REF##18397887##14##].</p>", "<p>Another member of the B\" subunit family is named G5PR. It has a wide expression pattern and can bind both PP2A<sub>D </sub>and PP5 [##REF##12167160##9##]. Furthermore, G5PR can interact with GANP, a DNA-primase which is selectively up-regulated in germinal centre B cells after immunisation with T cell-dependent antigens [##REF##12167160##9##]. B-cell-specific G5PR knockout mice display a decreased number of splenic B cells. This enhanced cell death, specifically induced upon antigen binding to the specific B cell receptor, is caused by an increased activation of c-Jun NH<sub>2</sub>-terminal protein kinase and Bim [##REF##16129705##21##]. In parallel, T-cell-specific G5PR knockout mice display a decreased number of thymocytes. The enhanced cell death, mainly seen in CD4 and CD8 double positive thymocytes, is accompanied by increased activation of both c-Jun NH2-terminal protein kinase and caspase-3, but not Bim [##REF##18022237##22##].</p>", "<p>mPR59 is a mouse-specific B\" subunit, discovered as an interacting protein of p107, a Rb related protein [##REF##9927208##10##]. No human orthologue of mPR59 is found so far. It is expressed in various tissues and overexpression can regulate p107 phosphorylation, causing an increase of cells in the G<sub>1 </sub>phase of the cell cycle [##REF##9927208##10##]. Furthermore, mPR59 co-purifies with the L-Type Calcium Channel Ca<sub>v</sub>1.2, a voltage-gated Ca<sup>2+ </sup>channel important for Ca<sup>2+ </sup>influx in cells of the cardiovascular system, heart and brain [##REF##16519540##23##].</p>", "<p>Undoubtedly, the generation of (additional) PR72/B\" knockout mice will be a valuable tool to obtain further insights into the potential physiological roles of the PR72/B\" regulatory subunits. To provide a framework for the generation and analysis of such PR72/B\" knockout mice, we present a comprehensive overview of the murine PR72/B\"-encoding genes, their exon/intron organisation, their (alternative) transcripts and their developmental and tissue-specific expression. Surprisingly and in contrast to the murine PR55/B [##REF##12473071##24##] and PR61/B' [##REF##15095873##25##] families of PP2A subunits, the murine PR72/B\" subunits have evolved somewhat differently compared to their human orthologues. As a consequence the current PR72/B\" nomenclature is confusing, and we now propose some changes to make it more consistent. At the cellular level, we determine the subcellular localisation of the major PR72/B\" subunits and their ability to affect cell cycle progression upon overexpression. Evidence is presented for at least one novel murine PR72/B\" isoform, adding to the high diversity of PP2A B-type subunits.</p>" ]
[ "<title>Methods</title>", "<title>Materials</title>", "<p>[<sup>35</sup>S]-Methionine (2.5 mCi/ml), [<sup>32</sup>P]UTP, Protein G Sepharose and Glutathione Sepharose beads were obtained from Amersham Pharmacia Biotech. Restriction enzymes and DNA-modifying enzymes were purchased from Fermentas. <italic>Pwo </italic>proofreading polymerase (used in all PCR reactions) was from Roche Molecular Biochemicals. DNA oligonucleotides were purchased from Sigma-Genosys.</p>", "<title>Bioinformatics</title>", "<p>To search for the murine B\" homologues in public databases we used the Nucleotide BLAST programs at NCBI <ext-link ext-link-type=\"uri\" xlink:href=\"http://blast.ncbi.nlm.nih.gov/Blast.cgi\"/>. For the alignment of proteins and generation of a phylogenetic tree, we used the multalin program <ext-link ext-link-type=\"uri\" xlink:href=\"http://bioinfo.genopole-toulouse.prd.fr/multalin/multalin.html\"/>. Percentages of similarity and identity between the amino acid sequence of two proteins were calculated using EMBOSS Pairwise alignment algorithm <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/emboss/align\"/>. Data regarding the localisation of genes on the murine chromosomes were obtained using the ENSEMBLE Contig View <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ensembl.org/index.html\"/>, while the corresponding regions in human chromosomes were found using the ENSEMBLE Synteny View. Putative nuclear localisation signals have been predicted using the PSORT program <ext-link ext-link-type=\"uri\" xlink:href=\"http://psort.ims.u-tokyo.ac.jp\"/>, whereas putative nuclear export signals were predicted using the NetNES 1.1 Server <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cbs.dtu.dk/services/NetNES\"/>.</p>", "<title>Antibodies</title>", "<p>Anti-GST monoclonal antibody was purchased from Sigma. Anti PP2A<sub>C </sub>and anti-PR65 monoclonal antibodies were generously supplied by Dr. S. Dilworth (Imperial College, London, UK). Anti-PR72<sub>N </sub>pept. antibody was a generous gift of Dr. B. Hemmings (FMI, Basel, CH). Anti-PR72<sub>rec. </sub>antibody has been previously described [##REF##12524438##11##]. For the generation of the anti- PR130 <sub>N rec. </sub>antibody, BL21-pLys(RP) <italic>E. coli </italic>cells were transformed with PR130 AA1-664 in pET15b and induced with 0.2 mM IPTG overnight at 16°C. The inclusion bodies were purified [##REF##12524438##11##], solubilised in 7 M guanidinium hydrochloride, and dialysed against 200 mM Tris. HCl pH 8.2 containing 0.5 M NaCl. The resulting protein solution was used to immunise rabbits (Unité d'Hormonologie, Marloie, Belgium). The antiserum was taken after 5 boosts with the antigen and used at 1/2000 for Western blotting and 1/100 for immunofluorescence. Before immunisation a sample of the preimmune serum was taken as a negative control.</p>", "<title>RNA isolation and RT-PCR</title>", "<p>Total RNA from NIH 3T3 cells and murine heart tissue was isolated using GenElute™ Mammalian Total RNA Kit (Sigma) and reverse transcribed using primer E<sub>3 </sub>(5'-CTG TGC GCC CAG GAA CTG CGC-3') for the mPR59 isoforms and primers E<sub>5 </sub>(5'-CCT TGC AGT CCT CTT CCA CCA GC-3'), E<sub>7 </sub>(5'-GAT TCT GTC AGG GCT CTA GCA GG-3') and E<sub>9 </sub>(5'-GGA TGC TGT GTA GAG ATG CTC TAG-3') for the mPR72/130 isoforms. The following primers were used to amplify the different mPR59 isoforms: B\"δ1 (5'-CTC TGC CAT CAG TCT CTG CCC-3'), Bδ2 (5'-CAG GCC ACA CCC ACG GAT TCG-3'), B\"δ3/4 (5'-CCA CAG GGT CTT CAG CCA CGC-3'), E<sub>1 </sub>(5'-GCC CTG CCC ACT CTG ACT ATG-3') and E<sub>2 </sub>(5'-GAC GAC CTG GAG CCT CTG TGA-3'). The primers used to amplify the different mPR72/130 isoforms are B<sub>130 </sub>(5'-ATG GCA GCA ACT TAC AGA CTT GTG-3'), B<sub>72 </sub>(5'-ATG ATC AAG GAA ACG TCC TTG CGA AG-3'), E<sub>4 </sub>(5'-CTG TTG CTT GGG CAG CCT GAC C-3'), E<sub>6 </sub>(5'-AGG TTC AGC TCC AAG AAG GGT GA-3') and E<sub>8 </sub>(5'-CTT CAG TCA GTG GAT GAA GAG TAG-3'). The approximate location of the various primers within the genes is indicated in Figure ##FIG##2##3##.</p>", "<title><italic>In vitro </italic>transcription-translation</title>", "<p>[<sup>35</sup>S]-Methionine-labelled proteins were obtained from pBluescript vectors containing the coding regions of the proteins, using the TNT-coupled rabbit reticulocyte lysate system (Promega) with the appropriate RNA polymerase (T3/T7).</p>", "<title>Immunoprecipitation of tissue extracts</title>", "<p>Wild-type mice were anaesthetised with an intraperitoneal injection of pentobarbital (Nembutal, CEVA), transcardially perfused with ice-cold saline (NaCl 0.9%, B Braun) and dissected tissues were freeze clamped in liquid nitrogen. Tissues were weighed and homogenised on ice within a threefold volume excess of homogenisation buffer (25 mM Tris pH 7.6, 150 mM NaCl, 1 mM EDTA and 1 mM EGTA) containing protease inhibitors (Complete Protease Inhibitor Cocktail, Roche) using a douncer with 20–30 strokes. The homogenates were centrifuged for 10' at 13,000 g and the supernatant was collected. After preclearing with protein G-Sepharose (Amersham Biosciences), tissue lysates were incubated overnight at 4°C with anti-PR72<sub>N rec.</sub>, anti-PR130<sub>N rec. </sub>or anti-PR72<sub>N pept.</sub>. After addition of 40 μl of protein G-Sepharose for 1 h, the immune complexes were washed 4 times with 1 ml TBS + 0.1% Nonidet P-40 before they were dissolved in Laemmli sample buffer. Bound proteins were separated by SDS/PAGE and transferred to nitrocellulose membranes. Individual proteins were detected with the specified antibodies, revealed by horseradish peroxidase-linked secondary antibodies (DAKO) and developed using the ECL kit (Amersham Biosciences).</p>", "<title>GST-pull down</title>", "<p>Monkey COS7 cells were transfected (FuGENE 6, Roche) with pGMEX-T1, hPR72/B\"α2-pGMEX, hPR130/B\"α1-pGMEX, hPR70/B\"β1-pGMEX, mPR59/B\"δ1-pGMEX, mPR59/B\"δ2-pGMEX, mPR59/B\"δ3-pGMEX and mG5PR/B\"γ-pGMEX in a 10-cm dish. 48 h after transfection, cells were rinsed with phosphate-buffered saline (PBS), lysed in 200 μl NET buffer (50 mM Tris pH 7.4, 150 mM NaCl, 15 mM EDTA and 1% NP-40) and centrifugated for 10' at 13,000 g. Cell lysates were incubated at 4°C for 1 h with NENT<sub>200 </sub>buffer (20 mM Tris-HCl pH 7.4, 1 mM EDTA, 0.1% Nonidet P-40, 25% glycerol, 200 mM NaCl) containing 1 mg/ml bovine serum albumin and 25 μl glutathione-Sepharose beads (Amersham Biosciences) on a rotating wheel. The beads were washed 2 times with 1 ml of NENT<sub>300 </sub>(NENT with 300 mM NaCl) containing 1 mg/ml bovine serum albumin and 2 times with 1 ml of NENT<sub>300</sub>. Bound proteins were eluted by addition of 20 μl of Laemmli sample buffer and boiling. The eluted proteins were analysed by SDS-PAGE and Western blotting.</p>", "<title>Construction of isoform-specific RNA probes</title>", "<p>To generate isoform-specific PCR products, we used following primers: 5'-ATG ATC AAG GAA ACG TCC TTG CGA AG-3' and 5' GAA GAG GCT GAA GTC ATT TCA G-3', generating mPR72/B\"α2 nt 1–120; 5'-ATG GCA GCA ACT TAC AGA CTT GTG-3' and 5'-GAA TAC CTG TAA CTT AAA GGA C-3', resulting in mPR130/B\"α1 nt 1–330; 5'-atg gac tgg a aa gac gtg ctt c-3' and 5'-gcc aaa ctc ctt cat aca gat-3', producing mG5PR/B\"γnt 1–450; 5'-ATG GCG CCG CTG ACG CCG CGG-3' and 5'-GAA CGA CGG GAC CCA GGA CG-3', producing mPR59/B\"δ1 nt 1–243; and 5'-AGG CCA CAC CCA CGG ATT CG-3' and GTT TCC GTC GCG CTT CCA GAA G-3', resulting in mPR59/B\"δ2 54 nt 5'UTR + nt 1–120 of coding region. We subcloned these PCR products into the pBluescript II SK-vector (Stratagene). The constructs were used to generate RNA probes. The RNA probes were transcribed and labeled with [<sup>32</sup>P]UTP using Ambion's Strippable RNA probe synthesis and removal kit.</p>", "<title>Northern blotting</title>", "<p>A Mouse Tissue MTN<sup>® </sup>Blot containing poly(A<sup>+</sup>) RNA from eight murine tissues and a Mouse Embryo MTN<sup>® </sup>Blot containing poly(A<sup>+</sup>) RNA from murine embryos at days 7, 11, 15 and 17 were obtained from Clontech Laboratories. The membranes were hybridised at 64°C using Ambion's Ultrahyb Ultrasensitive hybridisation buffer and washed with Northern Max Low and High Stringency Buffers from Ambion. They were exposed and analysed using ImageQuant (Molecular Dynamics). Probe removal was done using Ambion's Strippable RNA probe synthesis and removal kit. For quantification we used the ImageQuant program from Molecular Dynamics. Absolute values of the different tissues and embryonic stages were first divided by the absolute β-actin values of the corresponding tissue/embryonic stages. These values where then compared with the value of the most intense band on the tissue blot, which was given a value of 100%.</p>", "<title>Subcellular localisation</title>", "<p>PCR fragments of hPR72/B\"α2, hPR70/B\"β1, mG5PR/B\"γ, mPR59/B\"δ1 and mPR59/B\"δ2 were cloned into the <italic>Sma</italic>I restriction site of pEGFP-C1 (Clontech). 1 μg of each plasmid was transfected into COS7 cells grown on a glass coverslip in a 12-well dish. 24 h after transfection, cells were washed in PBS and fixed in PBS containing 4% paraformaldehyde for 10 min. After 3 washes with PBS, cells were mounted in Prolong gold antifade reagent with DAPI (Invitrogen). For immunofluorescence staining of endogenous PR130, COS7 cells grown on a glass coverslip were rinsed with PBS and fixed in PBS with 4% paraformaldehyde for 10 min. Subsequently, cells were incubated with PBS containing 1% bovine serum albumin (BSA, Sigma) for 30 min and then for 45 min with the PR130<sub>N rec. </sub>primary antibodies, followed by incubation with Alexa Fluor 488 donkey anti-rabbit secondary antibody (Invitrogen) in PBS with 1% BSA. Finally, the cells were washed 3 times in PBS, once in water and mounted in Prolong gold antifade reagent with DAPI (Invitrogen). All slides were examined with a LSM-510 laser scanning confocal microscope (Zeiss, Jena, Germany), using a Plan-Neofluar 40×/1.6 Oil DIC objective.</p>", "<title>FACS Analysis</title>", "<p>Asynchronously growing HeLa cells were transfected with pEGFP-C1 or the GFP fusion plasmids of the different B\" subunits in two 10-cm dishes per plasmid. If nocodazole (1 μg/ml) was used, it was added at this point for another 16 h before FACS analysis. 48 h after transfection, cells were trypsinised, washed in PBS and fixed for 5 min in 4% paraformaldehyde at room temperature. After washing, the cell pellet was incubated in 0.5 ml of PBS containing 100 μg/ml propidium iodide (Fluka) and 0.1% RNase for at least 1 h at room temperature. The samples were analysed with a Beckman Instruments Coulter Epics XL flow cytometer (Analis) on FL1 (for EGFP) and FL3 (for propidium iodide) using standard procedures, and the System II™ software (Analis) and WinMDI Version 2.8 software for quantification.</p>", "<title>Immunohistochemistry</title>", "<p>Wild-type mice were anaesthetised with pentobarbital (Nembutal, CEVA) and transcardially perfused with PBS containing 4% paraformaldehyde. Organs were removed, postfixed overnight at 4°C and paraffin embedded. Sections (7 μM) were mounted on silan coated glass slides (Menzel gläser). For antigen retrieval, dewaxed and rehydrated tissue sections were boiled in 10 mM citric acid (pH 6.0) for 10 min. After cooling the slides, endogenous peroxidase was removed with 0.03% H<sub>2</sub>O<sub>2 </sub>in methanol for 20 min and transferred to PBS solution. Subsequently, sections were encircled with a water-repellent PAP-pen (Zymed) and rinsed with PBS. After blocking with PBS containing 1% BSA for 45 min, sections were immunoreacted for 1 h at RT with primary antibodies, washed 3 times with PBS containing 0.05% Tween (PBS-T), incubated with biotinylated goat anti-Rabbit secondary antibody (Vector Labs) at RT for 30 min, and washed three times with PBS-T. After incubation for 30 min with the VECTASTAIN ABC Systems (Vector Labs), sections were washed 3 times in PBS-T, incubated with diaminobenzidine tetrahydrochloride (1 tablet dissolved in 10 ml 50 mM Tris pH 7.6, MP Biomedicals), washed three times in water and incubated with hematoxylin (Sigma) or iron hematoxylin (Sigma) for 2 min. Next, sections were washed in tap water for 5', dehydrated and mounted using Depex. Tissue sections were examined using a light microscope equipped with a digital camera (DC200, Leica Microsystems).</p>" ]
[ "<title>Results</title>", "<title>The murine B\" family isoforms</title>", "<p>In mammals, four genes of the B\" regulatory subunit family of PP2A have been described, giving rise to six isoforms: PR72 [##REF##8392071##6##], PR130 [##REF##8392071##6##], PR70 [##REF##12605688##7##], PPP2R3L product [##REF##11173861##8##], G5PR [##REF##12167160##9##] and mPR59, an isoform sofar only found in mice [##REF##9927208##10##].</p>", "<p>Upon searching the NCBI database for murine B\" family members via the BLAST algorithm, we retrieved the complete protein sequence of two murine PR72 isoforms: the human PR72 orthologue [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BAE21013\">BAE21013</ext-link>] (referred to as mPR72/B\"α2) and a shorter variant missing the last 41 residues [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BAC28935\">BAC28935</ext-link>] (referred to as mPR72/B\"α4). In addition, one mG5PR isoform [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"NP_067504\">NP_067504</ext-link>] (referred to as mG5PR/B\"γ) and four mPR59 isoforms were retrieved: mPR59 as described by Voorhoeve <italic>et al. </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAC98973\">AAC98973</ext-link>] (referred to as mPR59/B\"δ2) [##REF##9927208##10##], two mPR59 variants containing different N-termini [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAH59852\">AAH59852</ext-link> and BAE25309] (referred to as mPR59/B\"δ1 and mPR59/B\"δ3 respectively) and a mPR59/B\"δ3 variant harbouring an alternative C-terminus [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAH96544\">AAH96544</ext-link>] (referred to as mPR59/B\"δ4). Additionally, two partial protein sequences of murine PR130 homologues were retrieved, one containing the first 615 amino acids of the PR130 specific N-terminus [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BAC31413\">BAC31413</ext-link>] and the other containing the last 203 amino acids of the specific N-terminus followed by 12 additional residues [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BAC37349\">BAC37349</ext-link>], resembling <italic>Xenopus </italic>XN73 (referred to as mPR130/B\"α3). Of the full-length mPR130 (referred to as mPR130/B\"α1), only a predicted sequence [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XP_135153\">XP_135153</ext-link>] could be found.</p>", "<p>To confirm these <italic>in silico </italic>data and to get an idea about the abundancy of these clones, we scanned the NCBI database for murine EST-clones containing the B\"-specific N- or C-termini. Only very few EST-clones were found carrying the sequence of the mPR72 C-terminus lacking 41 residues [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BE852268\">BE852268</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"CA984544\">CA984544</ext-link>], the XN73-like truncated mPR130 C-terminus [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AV356684\">AV356684</ext-link>] and the alternative mPR59 C-terminus [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BB357167\">BB357167</ext-link>], making it rather unlikely that murine B\" family members containing these termini (mPR130/B\"α3, mPR72/B\"α4 and mPR59/B\"δ4) do abundantly exist. In contrast, five EST-clones of the specific N-terminus of mPR59/B\"δ3 and multiple (&gt; 15) EST-clones of the other B\"-specific N- and C-termini were found, suggesting that mPR72/B\"α2, mPR130/B\"α1, mG5PR/B\"γ, mPR59/B\"δ1, PR59/B\"δ2, and PR59/B\"δ3 are much more abundant, and probably represent the main murine B\" isoforms.</p>", "<p>Since mPR59/B\"δ1 and mPR59/B\"δ3 are putatively novel, and to further confirm their existence, we performed a reverse transcription on NIH 3T3 and murine heart RNA using primer E<sub>3 </sub>annealing in the mPR59 3' UTR. With the resulting cDNAs as templates and the use of five different primers, annealing with the specific N-termini of PR59/B\"δ1–4 (B<sub>δ1</sub>, B<sub>δ2</sub>, B<sub>δ3/4</sub>) and/or the specific C-termini of PR59/B\"δ1–3 (E<sub>2</sub>) and PR59/B\"δ4 (E<sub>1</sub>), we were able to generate several DNA fragments, which after sequencing, were shown to correspond to PR59/B\"δ1, PR59/B\"δ2 and PR59/B\"δ3. <italic>In vitro </italic>transcription-translation reactions of these cDNAs revealed proteins of about 60 kDa (PR59/B\"δ1), 55 kDa (PR59/B\"δ2) and 40 kDa (PR59/B\"δ3) (Figure ##FIG##0##1A##), implying that not only the messengers of PR59/B\"δ1,δ2,δ3 are present in murine cells, but that they can also be translated into proteins. A cDNA for mPR59/B\"δ4 could not be amplified using this approach, confirming its low occurrence in the EST database.</p>", "<p>A similar approach was undertaken to confirm the existence of the four mPR130/PR72 isoforms. In this case RT-PCR of NIH 3T3 or murine heart RNA led to the amplification of mPR130/B\"α1 and mPR72/B\"α2 cDNA, but not of mPR130/B\"α3 or mPR72/\"α4. To confirm this at the protein level, we prepared protein extracts of murine embryonic fibroblast NIH 3T3 cells and several murine adult tissues: heart, in which both human PR130 as PR72 are highly expressed [##REF##8392071##6##]; adrenal gland, the tissue from which the mPR130/B\"α3 EST-clone [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AV356684\">AV356684</ext-link>] was isolated, and brain. Three antibodies were used to perform immunoprecipitations of the different mPR130/PR72 isoforms: a PR72 AB, raised against full-length recombinant human PR72 [##REF##12524438##11##]; a PR130<sub>N </sub>AB, raised against the recombinant specific N-terminus of human PR130 (AA 1–664) and a peptide PR72 AB, raised against the first 19 amino acids of the human PR72-specific N-terminus. The results demonstrate immunoreactivity at 140 kDa and 70 kDa, corresponding to mPR130/B\"α1 and mPR72/B\"α2 (Figure ##FIG##0##1B##). No other specific bands could be observed, even upon prolonged exposure, confirming our <italic>in silico </italic>and RT-PCR analysis. Consequently, it can be concluded that should the mPR130/B\"α3 and/or mPR72/B\"α4 isoforms exist, their expression would be very low or highly restricted in time and/or space.</p>", "<p>Together, six main B\" family members are present in mice: mPR130/B\"α1, mPR72/B\"α2, mG5PR/B\"γ, mPR59/B\"δ1, mPR59/B\"δ2 and mPR59/B\"δ3, with mPR59/B\"δ1 and δ3 being novel ones. A protein alignment of these murine B\" isoforms (Figure ##FIG##1##2##) reveals that they all share a conserved region, harbouring both ASBD and both EF-hand motifs, with the exception of mPR59/B\"δ3 which misses 31 amino acids of the first ASBD. They mainly differ within their N- and/or C-termini, which may be responsible for isoform-specific functions. Surprisingly, no evidence of murine PR70 and PPP2R3L product orthologues could be found. The murine proteins most closely resembling these isoforms are PR59/B\"δ1 (58% identity, 66% similarity with hPR70) and PR59/B\"δ2 (57% identity; 66% similarity with the PPP2R3L product).</p>", "<title>Genomic organisation of the three murine B\" genes</title>", "<p>An alignment of the nucleotide sequences of the murine PR72/B\" isoforms suggests that all novel isoforms are the result of alternative splicing of the genes encoding previously described members. By BLASTN analysis of the murine genome using the murine cDNA sequences, we could solve the genomic organisation of these three murine B\" genes (Figure ##FIG##2##3##). The intron-exon boundaries were deduced by comparing the sequences obtained from the genomic clones and the respective cDNAs. Almost all boundaries were found to follow Chambon's rule (GT-AG) for splice donor and acceptor sites [##REF##6791577##26##] (Table ##TAB##0##1##).</p>", "<p>The genomic organisation of the gene giving rise to the murine PR130 and PR72 isoforms (<italic>3222402P14Rik</italic>, MGI: 2442104) was deduced from clone RP24-308L17 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AC120390\">AC120390</ext-link>], containing the entire gene. To retain the logic in the PR72/B\" gene nomenclature, we propose to rename this gene into <italic>Ppp2r3a</italic>, analogous to the human <italic>PPP2R3A </italic>gene. The mPR130/PR72 gene is located on chromosome 9F1 (101 Mb). This region corresponds to human chromosome 3q22, where <italic>PPP2R3A </italic>is located. The mPR130/PR72 gene (Figure ##FIG##2##3##) spans approximately 106 kb and consists of 15 exons. In addition to the exons giving rise to mPR130/B\"α1 and mPR72/B\"α2, we could also locate both the exon containing the sequence of the 12 specific C-terminal amino acids of mPR130/B\"α3 (exon 2), and the exon responsible for the premature ending of PR72/B\"α4 (exon 14B) at the correct sites within the gene. Since PR72/B\"α2 is the result of an intra-exonic splicing event within exon 14, PR72/B\"α4 might well be the result of a splicing error at this position, possibly explaining its low abundance. In the human PR130/PR72 gene (<italic>PPP2R3A</italic>), the exon responsible for the putative generation of a human PR72/B\"α4 isoform is also present. The NCBI human EST database contains exactly one EST [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"CA427549\">CA427549</ext-link>] containing this exon. In contrast, no sign of the specific C-terminal tail of either mPR130/B\"α3 or XN73 could be found in <italic>PPP2R3A</italic>. In addition, no human EST-clones were retrieved containing the PR130-specific N-terminus followed by nucleotides other than those of the common part of PR130 and PR72.</p>", "<p>To unravel the genomic organisation of the murine PR59 gene (MGI: 1335093), we analysed NCBI contig NW_001030923. Currently, this gene's symbol is <italic>Ppp2r3a</italic>, but as one could easily mistake it for the murine orthologue of <italic>PPP2R3A</italic>, we propose to change its name into <italic>Ppp2r3d </italic>(see also further). This gene spans approximately 9 kb and consists of 13 exons. The exons encoding the complete sequences of all mPR59 isoforms were found at the appropriate locations (Figure ##FIG##2##3##). To find the human <italic>Ppp2r3d </italic>homologue, we scanned the human genome using the BLASTN program with the different mPR59 isoforms as input. The closest match was the human PR70/PPP2R3L product gene (<italic>PPP2R3B/L</italic>), followed by the human PR130/PR72 gene (<italic>PPP2R3A</italic>), confirming the similarity between PR70/PPP2R3L and mPR59 at the genomic level. Therefore, the murine <italic>Ppp2r3d </italic>and the human <italic>PPP2R3B/L </italic>gene may have evolved from a common ancestor gene. This hypothesis looks promising since to our knowledge no organism contains both a PR70/PPP2R3L product and a PR59-encoding gene. A phylogenetic tree, based on the protein sequences of various PR72/B\" family members in man and mouse, further supports this hypothesis (Figure ##FIG##3##4##).</p>", "<p>Analysis of the mG5PR genomic structure is based on NCBI contig NW_001030500, which is located on chromosome 12C2 (56 Mb, ENSEMBLE ContigView) and contains the entire gene (<italic>Ppp2r3c</italic>, MGI: 193009). The mG5PR gene (Figure ##FIG##2##3##) contains 13 exons and closely mimics the genomic organisation of the human gene [##REF##12167160##9##].</p>", "<title>The mPR59/B\"δ3 protein is not a genuine PP2Aregulatory subunit</title>", "<p>With the exception of XN73 and mPR130/B\"α3, all B\" family members contain a conserved region and specific N- and C-termini. The conserved region, which contains two ASBDs and two EF-hand motifs, is necessary for PR65/A binding [##REF##11856313##3##]. Since the first ASBD is not intact in mPR59/B\"δ3 (Figure ##FIG##1##2##), we wondered whether this isoform is still able to bind PP2A. To this end, we made GST-fusion proteins of the main B\" family members and overexpressed these in COS-7 cells. After a GST-pull down assay, we evaluated the binding of both the PR65/A and catalytic subunit of PP2A via Western blotting. As expected, all main B\" isoforms (hPR130, hPR72, hPR70, mG5PR/B\"γ, mPR59/B\"δ1 and mPR59/B\"δ2) bind PP2A (Figure ##FIG##4##5##), and are therefore genuine B\" subunits. In contrast, mPR59/B\"δ3 fails to bind PP2A, suggesting it is not a regulatory subunit of PP2A (Figure ##FIG##4##5##). Like XN73 and mPR130/B\"α3, it might be involved in regulation of PP2A by competing with the other mPR59 isoforms for binding to other binding partners.</p>", "<title>Tissue distribution of mPR130/B\"α1, mPR72/B\"α2, mPR59/B\"δ1 and mPR59/B\"δ2</title>", "<p>We determined the mRNA expression of the genuine murine B\" regulatory subunits via Northern blot analysis (Figure ##FIG##5##6##). Using an antisense probe which hybridises to the first 330 nucleotides of mPR130, transcripts of 4.3 kb and 7.0 kb were detected. These transcripts are probably generated by the use of different polyadenylation sites. However, we can not exclude the possibility that the 4.3 kb transcript represents mPR130/B\"α3. Both mPR130 transcripts have a similar expression pattern, being high in kidney and heart, intermediate in skeletal muscle, brain, liver and testis and low in lung and spleen. These findings are consistent with the previously published ubiquitous expression of human PR130 [##REF##8392071##6##].</p>", "<p>A probe specific for the N-terminal sequence of the mPR72 isoforms, detected a single band at 4.4 kb, likely corresponding to mPR72/B\"α2. Similar to the expression pattern of PR72 in humans [##REF##8392071##6##], mPR72/B\"α2 is abundant in heart and skeletal muscle and barely detectable in other tissues (Figure ##FIG##5##6##).</p>", "<p>As for mPR130/B\"α1 and mPR72/B\"α2, the transcripts of mPR59/B\"δ1 and mPR59/B\"δ2 were visualised using probes hybridising with their specific N-termini. The expression profiles obtained reveal a wide tissue distribution with some minor differences between the two splice variants: mPR59/B\"δ1 expression is high in heart and liver, intermediate in kidney, brain and skeletal muscle and rather low in testis, lung and spleen, while mPR59/B\"δ2 is highly abundant in heart, liver, brain, kidney and testis and less abundant in lung, skeletal muscle and spleen.</p>", "<p>These results, together with the earlier observed broad mG5PR expression pattern [##REF##12167160##9##] indicate a general high expression of all murine PP2A B\" subunits in heart.</p>", "<title>Embryonic expression of the murine B\" regulatory subunits</title>", "<p>In order to generate viable knockout mice, it can be important to establish whether a function of the protein of interest can be expected during embryonic development, as a general knockout of such a gene might lead to an embryonic lethal phenotype. Expression of the mPR130/B\"α1, mPR72/B\"α2, mPR59/B\"δ1, mPR59/B\"δ2 and mG5PR/B\"γ subunits was examined via Northern blotting at embryonic days 7, 11, 15 and 17 (Figure ##FIG##6##7##). Hybridisation was performed using the same specific probes as for the tissue distribution determination. For mG5PR/B\"γ, a probe spanning the first 450 nucleotides of mG5PR/B\"γ was used. No additional splice variants, specific for embryonic stages, were observed for any of the main murine B\" subunits. Expression of both mPR130/B\"α1 and mPR72/B\"α2 transcripts increases as embryonic development proceeds. This might suggest a role for these proteins in fetal growth and development. In previous studies, both hPR130 as well as hPR72 have been reported to influence Wnt signalling [##REF##15687260##15##, ####REF##16567647##16##, ##REF##17372190##17####17372190##17##]. In contrast, the expression of mPR59/B\"δ1, mPR59/B\"δ2 and mG5PR/B\"γ remains constant during all stages of embryonic development (Figure ##FIG##6##7##).</p>", "<title>Immunohistochemical analysis of mPR130/B\"α1 and mPR72/B\"α2 in different muscle tissues</title>", "<p>All B\" isoforms are highly expressed in heart. Moreover, mPR72/B\"α2 expression is nearly restricted to muscle tissue. mPR130/B\"α1, which has a broader expression pattern, is also substantially present in the different muscle tissues represented on the Northern blot. Since we have specific antibodies for both isoforms (Figure ##FIG##0##1B##), we examined the distribution of mPR130/B\"α1 and mPR72/B\"α2 in the different types of muscle tissue (heart, skeletal and smooth muscle) in more detail via immunohistochemistry.</p>", "<p>In longitudinal sections of skeletal muscle, mPR130/B\"α1 and mPR72/B\"α2 both intensively stain the muscle fibers in a striated pattern (Figure ##FIG##7##8A, e–f##). To evaluate whether these bands represent the I- or A-bands, we counterstained the sections with iron hematoxylin. This dye gives the A-band a dark blue colour and colocalises with mPR130/B\"α1 and mPR72/B\"α2 (Figure ##FIG##7##8B, f–g##). In one of the muscle fibers stained with mPR130/B\"α1 AB, we could even observe a darker stained band positioned in the centre of the A-band (Figure ##FIG##7##8B, d##). This band probably represents the H-zone. In sections of murine myocardium, a similar striated staining pattern could be observed in the longitudinally positioned muscle fibers. Cross-sectional muscle fibers did not show staining of any specific structures (Figure ##FIG##7##8A, h–i##). In bladder, neither mPR130/B\"α1 AB nor mPR72/B\"α2 AB stained the smooth muscle fibers of the muscular layer. In contrast, mPR130/B\"α1 staining could be seen in the transitional epithelial layer, lining the inner cavity of the bladder (Figure ##FIG##7##8A, b–c##). In none of the cases, specific staining of any structures was observed using pre-immune serum (Figure ##FIG##7##8A, a,d,g##).</p>", "<p>In conclusion, PR130/B\"α1 and PR72/B\"α2 staining seems to be a specific feature of the A-band striations of heart and skeletal muscle, whereas staining is very low or absent in smooth muscle.</p>", "<title>Subcellular localisation of the murine B\" subunits</title>", "<p>Using GFP-fusion proteins, we determined the subcellular localisation of the hPR72, mPR59/B\"δ1, mPR59/B\"δ2, mG5PR/B\"γ and hPR70 subunits in COS7 cells. Since the PR130 AB is suitable for direct immunofluorescence, we also evaluated the subcellular localisation of endogenous PR130 (Figure ##FIG##8##9##). As previously described [##REF##12524438##11##], hPR72 is mainly present in the nucleus (N &gt;&gt; CP). PR130 is present in both the cytoplasm and the nucleus (N &gt; CP). mPR59/B\"δ1 (CP = N) and mPR59/B\"δ2 (CP &gt; N) appear both in cytoplasm and nucleus but are clearly less abundant in the nucleus than hPR72 and PR130. mG5PR/B\"γ is more abundant in the nucleus than the cytoplasm (N &gt; CP), corresponding to earlier published data [##REF##12167160##9##], and additionally is highly abundant in the cell periphery. Surprisingly, unlike PR48 [##REF##10629059##20##], hPR70 has a more pronounced cytoplasmic expression (CP &gt;&gt; N). This diversity in observed subcellular localisation patterns demonstrates that each B\" subunit may direct the phosphatase to distinct sites and substrates within the cell.</p>", "<title>All B\" subunits tested provoke a G1 arrest upon overexpression</title>", "<p>Several B\" family members have been suggested to influence cell cycle progression [##REF##12167160##9##, ####REF##9927208##10##, ##REF##12524438##11####12524438##11##,##REF##17991896##13##,##REF##18397887##14##,##REF##10629059##20##]. Therefore we analysed the cell cycle profile of HeLa cells upon overexpression of several EGFP-B\" fusion-proteins via FACS analysis. Compared to control cells (EGFP), overexpression of all EGFP-B\" fusion proteins tested (hPR72, hPR130, hPR70, mG5PR/B\"γ, mPR59/B\"δ1 and mPR59/B\"δ2) caused an increase in the G1 population of transfected cells (Figure ##FIG##9##10A##). This was even more pronounced in the presence of nocodazole, which arrests cycling cells in G2/M (Figure ##FIG##9##10B##). These results confirm and extend previously published effects of hPR72 [##REF##12524438##11##] and mPR59/B\"δ2 [##REF##9927208##10##] overexpression on cell cycle progression.</p>" ]
[ "<title>Discussion</title>", "<p>The importance of the B-type subunits for PP2A regulation can hardly be overestimated as they restrict PP2A activity spatially and temporarily within living organisms, thereby determining PP2A substrate selectivity and function. Although the B-type subunits can be classified into three distinct gene families (PR55/B, PR61/B' and PR72/B\"), their still increasing number, the sometimes confusing nomenclature and species-specific differences add to the complexity, especially for the non-specialist. In particular, for the PR72/B\" subunit family the current situation may be confusing. With the aim to make this more clear and at the same time to provide a platform for the generation of B\" knockout mice, we have presented here a comprehensive overview of the murine B\" family members-in analogy to previous reports of the murine B' [##REF##15095873##25##] and B [##REF##12473071##24##] families.</p>", "<p>In addition to the previously described murine-specific PR59 subunit [##REF##9927208##10##] and the murine orthologues of PR130, PR72 and G5PR, we have identified two novel splice variants of mPR59 as members of the murine B\" family. Based on the genomic organisation of the <italic>Ppp2r3d </italic>gene, we named the original isoform PR59/B\"δ2 and the novel isoforms PR59/B\"δ1 and PR59/B\"δ3. In contrast to PR59/B\"δ1 and δ2, no <italic>in vivo </italic>interaction of PR59/B\"δ3 with PP2A could be observed. Consequently, PR59/B\"δ3 can not be considered as a regulatory subunit of PP2A. In addition, other potential murine B\" family members (mPR130/B\"α3, mPR72/B\"α4 and mPR59/B\"δ4) were found to be present in the NCBI EST database. Although genomic evidence for these isoforms could be found, our attempts to confirm their existence at either the mRNA or protein level failed. This suggests that these isoforms are poorly expressed or might result from splicing errors. Nevertheless, we can not exclude the possibility that their expression is missed because of a tight regulation or restriction in space and/or time. Moreover, the existence of two B\" isoforms (PR130/B\"α3 and PR59/B\"δ3) which do not interact with the PP2A core dimer, gives further support to the idea that B\" subunits can exist- and be stable-independently of PP2A and that their function might extend beyond regulation of phosphatase activity as such (see [##REF##18291659##4##] for a discussion). Even if so far only minute amounts of these atypical unexpected messages could be detected, specific conditions might lead to an upregulation and possible functional consequences.</p>", "<p>To our surprise, no murine orthologues of human PR70 or PPP2R3L product could be found. These gene products arise from the <italic>PPP2R3B/L </italic>gene, and according to the genomic organisation of <italic>PPP2R3B/L</italic>, we propose to rename these two N-terminal splice variants PR70/B\"β1 and PR70/B\"β2 respectively (Table ##TAB##1##2##). Several arguments suggest that the murine <italic>Ppp2r3d </italic>gene (PR59) originates from a common ancestor of the human <italic>PPP2R3B/L </italic>gene (PR70), the <italic>Canis familiaris PPP2R3B </italic>gene (PR70) and the <italic>Xenopus laevis LOC398610 </italic>gene (PR70): (1) none of these organisms contain both the genes responsible for the expression of PR59 and PR70 isoforms; (2) these genes share the highest degree of homology amongst the B\" members, both on the protein as well as on the genomic level; (3) from both genes, two B\" regulatory subunits arise by using alternative first exons; and (4) human PR70/B\"β1 can interact with and regulate the phosphorylation status of pRb while murine PR59/B\"δ2 can associate with and regulate the phosphorylation status of p107, another member of the retinoblastoma family [##REF##9927208##10##]. Therefore, they may share some functional similarities. Despite these similarities, we do not consider PR59 as the murine orthologue of PR70. This is based on (1) the rather low sequence homology between the specific N-terminal regions of the different PR59 and PR70 isoforms and (2) the ubiquitous expression of both PR59 regulatory subunits in contrast to the skeletal muscle and heart-specific expression of hPR70/B\"β2[##REF##11173861##8##]. For these reasons, we suggested to rename the PR59-encoding gene <italic>Ppp2r3d</italic>, instead of <italic>Ppp2r3b</italic>. Because of the diversity between mice and humans, it can be concluded that in contrast with the PP2A PR55/B and PR61/B' families, the PR72/B\" family represents a less evolutionary conserved group of proteins. However, this does not mean that this family is not represented in lower species. PR72/B\" members are also found in <italic>Xenopus laevis </italic>[##REF##12605688##7##], <italic>Drosophila melanogaster </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"NM_142585\">NM_142585</ext-link>], <italic>Caenorhabditis elegans </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XM_001667031\">XM_001667031</ext-link>], various plants (<italic>Arabidopsis thaliana </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AF290025\">AF290025</ext-link>], <italic>Oryza sativa </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AC078829\">AC078829</ext-link>]), and some unicellular organisms (<italic>Tetrahymena thermophila </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XP_001015505\">XP_001015505</ext-link>], <italic>Paramecium tetraurelia </italic>[GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XP_001347004\">XP_001347004</ext-link>]), but are manifestly absent in yeast.</p>", "<p>Unlike the PR55/B and PR61/B' family, the nomenclature of both the genes and proteins of the PR72/B\" family members became very confusing. As mentioned earlier, we propose to change the murine PR72/B\" gene names in such a way that the relationship with their human counterparts becomes clearer (Table ##TAB##1##2##). As for the PR72/B\" proteins, until now, most of them have been named according to their molecular weight (with G5PR as an exception to this rule). In this report, we would like to propose a nomenclature which is in accordance with the corresponding gene name and the organisation of that gene, and is consistent with the nomenclature of the proteins of the PR55/B and PR61/B' families. For historical reasons we prefer to keep the original and commonly known PR130, PR72, PR70, PR59 and G5PR protein names combined with the B\" indication and an additional specification (Greek letter) to denote the specific isoform. As such, B\" proteins encoded by the <italic>PPP2R3A </italic>gene (PR130 and PR72) will get the 'α' extension, followed by a number to designate the specific splice variant; B\" proteins encoded by the <italic>PPP2R3B </italic>gene (PR70) get the 'β' extension etc. For the comfort of the authors and readers, once in a manuscript the subunit is clearly defined, the short names B\"α1, B\"γ1 etc. can be used. In Table ##TAB##1##2##, a summary of the (old and new) murine and human B\" nomenclature is given. If one intents to indicate the whole family, one can use PR72/B\" as a common denominator since this makes an unequivocal distinction with the other B-type third subunits: PR55/B and PR61/B'.</p>", "<p>We have also investigated the tissue distribution and developmental expression of all murine PP2A PR72/B\" subunits on the RNA level. Taking together previously published data [##REF##8392071##6##, ####REF##12605688##7##, ##REF##11173861##8####11173861##8##] and our northern blot analyses, it can be concluded that mPR130/B\"α1, hPR130/B\"α1, mPR59/B\"δ1, mPR59/B\"δ2, mG5PR/B\"γ and XPR70/B\"β1 are ubiquitously expressed, indicating that these subunits are involved in general cellular processes. In contrast, mPR72/B\"α2, hPR72/B\"α2, and hPR70/B\"β2 are highly expressed in heart and skeletal muscle and barely detectable in other tissues, suggesting a specific role for these subunits in striated muscle tissues. In fact, all B\" subunits seem to be highly expressed in heart, an observation which is given the presence of two EF-hands in their primary structures and their regulation by Ca<sup>2+ </sup>ions, of particular interest.</p>", "<p>Immunohistochemical analysis of longitudinal fibers of murine heart and skeletal muscle showed a striated pattern for mPR130/B\"α1 and mPR72/B\"α2. Counterstaining with iron hematoxylin revealed that they both colocalise with the A-band. In contrast to the I-band, the A-band does not only contain thin, actin-rich filaments but also thick, myosine-rich filaments. mPR130/B\"α1 and mPR72/B\"α2 staining is virtually absent in the smooth muscle of the bladder, suggesting that these subunits may play a specific role in striated muscle contraction only. Although mPR130/B\"α1 and mPR72/B\"α2 stain the nuclei in longitudinal as well as cross sections of heart and skeletal muscle, their nuclear localisation in these tissues is less pronounced compared to cultured fibroblast cell lines, probably due to their relatively high abundance in the contractile apparatus.</p>", "<p>During embryogenesis no significant differences in expression from E7 until E17 could be observed for transcripts of mG5PR/B\"γ, mPR59/B\"δ1 and mPR59/B\"δ2, indicating that the expression of these proteins is not regulated during early embryonic development. Meanwhile, mPR130/B\"α1 and mPR72/B\"α2 expression increases from E7 to E17. This might imply a role for these proteins during early embryogenesis. Alternatively, protein levels of these isoforms are simply building up for a more maximal expression during late development or after birth. In accordance with this increased expression during embryogenesis, depletion of XPR72 in <italic>Xenopus </italic>embryos caused several severe developmental defects (defects in somite formation, a short axis phenotype and lack of eye differentiation) [##REF##15687260##15##]. Depletion of XPR130 resulted in milder developmental defects such as a disruption of somite organisation and an underdeveloped tail [##REF##16567647##16##]. Together these data provide valuable information that will aid in the development of suitable strategies to generate future B\" knockout mice, as well as in their phenotypic analysis.</p>", "<p>At the cellular level, we have also determined the subcellular localisation of the main murine and human B\" subunits via expression of EGFP-fusion proteins in COS7 cells (for PR72/B\"α2, G5PR/B\"γ, PR70/B\"β1, PR59/B\"δ1 and PR59/B\"δ2) or via indirect immunofluorescence (for PR130/B\"α1). A high diversity was observed: whereas PR130/B\"α1, PR72/B\"α2 and G5PR/B\"γ are predominantly nuclear, PR59/B\"δ1 is equally well expressed in the cytoplasm, and PR70/B\"β1 and PR59/B\"δ2 are predominantly expressed in the cytoplasm. The latter observation further extends the relationship and functional homology between these two isoforms. On the other hand, the cytoplasmic localisation of PR70/B\"β1 is in contrast with the reported nuclear localisation of its truncated PR48 form [##REF##10629059##20##], suggesting that nuclear import/export of PR70 isoforms may be a regulated event. Further, putative nuclear localisation signals are predicted in G5PR/B\"γ (mono- and bipartite), PR130/B\"α1 and PR72/B\"α2 (monopartite), but not in PR59/B\"δ1, PR59/B\"δ2 or PR70/B\"β1, whereas a nuclear export signal is predicted in the common part of PR59/B\"δ1 and PR59/B\"δ2. Although the functionality of these motifs remains to be determined, their putative occurrence/absence fits quite well with our experimental observations.</p>", "<p>Upon overexpression, all B\" subunits tested can induce a G1/S cell cycle arrest in HeLa cells, suggesting that this property may be independent of their subcellular localisation. The failure to proceed to S-phase may be related to the suggestion that many B\" subunits play roles in DNA replication [##REF##12167160##9##,##REF##9927208##10##,##REF##17991896##13##,##REF##18397887##14##,##REF##10629059##20##]. However, the mechanism of this induced cell cycle arrest may equally well be the result of a dominant negative effect, such as for instance a competition between 'free' B\" [##REF##12524438##11##] and trimeric B\" for binding a substrate, or competition between B\" and other B-type subunits for binding to PP2A<sub>D</sub>.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, the murine PR72/B\" family exhibits an equal complexity and diversity as the PR55/B and PR61/B' families of PP2A subunits, with three genes encoding nine isoforms/splice variants, of which at least five are abundantly expressed and give rise to genuine PP2A subunits. An important difference with the PR55/B and PR61/B' families however, is the poorer relationship between human and murine genes, which might indicate that the B\" family is evolutionary more divergent. Moreover, the diversity of PR72/B\" members does not only originate from the number of isoforms, but extends to their specific tissue distribution, developmental expression and subcellular localisation. These differences add to the notion that no matter how similar their primary structures may be, different PP2A B-type isoforms likely display different <italic>in vivo </italic>functions. To explore these functions in mouse models remains an exciting challenge for the future.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Protein phosphatase 2A (PP2A) is a serine/threonine-specific phosphatase displaying vital functions in growth and development through its role in various signalling pathways. PP2A holoenzymes comprise a core dimer composed of a catalytic C and a structural A subunit, which can associate with a variable B-type subunit. The importance of the B-type subunits for PP2A regulation cannot be overestimated as they determine holoenzyme localisation, activity and substrate specificity. Three B-type subunit families have been identified: PR55/B, PR61/B' and PR72/B\", of which the latter is currently the least characterised.</p>", "<title>Results</title>", "<p>We deduced the sequences and genomic organisation of the different murine PR72/B\" isoforms: three genes encode nine isoforms, five of which are abundantly expressed and give rise to genuine PP2A subunits. Thereby, one novel subunit was identified. Using Northern blotting, we examined the tissue-specific and developmental expression of these subunits. All subunits are highly expressed in heart, suggesting an important cardiac function. Immunohistochemical analysis revealed a striated expression pattern of PR72 and PR130 in heart and skeletal muscle, but not in bladder smooth muscle. The subcellular localisation and cell cycle regulatory ability of several PR72/B\" isoforms were determined, demonstrating differences as well as similarities.</p>", "<title>Conclusion</title>", "<p>In contrast to PR55/B and PR61/B', the PR72/B\" family seems evolutionary more divergent, as only two of the murine genes have a human orthologue. We have integrated these results in a more consistent nomenclature of both human and murine PR72/B\" genes and their transcripts/proteins. Our results provide a platform for the future generation of PR72/B\" knockout mice.</p>" ]
[ "<title>List of abbreviations</title>", "<p>AB (antibody), ASBD (A subunit binding domain), CG-NAP (centrosome and Golgi localized PKN-associated protein), DARPP-32 (dopamine- and cAMP-regulated phosphoprotein of 32 kDa), FeH (iron hematoxylin), hPR130 (human PR130), PKA (protein kinase A), mPR59 (murine PR59), PKN (novel protein kinase), PP1 (protein phosphatase 1), PP2A (protein phosphatase 2A), PP2A<sub>D </sub>(PP2A heterodimer), PP2A<sub>TX </sub>(PP2A trimer with x as third subunit), Nkd (Naked Cuticle), PP5 (protein phosphatase 5), pRb (retinoblastoma protein)</p>", "<title>Authors' contributions</title>", "<p>KZ has contributed to the design and execution of most of the presented experiments, the analysis and interpretation of the obtained data and writing of the manuscript. JVL has contributed to the design and realisation of immunohistochemistry experiments and critical assessment of the paper. JG has contributed to the design of experiments, the analysis and interpretation of the data and critical assessment of the manuscript. VJ has contributed to the design and execution of experiments, the analysis and interpretation of the data and writing of the manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We highly appreciate the expert technical assistance of Mrs. Elke Verhoeven. We acknowledge Prof. F. Van Leuven (CME, K.U. Leuven) and the Cell Imaging Core (K.U. Leuven) for the use of microscope facilities and Dr. S. Dilworth and Dr. B. Hemmings for antibodies. This work is supported by grants of the 'Geconcerteerde OnderzoeksActies' of the Flemish government, the 'Interuniversitary Attraction Poles' of the Belgian Science Policy and the 'Fonds voor Wetenschappelijk Onderzoek-Vlaanderen'. V.J. is funded by a fellowship of the K.U. Leuven Research Fund.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Diversity of the murine PR130/72 and PR59 isoforms</bold>. <bold>A: </bold>Autoradiography of [<sup>35</sup>S]-labelled <italic>in vitro </italic>transcription-translation products of plasmids (1 μg) containing cDNA of murine PR59/B\"δ1 (lane δ1), PR59/B\"δ2 (lane δ2) and PR59/B\"δ3 (lane δ3). <bold>B: </bold>Immunoprecipitation of mPR72/PR130 isoforms from different murine cells and tissues (N = NIH 3T3, H = heart, B = brain, A = adrenal gland) using pre-immunisation sera (as negative control) and three different PR72/PR130 antibodies: PR72<sub>rec. </sub>AB recognizes all putative PR72 isoforms and the PR130 isoforms which contain the common C-terminal region, PR130<sub>N rec. </sub>AB recognizes all PR130 isoforms, and PR72<sub>N pept. </sub>AB recognizes all PR72 isoforms.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Alignment of the amino acid sequences of the murine B\" isoforms</bold>. Residues identical for all isoforms are shown in bold. The two ASBDs are marked in grey shade and the boxes represent the two conserved EF-hand motifs. For each isoform, the start Methionine is underlined. The first 8 lines represent only the specific sequences of the PR130 N-terminus.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Organisation of the murine B\"-encoding genes and their transcripts</bold>. The boxes represent the exons (on scale) and the lines represent the introns (not on scale). The coding regions (black boxes) and untranslated regions (white boxes) are indicated. Splicing events are denoted by dotted lines. Below the genomic structures, all putative transcripts are indicated: the main transcripts are coloured in black (at least 5 EST clones were retrieved), while less abundant transcripts are coloured in grey (less than 5 EST clones were found). Primers used for RT-PCR are also indicated (forward primers: B<sub>72</sub>, B<sub>130</sub>, B<sub>δ1</sub>, B<sub>δ2 </sub>and B<sub>δ3/4</sub>; reverse primers: E1 to E9).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Phylogenetic tree (MULTALIN)</bold>. Phylogenetic tree based on protein sequence of human and murine B\" regulatory subunits (human and murine PR72/B\"α2 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"NP_871626\">NP_871626</ext-link> and BAE21013], human and murine PR130/B''α1 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"Q06190\">Q06190</ext-link> and <ext-link ext-link-type=\"gen\" xlink:href=\"XP_135153\">XP_135153</ext-link>], murine PR59/B''δ1,2,3 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAH59852\">AAH59852</ext-link>, AAC98973 and BAE25309], human PR70 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"DAA00385\">DAA00385</ext-link>], human PPP2R3L product [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"CAI41975\">CAI41975</ext-link>], human and murine G5PR/B''γ [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"BAA91308\">BAA91308</ext-link>, <ext-link ext-link-type=\"gen\" xlink:href=\"NP_067504\">NP_067504</ext-link>].</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Association of the main human and murine PR72/B\" isoforms with PP2A<sub>D</sub></bold>. COS7 cells were transfected with GST, hPR130-GST, hPR72-GST, hPR70-GST, mG5PR/B\"γ-GST, mPR59/B\"δ1-GST, mPR59/B\"δ2-GST and mPR59/B\"δ3-GST. 48 h after transfection, a GST pull down assay was performed and binding of PR65/A and C subunits was evaluated via Western blotting using specific antibodies.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Tissue distribution of the murine PR130/B\"α1, PR72/B\"α2, PR59/B\"δ1/2 transcripts</bold>. A first choice Mouse Blot I containing poly(A<sup>+</sup>) RNAs from ten different murine tissues (Clontech) was hybridised with isoform-specific RNA probes. For quantification we used the ImageQuant program from Molecular Dynamics. Percentages of expression are relative to the most intense band on each blot, which was given a value of 100%. Therefore only transcripts present on the same blot can be compared. A β-actin control hybridisation is shown at the bottom.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Expression pattern of the main murine B\" regulatory subunits in embryos</bold>. A murine embryo multiple tissue Northern Blot containing poly(A<sup>+</sup>) RNAs from murine embryos at days 7, 11, 15 and 17 (Clontech) was hybridised with isoform-specific RNA probes. Embryonic expression of mPR130/B\"α1, mPR72/B\"α2, mPR59/B\"δ1, mPR59/B\"δ2 and mG5PR/B\"γ is shown. For quantification we used the ImageQuant program from Molecular Dynamics. Percentages of expression are relative to the most intense band of the murine tissues blot (Figure 6). This way, a comparison with the relative expression of a specific isoform in the different tissues (Figure 6) can be made. The β-actin control hybridisation is shown at the bottom and was used to correct for equal loading.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Immunohistochemistry of mPR130/B\"α1 and mPR72/B\"α2 in muscle tissue (heart, skeletal muscle and smooth muscle)</bold>. <bold>A</bold>: Sections of bladder (a-c), skeletal muscle (d-f) and heart (g-i) were costained with the nuclei-specific hematoxylin dye (blue) and antibodies specific for mPR130/B\"α1 (b, e, h) or mPR72/B\"α2 (c, f, i). As a control, sections were stained with pre-immune sera (a, d, g). <bold>B: </bold>Staining of longitudinal sections of skeletal muscle with iron hematoxylin (a, b), PR130-specific AB (c,d), PR72-specific AB (e) and costaining with iron hematoxylin and PR130 AB (f) or PR72 AB (g).</p></caption></fig>", "<fig position=\"float\" id=\"F9\"><label>Figure 9</label><caption><p><bold>Subcellular localisation of the main PR72/B\" regulatory subunits in COS-7 cells</bold>. Endogenous PR130 was visualised using PR130<sub>N </sub><sub>rec</sub>. AB, while the other main B\" regulatory subunits (hPR72, hPR70, mG5PR/B\"γ, mPR59/B\"δ1 and mPR59/B\"δ2) were visualised as EGFP fusion proteins. Pictures were taken with a LSM-510 laser scanning confocal microscope.</p></caption></fig>", "<fig position=\"float\" id=\"F10\"><label>Figure 10</label><caption><p><bold>Cell cycle profile of EGFP, hPR72-EGFP, hPR130-EGFP, hPR70-EGFP, mG5PR/B\"γ-EGFP, mPR59/B\"δ1-EGFP and mPR59/B\"δ2-EGFP expressing HeLa cells</bold>. 48 h after transfection with the different expression plasmids, HeLa cells were stained for DNA content (propidium iodide), and the cell cycle profile of EGFP-positive cells was measured. Panel A contains untreated cells whereas cells of panel B were treated with nocodazole (16 h, 1 mg/ml) prior to analysis. Quantification of the percentages of cells in G1, S and G2/M stages of the cell cycle are included. These are results from one typical experiment. Each condition was performed at least 4 times giving similar results but the outcome was highly dependent on the (over)expression level.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Exon-intron organisation of the murine PR72/B\" genes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold><italic>Ppp2r3a</italic></bold></td><td align=\"center\">exon</td><td align=\"center\">exon size (bp)</td><td align=\"center\">5'-splice donor</td><td align=\"center\">intron size (bp)</td><td align=\"center\">3'-splice acceptor</td></tr></thead><tbody><tr><td/><td align=\"center\">1</td><td align=\"center\">2434</td><td align=\"center\">ATCAAG<bold>gt</bold>aaga</td><td align=\"center\">4838</td><td align=\"center\">tctt<bold>ag</bold>ATTAGA</td></tr><tr><td/><td align=\"center\">2</td><td align=\"center\">98</td><td align=\"center\">AGGTCT<bold>gt</bold>aacc</td><td align=\"center\">7123</td><td align=\"center\">tccc<bold>ag</bold>AAGGCT</td></tr><tr><td/><td align=\"center\">3</td><td align=\"center\">441</td><td align=\"center\">ACACAG<bold>gt</bold>ttga</td><td align=\"center\">4205</td><td align=\"center\">ttcc<bold>ag</bold>ATTCAA</td></tr><tr><td/><td align=\"center\">4</td><td align=\"center\">267</td><td align=\"center\">GCAAAG<bold>gt</bold>aaca</td><td align=\"center\">8122</td><td align=\"center\">ttgc<bold>ag</bold>GTCTGT</td></tr><tr><td/><td align=\"center\">5</td><td align=\"center\">104</td><td align=\"center\">GAAAAA<bold>gt</bold>aagt</td><td align=\"center\">5495</td><td align=\"center\">ttgt<bold>ag</bold>GTTGCT</td></tr><tr><td/><td align=\"center\">6</td><td align=\"center\">103</td><td align=\"center\">CTTCAG<bold>gt</bold>gata</td><td align=\"center\">4932</td><td align=\"center\">tta<bold>ag</bold>GATGTG</td></tr><tr><td/><td align=\"center\">7</td><td align=\"center\">75</td><td align=\"center\">ACCACG<bold>gt</bold>agga</td><td align=\"center\">21453</td><td align=\"center\">aact<bold>ag</bold>GTTGTT</td></tr><tr><td/><td align=\"center\">8</td><td align=\"center\">87</td><td align=\"center\">TTGCAA<bold>gt</bold>atgc</td><td align=\"center\">5049</td><td align=\"center\">tttc<bold>ag</bold>ACTCTG</td></tr><tr><td/><td align=\"center\">9</td><td align=\"center\">157</td><td align=\"center\">ACCAGG<bold>gt</bold>aagt</td><td align=\"center\">3058</td><td align=\"center\">attt<bold>ag</bold>CTTCAT</td></tr><tr><td/><td align=\"center\">10</td><td align=\"center\">49</td><td align=\"center\">AACAAG<bold>gt</bold>atga</td><td align=\"center\">1074</td><td align=\"center\">cttc<bold>ag</bold>AGGAAA</td></tr><tr><td/><td align=\"center\">11</td><td align=\"center\">90</td><td align=\"center\">TACCAG<bold>gt</bold>aaag</td><td align=\"center\">17238</td><td align=\"center\">tcct<bold>ag</bold>CATTGA</td></tr><tr><td/><td align=\"center\">12</td><td align=\"center\">176</td><td align=\"center\">GTGATG<bold>gt</bold>aagg</td><td align=\"center\">469</td><td align=\"center\">tcat<bold>ag</bold>GCAGAA</td></tr><tr><td/><td align=\"center\">13</td><td align=\"center\">119</td><td align=\"center\">CAGAAG<bold>gt</bold>aaca</td><td align=\"center\">1179</td><td align=\"center\">aatc<bold>ag</bold>GATGTT</td></tr><tr><td/><td align=\"center\">14a</td><td align=\"center\">107</td><td align=\"center\">AGAAGG</td><td align=\"center\">/</td><td align=\"center\"><bold>GT</bold>GAGT</td></tr><tr><td/><td align=\"center\">14b</td><td align=\"center\">1174</td><td align=\"center\">GAGATA<bold>gt</bold>cctc</td><td align=\"center\">15879</td><td align=\"center\">ttcc<bold>ag</bold>CTCATT</td></tr><tr><td/><td align=\"center\">15</td><td align=\"center\">1575</td><td/><td/><td/></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"center\"><bold><italic>Ppp2r3c</italic></bold></td><td align=\"center\">exon</td><td align=\"center\">exon size (bp)</td><td align=\"center\">5'-splice donor</td><td align=\"center\">intron size (bp)</td><td align=\"center\">3'-splice acceptor</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\">1</td><td align=\"center\">866</td><td align=\"center\">CAAAGA<bold>gt</bold>gagt</td><td align=\"center\">3572</td><td align=\"center\">tact<bold>ag</bold>GGAAAA</td></tr><tr><td/><td align=\"center\">2</td><td align=\"center\">128</td><td align=\"center\">TACAGG<bold>gt</bold>aagt</td><td align=\"center\">378</td><td align=\"center\">ttta<bold>ag</bold>TTGCCA</td></tr><tr><td/><td align=\"center\">3</td><td align=\"center\">105</td><td align=\"center\">TTGCAG<bold>gt</bold>acga</td><td align=\"center\">520</td><td align=\"center\">ctta<bold>ag</bold>AACTTA</td></tr><tr><td/><td align=\"center\">4</td><td align=\"center\">113</td><td align=\"center\">GTGCAA<bold>gt</bold>aaga</td><td align=\"center\">3898</td><td align=\"center\">tttt<bold>ag</bold>GCAATT</td></tr><tr><td/><td align=\"center\">5</td><td align=\"center\">98</td><td align=\"center\">GAAAAG<bold>gt</bold>gatt</td><td align=\"center\">1207</td><td align=\"center\">tgtc<bold>ag</bold>TTTGGC</td></tr><tr><td/><td align=\"center\">6</td><td align=\"center\">71</td><td align=\"center\">GAATCA<bold>gt</bold>gagt</td><td align=\"center\">2711</td><td align=\"center\">tccc<bold>ag</bold>GACCTG</td></tr><tr><td/><td align=\"center\">7</td><td align=\"center\">133</td><td align=\"center\">GAACAG<bold>gt</bold>aaaa</td><td align=\"center\">913</td><td align=\"center\">ccca<bold>ag</bold>GGAAGA</td></tr><tr><td/><td align=\"center\">8</td><td align=\"center\">156</td><td align=\"center\">CTGGAG<bold>gt</bold>aaat</td><td align=\"center\">76</td><td align=\"center\">cttt<bold>ag</bold>CTAAGA</td></tr><tr><td/><td align=\"center\">9</td><td align=\"center\">76</td><td align=\"center\">TCTATG<bold>gt</bold>aggc</td><td align=\"center\">381</td><td align=\"center\">tgca<bold>ag</bold>GTCAGT</td></tr><tr><td/><td align=\"center\">10</td><td align=\"center\">137</td><td align=\"center\">GAAATG<bold>gt</bold>agtt</td><td align=\"center\">3006</td><td align=\"center\">ctgt<bold>ag</bold>GACTAT</td></tr><tr><td/><td align=\"center\">11</td><td align=\"center\">138</td><td align=\"center\">TTTAGG<bold>gt</bold>aagt</td><td align=\"center\">1803</td><td align=\"center\">atac<bold>ag</bold>GCCATA</td></tr><tr><td/><td align=\"center\">12</td><td align=\"center\">60</td><td align=\"center\">GTCAAG<bold>gt</bold>tatt</td><td align=\"center\">1108</td><td align=\"center\">ttat<bold>ag</bold>GATGAA</td></tr><tr><td/><td align=\"center\">13</td><td align=\"center\">982</td><td/><td/><td/></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"center\"><bold><italic>Ppp2r3d</italic></bold></td><td align=\"center\">exon</td><td align=\"center\">exon size (bp)</td><td align=\"center\">5'-splice donor</td><td align=\"center\">intron size (bp)</td><td align=\"center\">3'-splice acceptor</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\">1</td><td align=\"center\">567</td><td align=\"center\">CAGGACggtgag</td><td align=\"center\">358</td><td align=\"center\">gcac<bold>ag</bold>ACCCGA</td></tr><tr><td/><td align=\"center\">2</td><td align=\"center\">321</td><td align=\"center\">CGGCAG<bold>gt</bold>gggc</td><td align=\"center\">405</td><td align=\"center\">ccac<bold>ag</bold>GTCCAT</td></tr><tr><td/><td align=\"center\">3</td><td align=\"center\">198</td><td align=\"center\">GCCAAG<bold>gt</bold>atgt</td><td align=\"center\">298</td><td align=\"center\">cccc<bold>ag</bold>GCCTGT</td></tr><tr><td/><td align=\"center\">4</td><td align=\"center\">105</td><td align=\"center\">CGCAAGtgagtg</td><td align=\"center\">409</td><td align=\"center\">acag<bold>ag</bold>TCCTGC</td></tr><tr><td/><td align=\"center\">5</td><td align=\"center\">101</td><td align=\"center\">CTGCAG<bold>gt</bold>gggc</td><td align=\"center\">672</td><td align=\"center\">tggc<bold>ag</bold>GATGTG</td></tr><tr><td/><td align=\"center\">6a</td><td align=\"center\">75</td><td align=\"center\">ACCACA</td><td align=\"center\">/</td><td align=\"center\"><bold>GT</bold>GAGC</td></tr><tr><td/><td align=\"center\">6b</td><td align=\"center\">373</td><td align=\"center\">TTCC<bold>AG</bold></td><td align=\"center\">/</td><td align=\"center\"><bold>GT</bold>GATT</td></tr><tr><td/><td align=\"center\">6c</td><td align=\"center\">87</td><td align=\"center\">CTGCAG<bold>gt</bold>gtgg</td><td align=\"center\">452</td><td align=\"center\">ctgc<bold>ag</bold>GCTGTG</td></tr><tr><td/><td align=\"center\">7</td><td align=\"center\">157</td><td align=\"center\">AGCGGG<bold>gt</bold>gagt</td><td align=\"center\">260</td><td align=\"center\">ccgc<bold>ag</bold>CCATCT</td></tr><tr><td/><td align=\"center\">8</td><td align=\"center\">49</td><td align=\"center\">CACCAG<bold>gt</bold>gagt</td><td align=\"center\">276</td><td align=\"center\">ccgc<bold>ag</bold>GGCGAG</td></tr><tr><td/><td align=\"center\">9</td><td align=\"center\">90</td><td align=\"center\">CACCAG<bold>gt</bold>gagg</td><td align=\"center\">402</td><td align=\"center\">ccac<bold>ag</bold>CACCGA</td></tr><tr><td/><td align=\"center\">10</td><td align=\"center\">176</td><td align=\"center\">GCCCCG<bold>gt</bold>gagc</td><td align=\"center\">220</td><td align=\"center\">cccc<bold>ag</bold>GCCGGA</td></tr><tr><td/><td align=\"center\">11</td><td align=\"center\">119</td><td align=\"center\">CCGCAG<bold>gt</bold>gggt</td><td align=\"center\">412</td><td align=\"center\">ccac<bold>ag</bold>GACACT</td></tr><tr><td/><td align=\"center\">12</td><td align=\"center\">113</td><td align=\"center\">CGAAGG<bold>gt</bold>taga</td><td align=\"center\">296</td><td align=\"center\">tcac<bold>ag</bold>GCCCTG</td></tr><tr><td/><td align=\"center\">13a</td><td align=\"center\">37</td><td align=\"center\">CCC<bold>AG</bold></td><td align=\"center\">/</td><td align=\"center\"><bold>GT</bold>CCGA</td></tr><tr><td/><td align=\"center\">13b</td><td align=\"center\">256</td><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Proposed nomenclature of PP2A PR72/B\" regulatory subunits in <italic>Homo sapiens </italic>and <italic>Mus musculus</italic>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\" colspan=\"4\">GENE SYMBOL</td><td align=\"center\" colspan=\"4\">PROTEIN</td></tr><tr><td align=\"center\" colspan=\"2\">HUMAN</td><td align=\"center\" colspan=\"2\">MOUSE</td><td align=\"center\" colspan=\"2\">HUMAN</td><td align=\"center\" colspan=\"2\">MOUSE</td></tr></thead><tbody><tr><td align=\"center\">name</td><td align=\"center\">alias</td><td align=\"center\">(new) name</td><td align=\"center\">alias</td><td align=\"center\">new name</td><td align=\"center\">alias</td><td align=\"center\">new name</td><td align=\"center\">alias</td></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\"><italic>PPP2R3A</italic></td><td align=\"center\"><italic>PPP2R3</italic></td><td align=\"center\"><italic>Ppp2r3a</italic></td><td align=\"center\"><italic>3222402P14Rik</italic></td><td align=\"center\"><bold>PR130/B\"α1</bold></td><td align=\"center\"><bold>PR130</bold></td><td align=\"center\"><bold>PR130/B\"α1</bold></td><td align=\"center\"><bold>PR130</bold></td></tr><tr><td/><td/><td/><td/><td align=\"center\"><bold>PR72/B\"α2</bold></td><td align=\"center\"><bold>PR72</bold></td><td align=\"center\"><bold>PR72/B\"α2</bold></td><td align=\"center\"><bold>PR72</bold></td></tr><tr><td/><td/><td/><td/><td/><td/><td align=\"center\">PR130/B\"α3</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td align=\"center\">PR72/B\"α4</td><td/></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\"><italic>PPP2R3B</italic></td><td align=\"center\"><italic>NY-REN-8</italic></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\"><bold>PR70/B\"β1</bold></td><td align=\"center\"><bold>PR70*</bold></td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td/><td align=\"center\"><italic>PPP2R3L</italic></td><td/><td/><td align=\"center\"><bold>PR70/B\"β2</bold></td><td align=\"center\"><bold>PPP2R3L product *</bold></td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\"><italic>PPP2R3C</italic></td><td align=\"center\"><italic>C14orf10</italic></td><td align=\"center\"><italic>Ppp2r3c</italic></td><td align=\"center\"><italic>MGC55473</italic></td><td align=\"center\"><bold>G5PR/B\"γ</bold></td><td align=\"center\"><bold>G5PR</bold></td><td align=\"center\"><bold>G5PR/B\"γ</bold></td><td align=\"center\"><bold>G5PR</bold></td></tr><tr><td colspan=\"8\"><hr/></td></tr><tr><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\"><italic>Ppp2r3d</italic></td><td align=\"center\"><italic>AI118493</italic></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\"><bold>PR59/B\"δ1</bold></td><td/></tr><tr><td/><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\"><bold>PR59/B\"δ2</bold></td><td align=\"center\"><bold>mPR59</bold></td></tr><tr><td/><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">PR59/B\"δ3</td><td/></tr><tr><td/><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">PR59/B\"δ4</td><td/></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
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[ "<table-wrap-foot><p>Nucleotides in uppercase represent exonic sequences while nucleotides in lowercase represent intronic sequences. Bold letters indicate the exon-intron boundaries based on the AT-GT rule.</p></table-wrap-foot>", "<table-wrap-foot><p>bold: main PR72/B\" regulatory subunits</p><p>*: protein of which first a partial clone, PR48, was discovered</p></table-wrap-foot>" ]
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[]
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{ "acronym": [], "definition": [] }
26
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Aug 20; 9:393
oa_package/e4/44/PMC2529318.tar.gz
PMC2529319
18671852
[ "<title>Background</title>", "<p>The Core Binding Factor (CBF) is a transcriptional regulator complex, which is composed of two sub-units [##REF##2168969##1##]. Mammals have three genes coding for the α-subunits, <italic>RUNX1</italic>, <italic>RUNX2 </italic>and <italic>RUNX3 </italic>[##REF##15156175##2##], and one coding for the β-subunit, <italic>CBFβ </italic>. The α-subunits recognize a specific sequence (TGT/cGGT) in the regulatory regions of their target genes in order to bind DNA directly, while the β-subunit heterodimerizes with the α-subunits but does not interact directly with the DNA. The interaction with CBFβ stabilizes the RUNX-DNA complex [##REF##8341710##3##,##REF##8497254##4##] and protects the RUNX proteins from degradation [##REF##11179217##5##].</p>", "<p>In humans, the CBF complex containing RUNX1 as the α-subunit is one of the most frequent targets of chromosomal and genetic alterations in leukemia. Chromosomal rearrangements involving <italic>RUNX1 </italic>or <italic>CBFβ </italic>[##REF##10717473##6##], somatic point mutations in <italic>RUNX1 </italic>[##REF##15156185##7##] and amplification of <italic>RUNX1 </italic>[##REF##12529654##8##] have all been described in acute leukemia. In addition to somatic alterations, germ-line point mutations in <italic>RUNX1 </italic>are responsible for an autosomal dominant platelet disorder with a propensity to develop leukemia (FPD-AML, OMIM 601399) [##REF##11830488##9##,##REF##10508512##10##]. Interestingly, the dosage of RUNX1 protein seems to play a role in the determination of the leukemic phenotype. Indeed, low dosage of RUNX1, resulting from haploinsufficient or dominant negative mutations, lead to the development of myeloid leukemia [##REF##11830488##9##, ####REF##10508512##10##, ##REF##10068652##11####10068652##11##], whereas amplification of <italic>RUNX1 </italic>gene is more often observed in lymphoid leukemia, particularly pediatric ALL [##REF##11960347##12##]. A number of observations also suggest that although <italic>RUNX1 </italic>is involved in the first steps of leukemia development, additional somatic mutations are necessary and probably determinant for the leukemic phenotype: 1) The predisposition to develop leukemia in FPD-AML patients shows that germline <italic>RUNX1 </italic>mutations are not sufficient for the development of the disease [##REF##10508512##10##]. 2) Somatic translocations are not able to induce leukemia in mouse cells on their own [##REF##10979955##13##]. 3) The translocation t(12;21), which fuses <italic>ETV6 (TEL) </italic>to <italic>RUNX1</italic>, can arise <italic>in utero </italic>but does not trigger leukemia until later in childhood, with as much as nine years latency [##REF##9539781##14##]. These additional mutations are likely to occur in molecules involved in the same biological pathways as RUNX1, as hemizygous loss of several molecules in the same biological pathway (e.g. RUNX1 and SPI1) is thought to be almost as tumorigenic as homozygous loss of one molecule (e.g. homozygous RUNX1 mutation in AML-M0) [##REF##10918600##15##]. Therefore the identification of downstream targets of RUNX1, with care to the model systems including species and cell type of origin, is of great interest in order to identify novel candidate molecules involved in leukemogenesis.</p>", "<p>The identification of the biological pathways regulated by RUNX1 is also of importance to shed light on its <italic>in vivo </italic>function and role in leukemia development. The observation that <italic>Runx1 </italic>knockout mice show a lack of definitive hematopoietic maturation and die at embryonic stage 12 from hemorrhages in the central nervous system demonstrates that RUNX1 plays a critical role during development of the hematopoietic system [##REF##8565077##16##,##REF##8622955##17##]. In addition, RUNX1 might also play a role in other systems as it is expressed in many other embryonic tissues [##REF##10226014##18##, ####REF##11731260##19##, ##REF##12811823##20####12811823##20##] and in epithelial cells [##REF##11731260##19##,##REF##12811823##20##]. It is furthermore overexpressed in endometrioid carcinoma [##REF##15604243##21##] and down-regulated in gastric cancer [##REF##15386419##22##]. The <italic>in vivo </italic>function of RUNX1 is therefore yet to be fully understood.</p>", "<p>Here we describe the combination of a number of genomic and bioinformatic approaches to identify biological pathways downstream of RUNX1, and report on a number of processes in which RUNX1 is likely to be involved. We also took advantage of the integration of these approaches in order to identify novel RUNX1 target genes.</p>" ]
[ "<title>Methods</title>", "<title>Adenovirus production</title>", "<p>Recombinant adenoviruses expressing <italic>RUNX1 </italic>p49 isoform [##REF##11179664##49##] or CBFβ were generated as described [##REF##14500759##50##], except that VmRL-CMV1 and pSCOT were used as the adenovirus backbone and transfer vector respectively. For details, see Additional File ##SUPPL##0##1##.</p>", "<title>Cell lines and RNA extraction</title>", "<p>EBV-transformed lymphoblasts generating B cell lines from FPD-AML patients (Pedigree 2, individuals V:1 and V:2;) [##REF##11830488##9##] and related unaffected individuals (Pedigree 2, individuals IV:1 and V:3) were used for the FPD microarray dataset. HeLa cells (4 × 10<sup>7</sup>) were infected with a multiplicity of infection (MOI) of 100 for each adenovirus and incubated for 48 hours. The Qiagen RNeasy maxikit was used for the extraction of total RNA in each case. <italic>Runx1 </italic>knockout and wild-type embryo propers at embryonic stages E8.5 and E12 were homogenized in Trizol (Invitrogen) and total RNA extracted following the manufacturer's protocol.</p>", "<title>Mouse samples</title>", "<p>Runx1 knockout mice have been previously described [##REF##8565077##16##]. They are maintain on a BalbC genetic background at the Biological Resource Center, (Biopolis, Singapore) and all animal experiments followed the guidelines set by the National Advisory Committee for Laboratory Animal Research. Wild-type and Runx1 knockout mouse embryo propers were harvested at embryonic stages E8.5 and E12.</p>", "<title>cDNA Microarray hybridization</title>", "<p>cDNA microarrays were printed by the Australian Genome Research Facility (AGRF) with the Hs8k cDNA clone library from Research Genetics and a selection of control spots. In total there were 8132 EST probes printed in duplicate. The array also contained 12 copies of the Lucidea Universal ScoreCard controls (Amersham). Labeling, hybridization, and washing were performed as described [##REF##15657102##51##]. In the case of the FPD dataset, four hybridizations were performed comparing two affected individuals against two unaffected individuals of pedigree 2. For the overexpression system, 2 different RNA samples from HeLa cells overexpressing EGFP were used as reference and 2 different RNA samples from HeLa cells overexpressing RUNX1 and CBFβ were used as experimental RNAs. Seven hybridizations (including 3 dyeswaps) were performed. The data were filtered for genes whose difference in expression was due to EGFP, using four hybridizations between EGFP expressing cells and normal HeLa cells.</p>", "<title>Affymetrix genechip hybridization</title>", "<p>Labelling, hybridization and washing were performed by the AGRF following the Affymetrix protocol (701725 rev5). Briefly, total RNA (100 ng) was amplified using T7-oligo dT and the Megascript T7 kit (Ambion). A second round of cDNA synthesis was performed using the total amount of the amplified RNA. Biotin-labeled RNA was subsequently synthesized using the GeneChip IVT Labeling Kit. Labelled RNA (15 μg) was fragmented and the mouse genome 430 2.0 arrays were hybridized overnight and washed as described before being scanned using a GeneChip scanner 3000 (Affymetrix). Two biological replicates were used for each condition.</p>", "<title>Microarray analysis</title>", "<p>The cDNA microarray images were analyzed using SPOT software [##UREF##0##52##]. Spots were assigned quality weights based on their segmented pixel areas and the log-ratios were print-tip loess normalized [##REF##14597310##53##]. Duplicate printings of each probe on each array were combined using the common correlation method of [##REF##15657102##51##]. For the mouse Affymetrix GeneChips, the intensities for each probe set were normalized and summarized using the Robust Multi-array Analysis algorithm [##REF##12582260##54##]. Differential expression was assessed using empirical Bayes moderated t- and F-statistics from the LIMMA package [##REF##16646809##55##]. Recognizing that p-value calculations make normality and other distributional assumptions, which are hard to verify for microarray data, we decided to use control probes and appropriate plots to guide our criteria for differential expression as far as possible. For the cDNA data, conservative threshold values for differential expression were chosen to minimize the false-positive and false-negative rates estimated from Scorecard control probes printed on the arrays. This resulted in a threshold value of |t|&gt;4 for the FPD data. Of 204 calibration control probes printed on the arrays, none reached this cutoff for statistical significance, suggesting a false discovery rate less than 1/204, without relying on any distributional assumptions. For the mouse Affymetrix data, a threshold of |t|&gt;3 was chosen from a q-q plot of the moderated t-statistics.</p>", "<p>For the overexpression system arrays, a combination of criteria was used to assess differential expression. These arrays were analyzed as part of a larger microarray study using the same overexpression system to study a range of AML related genes. Genes with |t|&gt;4 were initially assigned as differentially expression, with only one calibration control probe reaching this threshold. A series of nested F-tests (with p-value cutoff 1e-5) was also performed using the larger dataset in order to get an improved estimate of the number of genes significantly differentially expressed in more than one condition simultaneously. This increased the number of differentially expressed genes by a third. Finally, genes were removed from the differentially expressed list if their response to <italic>RUNX1/CBFβ </italic>transduction was not significantly greater than their response to the adenovirus alone.</p>", "<p>All the analyzed datasets have been deposited at the NCBI Gene Expression Omnibus <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/geo/\"/> under accession numbers GSE2592 (mouse Affymetrix data), GSE2593 (overexpression experiment) and GSE2594 (FPD-AML arrays).</p>", "<title>Mean-rank gene set enrichment tests (MR-GSE)</title>", "<p>A version of statistical gene set testing was used to investigate associations between the expression profiles obtained from different experiments. Each test uses a set of genes selected as differentially expressed in one data set (the reference dataset) and determines whether the gene set tends to be highly ranked in another dataset (the test dataset). The test statistic is the mean rank of the gene set in the test dataset. This approach, which we call mean-rank gene set enrichment (MR-GSE), is very similar to Tian et al's T<sub>k </sub>test [##REF##16174746##56##] and Kim and Volsky's PAGE test [##REF##15941488##57##]. The main difference is that MR-GSE averages the ranks of t-statistics instead of t-statistics themselves, which makes it less influenced by individual genes in the gene set. This has the advantage of giving more weight to gene sets with a larger number of active genes, and it also allows us to use the same testing procedure with a range of ranking procedures other than t-statistics. Where possible, MR-GSE is used with moderated t-statistics rather than ordinary t-statistics, as these are preferable for microarray analysis including gene set testing [##REF##16174746##56##,##REF##17612399##58##]. Unlike earlier Gene Set Enrichment Analysis methods [##REF##16199517##59##], MR-GSE can be used to test individual gene sets in isolation and has good power even for microarray experiments with small to moderate sample sizes.</p>", "<p>The null hypothesis tested by MR-GSE is that the gene set is randomly chosen. When the reference and test datasets share the same microarray platform, p-values can be computed using Wilcoxon two-sample rank tests [##UREF##1##60##]. When the reference and test datasets are based on different microarray platforms (cDNA vs Affymetrix), the p-values were instead computed using random permutations of probes on the reference arrays. This was done to avoid any bias arising from probe selection on the cDNA platform or from multiple probe-sets for individual genes on the Affymetrix platform.</p>", "<p>For the integration of gene expression profiling data and biological processes regulated by RUNX1, genes were ranked in the test datasets by absolute moderated t-statistic. For the correlation with clinical AML samples, the test dataset was the previously published expression profiling data on 285 AML patients and 8 healthy individuals [##REF##15084694##23##]. In this case, the Affymetrix probe-sets were ranked according to their correlation with the 11 RUNX1 probe-sets across the 293 RNA samples. Correlations were computed using Gene Recommender [##REF##12902378##61##], which provides a very robust correlation measure suitable for this purpose. Probe-sets were also ranked by moderated t-statistic on their ability to distinguish the healthy patients from the 22 patients with t(8;21) or from the 18 patients with inv(16).</p>", "<p>The MR-GSE p-values are computed by permuting genes rather than permuting arrays. This is necessary because the tests are designed for use with small numbers of arrays. The computation necessarily assumes that different genes have statistically independent expression values within experimental groups. When the gene set contains genes which are highly interdependent, and which vary substantially between biological replicates, the test may be anti-conservative. We checked the independence assumption for our data by computing average inter-gene correlations using REML. The inter-gene correlations were found to be generally very small at the expression level (data not shown), suggesting that the MS-GSE results are meaningful on our data.</p>", "<title>Bioinformatic identification of biological processes and cross-platform comparison</title>", "<p>Enrichment of a gene ontology annotation in a dataset of differentially expressed genes compared to the genes present on the array was determined using the GOStat program <ext-link ext-link-type=\"uri\" xlink:href=\"http://gostat.wehi.edu.au/\"/>[##REF##14962934##62##]. For the MR-GSE test, relevant gene sets were taken from published reviews or independent microarray data (see Additional File ##SUPPL##0##1##: Table S4)</p>", "<title>BrdU proliferation assay</title>", "<p>The Cell Proliferation ELISA, BrdU kit (Roche) was used to measure proliferation of cell lines derived from two independent families, including the family used for the microarray experiment (Pedigree 2) [##REF##11830488##9##] and an additional family harboring a nonsense mutation Y260X present outside of the Runt domain (Pedigree 3, affected individuals III:7 and IV:4 and one unaffected individual III:8 [##REF##11830488##9##]). Briefly, the cells were split into 96-well plates at an equal density. BrdU was added to the cells for 4 hours and the cells were then treated according to the manufacturer's protocol. The optical density (OD<sub>450</sub>) was measured on an ELISA plate reader. Technical triplicates and two independent experiments were performed. A two-way ANOVA (analysis of variance) test was performed.</p>", "<title>Tubulin polymerization assay</title>", "<p>Soluble (cytosolic) and polymerized (cytoskeletal) fractions of tubulin were separated from the cell lines treated with or without 4 μg/ml of Taxol as described [##REF##12890533##63##]. The same cell lines used for the proliferation assay were assessed. Results were expressed as a percentage of polymerized tubulin by dividing the densitometric value of polymerized tubulin (insoluble) by the total tubulin content (sum of densitometric value of soluble and polymerized tubulin). Three independent experiments were performed and a two-way ANOVA was done.</p>", "<title>Glycophorin A assay</title>", "<p>Blood samples were collected in EDTA-tubes, with informed consent, from seven individuals heterozygous (MN phenotype) at the glycophorin A locus. These include: a FPD-AML patient harboring a frameshift mutation (N69fsX94) and her unaffected sister, a second FPD-AML patient harboring a nonsense mutation (Pedigree 3 (Y260X), individual IV:4) [##REF##11830488##9##] and 4 independent unaffected individuals. The assay is described in detail in Additional File ##SUPPL##0##1##. A two-way ANOVA test was performed to compare the 5 controls to the 2 affected individuals.</p>", "<title>Luciferase reporter assay</title>", "<p>Genomic regions overlapping the conserved binding sites (300–400 bps) were amplified from BACs and cloned into pGL3-Basic vector (Promega #E1751). Each construct was co-transfected into HeLa cells using lipofectamine 2000 (Invitrogen) along with pSCOT plasmids expressing RUNX1 and CBFβ or empty vector to keep the amount of plasmid constant. For normalization, 20 ng of pRL-TK vector (Renilla luciferase Promega #E2241) was also co-transfected. The luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega #E1910). The increase or decrease in luciferase activity was determined as a function of the endogenous activity of each construct.</p>", "<title>cDNA panel production</title>", "<p>The human cDNA panel was generated as described [##REF##10950928##26##]. The relative amount of each cDNA was normalized according to housekeeping gene levels. More details are described in Additional File ##SUPPL##0##1##.</p>" ]
[ "<title>Results</title>", "<title>Gene expression profiling of cells harboring different levels of RUNX1</title>", "<p>Three different model systems were used to identify the biological pathways regulated by the RUNX1 transcription factor. These were haploinsufficiency using FPD-AML patient B cell lines (FPD), overexpression of CBF complex (CBF) in HeLa cells and Runx1 deficiency in mouse embryos (E8.5 and E12) (Figure ##FIG##0##1##).</p>", "<p>Lymphoblastic cells derived from FPD patients heterozygous for a RUNX1 frameshift mutation (R135fs) were first analyzed. This mutation results in haploinsufficiency of RUNX1, as the mutant protein has lost its capacity to bind DNA and to transactivate the expression of the target genes [##REF##11830488##9##]. Quantitative RT-PCR on these non-leukemic lymphoblastic cells showed that affected individuals express approximately 55% of the transcript level observed in unaffected individuals (see Additional File ##SUPPL##0##1## :Figure S1). The genes differentially expressed between two affected and two non-affected cell lines are therefore largely the result of a low dosage of RUNX1 protein. Using human cDNA microarrays with the Hs8k cDNA clone library from Research Genetics and a selection of control spots, 366 genes were identified as differentially expressed, of which 52% (192/366) were down-regulated in affected individuals (Figure ##FIG##0##1## and see Additional File ##SUPPL##1##2##).</p>", "<p>For overexpression studies, HeLa epithelial cells were transduced using adenoviral vectors. FACS analysis showed that over 90% of HeLa cells were transduced by a EGFP-expressing adenovirus (data not shown). This system results in a highly homogenous cell population in which small changes of expression can be identified. The wild type CBF complex α-subunit, RUNX1, was overexpressed together with the β-subunit, CBFβ (see Additional File ##SUPPL##0##1##: Figure S2) and seven hybridizations were performed. Following overexpression of the CBF complex, 721 genes were differentially expressed including the up-regulation of 42% of the genes (300/721; Figure ##FIG##0##1## and see Additional File ##SUPPL##1##2##).</p>", "<p>Finally, we compared the expression profiles of two wild type and two <italic>Runx1 </italic>knockout mouse embryo propers at each embryonic stages E8.5 and E12 using Affymetrix chips. Despite the heterogeneity of the samples, 931 and 297 genes were differentially expressed at embryonic stages E8.5 and E12, respectively. Of these genes, 57% (533/931) and 72% (214/297) were down-regulated in the knockout embryos (Figure ##FIG##0##1## and see Additional File ##SUPPL##2##3##). These differences in expression are likely to reflect the lack of hematopoiesis and the premature death, respectively, observed in the Runx1 embryos.</p>", "<p>We then compared the different datasets using a mean-rank gene set enrichment test (MR-GSE) in order to determine the level of connection between the 3 approaches (FPD cell lines, CBF overexpression and <italic>Runx1 </italic>knockout mouse embryos), disregarding the cell type and the organism. High correspondence was observed between the two human datasets. The correspondence between the human and the mouse datasets was not as good, although still significant. This might partially be explained by the difficulties of matching human and mouse platforms (see Additional File ##SUPPL##0##1##: Figure S3).</p>", "<title>Correlation with clinical AML samples</title>", "<p>It was first necessary to determine whether the genes identified in nonmyeloid cells in this study may play a role in myeloid leukemia development. We therefore compared our data to previously published microarray data obtained from 285 AML and 8 healthy samples [##REF##15084694##23##], using the MR-GSE test. The high correspondence between the FPD-AML and CBF datasets had already suggested that a large number of downstream genes were similar between epithelial and lymphocytic cells. Therefore we used each approach as representative of the <italic>RUNX1 </italic>gene dosage, regardless of the cell type. The AML samples used in the comparison include 22 patients with a t(8;21) translocation, which fuses <italic>RUNX1 </italic>to <italic>ETO</italic>, and 18 patients with inv(16), which fuses the co-factor <italic>CBFβ </italic>to <italic>MYH11</italic>. The other samples include a range of common alterations or no identified mutations. RUNX1 activation targets should be positively correlated with RUNX1 expression whereas repression targets should be negatively correlated. Therefore we ranked all the probes-sets on the microarrays according to their correlation with RUNX1 across the 293 AML and normal samples (Figure ##FIG##1##2A##). MR-GSE tests demonstrated that genes up-regulated in the FPD-AML patients (likely to represent genes repressed by RUNX1), had an expression trend opposite to RUNX1 in the AML patients, suggesting indeed that these genes are repressed <italic>in vivo </italic>in the presence of RUNX1 (p = 7 × 10<sup>-6</sup>; Figure ##FIG##1##2B##). On the other hand, the down-regulated genes do not show any statistically significant trend (Figure ##FIG##1##2C##). Similarly, the genes activated by the exogenous CBF complex had an expression pattern similar to RUNX1 across the clinical samples (p = 1 × 10<sup>-4</sup>; Figure ##FIG##1##2D##), whereas genes repressed by the CBF complex had an expression pattern opposite to RUNX1 (p = 2 × 10<sup>-5</sup>; Figure ##FIG##1##2E##).</p>", "<p>MR-GSE tests also showed that genes differentially expressed in the B cell lines derived from FPD-AML patients tended to be differentially expressed in the blasts and mononuclear cells of 22 clinical patients with a t(8;21) translocation (p = 10<sup>-10</sup>) and of 18 patients with the inv(16) abnormality (p = 3.5 × 10<sup>-9</sup>). For example, the top 14 differentially expressed genes in the FPD-AML dataset that are also differentially expressed in the clinical samples are shown in Additional File ##SUPPL##0##1## (Table S3). As a whole, these results demonstrate that the genes identified in our study are likely to play an important role in the development of the disease.</p>", "<title>Biological processes regulated by RUNX1: bioinformatic approaches</title>", "<p>Bioinformatics tools taking into account all differentially expressed genes (direct and indirect RUNX1 targets) were used to systematically identify the biological processes in which RUNX1 may be involved. A number of gene ontology (GO) annotations were significantly enriched in each dataset (Table ##TAB##0##1##). Some were identified in more than one dataset such as \"cadmium ion binding\" and \"immune response\". Other significantly represented processes were identified through the use of Ingenuity Pathways Analysis (Ingenuity Systems, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ingenuity.com\"/>) (Figure ##FIG##2##3##). These include cancer related genes as well as genes involved in hematological disorders. To complete this analysis, a MR-GSE was also performed using a number of published gene sets related to thrombocytopenia, leukemia and cancer (Figure ##FIG##3##4##, see Additional File ##SUPPL##0##1##: Table S4 and Additional File ##SUPPL##3##4##). Significant correlation was obtained between the microarray datasets and a number of these sets of genes, including genes involved in megakaryopoiesis and cytokinesis, genes differentially expressed following irradiation of lymphoblasts, and genes consistently differentially expressed in solid-tissue tumors.</p>", "<title>Biological processes regulated by RUNX1: <italic>in vivo </italic>confirmations</title>", "<p>We designed a series of assays that were performed on either cell lines, or directly on samples from FPD-AML patients with RUNX1 mutations, to confirm the disturbance of several interesting biological processes identified by the above approaches.</p>", "<title>Heterozygous <italic>RUNX1 </italic>point mutations affect proliferation</title>", "<p>RUNX1 is thought to be involved in the balance between cell proliferation and differentiation, whose disruption leads to leukemia development. However, the molecular mechanisms behind this regulation are not known. We observed that genes participating in cellular proliferation were significantly enriched in both FPD and CBF datasets (Table ##TAB##0##1## and Figure ##FIG##2##3##). The genes responsible for this enrichment are indicated in Additional File ##SUPPL##0##1## (Table S5). We therefore performed a BrdU proliferation assay in order to determine whether a subtle proliferation defect was present when RUNX1 level was lower in FPD-AML patients. A slower proliferation was indeed observed in FPD-AML lymphoblasts derived from two independent families compared to unaffected cells (Figure ##FIG##4##5A##, p &lt; 0.001).</p>", "<title>RUNX1 modulates microtubule stability</title>", "<p>A significant enrichment of molecules containing a common tubulin motif was observed following overexpression of the CBF complex (Table ##TAB##0##1##). Five tubulin isoforms were down-regulated following overexpression of the CBF complex. These data led to the observation that CBF overexpression affected the expression of 57 genes associated with cytoskeletal structures according to GO annotation (see Additional File ##SUPPL##0##1##: Table S6). This class of genes was not significantly represented in the dataset from the FPD-AML cell lines, however this may be the result of the not complete knock-down of RUNX1 in the affected individuals leading to small changes that are not detected by microarray analysis. Therefore we also tested whether microtubule stability was affected in these cell lines. Significantly higher microtubule polymer levels were observed in the affected patients compared to the unaffected individuals (Figure ##FIG##4##5B## and ##FIG##4##5C##; p &lt; 0.002). Furthermore, the microtubules in affected cells could not be stabilized using the drug Taxol to the same extent as the unaffected cells (Figure ##FIG##4##5D##; p &lt; 0.0003). This might result from the inability of the drug to bind to the microtubule molecule because of the unusual presence of other microtubule stabilizing proteins or from a lack of soluble tubulin molecules in the cellular environment. In any case, these results suggest that RUNX1 is involved in microtubule dynamics.</p>", "<p>Neither the proliferation nor the tubulin defects are due to the EBV transformation of the cell lines as many independent proliferation and tubulin polymerization assays performed on lymphoblastic cell lines derived from families with predispositions to various haematological malignancies do not show similar familial clustering (data not shown).</p>", "<title>Genomic instability</title>", "<p>Highly significant correspondence was observed between the FPD, CBF and mouse datasets and the genes switched on after irradiation of lymphoblasts (Figure ##FIG##3##4##). We used a glycophorin A assay to test whether the FPD-AML patients are more prone to somatic genetic mutations than unaffected individuals. This test assesses the frequency of mutation events occurring at the glycophorin A locus in erythroid progenitors in blood of heterozygous individuals (MN phenotype) [##REF##9354918##24##]. Although more samples would be necessary for corroboration, a significant trend was present between the blood of two affected patients and five unaffected individuals, suggesting that a subtle increase of mutation rate may occur when RUNX1 activity is impaired (Figure ##FIG##4##5E##; p &lt; 0.01). This increased mutation rate appears to be higher in the assay that would detect deletions (NO), that are the predominate mutations arising due to ionizing irradiation [##REF##16084534##25##].</p>", "<title>Identification of potential novel RUNX1 target genes – co-expression in human tissues and hematopoietic cell lines</title>", "<p>We reasoned that direct RUNX1 target genes must be expressed in the same tissues or cells as RUNX1. Thus, the expression patterns of a number of differentially expressed genes, chosen due to potential functions in leukemia development, were compared to that of RUNX1 (see Additional File ##SUPPL##4##5##). The expression of 22 genes in 20 human tissues, 19 hematopoietic cell lines and normal human bone cells was assessed using cDNA panels [##REF##10950928##26##]. 9 of these genes show a high expression in a number of hematopoietic cell lines and all the others show common expression with RUNX1 in various tissues such as liver and peripheral blood leukocytes (PBLs).</p>", "<title>Identification of potential novel RUNX1 target genes – data overlaps</title>", "<p>In order to distinguish between the direct RUNX1 target genes and those effected further downstream by a disregulation of RUNX1 level, we hypothesized that the genes in common in more than one dataset were more likely to be at the top of the genetic pathways regulated by RUNX1 and to be enriched for direct target genes. As suggested by the significant MR-GSE results, we observed statistically significant overlap between each dataset. Among the 366 genes differentially expressed in FPD-AML cell lines, 69 genes were also differentially expressed following overexpression of the CBF complex, while only 32 were expected by chance (Figure ##FIG##5##6A##). As anticipated when comparing an under- and overexpression system, 61% (42/69) of the genes in this overlap were differentially expressed in the opposite direction. Among these 69 genes 16 were also differentially expressed in at least one embryonic stage of the <italic>Runx1 </italic>knockout embryos (Table ##TAB##1##2##, Figure ##FIG##5##6A##).</p>", "<title>Identification of potential novel RUNX1 target genes – regulatory region analysis</title>", "<p>In order to accumulate evidence that some of the genes present in these overlaps are direct target genes, we searched for human RUNX1 binding sites, which were conserved in mouse using the oPOSSUM software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cisreg.ca/cgi-bin/oPOSSUM/opossum\"/> see Additional File ##SUPPL##0##1##) [##REF##15933209##27##]. Many differentially expressed genes contained at least one conserved RUNX1 binding site in their regulatory regions and the overlaps between the datasets show a higher enrichment for such genes as hypothesized above (Figure ##FIG##5##6A##).</p>", "<p>The regions flanking five putative conserved binding sites identified in three differentially expressed genes, and one negative control region, were cloned upstream of a luciferase reporter gene and co-transfected together with plasmids expressing RUNX1 and CBFβ. These genes were selected because of their presence in the overlap between the human datasets and/or their interesting functions; ANXA1 (Annexin 1) is involved in cell proliferation and cytoskeleton regulation; ARMET (Arginine-rich, mutated in early stage tumors) is mutated in cancer; CYR61 (Cysteine-rich, angiogenic inducer, 61) promotes proliferation and angiogenesis. An increase in luciferase activity was observed for ANXA1 binding sites and for one of the ARMET binding sites and a diminution of the luciferase activity was observed for the CYR61 binding site (Figure ##FIG##5##6B##). No modification of the luciferase activity was observed for a sequence derived from the negative control CASP3 regulatory region (where no conserved binding site was identified by the oPOSSUM program). It is likely that a combination of a number of binding sites and the presence of additional co-factors are necessary for a correct and synergistic <italic>in vivo </italic>regulation of these genes and it might explain the small activity observed for the ARMET binding sites. It might also explain the activation of the ANXA1 site while this gene was repressed by the overexpression of the CBF complex.</p>" ]
[ "<title>Discussion</title>", "<p><italic>RUNX1 </italic>is one of the most frequent targets of somatic mutations in leukemia and is mutated in an autosomal dominant disorder affecting platelets and predisposing to leukemia development. Better characterization of its <italic>in vivo </italic>function is likely to give insight into the mechanisms leading to the development of leukemia, and will provide new candidate genes for leukemogenesis. We do not believe that as a transcription factor and master regulator of hematological cancers, RUNX1 will alter the function of only one oncogenic molecule, but multiple molecules in the same pathways, and our analyses and functional assays are carefully designed to study these effects. We have described a combination of genomic and bioinformatic approaches to identify the biological pathways and genes regulated by RUNX1, an overview of which is in Figure ##FIG##6##7##. Each approach independently provides a large source of data to identify RUNX1 targets according to <italic>RUNX1 </italic>gene dosage. However, the combination of them is powerful because of their convergence. Although the approaches described here are not the ideal models to study myeloid leukemia, each of them has their own advantages and their integration compensates for their limitations: 1) The use of cells derived from patients harbouring a <italic>RUNX1 </italic>mutation but who have not yet developed leukemia allow us to observe effects, largely due to changes in RUNX1 dosage. However, it should be kept in mind that due to the difficulties of obtaining myeloid cell lines, these studies were performed in lymphoid cells. 2) The overexpression system using HeLa cells provided a highly homogenous cell population, which is necessary to perform gene expression profiling. 3) The knockout mouse embryos represent various cell types, however they give us global information of the complete absence of RUNX1, which is difficult to obtain using cell lines. Efficient and homogenous knockdown levels are indeed difficult to obtain using siRNA especially in hematopoietic cells [##REF##15782549##28##].</p>", "<p>The highly significant correlation observed between the genes identified in the FPD-AML cells and the overexpression system and clinical data on AML samples supports the hypothesis that large number of genes would be broadly regulated by RUNX1 in our various approaches disregarding of the cell type. Genes identified as differentially expressed following disregulation of RUNX1 expression level and/or in these AML samples are good candidates for targets of secondary hits during leukemogenesis downstream of RUNX1 mutation. The various approaches described in this study, including conserved binding sites and co-expression studies, will also help to further prioritize genes that might sustain secondary hits. For example, the gene encoding the Cyclin D3 (CCND3) was differentially expressed following overexpression of the CBF complex and mutations in this gene have been described in acute myeloid leukemia patients [##REF##15667533##29##].</p>", "<p>In order to generate insights into the <italic>in vivo </italic>role of RUNX1, we employed bioinformatics tools to identify processes that were changed following alteration of RUNX1 expression level. We have shown that genes involved in megakaryopoiesis tend to be differentially expressed in the FPD and CBF datasets, demonstrating that a large number of the differentially expressed genes may play a role in platelet formation. Enrichment for genes involved in cell proliferation was also observed in both the FPD and CBF datasets, and functional assays on the FPD-AML cell lines showed that heterozygous mutation of <italic>RUNX1 </italic>reduced proliferation of lymphoblasts. These data validate our integrative approach as they confirm studies in transgenic mice expressing the fusion proteins CBFβ-MYH11 [##REF##9315100##30##] and RUNX1-ETO [##REF##10979955##13##], which both act in a dominant negative fashion over the wild-type protein. These mice show a decrease in both lymphoid and myeloid cell proliferation. This observation also correlates with mouse data showing that Runx1 promotes cell cycle progression from G1 to S phase [##REF##10652337##31##]. An anti-proliferative effect of a RUNX1 mutant protein may have an oncogenic effect due to an improper balance between proliferation and differentiation. For example, overexpression of RUNX1 usually results in ALL while complete or partial loss of RUNX1 results in AML development.</p>", "<p>Our integrative approach unraveled a novel process that may play an important role in RUNX1 function, involving the cytoskeletal dynamics. Indeed following the finding that an enrichment of microtubule and cytoskeleton related molecules was observed when the CBF complex was overexpressed, functional assay using the FPD-AML cells demonstrated an increase of polymerized microtubules in FPD-AML affected cells compared to cells from unaffected individuals. Microtubules are important in many processes such as cell migration, cell division, cellular transport and signal transduction [##REF##15057285##32##] and microtubule remodeling is essential during the cell cycle, especially during mitosis when a correct microtubule network is essential for proper chromosomal segregation [##REF##15173827##33##]. Interestingly, the fusion protein, CBFβ-MYH11 that results from inv(16), co-localizes with the actin cytoskeleton and disorganizes stress fibers and F-actin structures [##REF##9264408##34##]. A mild microtubule defect might partially explain the platelet defect observed in FPD-AML patients, as microtubules are necessary at several different stages of megakaryopoiesis including endomitosis, production of platelets from mature polyploid megakaryocytes, and release of the content of platelet granules [##REF##10942379##35##]. Moreover, mutations in the actin-binding protein WASP and the myosin heavy chain MYH9 cause the Wiskott-Aldrich [##REF##8069912##36##] and May-Hegglin [##REF##10973259##37##] syndromes of thrombocytopenia, respectively. However, RUNX1 is likely to regulate only specific tubulin isoforms or tissue-specific cytoskeleton-associated proteins as a strong cytoskeleton defect would be more detrimental to the whole organism. In addition, the dosage of normal RUNX1 activity necessary for normal function might differ according to cell type, and some cell types may be more susceptible than others to perturbation in RUNX1 levels. Interestingly, Taxol resistant leukemic cells have been shown to have a reduced total level of tubulin and an increased level of polymerized tubulin [##REF##15568225##38##], similar to the results seen in the FPD-AML cells. Furthermore, a high level of survivin (BIRC5), which was down-regulated following overexpression of the CBF complex, is associated with resistance to Taxol [##REF##12363043##39##]. This is the first evidence demonstrating a relationship between RUNX1 and microtubule dynamics.</p>", "<p>Finally, we showed that the predisposition of FPD-AML to develop leukemia may be due to an increased rate of mutation in <italic>RUNX1 </italic>heterozygous cells. Every dataset showed significant correspondence with genes involved in DNA damage response. Although not conclusive, the glycophorin A assay, which measures the frequency of the progeny of mutated erythrocyte precursors in blood, showed a mild increase in mutation frequency in FPD-AML patients compared to unaffected individuals. Recently, it was shown that the RUNX1-ETO fusion protein induces mutations in transfected U937 myeloid cells [##REF##14660751##40##]. This study demonstrated that the fusion protein regulates many genes involved in the base excision repair pathway, which mainly corrects for point mutations. Furthermore, a higher incidence of leukemia in CBFβ-MYH11 chimeras compared to normal chimeras when exposed to ENU mutagenesis has also been observed [##REF##10508507##41##,##REF##11526243##42##]. This demonstrates that alteration of RUNX1 function may increase the rate of mutation and lead to an accumulation of mutated cells.</p>", "<p>The three processes described here (proliferation, cytoskeleton stability and genomic instability) are tightly interconnected and may explain the phenotype observed in FDP-AML patients. Indeed, a proliferation defect would have an impact on megakaryopoiesis and cytoskeleton remodeling. In turn, a cytoskeleton defect could also affect proliferation and trigger chromosomal aberrations. The necessary threshold level of RUNX1 expression is likely to be cell-specific, explaining why <italic>RUNX1 </italic>heterozygous mutation affects only hematopoietic cells; nevertheless, our observations could conceivably suggest possible involvement of RUNX1 in solid-tissue tumor.</p>", "<p>We also identified new potential RUNX1 target genes by analyzing the regulatory regions and the expression pattern of the differentially expressed genes present in the overlaps between the different platforms. Many RUNX1 target genes have already been described in the literature, mainly from <italic>in vitro </italic>studies and in mouse cells [##REF##12643014##43##,##REF##9811459##44##]. Four of the published target genes, CSF1R, MYB, MPO and TIMP1, were differentially expressed in the <italic>Runx1 </italic>knockout embryos. In addition, target genes that were described more recently, including CCND3 [##REF##14747476##45##] and IGFBP3 [##REF##15592512##46##], were identified following overexpression of the CBF complex. That there was not more correlation may be due to incomplete microarray platforms, but more importantly is likely to reflect the bias present in the published RUNX1 target genes that were identified because of their primary role in hematopoiesis and these may not represent the most common RUNX1 target genes. Interesing candidates were among the 16 genes differentially expressed in every dataset, such as Annexin I (ANXA1), which was shown to reduce inflammation, by inhibiting neutrophil recruitment [##REF##8409403##47##] and has an anti-proliferative effect by inducing aberrant cytoskeleton formation [##REF##14516791##48##]. This gene is likely to play an important role downstream of RUNX1.</p>" ]
[ "<title>Conclusion</title>", "<p>In summary, this combination of gene expression profiling platforms allowed prioritization of novel candidate genes for leukemogenesis according to distinct parameters and has shed light on RUNX1 functions by identifying biological pathways downstream of RUNX1 such as microtubule stability and genomic instability and identified a large number of potential novel RUNX1 target genes. Whether or not these are direct RUNX1 targets remains to be demonstrated by further research.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The <italic>RUNX1 </italic>transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia.</p>", "<title>Results</title>", "<p>Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFβ, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either <italic>RUNX1 </italic>or its cofactor, <italic>CBFβ</italic>. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous <italic>RUNX1 </italic>point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes.</p>", "<title>Conclusion</title>", "<p>This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both familial and sporadic leukemia as well as therapeutic implications.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>JM designed the experiments and analysis, performed the majority of the experiments and wrote the manuscript. KMS performed the statistical analysis of the Affymetrix data and participated in the Gene Set Enrichment analysis. RE participated in the design of the experiments. KBP participated in the generation of adenovirus particles. TB participated in the design of the bioinformatics analyses. CC participated in the luciferase reporter assay. MER participated in the microarray analyses. FS performed the ANOVA tests and participated in the cross-platform comparison. PC participated in the luciferase reporter assay. ML performed the tubulin polymerization assay. XS performed the GPA assay. YI provided vital Runx1 knockout embryos. WHR provided vital patient samples. MSH provided vital patient samples. MO provided vital Runx1 knockout embryos. DRT participated in the design and analysis of the GPA assay. TPS participated in the design of the bioinformatics analyses. MK participated in the design and analysis of the tubulin polymerization assay. GKS generated the statistical analysis of the cDNA microarray data, the statistical comparison to AML samples and supervised the statistical components of the article. HSS designed the experiments and analysis and participated in the writing of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Dr. S. Brenz Verca and Prof S. Rusconi for providing the adenoviral backbone, transfer vector (pScot) and the HER911 packaging cell line. We thank Prof. S.E. Antonarakis for his support during the adenovirus production. This project was supported by grants from the Ligue Genevoise Contre le Cancer, the Fondation Pour la Lutte Contre le Cancer, the Fondation Dr Henri Dubois-Ferrière Dinu Lipatti, the Nossal Leadership Fellowship from the Walter and Eliza Hall Institute of Medical Research, NHMRC Grants (257501, 257529) and NHMRC fellowship 171601 to HSS; International Postgraduate Research (Australian government) and Melbourne International Research scholarships to JAM and FS, an Australian postgraduate award to MER, an NHMRC Dora Lush Postgraduate Award (305552) to CC, a Swiss National Science Foundation and Bernische Krebsliga fellowships to RE, NIH (DK58161 and HL079507), to MH and NHMRC Career Development Award 300580 to MK.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Gene expression profiles and overlaps</bold>. The three platforms used in this study are indicated. The number of up-, down- or all differentially expressed genes (DEGs) are indicated below each platform.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Correlation with clinical AML data</bold>. A. Published microarray data on 285 AML patients [##REF##15084694##23##] were ordered using Gene Recommender according to the expression pattern of the 11 probe sets for RUNX1. The patients with t(8;21) are marked in orange and those with inv(16) in red. Probes co-regulated with RUNX1 are highly ranked (yellow bar), whereas probes showing an expression pattern the least similar to RUNX1 are ranked lowest (blue bar). B-C. Random permutations were performed to compare the rank of the genes differentially expressed in FPD platform and random set of genes. The histograms show the percentage of up- or down-regulated genes in FPD relative to their rank with \"0\" being the probes co-regulated with RUNX1 (yellow) and \"1\" being the probes the least similar to RUNX1 (blue). The trends observed in the histograms are represented as triangles or rectangle. D-E. Similar histograms showing percentage of up- or down-regulated genes in CBF relative to their rank.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Processes identified by Ingenuity Pathways Analysis</bold>. Evidence that each dataset is involved in the given function as determined by the use of Ingenuity Pathways Analysis (Ingenuity Systems, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ingenuity.com\"/>). The threshold for the significance is indicated by a vertical bar and represents a p-value of 0.05.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>MR-GSE test</bold>. Representation of the p-values (corrected for multiple testing) resulting from the MR-GSE test for each dataset and 10 gene sets specified in Additional File ##SUPPL##0##1## (Table S4). In brief they are gene sets Mekagaryocyte differentiation, Identification of genes involved in the differentiation of megakaryocytes. DEGs between stem cells and differentiated megakaryocytes; Platelets, Transcription profiling of human blood platelet; ; Normal megakaryocytes, Genes highly expressed in megakaryocytes; ET megakaryocytes, Genes highly expressed in essential thrombocytopenia megakaryocytes; Cytokinesis proteome, Identification of proteins present in the midbody during cytokinesis; Spindle checkpoint, Review ; DNA repair, Review; Lymphoblast irradiation; high dose, Effect of ionising radiation on lymphoblasts; Lymphoblast irradiation; low dose, Effect of ionising radiation on lymphoblasts; Genes DE in cancer, Meta-analysis of cancer microarray data to identify genes consistently DE in tumours. This represents whether the genes present in the published gene sets are also differentially expressed in our expression profiles. For example, the genes expressed in normal or diseased megakaryocytes (lines 3 and 4) are significantly represented in the differentially expressed genes identified in the FPD and CBF approaches.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Functional assays on FPD-AML cell lines</bold>. A. The results of a BrdU proliferation assay are indicated for each cell line. Dark bars indicate affected individuals. The standard errors of two independent replicates are shown. A two-way ANOVA resulted in a significant p-value (p &lt; 0.001) between affected and unaffected individuals. B. Examples of the tubulin polymerization assay for an affected and an unaffected individuals in each family. s:soluble tubulin; p:polymerized tubulin. C. The percentage of polymerized tubulin is shown for each cell line. Dark bars indicate affected individuals. The standard errors of three independent replicates are indicated. A two-way ANOVA resulted in a significant p-value (p &lt; 0.002) between affected and unaffected individuals. D. Percentage of polymerized tubulin in the same cell lines before (darker left bars) and after (second bars) induction of polymerization by Taxol. A significant smaller induction is observed in affected individuals (dark bars) as demonstrated by an ANOVA (p &lt; 0.0003). E. Glycophorin A assay. The numbers of N0 (loss of the M allele), NN (mutation changing M to N allele) or total mutant (both N0 and NN) cells are indicated for each individual. The standard errors of three to five technical replicates are indicated. Dark bars represent affected individuals (A1-A2). The control C5 is the unaffected sister of patient A1. ANOVAs were performed for each kind of mutation and the p-values are indicated.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>A. Overlaps between the datasets and percentage of genes with a RUNX1 binding site in their regulatory regions</bold>. The overlaps between the different platforms are represented with arrows. * indicates that the genes differentially expressed in at least one of the mouse datasets are considered for the following overlap. The number of differentially expressed genes (DEGs) containing a conserved RUNX1 binding site (with CBS) in their regulatory regions, as determined by the oPOSSUM program [##REF##15933209##27##], over the number of analyzed genes is indicated for each dataset and overlap. The corresponding percentage is indicated in brackets. B. Luciferase assay for 5 RUNX1 binding sites corresponding to 3 differentially expressed genes. The transactivation activity of RUNX1 over these sites was measured as the fold change of the luciferase activity in the presence of the CBF complex compared to the endogenous activity of each construct. The standard errors of three independent replicates are shown. CASP3 was shown as a negative control as no binding site was found for this gene. The difference in expression for the three genes in each dataset is indicated in the table. 0 means no difference in expression, ↓ stands for down-regulated and ↑ stands for up-regulated.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Part of the networks downsteam of RUNX1</bold>. Additional data from the literature and our studies were used to update the standard Ingenuity Pathway System (Ingenuity<sup>® </sup>Systems, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ingenuity.com\"/>) network analyses. Genes up-regulated (red) or down-regulated (green) in either FPD or CBF are indicated. Selected chosen functions with significant network nodes are shown including all the genes involved in cytoskeleton organization. Grey arrows represent transcriptional regulation, grey lines represent direct interaction, dotted lines represent indirect link. Each kind of molecule is represented by a different symbol (see <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ingenuity.com\"/>).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Gene ontology enrichment</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">FPD</td><td align=\"center\">CBF</td><td align=\"center\">E8.5</td><td align=\"center\">E12</td></tr></thead><tbody><tr><td align=\"center\">GO: Biological processes</td><td align=\"center\"><bold>Immune response p = 6.5 × 10<sup>-5 </sup>36 genes</bold></td><td align=\"center\">Macromolecular complex assembly p = 0.02 47 genes</td><td align=\"center\">Blood vessel development p = 0.06 15 genes</td><td align=\"center\">Response to external stimulus* p = 0.0003 18 genes</td></tr><tr><td/><td align=\"center\">Negative regulation of apoptosis p = 0.002 16 genes</td><td align=\"center\">Cell growth p = 0.02 21 genes</td><td/><td align=\"center\">Behavior p = 0.0003 14 genes</td></tr><tr><td/><td align=\"center\">Response to biotic stimulus p = 0.002 19 genes</td><td/><td/><td align=\"center\"><bold>Immune system process p = 0.0006 18 genes</bold></td></tr><tr><td/><td align=\"center\">Cell proliferation p = 0.01 36 genes</td><td/><td/><td/></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"center\">GO: Molecular functions</td><td align=\"center\"><bold>Cadmium ion binding p = 0.002 4 genes</bold></td><td align=\"center\">RNA binding p = 0.03 50 genes</td><td/><td align=\"center\">IgG binding p = 0.006 3 genes</td></tr><tr><td/><td/><td align=\"center\"><bold>Cadmium ion binding p = 0.03 4 genes</bold></td><td/><td align=\"center\">Ferric-chelate reductase activity p = 0.03 2 genes</td></tr><tr><td/><td/><td/><td/><td align=\"center\">Polysaccharide binding p = 0.03 6 genes</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"center\">GO: cellular component</td><td/><td align=\"center\">Spindle p = 0.06 11 genes</td><td align=\"center\">Cell junction p = 0.06 14 genes</td><td align=\"center\">Cell surface p = 0.05 9 genes</td></tr><tr><td/><td/><td/><td/><td align=\"center\">Extracellular space p = 0.06 37 genes</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"center\">InterPro motifs (FatiGo)</td><td align=\"center\"><bold>Vertebrate metallothionein p = 0.0001</bold></td><td align=\"center\"><bold>Vertebrate metallothionein p = 0.02</bold></td><td/><td/></tr><tr><td/><td/><td align=\"center\">Tubulin p = 0.04</td><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Genes differentially expressed in FPD, CBF and in E8.5/E12</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Gene name</bold></td><td align=\"center\"><bold>RefSeq</bold></td><td align=\"center\"><bold>RUNX1 BS</bold></td></tr></thead><tbody><tr><td align=\"left\">ITM2C</td><td align=\"center\">NM_030926</td><td align=\"center\">y</td></tr><tr><td align=\"left\">GLO1</td><td align=\"center\">NM_006708</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">OGT</td><td align=\"center\">NM_003605</td><td align=\"center\">y</td></tr><tr><td align=\"left\">ALAS1</td><td align=\"center\">NM_000688</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">HSPA4L</td><td align=\"center\">NM_014278</td><td align=\"center\">y</td></tr><tr><td align=\"left\">PPIB</td><td align=\"center\">NM_000942</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">CIB1</td><td align=\"center\">NM_006384</td><td align=\"center\">N</td></tr><tr><td align=\"left\">BASP1</td><td align=\"center\">NM_006317</td><td align=\"center\">y</td></tr><tr><td align=\"left\">TACC1</td><td align=\"center\">NM_006283</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">CTSC</td><td align=\"center\">NM_001814</td><td align=\"center\">y</td></tr><tr><td align=\"left\">PBX3</td><td align=\"center\">NM_006195</td><td align=\"center\">y</td></tr><tr><td align=\"left\">TGFBR3</td><td align=\"center\">NM_003243</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">ANZA1</td><td align=\"center\">NM_000700</td><td align=\"center\">y</td></tr><tr><td align=\"left\">ELF1</td><td align=\"center\">NM_172373</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">IFRD1</td><td align=\"center\">NM_001550</td><td align=\"center\">ND</td></tr><tr><td align=\"left\">MT1G</td><td align=\"center\">NM_005950</td><td align=\"center\">ND</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional File 1</title><p>Additional Methods, Figures S1 to S4 and Tables S3 to S6. Additional figures and tables to support the statistical and bioinformatics analyses described in the manuscript.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional File 2</title><p>Table S1. Gene expression profiling results. Summary of the gene expression profiling results, oPOSSUM and corresponding mouse Affymetrix data for each clone Accession number present on the human cDNA array.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional File 3</title><p>Table S2. Gene expression profiling data for E8.5 and E12 <italic>Runx1 </italic>knockout embryos.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional File 4</title><p>Figure S4. Supporting graphs for the Gene Set Enrichment analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional File 5</title><p>Figure S5. Expression pattern of RUNX1 and a subset of differentially expressed genes.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>The most significant gene ontology annotations are indicated for each dataset as identified through GOStat in April 2007. InterPro motifs were identified through the FatiGo program. The p-values are corrected for multiple testing (False discovery rate, Benjamini).</p></table-wrap-foot>", "<table-wrap-foot><p>RUNX1 BS: presence of a RUNX1 binding site in the regulatory region of the gene as determined by oPOSSUM. y stands for the presence of binding site and ND stands for not determined due to the absence of the gene in oPOSSUM.</p></table-wrap-foot>" ]
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[{"surname": ["Buckley"], "given-names": ["MJ"], "article-title": ["Spot User's Guide"], "source": ["CSIRO Mathematical and Information Sciences, Sydney, Australia"], "year": ["2000"]}, {"surname": ["Hollander", "Wolfe"], "given-names": ["M", "DA"], "source": ["Nonparametric stitistical inference"], "year": ["1973"], "publisher-name": ["New York , John Wiley & Sons"], "fpage": ["27"], "lpage": ["33"]}]
{ "acronym": [], "definition": [] }
63
CC BY
no
2022-01-12 14:47:26
BMC Genomics. 2008 Jul 31; 9:363
oa_package/64/d9/PMC2529319.tar.gz
PMC2529320
18691399
[ "<title>Background</title>", "<p>Grape berry is a typical true fruit originating from the ovary and is formed by skin, flesh, seeds and a complete vascular system. They all have specific properties that are directly linked to their particular physiological roles during berry development and seed dispersal.</p>", "<p>The growth of this non-climacteric fruit is summarized by the well known double-sigmoidal curve and is divided into an initial and rapid growth, a subsequent lag phase and a second period of growth corresponding to berry ripening [##UREF##0##1##,##UREF##1##2##]. During the first phase, embryo formation takes place in the seeds and the berry enlarges through frequent cell divisions, accompanied by the accumulation of many solutes, such as malic acid, tartaric acid and tannins [##UREF##2##3##,##UREF##3##4##]. The lag phase is characterized by the lack of any changes in berry weight and volume and its end coincides with the onset of ripening. This stage, which is referred to the French word <italic>véraison</italic>, is detectable in red cultivars where the change in skin colour takes place due to the start of anthocyanins synthesis. It is important to observe that at this time phloem unloading shifts to an apoplasmic pathway that is accompanied by a parallel change of the role of xylem in the water budgets [##REF##16207748##5##, ####REF##16868045##6##, ##REF##16861573##7####16861573##7##]. Furthermore, ripening is characterized by profound changes in berry composition. The concentrations of some metabolites, among which malic acid is the most important, decrease while the levels of other molecules, such as glucose, fructose, volatile aroma compounds and anthocyanins (in red cultivars), greatly increase [##UREF##3##4##,##UREF##4##8##, ####REF##12226348##9##, ##REF##16469915##10####16469915##10##]. Moreover, berries start to soften at <italic>véraison </italic>and this event is mainly linked to significant changes in the cell wall composition [##REF##10712544##11##, ####REF##9808722##12##, ##UREF##5##13##, ##REF##11800390##14####11800390##14##].</p>", "<p>In all growth phases, the very active metabolism of the skin deeply influences the final characteristics of the grape berry. This tissue, which is formed by a single layer of clear epidermal cells and a few hypodermal layers beneath the epidermis, is in fact the site of the synthesis of anthocyanins and aroma compounds [##UREF##3##4##,##UREF##4##8##,##REF##16469915##10##,##UREF##6##15##] and also represents a fundamental protective barrier against damage by physical injuries and pathogen attacks [##REF##15710631##16##]. The composition of this tissue depends on both the particular genetic background of the cultivar and the environmental conditions. These factors play a central role in influencing colour, aroma and other organoleptic properties of wine [##UREF##3##4##,##REF##17584945##17##, ####REF##18034875##18##, ##UREF##7##19##, ##REF##17897409##20##, ##UREF##8##21####8##21##].</p>", "<p>The impact of gene and protein expression patterns in determining the specificity of the skin in comparison to the other berry tissues is a crucial aspect that must be considered. In this view, two recent studies of the mRNA expression profiles in isolated skins have been published using oligonucleotide or cDNA microarrays [##REF##17584945##17##,##REF##15480888##22##]. Waters and co-workers provided a first description on the main events characterizing the shift in gene expression in this tissue around <italic>véraison </italic>[##REF##15480888##22##]. On the other hand, Grimplet and co-workers compared the mRNA expression profiles of the three major tissues of the berry (skin, pulp and seeds) at maturity. The results of this analysis highlighted that the skin transcriptome presented the most distant fingerprint from the global set, since the categories related to housekeeping processes (<italic>i.e</italic>. protein fate, cell cycle and DNA processing) were under-represented while those related to secondary, amino acid and lipid metabolism were highly expressed, if compared to pulp and seeds [##REF##17584945##17##].</p>", "<p>The widening of genomic information obtained in the last few years has also paved the way to the study of protein expression. Recently, some proteomic studies have been performed on grape berry. A 2-DE analysis of the mesocarp profile conducted by Sarry and co-workers [##REF##14730682##23##] allowed the identification of 67 proteins using MALDI-MS, thus providing clues to the sugar and organic acid metabolism in ripe berry pulp. More recently, the first analysis of the skin proteome has been performed by comparing, two by two, three different ripening stages in Cabernet Sauvignon berries [##REF##17426054##24##]. This paper mainly reports differences in the expression of pathogenesis-related proteins and of some enzymes involved in anthocyanin biosynthesis. Giribaldi and co-workers [##REF##17683049##25##], on the other hand, focused their attention on the proteome of whole berries of cv. Nebbiolo during a longer period of time, ranging from one month after flowering to complete ripening. These studies provided a first profile of grape proteomes, also describing some dynamic changes taking place in growing berries, although further efforts are still necessary in order to unravel the physiological events that characterize the grape berry ripening and the specific roles of the different tissues at the protein level.</p>", "<p>A crucial step in a 2-DE analysis is the procedure adopted for protein extraction. As with many other fruits, grape is a recalcitrant plant material because of the high concentration of interfering compounds such as phenolics, terpenes, organic acids, ions, carbohydrates and proteolytic and oxidative enzymes [##REF##10786879##26##, ####REF##15543535##27##, ##REF##15315634##28##, ##REF##15352226##29##, ##REF##15912556##30##, ##REF##16586412##31####16586412##31##]. This aspect is particularly onerous for investigations of the skin, where some of these compounds are present at very high concentrations. For this tissue, the phenol extraction method followed by ammonium acetate in methanol precipitation appears to be the most appropriate protocol up to now [##REF##17426054##24##,##REF##16664906##32##].</p>", "<p>In order to obtain further information on protein expression changes in the skin during berry ripening, a comparative 2-DE analysis was performed on a time-course experimental design made up of five different stages from <italic>véraison </italic>to full ripening of Barbera, a widely cultivated red variety typical of northern Italy. In order to associate the proteome changes to the events characterizing the ripening process, some biochemical parameters were also measured. In this study, it was reported that 80 spots significantly changed their relative volumes among the different stages. Sixty-nine of them were identified by LC-ESI-MS/MS and the corresponding proteins were classified on the basis of their putative functions. Some of these proteins were associated with glycolysis and other carbohydrate pathways of the primary metabolism and were found to increase in the skin tissue during ripening.</p>" ]
[ "<title>Methods</title>", "<title>Plant material and experimental design</title>", "<p>Experimental material was harvested during the 2005 growing season from <italic>Vitis vinifera </italic>L. cv. Barbera grapevines, grown at the Experimental Station of the Ente Regionale per i Servizi all'Agricoltura e alle Foreste (E.R.S.A.F.) of Regione Lombardia (Pavia, Italy).</p>", "<p>Samples were collected at five different ripening stages from <italic>véraison</italic>, until full ripening (corresponding to 58, 72, 86, 100, 107 days after blooming). We considered the <italic>véraison </italic>stage as the moment when 50% of the berries started to change colour.</p>", "<p>Two hundred berries were collected at each sampling date. Berries were equally sampled on a single cluster per plant across 20 plants. Immediately after harvest, the skins were collected by squishing the berries in order to remove seeds and the bulk of the mesocarp, then pressing and smearing the inner part of the skin on two layers of cheesecloth to completely take away the residual pulp. Skins samples were split into two technical replicates. The samples were frozen in liquid nitrogen and stored at -80°C until use. Each technical replicates was subjected to independent protein extraction. Three gels were run for each extraction. At all stages, samples of whole fresh berries, obtained as described above, were immediately used to measure total soluble solids, pH and titratable acidity.</p>", "<title>Determination of physiological parameters</title>", "<p>In order to assess the progress of grape berry ripening and to associate the physiological phases to the observed changes in protein expression, total solids, pH, titratable acidity and anthocyanins were evaluated on five stages of ripening, starting from <italic>véraison </italic>to full maturation.</p>", "<p>Total soluble solids (°BRIX), pH and titratable acidity were measured in grape juice, obtained by pressing fresh berries with a small hand-crank press, using a hand held refractometer (ATAGO CO., Ltd), a pH meter (Hanna HI 221) and an automatic titrator (Crison Compact Titrator) titrating in the presence of NaOH. Anthocyanins were extracted from the skins as previously described by Fumagalli and co-workers [##REF##16848515##68##]. The anthocyanins concentration was evaluated by measuring the absorbance of the extract at a wavelength of 535 nm and referring the values to a malvidin-3-glucoside calibration curve.</p>", "<p>Considering the whole period, a sharp increase in the anthocyanin content of the skin, soluble solids and pH in berry juice was measured, while a reduction in titratable acidity occurred at the same time (Figure ##FIG##7##8##). In detail, we observed a 10-fold surge in the anthocyanin level and a 2-fold upturn of soluble solids, accompanied by a pH shift of 0.4 and a 3-fold decrease in titratable acidity. The rate of sugars and anthocyanins accumulation as well as the changes in pH and titratable acidity were almost constant until the fourth stage, while no significant variations for these parameters were observed between the fourth and the fifth sampling stages.</p>", "<title>Protein extraction and quantification</title>", "<p>Frozen samples (5 g) were finely powdered in liquid nitrogen using a pestle and mortar, homogenized with cold (-20°C) acetone, washed twice on Whatman 41 filter paper (Whatman International Ltd) with cold acetone and finally dried under vacuum. The acetone powder was then resuspended in 20 mL of extraction buffer [0.7 M sucrose, 0.5 M Tris-HCl pH 8, 10 mM disodium EDTA salt, 4 mM ascorbic acid, 1 mM PMSF, 1 μM leupeptin, 0.1 mg mL<sup>-1 </sup>Pefabloc (Fluka), 0.4% (v/v) β-mercaptoethanol] on ice, incubated in a 4°C cold room under shaking for 30 min and then centrifuged at 13000 <italic>g </italic>for 30 min. The resultant supernatant was extracted as previously described by Hurkman and Tanaka [##REF##16664906##32##] by the addition of an equal volume of ice-cold Tris-buffered phenol (pH 8). The sample was shaken for 30 min at 4°C, incubated for 2 h at 4°C and finally centrifuged at 5000 <italic>g </italic>for 20 min at 4°C to separate the phases. The upper phenol phase was collected, while the aqueous phase at the bottom was back-extracted with an equal volume of phenol. Proteins were precipitated by the addition of five volumes of ice-cold 0.1 M ammonium acetate in methanol to the phenol phase, then vortexed briefly and finally incubated at -20°C overnight. Precipitated proteins were recovered by centrifuging at 13000 <italic>g </italic>for 30 min, then washed again with cold methanolic ammonium acetate and three times with cold 80% (v/v) acetone. The final pellet was dried under vacuum and dissolved in IEF buffer [7 M urea, 2 M thiourea, 3% (w/v) CHAPS, 1% (v/v) NP-40, 50 mg mL<sup>-1 </sup>DTT and 2% (v/v) IPG Buffer pH 3–10 (GE Healthcare)] by vortexing and incubating for 1 h at room temperature. The sample was centrifuged at 10000 <italic>g </italic>for 10 min and the supernatant stored at -80°C until further use. The protein concentration was determined by 2-D Quant Kit (GE Healthcare).</p>", "<title>2-DE</title>", "<p>The protein sample (200 μg) was loaded on pH 3–10, 24 cm IPG strips passively rehydrated overnight in 7 M urea, 2 M thiourea, 3% (w/v) CHAPS, 1% (v/v) NP-40, 10 mg mL<sup>-1 </sup>DTT and 0.5% (v/v) IPG Buffer pH 3–10. IEF was performed at 20°C with current limit of 50 μA/strip for about 90 kVh in an Ettan IPGphor (GE Healthcare) using the following settings: 5 min gradient 200 V, 1 h at 200 V, 5 min gradient 500 V, 1 h at 500 V, 5 min gradient 1000 V, 6 h at 1000 V, 3 h gradient 8000 V and 9 h at 8000 V. After IEF, strips were equilibrated by gentle shaking for 15 min in an equilibration buffer [100 mM Tris-HCl pH 6.8, 7 M urea, 2 M thiourea, 30% (w/v) glycerol, 2% (w/v) SDS] added with 0.5% (w/v) DTT for disulfide bridges reduction and for an additional 15 min in the same equilibration buffer to which was added 0.002% (w/v) bromophenol blue and 4.5% w/v iodoacetamide for cysteine alkylation. Second-dimensional SDS-PAGE [##REF##5432063##69##] was run in 12.5% acrylamide gels using the ETTAN DALT <italic>six </italic>apparatus (GE Healthcare). Running was first conducted at 5 W/gel for 30 min followed by 15 W/gel until the bromophenol blue line ran off.</p>", "<title>Protein visualization and image and data analysis</title>", "<p>Proteins were stained using the colloidal Coomassie Brilliant Blue G-250 (cCBB) procedure, as previously described by Neuhoff and co-workers [##REF##2466658##70##]. The gels were scanned in an Epson Expression 1680 Pro Scanner and analyzed with ImageMaster 2-D Platinum Software (GE Healthcare). Automatic matching was complemented by manual matching. Molecular weights of the spots were deduced on the basis of the migration of SigmaMarkers™ wide range (MW 6.500 – 205.000), while p<italic>I </italic>was determined according to the strip manufacturer's instructions (GE Healthcare).</p>", "<p>Relative spot volumes of the six replicate gels of the five ripening stages were compared and were analyzed according to the ANOVA test to verify whether the changes were statistically significant (<italic>p </italic>&lt; 0.01). Only spots showing at least a two-fold change in their relative volumes were considered for successive analysis. Significant differences were analyzed through the two-way hierarchical clustering methodology using the software PermutMatrix [##REF##15546938##71##,##REF##17203979##72##]. For this purpose, the data produced by the analysis of 2-DE gels were converted into a binary matrix replacing the missing values by zero. The row by row normalization of data was performed using the classical zero-mean and unit-standard deviation technique. Pearson's distance and Ward's algorithm were used for the analysis.</p>", "<title>In-gel digestion, mass spectrometry and protein characterization</title>", "<p>Spots were excised from cCBB-stained 2-DE gels and in-gel digested as previously described by Magni and co-workers [##REF##17320919##73##]. The extracted tryptic fragments were resuspended in 0.1% (v/v) formic acid and analysed by LC-ESI-MS/MS. For all the experiments a Finnigan LCQ Deca XP MAX IT mass spectrometer equipped with a Finnigan Surveyor (MS Pump Plus) HPLC system (Thermo Electron Corporation) was used. Chromatography separations were conducted on a BioBasic C18 column (180 μm I.D. × 150 mm length and 5 μm particle size), using a linear gradient from 5% to 80% solvent B [solvent A: 0.05% (v/v) formic acid; solvent B: ACN containing 0.05% (v/v) formic acid] with a flow of 2.5 μL/min. The capillary temperature and the spray voltage were set at 220°C and at 3.0 kV, respectively. For MS/MS scans the normalized collision energy was set at 35%. Acquisitions were performed in data-dependent MS/MS scanning mode and enabling a dynamic exclusion window of 3 min.</p>", "<p>Protein identifications were conducted by using TurboSEQUEST<sup>® </sup>incorporated in BioworksBrowser 3.2 (Thermo Electron Corporation) by correlation of uninterpreted spectra to the entries of NCBI NR non-redundant (<italic>i</italic>), <italic>Vitis </italic>protein subset (<italic>ii</italic>) and <italic>Vitis </italic>EST subset (<italic>iii</italic>) databases extracted from the NCBI NR non-redundant database (<italic>ii</italic>) and ESTdb others (<italic>iii</italic>), downloaded from the National Center for Biotechnology Information (NCBI). The software was set to allow two missed cleavages per peptide and to take into account fixed modification of cysteine carboxyamidomethylation and variable modification of methionine oxidation. The parent ion and fragment ion mass tolerance were set to ± 2 Da and ± 1 Da, respectively. In order to identify proteins, only peptides with Xcorr ≥ 1.5 (+1 charge state), ≥ 2.0 (+2 charge state), ≥ 2.5 (≥ 3 charge state), peptide probability &lt; 1 × 10<sup>-3</sup>, ΔCn ≥ 0.1 and Sf ≥ 0.70 were considered. Regarding protein identification by sequence similarity search, identified peptides were aligned against the NCBI NR non-redundant database using the FASTS algorithm [##REF##12096132##74##]<ext-link ext-link-type=\"uri\" xlink:href=\"http://fasta.bioch.virginia.edu/fasta_www2/fasta_www.cgi?rm=select&amp;pgm=fs\"/>. Theoretical molecular weight and p<italic>I </italic>of characterized proteins were calculated by processing sequence entries at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.expasy.org/tools/pi_tool.html\"/>. Protein functions were assigned to MIPS FunCat <ext-link ext-link-type=\"uri\" xlink:href=\"http://mips.gsf.de/projects/funcat\"/> according to their role described in the literature.</p>" ]
[ "<title>Results and discussion</title>", "<title>2-DE and image analysis</title>", "<p>2-DE analysis was performed on five consecutive stages of ripening that, as described in the Methods section, were also defined through the determination of some physiological parameters.</p>", "<p>Proteins were extracted from the berry skin samples of cultivar Barbera previously washed in acetone through a protocol which made use of phenol followed by precipitation in ammonium acetate in methanol, which was previously indicated to be appropriate for this recalcitrant tissue [##REF##17426054##24##]. 2-DE gels are shown in Fig. ##FIG##0##1##. The average number of detected spots was about 850 for each stage and did not vary significantly among the five different conditions. To ascertain the quantitative changes in the proteomic maps, their relative spot volumes (<italic>%Vol</italic>) were evaluated by software-assisted analysis. The ANOVA test (<italic>p </italic>&lt; 0.01), coupled with a threshold of two-fold change in level, revealed 80 spots as being differentially expressed throughout berry ripening.</p>", "<title>Hierarchical clustering analysis</title>", "<p>The differentially expressed spots were subjected to two-way hierarchical clustering analysis using the PermutMatrix software (Figure ##FIG##1##2##). Looking at the clustering of columns, which mirrors the distances among the different stages of berry ripening, it is evident that the bunch order reflects the sequential succession of samples, while there is a clear difference between the first two samplings and the following three. These results suggested that the most important changes in protein expression took place between the second and the third stage. Nevertheless, this behaviour appeared different from the data emerging from oligo/microarrays studies in which the most dramatic changes in the transcriptome were found immediately after <italic>véraison </italic>and appeared well correlated with the start of the ripening process [##REF##15480888##22##,##REF##16151847##33##]. The fact that most of the observed changes in this proteomic analysis did not refer to <italic>véraison</italic>, but to a period between 14 and 28 days after <italic>véraison </italic>(DAV), may reflect peculiar features of cv. Barbera that is characterized by a longer period of ripening, compared to the cultivar Shiraz which was used in the works cited above. Anyway, it is important to underline that comparisons among studies using different genotypes need to be evaluated with extreme caution. In addition, when dealing with in-field grown plants, the relevance of environmental factors should not be excluded since they affect the gene and/or protein expression [##REF##18034875##18##].</p>", "<p>As for the row clustering, two main trends were observed: one related to proteins whose levels increase during maturation, the other describing those declining as the berry ripens. Most of the spots belonged to the first class (62.5%), agreeing with the observation that the number of genes whose expression is switched on during ripening is far greater than the amount of genes switched off [##REF##15480888##22##]. The clustering analysis also indicated that the trends of expression relative to the last two ripening stages were closely grouped, suggesting that no evident expression changes took place in that period.</p>", "<title>Protein identification and functional distribution</title>", "<p>Among the 80 differentially expressed spots analyzed by LC-ESI-MS/MS, 69 were identified, listed in Table ##TAB##0##1## and shown in Figure ##FIG##2##3## which is referred to a gel of the fifth stage. The functional distribution of the identified proteins was performed according to MIPS FunCat annotation and is shown in Figure ##FIG##3##4##.</p>", "<p>Most of the observed variations are related to response to biotic or abiotic stresses (38%), glycolysis and gluconeogenesis (13%), C-compound and carbohydrate metabolism (13%) and amino acid metabolism (10%). The proportion of proteins involved in stress responses was quite high if compared to the functional distributions previously observed in the proteome of whole berries and isolated mesocarp, in which these proteins ranged from 8% to 19% of the identified spots [[##REF##17683049##25##] and [##REF##14730682##23##], respectively]. These results paralleled a recent large-scale mRNA expression analysis on the three main berry tissues [##REF##17584945##17##] as well as the skin proteome analysis of cultivar Cabernet Sauvignon where most of the proteins over-expressed at maturity were involved in pathogen response [##REF##17426054##24##]. This massive expression of proteins involved in stress responses may be essential to the protective function of the skin as a physical barrier between the environment and the inner tissues.</p>", "<p>Although it is known that the biosynthesis of anthocyanins and the transcription of related genes are induced at <italic>véraison </italic>[##REF##12226348##9##], no proteins related to this pathway were found. This failure could be ascribed to the experimental conditions used in this work. In fact, in a very preliminary analysis conducted on different genotypes using a narrower pH range (4–7) we found some really low expressed spots that were referable to enzymes involved in anthocyanin synthesis (data not shown). Nevertheless, Robinson and Davies reported that enzymes involved in this pathway are present at low levels making their assay difficult [##UREF##5##13##].</p>", "<title>Pathogenesis-related proteins</title>", "<p>Pathogenesis-related (PR) proteins belonging to class IV chitinases (Chit4), β-1,3-glucanases (Glucβ) and thaumatin-like protein (TLP) were found (Table ##TAB##0##1## and Figure ##FIG##2##3##). PR proteins matched to a group of spots, generally low expressed at <italic>véraison</italic>, whose abundance abruptly rose up to the point of representing about the 20% of the total spot volume in the protein profile of ripe berries (Figure ##FIG##4##5##). Both chitinase and β-1,3-glucanase are known to have antifungal activity and presumably hydrolyse the cell walls of fungal hyphae [##REF##9232867##38##,##REF##10820071##39##]. In agreement with the previous proteomic studies on grape ripening [##REF##17426054##24##,##REF##17683049##25##], two spots were identified corresponding to β-1,3-glucanase (spots 1071 and 1075) which accumulated after <italic>véraison</italic>. However, data regarding the behaviour of this enzyme during berry ripening are contradictory. More than one study [##REF##9232867##38##,##REF##16219919##40##] pointed out that, beside the surge of chitinase activity during ripening, no β-1,3-glucanase activity was detected in grape at any stages of berry development while it was reported that the gene is expressed [##UREF##10##37##]. In spite of this, Deytieux and co-workers [##REF##17426054##24##] associated the assay of the enzyme activity to the proteomic profile and found that, even if weakly correlated, both the expression and the activity of β-1,3-glucanase increased during ripening.</p>", "<p>In addition to their involvement in osmotic stress, a role in defence against fungi in grape berries has also been suggested for thaumatin-like proteins [##REF##9232867##38##]. It is interesting to observe that several studies provide evidence of the fact that chitinases and thaumatin-like proteins accumulate during berry ripening even in the absence of pathogen infections [##REF##17426054##24##,##REF##9232867##38##, ####REF##10820071##39##, ##REF##16219919##40####16219919##40##]. According to these results, we observed a sharp increase of these proteins moving from <italic>véraison </italic>to full maturation, suggesting that their expression may be developmentally regulated (Figure ##FIG##4##5##).</p>", "<title>Oxidative stress-related proteins</title>", "<p>It has been proposed that the oxidative stress may play a developmental role in the ripening process [##REF##11882944##41##, ####REF##12114557##42##, ##REF##16739135##43####16739135##43##]. As far as it concerns grape, this hypothesis is still a matter of debate. The data regarding the expression and the activities of proteins involved in ROS detoxification still remain unclear [##REF##17683049##25##,##REF##16151847##33##,##UREF##10##37##,##UREF##11##44##]. Recently, Pilati and co-workers [##REF##18034875##18##] found that an oxidative burst occurs at <italic>véraison </italic>and that this event may modulate the expression of a gene set. Nevertheless, among the differentially expressed proteins during ripening, we identified some enzymes that are known to be involved in the oxidative stress response (<italic>e.g</italic>. PPO, polyphenol oxidase; GPOX, glutathione peroxidase; CAT, catalase; TInLi, temperature-induced lipocalin, Figures ##FIG##2##3## and ##FIG##4##5##).</p>", "<p>Polyphenol oxidases catalyze the formation of <italic>o</italic>-quinones, molecules involved in browning reactions as a consequence of pathogen infection, wounding and organ senescence, through the O<sub>2</sub>-dependent oxidation of monophenols and <italic>o</italic>-diphenols [##UREF##12##45##]. In addition to the described defensive role, these ubiquitous enzymes may contribute to the biosynthetic pathways leading to proanthocyanidin [##REF##15720617##46##] and aurone [##REF##11073455##47##]. In our work we identified 8 spots corresponding to PPO, whose expression was high at <italic>véraison </italic>and dropped during ripening (Table ##TAB##0##1##, Figures ##FIG##2##3## and ##FIG##4##5##). This trend is in agreement with previous reports on this class of enzymes which are generally highly expressed and active in young developing tissues [##REF##1391768##48##, ####REF##1633491##49##, ##REF##7678763##50##, ##REF##7888632##51####7888632##51##]. Dry and Robinson [##REF##7948897##52##] described that the protein is synthesized as a 67 kDa precursor which is imported into the chloroplast and processed to remove a 10.6 kDa chloroplast transit peptide from the N-terminus and a 16.2 kDa peptide of unknown function from the C-terminus, thus resulting in a catalytic unit of 40.5 kDa. On the basis of the matched peptides and the deduced masses, five of the characterized proteins (spots 810, 819, 826, 843 and 876) may represent the catalytic unit. Interestingly, it is possible that spots 1481, 1482 and 1768, identified as PPO and having deduced masses of around 18 kDa, may correspond to the C-terminus of the enzyme. This was supported by the similarity of the molecular weight and by the evidence that the detected tryptic peptides are comprised in the part of the sequence between the hypothesized cleavage site and the C-terminus. This may indicate that the small terminal portion of PPO is maintained in skin cells after the cleavage from the catalytic unit. The role of this fragment is not known but it was recently indicated that its tertiary structure is likely to be similar to that of hemocyanin, an oxygen-binding protein isolated in the blood of molluscs whose main function resides in O<sub>2</sub>-storage and transport [##REF##16332393##53##].</p>", "<p>A spot corresponding to catalase (CAT, spot 521) presented a four-fold increase in abundance during ripening. An opposite behaviour was described for this enzyme in some recent reports on whole berries [##REF##17683049##25##,##REF##16219919##40##]. Although the influence of some factors can not be excluded, such as the genetic background and the environmental and seasonal conditions, these results could be explained by considering them as specific traits of the skin. For instance, it was recently discovered that the concentrations of ascorbate and glutathione in apple epidermis were 3- to 7-fold higher than in the underlying mesocarp [##UREF##13##54##]. In this view, we also observed a clear increase in the expression of a glutathione peroxidase (GPOX, spot 1408, Figure ##FIG##4##5##).</p>", "<title>Proteins involved in C-metabolism</title>", "<p>Among the characterized proteins, many are involved in primary activities, such as glycolysis, gluconeogenesis, C-compounds and carbohydrate metabolism (Table ##TAB##0##1## and Figure ##FIG##2##3##). A general picture of some traits of carbon metabolism showing the trend of these proteins is depicted in Figure ##FIG##6##7##.</p>", "<p>The understanding of grape assimilate partitioning, <italic>i.e</italic>. the process which determines the way carbohydrates are transported to the berry and how they are allocated, significantly improved in recent years. Sucrose is the preferred sugar for long-distance transfer in this species and is produced through photosynthesis in the mesophyll of mature leaves and conveyed to the berry from the phloem [##REF##16655073##55##]. Until <italic>véraison </italic>most of the sugar imported into the berry is metabolized and so there is little storage. After <italic>véraison</italic>, there is an upturn in sugar levels, among which glucose and fructose, that are the most representative carbohydrates, are accumulated in roughly equal amounts in the vacuoles of the mesocarp cells [##UREF##3##4##]. A number of reports indicates that, during ripening, the localization of sucrose hydrolysis shifts from the vacuole to the apoplast [##REF##16861573##7##,##REF##15480888##22##,##REF##8685267##56##]. This transition is associated to a decrease in the expression and activity of vacuolar invertases and a concomitant upturn of apoplastic acid invertases [##REF##16861573##7##]. In agreement with these reports, we identified two spots (spots 412 and 431) corresponding to a vacuolar invertase, GIN1, showing a strong reduction in their expression after <italic>véraison </italic>(Figure ##FIG##5##6##).</p>", "<p>The measured drop in titratable acidity is mainly ascribed to the catabolism of malate accumulated in the vacuole during stages I and II of berry development [##UREF##14##57##]. It has been suggested that this acid is degraded in grape via at least three pathways, mainly by the cytosolic NADP-malic enzyme (NADP-ME), which catalyzes the oxidative decarboxylation of malate into pyruvate and CO<sub>2 </sub>[##UREF##15##58##], and, to a lesser extent, by PEP carboxykinase and the cytosolic malate dehydrogenase (cMDH) [##REF##10938859##59##]. In our work we identified a spot corresponding to NADP-ME whose amount gradually increased during ripening (Figure ##FIG##5##6##). The role of this enzyme during berry development is still a matter of debate: in their tissue-specific transcriptional profile of ripe skins, Grimplet and co-workers [##REF##17584945##17##] recently pointed out that the mRNA levels of several enzymes involved in malate metabolism are higher in the skins than in pulp and seeds.</p>", "<p>In the past, several papers concerning the whole berry [##REF##17683049##25##] and isolated pulp or seeds [##REF##10938859##59##] reported that glycolysis is down-regulated after <italic>véraison</italic>. Differently, in some transcriptomic analysis conducted on the whole berry it was found that some enzymes belonging to this pathway were induced during ripening [##REF##16151847##33##,##REF##16219919##40##]. We have been the first, to our knowledge, who found that several glycolytic enzymes strongly increased in the skin during ripening (Figure ##FIG##5##6##). Most of them, <italic>e.g</italic>. phosphoglycerate mutase (PGlyM, spots 397 and 1767), enolase (ENO, spots 561 and 596), glyceraldeyde-3-phosphate dehydrogenase (G3PDH, spot 902 and 937) and phosphoglycerate kinase (PGK, spot 863), related to the energy-conserving reactions of glycolysis. These data underline the importance of distinguishing among the different berry tissues in order to understand the ripening process. In other words, the tissues could express different trends for glycolysis during ripening. In this view, we also found the concomitant high expression of NADP-ME as well as of the non-oxidative activities of the pentose phosphate pathway, such as the highly induced transketolase (TK, spots 325 and 327). These enzymes may be required in the skin for satisfying the large demand for carbon skeletons of the biosynthetic pathways operating in this tissue during ripening (<italic>e.g</italic>. anthocyanin synthesis).</p>", "<p>Pyruvate may be channelled into the Krebs cycle and is converted to Acetyl-CoA by the pyruvate dehydrogenase. According to an increase in fluxes towards TCA cycle, it has been found that the subunit E1 of this enzyme (PDHE1, spot 851) is more abundantly expressed towards maturity. Aconitase (ACO) is an enzyme of the TCA and glyoxylate cycles catalyzing the reversible conversion of citrate to isocitrate. The importance of this enzyme was emphasized by Carrari and co-workers [##REF##14551334##60##] who studied the <italic>Aco-1 </italic>tomato mutant which is characterized by a reduced expression of aconitase. Biochemical analysis of the leaves of this genotype suggested that <italic>Aco-1 </italic>exhibited a restricted flux through the Krebs cycle and reduced levels of Krebs cycle intermediates, with an elevated rate of photosynthesis and sucrose synthesis. The fact that <italic>Aco-1 </italic>leaves were also characterized by a different amino acid profile, indicates that this activity may have a role in controlling the C/N ratio and amino acid biosynthesis. We observed a spot corresponding to ACO (spot 191) whose expression sharply increased during ripening (Figure ##FIG##5##6##) as previously reported for cv. Cabernet Sauvignon skins [##REF##17426054##24##] and, at the transcriptomic level, for citrus fruit flesh [##REF##16897468##61##].</p>", "<p>Oxalyl-CoA decarboxylase (OxD, spot 413) is another protein whose levels increased during ripening. This enzyme catalyses the irreversible decarboxylation of Oxalyl-CoA, derived from glyoxylic acid, to produce formyl-CoA. This activity has already been associated to grape skin during ripening [##REF##17426054##24##], but further analyses are required in order to clarify its role in this process, as far as that of one- and two-carbon compounds.</p>", "<title>Proteins involved in N-metabolism</title>", "<p>It has been observed that the amino acid content of the berry rises significantly during maturation and that the relative amount of different amino acids changes, with proline and arginine generally being predominant [##UREF##16##62##]. Stines and co-workers [##REF##10398729##63##] suggested that proline accumulation may be achieved <italic>via </italic>the ornithine pathway under the control of ornithine aminotransferase (OAT), which constitutes a bridge between proline and arginine metabolism. In support of this view, we identified a very low abundance spot (&lt; 0.1 %Vol) corresponding to OAT (spot 654) which sharply increased in expression during ripening (Figure ##FIG##5##6##).</p>", "<p>As previously described by Giribaldi and co-workers [##REF##17683049##25##], in our study we found the protein cobalamin-independent methionine synthase (MetSy, spots 270 and 273), which catalyzes the final step of methionine biosynthesis. The exact role of this enzyme, whose expression peaked in the middle of ripening, still remains unclear.</p>", "<p>Interestingly, we identified a spot corresponding to a subunit precursor of the enzyme γ-aminobutyrate transaminase (ATpL3, spot 612) which is involved in the shunt of the aminoacid γ-aminobutyrate (GABA). To our knowledge there is no evidence of the involvement of this enzyme in the maturation of the grape berry, but it is known that it is involved in the ripening of other non-climacteric fruits, such as citrus [##REF##16897468##61##,##REF##17541628##64##]. According to the hypothesis proposed for citrus fruit, the GABA shunt may be active, among other things, in the regulation of cytoplasmic pH, due to the H<sup>+</sup>-consuming decarboxylation of glutamate, during the period of late development and ripening following citrate release from the vacuole [##REF##16897468##61##].</p>", "<title>Other proteins</title>", "<p>The most abundant protein found in the present work belongs to the family of ABA stress responsive elements (ASR, ca. 13% of the total volume at the first stage and ca. 6% thereafter). According to previous results, the spots 1318, 1358 and 1417 (Figure ##FIG##2##3##), which are referable to ASR, showed a downward trend during ripening (Figure ##FIG##4##5##). ASR are known to be involved in abiotic stress and fruit ripening, even though their exact role is still elusive [##REF##12953118##65##,##REF##17211513##66##]. The effective function has been questioned because of their very high expression level, the fact that the observed molecular masses were higher than the predicted theoretical values by a range of about 5–10 kDa and because they were found mainly in the cell wall enriched fraction [##REF##14730682##23##,##REF##18171594##67##].</p>", "<p>Heat shock proteins (HSP) are usually involved in stabilizing protein folding in response to different kinds of stimuli. We identified three spots corresponding to chaperones of a predicted mass of around 18 kDa (MChap and Hsp18.2, spots 1449, 1513 and 1533, Figure ##FIG##2##3##) and a heat shock chaperonin binding motif protein (HSC, spot 490) whose levels decreased after <italic>véraison </italic>(Figure ##FIG##5##6##). This evidence reinforces the conclusions of da Silva and co-workers [##REF##11073455##47##] who supposed that the peak of several HSPs expression at <italic>véraison</italic>, followed by their sudden drop, could be linked to the intense redirection of metabolism that is necessary to stabilize old and newly synthesized proteins.</p>", "<p>Finally, some proteins characterized in this study were involved in transcription (spots 1189 and 1511), protein synthesis (spot 1606), signal transduction (spot 1016) and secondary metabolism (spots 986, 1008 and 1028). Further work is necessary to define the effective role of these proteins in the skin during ripening.</p>" ]
[ "<title>Results and discussion</title>", "<title>2-DE and image analysis</title>", "<p>2-DE analysis was performed on five consecutive stages of ripening that, as described in the Methods section, were also defined through the determination of some physiological parameters.</p>", "<p>Proteins were extracted from the berry skin samples of cultivar Barbera previously washed in acetone through a protocol which made use of phenol followed by precipitation in ammonium acetate in methanol, which was previously indicated to be appropriate for this recalcitrant tissue [##REF##17426054##24##]. 2-DE gels are shown in Fig. ##FIG##0##1##. The average number of detected spots was about 850 for each stage and did not vary significantly among the five different conditions. To ascertain the quantitative changes in the proteomic maps, their relative spot volumes (<italic>%Vol</italic>) were evaluated by software-assisted analysis. The ANOVA test (<italic>p </italic>&lt; 0.01), coupled with a threshold of two-fold change in level, revealed 80 spots as being differentially expressed throughout berry ripening.</p>", "<title>Hierarchical clustering analysis</title>", "<p>The differentially expressed spots were subjected to two-way hierarchical clustering analysis using the PermutMatrix software (Figure ##FIG##1##2##). Looking at the clustering of columns, which mirrors the distances among the different stages of berry ripening, it is evident that the bunch order reflects the sequential succession of samples, while there is a clear difference between the first two samplings and the following three. These results suggested that the most important changes in protein expression took place between the second and the third stage. Nevertheless, this behaviour appeared different from the data emerging from oligo/microarrays studies in which the most dramatic changes in the transcriptome were found immediately after <italic>véraison </italic>and appeared well correlated with the start of the ripening process [##REF##15480888##22##,##REF##16151847##33##]. The fact that most of the observed changes in this proteomic analysis did not refer to <italic>véraison</italic>, but to a period between 14 and 28 days after <italic>véraison </italic>(DAV), may reflect peculiar features of cv. Barbera that is characterized by a longer period of ripening, compared to the cultivar Shiraz which was used in the works cited above. Anyway, it is important to underline that comparisons among studies using different genotypes need to be evaluated with extreme caution. In addition, when dealing with in-field grown plants, the relevance of environmental factors should not be excluded since they affect the gene and/or protein expression [##REF##18034875##18##].</p>", "<p>As for the row clustering, two main trends were observed: one related to proteins whose levels increase during maturation, the other describing those declining as the berry ripens. Most of the spots belonged to the first class (62.5%), agreeing with the observation that the number of genes whose expression is switched on during ripening is far greater than the amount of genes switched off [##REF##15480888##22##]. The clustering analysis also indicated that the trends of expression relative to the last two ripening stages were closely grouped, suggesting that no evident expression changes took place in that period.</p>", "<title>Protein identification and functional distribution</title>", "<p>Among the 80 differentially expressed spots analyzed by LC-ESI-MS/MS, 69 were identified, listed in Table ##TAB##0##1## and shown in Figure ##FIG##2##3## which is referred to a gel of the fifth stage. The functional distribution of the identified proteins was performed according to MIPS FunCat annotation and is shown in Figure ##FIG##3##4##.</p>", "<p>Most of the observed variations are related to response to biotic or abiotic stresses (38%), glycolysis and gluconeogenesis (13%), C-compound and carbohydrate metabolism (13%) and amino acid metabolism (10%). The proportion of proteins involved in stress responses was quite high if compared to the functional distributions previously observed in the proteome of whole berries and isolated mesocarp, in which these proteins ranged from 8% to 19% of the identified spots [[##REF##17683049##25##] and [##REF##14730682##23##], respectively]. These results paralleled a recent large-scale mRNA expression analysis on the three main berry tissues [##REF##17584945##17##] as well as the skin proteome analysis of cultivar Cabernet Sauvignon where most of the proteins over-expressed at maturity were involved in pathogen response [##REF##17426054##24##]. This massive expression of proteins involved in stress responses may be essential to the protective function of the skin as a physical barrier between the environment and the inner tissues.</p>", "<p>Although it is known that the biosynthesis of anthocyanins and the transcription of related genes are induced at <italic>véraison </italic>[##REF##12226348##9##], no proteins related to this pathway were found. This failure could be ascribed to the experimental conditions used in this work. In fact, in a very preliminary analysis conducted on different genotypes using a narrower pH range (4–7) we found some really low expressed spots that were referable to enzymes involved in anthocyanin synthesis (data not shown). Nevertheless, Robinson and Davies reported that enzymes involved in this pathway are present at low levels making their assay difficult [##UREF##5##13##].</p>", "<title>Pathogenesis-related proteins</title>", "<p>Pathogenesis-related (PR) proteins belonging to class IV chitinases (Chit4), β-1,3-glucanases (Glucβ) and thaumatin-like protein (TLP) were found (Table ##TAB##0##1## and Figure ##FIG##2##3##). PR proteins matched to a group of spots, generally low expressed at <italic>véraison</italic>, whose abundance abruptly rose up to the point of representing about the 20% of the total spot volume in the protein profile of ripe berries (Figure ##FIG##4##5##). Both chitinase and β-1,3-glucanase are known to have antifungal activity and presumably hydrolyse the cell walls of fungal hyphae [##REF##9232867##38##,##REF##10820071##39##]. In agreement with the previous proteomic studies on grape ripening [##REF##17426054##24##,##REF##17683049##25##], two spots were identified corresponding to β-1,3-glucanase (spots 1071 and 1075) which accumulated after <italic>véraison</italic>. However, data regarding the behaviour of this enzyme during berry ripening are contradictory. More than one study [##REF##9232867##38##,##REF##16219919##40##] pointed out that, beside the surge of chitinase activity during ripening, no β-1,3-glucanase activity was detected in grape at any stages of berry development while it was reported that the gene is expressed [##UREF##10##37##]. In spite of this, Deytieux and co-workers [##REF##17426054##24##] associated the assay of the enzyme activity to the proteomic profile and found that, even if weakly correlated, both the expression and the activity of β-1,3-glucanase increased during ripening.</p>", "<p>In addition to their involvement in osmotic stress, a role in defence against fungi in grape berries has also been suggested for thaumatin-like proteins [##REF##9232867##38##]. It is interesting to observe that several studies provide evidence of the fact that chitinases and thaumatin-like proteins accumulate during berry ripening even in the absence of pathogen infections [##REF##17426054##24##,##REF##9232867##38##, ####REF##10820071##39##, ##REF##16219919##40####16219919##40##]. According to these results, we observed a sharp increase of these proteins moving from <italic>véraison </italic>to full maturation, suggesting that their expression may be developmentally regulated (Figure ##FIG##4##5##).</p>", "<title>Oxidative stress-related proteins</title>", "<p>It has been proposed that the oxidative stress may play a developmental role in the ripening process [##REF##11882944##41##, ####REF##12114557##42##, ##REF##16739135##43####16739135##43##]. As far as it concerns grape, this hypothesis is still a matter of debate. The data regarding the expression and the activities of proteins involved in ROS detoxification still remain unclear [##REF##17683049##25##,##REF##16151847##33##,##UREF##10##37##,##UREF##11##44##]. Recently, Pilati and co-workers [##REF##18034875##18##] found that an oxidative burst occurs at <italic>véraison </italic>and that this event may modulate the expression of a gene set. Nevertheless, among the differentially expressed proteins during ripening, we identified some enzymes that are known to be involved in the oxidative stress response (<italic>e.g</italic>. PPO, polyphenol oxidase; GPOX, glutathione peroxidase; CAT, catalase; TInLi, temperature-induced lipocalin, Figures ##FIG##2##3## and ##FIG##4##5##).</p>", "<p>Polyphenol oxidases catalyze the formation of <italic>o</italic>-quinones, molecules involved in browning reactions as a consequence of pathogen infection, wounding and organ senescence, through the O<sub>2</sub>-dependent oxidation of monophenols and <italic>o</italic>-diphenols [##UREF##12##45##]. In addition to the described defensive role, these ubiquitous enzymes may contribute to the biosynthetic pathways leading to proanthocyanidin [##REF##15720617##46##] and aurone [##REF##11073455##47##]. In our work we identified 8 spots corresponding to PPO, whose expression was high at <italic>véraison </italic>and dropped during ripening (Table ##TAB##0##1##, Figures ##FIG##2##3## and ##FIG##4##5##). This trend is in agreement with previous reports on this class of enzymes which are generally highly expressed and active in young developing tissues [##REF##1391768##48##, ####REF##1633491##49##, ##REF##7678763##50##, ##REF##7888632##51####7888632##51##]. Dry and Robinson [##REF##7948897##52##] described that the protein is synthesized as a 67 kDa precursor which is imported into the chloroplast and processed to remove a 10.6 kDa chloroplast transit peptide from the N-terminus and a 16.2 kDa peptide of unknown function from the C-terminus, thus resulting in a catalytic unit of 40.5 kDa. On the basis of the matched peptides and the deduced masses, five of the characterized proteins (spots 810, 819, 826, 843 and 876) may represent the catalytic unit. Interestingly, it is possible that spots 1481, 1482 and 1768, identified as PPO and having deduced masses of around 18 kDa, may correspond to the C-terminus of the enzyme. This was supported by the similarity of the molecular weight and by the evidence that the detected tryptic peptides are comprised in the part of the sequence between the hypothesized cleavage site and the C-terminus. This may indicate that the small terminal portion of PPO is maintained in skin cells after the cleavage from the catalytic unit. The role of this fragment is not known but it was recently indicated that its tertiary structure is likely to be similar to that of hemocyanin, an oxygen-binding protein isolated in the blood of molluscs whose main function resides in O<sub>2</sub>-storage and transport [##REF##16332393##53##].</p>", "<p>A spot corresponding to catalase (CAT, spot 521) presented a four-fold increase in abundance during ripening. An opposite behaviour was described for this enzyme in some recent reports on whole berries [##REF##17683049##25##,##REF##16219919##40##]. Although the influence of some factors can not be excluded, such as the genetic background and the environmental and seasonal conditions, these results could be explained by considering them as specific traits of the skin. For instance, it was recently discovered that the concentrations of ascorbate and glutathione in apple epidermis were 3- to 7-fold higher than in the underlying mesocarp [##UREF##13##54##]. In this view, we also observed a clear increase in the expression of a glutathione peroxidase (GPOX, spot 1408, Figure ##FIG##4##5##).</p>", "<title>Proteins involved in C-metabolism</title>", "<p>Among the characterized proteins, many are involved in primary activities, such as glycolysis, gluconeogenesis, C-compounds and carbohydrate metabolism (Table ##TAB##0##1## and Figure ##FIG##2##3##). A general picture of some traits of carbon metabolism showing the trend of these proteins is depicted in Figure ##FIG##6##7##.</p>", "<p>The understanding of grape assimilate partitioning, <italic>i.e</italic>. the process which determines the way carbohydrates are transported to the berry and how they are allocated, significantly improved in recent years. Sucrose is the preferred sugar for long-distance transfer in this species and is produced through photosynthesis in the mesophyll of mature leaves and conveyed to the berry from the phloem [##REF##16655073##55##]. Until <italic>véraison </italic>most of the sugar imported into the berry is metabolized and so there is little storage. After <italic>véraison</italic>, there is an upturn in sugar levels, among which glucose and fructose, that are the most representative carbohydrates, are accumulated in roughly equal amounts in the vacuoles of the mesocarp cells [##UREF##3##4##]. A number of reports indicates that, during ripening, the localization of sucrose hydrolysis shifts from the vacuole to the apoplast [##REF##16861573##7##,##REF##15480888##22##,##REF##8685267##56##]. This transition is associated to a decrease in the expression and activity of vacuolar invertases and a concomitant upturn of apoplastic acid invertases [##REF##16861573##7##]. In agreement with these reports, we identified two spots (spots 412 and 431) corresponding to a vacuolar invertase, GIN1, showing a strong reduction in their expression after <italic>véraison </italic>(Figure ##FIG##5##6##).</p>", "<p>The measured drop in titratable acidity is mainly ascribed to the catabolism of malate accumulated in the vacuole during stages I and II of berry development [##UREF##14##57##]. It has been suggested that this acid is degraded in grape via at least three pathways, mainly by the cytosolic NADP-malic enzyme (NADP-ME), which catalyzes the oxidative decarboxylation of malate into pyruvate and CO<sub>2 </sub>[##UREF##15##58##], and, to a lesser extent, by PEP carboxykinase and the cytosolic malate dehydrogenase (cMDH) [##REF##10938859##59##]. In our work we identified a spot corresponding to NADP-ME whose amount gradually increased during ripening (Figure ##FIG##5##6##). The role of this enzyme during berry development is still a matter of debate: in their tissue-specific transcriptional profile of ripe skins, Grimplet and co-workers [##REF##17584945##17##] recently pointed out that the mRNA levels of several enzymes involved in malate metabolism are higher in the skins than in pulp and seeds.</p>", "<p>In the past, several papers concerning the whole berry [##REF##17683049##25##] and isolated pulp or seeds [##REF##10938859##59##] reported that glycolysis is down-regulated after <italic>véraison</italic>. Differently, in some transcriptomic analysis conducted on the whole berry it was found that some enzymes belonging to this pathway were induced during ripening [##REF##16151847##33##,##REF##16219919##40##]. We have been the first, to our knowledge, who found that several glycolytic enzymes strongly increased in the skin during ripening (Figure ##FIG##5##6##). Most of them, <italic>e.g</italic>. phosphoglycerate mutase (PGlyM, spots 397 and 1767), enolase (ENO, spots 561 and 596), glyceraldeyde-3-phosphate dehydrogenase (G3PDH, spot 902 and 937) and phosphoglycerate kinase (PGK, spot 863), related to the energy-conserving reactions of glycolysis. These data underline the importance of distinguishing among the different berry tissues in order to understand the ripening process. In other words, the tissues could express different trends for glycolysis during ripening. In this view, we also found the concomitant high expression of NADP-ME as well as of the non-oxidative activities of the pentose phosphate pathway, such as the highly induced transketolase (TK, spots 325 and 327). These enzymes may be required in the skin for satisfying the large demand for carbon skeletons of the biosynthetic pathways operating in this tissue during ripening (<italic>e.g</italic>. anthocyanin synthesis).</p>", "<p>Pyruvate may be channelled into the Krebs cycle and is converted to Acetyl-CoA by the pyruvate dehydrogenase. According to an increase in fluxes towards TCA cycle, it has been found that the subunit E1 of this enzyme (PDHE1, spot 851) is more abundantly expressed towards maturity. Aconitase (ACO) is an enzyme of the TCA and glyoxylate cycles catalyzing the reversible conversion of citrate to isocitrate. The importance of this enzyme was emphasized by Carrari and co-workers [##REF##14551334##60##] who studied the <italic>Aco-1 </italic>tomato mutant which is characterized by a reduced expression of aconitase. Biochemical analysis of the leaves of this genotype suggested that <italic>Aco-1 </italic>exhibited a restricted flux through the Krebs cycle and reduced levels of Krebs cycle intermediates, with an elevated rate of photosynthesis and sucrose synthesis. The fact that <italic>Aco-1 </italic>leaves were also characterized by a different amino acid profile, indicates that this activity may have a role in controlling the C/N ratio and amino acid biosynthesis. We observed a spot corresponding to ACO (spot 191) whose expression sharply increased during ripening (Figure ##FIG##5##6##) as previously reported for cv. Cabernet Sauvignon skins [##REF##17426054##24##] and, at the transcriptomic level, for citrus fruit flesh [##REF##16897468##61##].</p>", "<p>Oxalyl-CoA decarboxylase (OxD, spot 413) is another protein whose levels increased during ripening. This enzyme catalyses the irreversible decarboxylation of Oxalyl-CoA, derived from glyoxylic acid, to produce formyl-CoA. This activity has already been associated to grape skin during ripening [##REF##17426054##24##], but further analyses are required in order to clarify its role in this process, as far as that of one- and two-carbon compounds.</p>", "<title>Proteins involved in N-metabolism</title>", "<p>It has been observed that the amino acid content of the berry rises significantly during maturation and that the relative amount of different amino acids changes, with proline and arginine generally being predominant [##UREF##16##62##]. Stines and co-workers [##REF##10398729##63##] suggested that proline accumulation may be achieved <italic>via </italic>the ornithine pathway under the control of ornithine aminotransferase (OAT), which constitutes a bridge between proline and arginine metabolism. In support of this view, we identified a very low abundance spot (&lt; 0.1 %Vol) corresponding to OAT (spot 654) which sharply increased in expression during ripening (Figure ##FIG##5##6##).</p>", "<p>As previously described by Giribaldi and co-workers [##REF##17683049##25##], in our study we found the protein cobalamin-independent methionine synthase (MetSy, spots 270 and 273), which catalyzes the final step of methionine biosynthesis. The exact role of this enzyme, whose expression peaked in the middle of ripening, still remains unclear.</p>", "<p>Interestingly, we identified a spot corresponding to a subunit precursor of the enzyme γ-aminobutyrate transaminase (ATpL3, spot 612) which is involved in the shunt of the aminoacid γ-aminobutyrate (GABA). To our knowledge there is no evidence of the involvement of this enzyme in the maturation of the grape berry, but it is known that it is involved in the ripening of other non-climacteric fruits, such as citrus [##REF##16897468##61##,##REF##17541628##64##]. According to the hypothesis proposed for citrus fruit, the GABA shunt may be active, among other things, in the regulation of cytoplasmic pH, due to the H<sup>+</sup>-consuming decarboxylation of glutamate, during the period of late development and ripening following citrate release from the vacuole [##REF##16897468##61##].</p>", "<title>Other proteins</title>", "<p>The most abundant protein found in the present work belongs to the family of ABA stress responsive elements (ASR, ca. 13% of the total volume at the first stage and ca. 6% thereafter). According to previous results, the spots 1318, 1358 and 1417 (Figure ##FIG##2##3##), which are referable to ASR, showed a downward trend during ripening (Figure ##FIG##4##5##). ASR are known to be involved in abiotic stress and fruit ripening, even though their exact role is still elusive [##REF##12953118##65##,##REF##17211513##66##]. The effective function has been questioned because of their very high expression level, the fact that the observed molecular masses were higher than the predicted theoretical values by a range of about 5–10 kDa and because they were found mainly in the cell wall enriched fraction [##REF##14730682##23##,##REF##18171594##67##].</p>", "<p>Heat shock proteins (HSP) are usually involved in stabilizing protein folding in response to different kinds of stimuli. We identified three spots corresponding to chaperones of a predicted mass of around 18 kDa (MChap and Hsp18.2, spots 1449, 1513 and 1533, Figure ##FIG##2##3##) and a heat shock chaperonin binding motif protein (HSC, spot 490) whose levels decreased after <italic>véraison </italic>(Figure ##FIG##5##6##). This evidence reinforces the conclusions of da Silva and co-workers [##REF##11073455##47##] who supposed that the peak of several HSPs expression at <italic>véraison</italic>, followed by their sudden drop, could be linked to the intense redirection of metabolism that is necessary to stabilize old and newly synthesized proteins.</p>", "<p>Finally, some proteins characterized in this study were involved in transcription (spots 1189 and 1511), protein synthesis (spot 1606), signal transduction (spot 1016) and secondary metabolism (spots 986, 1008 and 1028). Further work is necessary to define the effective role of these proteins in the skin during ripening.</p>" ]
[ "<title>Conclusion</title>", "<p>This work gives new insights to the skin proteome evolution during ripening, focusing on some interesting traits of this tissue. In this view, we observed the ripening-related induction of the enzymes of the last five steps of glycolysis, although they had been described as down-regulated in previous studies performed on whole fruit. These variations were accompanied by the rise of the levels of other important proteins of primary metabolism, such as malic enzyme, aconitase, pyruvate dehydrogenase and transketolase.</p>", "<p>These results paved the way for investigations on the role of this tissue that has to respond to specific metabolic requests being the site of important biosynthetic pathway (<italic>e.g</italic>. anthocyanin). Moreover, the data emphasize the relevance of the skin as a physical barrier playing an important role in berry protection. In fact, the levels of many proteins known to take part in (a)biotic stress responses vary during the five analyzed stages. Many of them (<italic>i.e</italic>. chitinase, thaumatin-like, abscissic stress ripening protein, polyphenol oxidase) are the most expressed proteins found in this work and are characterized by the most abrupt variations in accordance to their possible developmental regulation.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Grape ripening represents the third phase of the double sigmoidal curve of berry development and is characterized by deep changes in the organoleptic characteristics. In this process, the skin plays a central role in the synthesis of many compounds of interest (<italic>e.g</italic>. anthocyanins and aroma volatiles) and represents a fundamental protective barrier against damage by physical injuries and pathogen attacks. In order to improve the knowledge on the role of this tissue during ripening, changes in the protein expression in the skin of the red cultivar Barbera at five different stages from <italic>véraison </italic>to full maturation were studied by performing a comparative 2-DE analysis.</p>", "<title>Results</title>", "<p>The proteomic analysis revealed that 80 spots were differentially expressed throughout berry ripening. Applying a two-way hierarchical clustering analysis to these variations, a clear difference between the first two samplings (up to 14 days after <italic>véraison</italic>) and the following three (from 28 to 49 days after <italic>véraison</italic>) emerged, thus suggesting that the most relevant changes in protein expression occurred in the first weeks of ripening. By means of LC-ESI-MS/MS analysis, 69 proteins were characterized. Many of these variations were related to proteins involved in responses to stress (38%), glycolysis and gluconeogenesis (13%), C-compounds and carbohydrate metabolism (13%) and amino acid metabolism (10%).</p>", "<title>Conclusion</title>", "<p>These results give new insights to the skin proteome evolution during ripening, thus underlining some interesting traits of this tissue. In this view, we observed the ripening-related induction of many enzymes involved in primary metabolism, including those of the last five steps of the glycolytic pathway, which had been described as down-regulated in previous studies performed on whole fruit. Moreover, these data emphasize the relevance of this tissue as a physical barrier exerting an important part in berry protection. In fact, the level of many proteins involved in (a)biotic stress responses remarkably changed through the five stages taken into consideration, thus suggesting that their expression may be developmentally regulated.</p>" ]
[ "<title>Abbreviations</title>", "<p>NP-40 octylphenoxy polyethoxy ethanol; cCBB Colloidal Coomassie Brilliant Blue.</p>", "<title>Authors' contributions</title>", "<p>ASN carried out protein extraction, 2-DE, gel analysis, clustering and statistical analysis and wrote the initial manuscript draft. BP contributed to the conception of the experimental design, carried out protein characterization by LC-ESI-MS/MS, analyzed the MS data, participated in writing the methods section of the manuscript. MR performed metabolite analyses. OF and AS participated to the manuscript revision. MC contributed to the interpretation of the results and took part to the critical revision of the manuscript. LE conceived the study, coordinated the experiments, wrote and edited the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported by a grant promoting excellence in the industrial district of the Regione Lombardia in the frame of the research project PIDICEUVE (Creation of a diagnostic platform for the certification of grapes for the winemaking industry). The authors wish to thank Dr. Chiara Fedeli for her valuable contribution during the writing of this manuscript.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>2-DE maps of five stages through the ripening of Barbera</bold>. 2-DE maps of five different ripening stages from <italic>véraison </italic>until full ripeness of cultivar Barbera berry skins. The <italic>véraison </italic>stage (0 DAV) was considered as the moment when 50% of the berries started to change colour. Proteins (200 μg) were separated by IEF at pH 3–10, followed by 12.5% SDS PAGE and visualized by cCBB-staining.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Clustering analysis of the spots that resulted to change their relative volumes during ripening</bold>. Two-way hierarchical clustering analysis of the 80 spots that showed at least a two-fold change in the relative spot volumes (ANOVA, <italic>p </italic>&lt; 0.01) in the five different ripening stages of grape berry skins of cultivar Barbera. The <italic>véraison </italic>stage (0 DAV) was considered as the moment when 50% of the berries started to change colour. The clustering analysis was performed with PermutMatrix graphical interface after Z-score normalization of the averages of relative spot values (n = 6). Pearson's distance and Ward's algorithm were used for the analysis. Each coloured cell represents the average of the relative spot value, according to the colour scale at the bottom of the figure.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Protein profiles of identified proteins</bold>. Identified proteins are indicated in a 2-DE gel representative of the fifth ripening stage with spot name abbreviation corresponding to those in Table 1, Figure 6 and 7. Spots showing an increased or a decreased expression during ripening are indicated in red and in green, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Functional categories distribution of the identified proteins</bold>. Functional distribution of the identified proteins (Table 1) according to the annotation in the MIPS FunCat.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Changes in the expression of proteins involved in stress response</bold>. Changes in the relative spot volumes of the proteins (Table 1) involved in stress responses during five different ripening stages from <italic>véraison </italic>until full ripening of cultivar Barbera grape berry skins. The <italic>véraison </italic>stage (0 DAV) was considered as the moment when 50% of the berries started to change colour. Proteins were grouped according to their functions. Values are the mean ± SE of six 2-DE gels derived from two independent biological samples analyzed in triplicate.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Changes in the expression of proteins involved in C- and N-metabolism or with other functions</bold>. Changes in the relative spot volumes of the identified proteins belonging to the indicated functional categories (Table 1), during five different ripening stages of cv. Barbera grape berry skins from <italic>véraison </italic>until full ripening. The <italic>véraison </italic>stage (0 DAV) was considered as the moment when 50% of the berries started to change colour. Proteins were grouped according to their functions. Values are the mean ± SE of six 2-DE gels derived from two independent biological samples analyzed in triplicate.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Schematic overview of the enzymes involved in sugar and organic acid metabolisms and their connection with some intermediary activities that changed in expression in grape berry skins during five different ripe stages from <italic>véraison </italic>until full ripening</bold>. The expression was evaluated by measuring relative spot volumes in the 2-DE analysis. Green or red arrows indicate whether the abundance of the identified proteins decreased or increased during ripening, respectively. IRV1, cell wall invertase, GIN1, vacuolar invertase; Susy, sucrose synthase; UGP, UDP-glucose-pyrophosphorylase; PGluM, phosphogluco-mutase; PGI, phosphogluco-isomerase; PFK, phosphofructokinase; ALD, aldolase; TPI, triosephosphate-isomerase; G3PDH, glyceraldehyde-3-phosphate-dehydrogenase; PGK, phosphoglycerate-kinase; PGlyM, phosphoglycerate-mutase; ENO, enolase; PK, pyruvate kinase; PDC, pyruvate decarboxylase; NADP-ME, NADP-dependent malic enzyme; ADH, alcohol dehydrogenase; PDH, Pyruvate dehydrogenase.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Biochemical changes occurring during the ripening of Barbera berries</bold>. Changes in the physiological parameters were measured during five different ripening stages of cultivar Barbera grape berries from <italic>véraison </italic>until full ripening. The <italic>véraison </italic>stage (58 days after blooming) was considered as the moment when 50% of the berries started to change colour. A, total soluble solids; B, titratable acidity; C, berry juice pH; D, total anthocyanin contents. The data are the means ± SE of three experiments run in triplicate (n = 9).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>List of spots identified by LC-ESI-MS/MS and bioinformatic analysis. Proteins were classified according to MIPS FunCat. Additional data about mass spectrometry are reported in the additional file ##SUPPL##0##1##.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Spot ID</td><td align=\"center\">Accession number</td><td align=\"center\">Protein description</td><td align=\"center\">Name abbreviation</td><td align=\"center\">M<sub>r</sub><sup><bold><italic>a</italic></bold></sup></td><td align=\"center\">M<sub>r</sub><sup><bold><italic>b</italic></bold></sup></td><td align=\"center\">p<italic>I</italic><sup><bold><italic>a</italic></bold></sup></td><td align=\"center\">p<italic>I</italic><sup><bold><italic>b</italic></bold></sup></td><td align=\"center\">a.a. cov.<sup><bold><italic>c </italic></bold></sup>(%)</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"9\"><bold>Glycolysis and gluconeogenesis</bold></td></tr><tr><td align=\"center\">365</td><td align=\"center\">Q9ZSQ4</td><td align=\"center\">Cytoplasmic phosphoglucomutase</td><td align=\"center\"><bold>PGluM</bold></td><td align=\"center\">68.17</td><td align=\"center\">63.12</td><td align=\"center\">6.16</td><td align=\"center\">5.49</td><td align=\"center\">9.6</td></tr><tr><td align=\"center\">397</td><td align=\"center\">Q42908</td><td align=\"center\">2,3-bisphosphoglycerate-independent phosphoglycerate mutase</td><td align=\"center\"><bold>PGlyM-1</bold></td><td align=\"center\">63.34</td><td align=\"center\">61.18</td><td align=\"center\">5.83</td><td align=\"center\">5.39</td><td align=\"center\">5.7</td></tr><tr><td align=\"center\">561</td><td align=\"center\">P42896</td><td align=\"center\">Enolase</td><td align=\"center\"><bold>ENO-1</bold></td><td align=\"center\">52.48</td><td align=\"center\">47.91</td><td align=\"center\">5.89</td><td align=\"center\">5.56</td><td align=\"center\">35.5</td></tr><tr><td align=\"center\">596</td><td align=\"center\">P42896</td><td align=\"center\">Enolase</td><td align=\"center\"><bold>ENO-2</bold></td><td align=\"center\">52.04</td><td align=\"center\">47.91</td><td align=\"center\">6.14</td><td align=\"center\">5.56</td><td align=\"center\">31.5</td></tr><tr><td align=\"center\">829</td><td align=\"center\">CAN81988</td><td align=\"center\">Phosphoglycerate kinase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>PGK-1</bold></td><td align=\"center\">40.66</td><td align=\"center\">42.42</td><td align=\"center\">6.15</td><td align=\"center\">6.29</td><td align=\"center\">27.9</td></tr><tr><td align=\"center\">863</td><td align=\"center\">CAN81988</td><td align=\"center\">Phosphoglycerate kinase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>PGK-2</bold></td><td align=\"center\">39.06</td><td align=\"center\">42.42</td><td align=\"center\">6.21</td><td align=\"center\">6.29</td><td align=\"center\">37.1</td></tr><tr><td align=\"center\">902</td><td align=\"center\">ABC75834</td><td align=\"center\">Glyceraldehyde-3-phosphate dehydrogenase</td><td align=\"center\"><bold>G3PDH-1</bold></td><td align=\"center\">37.48</td><td align=\"center\">36.76</td><td align=\"center\">7.48</td><td align=\"center\">6.72</td><td align=\"center\">25.1</td></tr><tr><td align=\"center\">937</td><td align=\"center\">P26518</td><td align=\"center\">Glyceraldehyde-3-phosphate dehydrogenase</td><td align=\"center\"><bold>G3PDH-2</bold></td><td align=\"center\">36.77</td><td align=\"center\">36.98</td><td align=\"center\">7.94</td><td align=\"center\">7.09</td><td align=\"center\">22.0</td></tr><tr><td align=\"center\">1767</td><td align=\"center\">Q42908</td><td align=\"center\">2,3-bisphosphoglycerate-independent phosphoglycerate mutase</td><td align=\"center\"><bold>PGlyM-2</bold></td><td align=\"center\">62.15</td><td align=\"center\">61.18</td><td align=\"center\">5.78</td><td align=\"center\">5.39</td><td align=\"center\">16.3</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>C-compound and carbohydrate metabolism</bold></td></tr><tr><td align=\"center\">191</td><td align=\"center\">AAC26045</td><td align=\"center\">Aconitase-iron regulated protein 1</td><td align=\"center\"><bold>ACO</bold></td><td align=\"center\">102.81</td><td align=\"center\">98.09</td><td align=\"center\">5.96</td><td align=\"center\">5.95</td><td align=\"center\">14.2</td></tr><tr><td align=\"center\">325</td><td align=\"center\">CAN60522</td><td align=\"center\">Transketolase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>TK-1</bold></td><td align=\"center\">74.77</td><td align=\"center\">73.77</td><td align=\"center\">5.97</td><td align=\"center\">6.36</td><td align=\"center\">15.3</td></tr><tr><td align=\"center\">327</td><td align=\"center\">CAN60522</td><td align=\"center\">Transketolase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>TK-2</bold></td><td align=\"center\">74.34</td><td align=\"center\">73.77</td><td align=\"center\">6.03</td><td align=\"center\">6.36</td><td align=\"center\">10.1</td></tr><tr><td align=\"center\">378</td><td align=\"center\">P51615</td><td align=\"center\">NADP-dependent malic enzyme</td><td align=\"center\"><bold>NADP-ME</bold></td><td align=\"center\">66.00</td><td align=\"center\">65.23</td><td align=\"center\">6.10</td><td align=\"center\">6.09</td><td align=\"center\">20.6</td></tr><tr><td align=\"center\">412</td><td align=\"center\">AAB47171</td><td align=\"center\">Vacuolar invertase 1</td><td align=\"center\"><bold>GIN1-1</bold></td><td align=\"center\">59.07</td><td align=\"center\">71.55</td><td align=\"center\">4.27</td><td align=\"center\">4.60</td><td align=\"center\">7.9</td></tr><tr><td align=\"center\">413</td><td align=\"center\">CAN69570</td><td align=\"center\">Putative oxalyl-CoA decarboxylase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>OxD</bold></td><td align=\"center\">60.17</td><td align=\"center\">61.06</td><td align=\"center\">5.98</td><td align=\"center\">5.94</td><td align=\"center\">23.8</td></tr><tr><td align=\"center\">431</td><td align=\"center\">AAB47171</td><td align=\"center\">Vacuolar invertase 1</td><td align=\"center\"><bold>GIN1-2</bold></td><td align=\"center\">59.20</td><td align=\"center\">71.55</td><td align=\"center\">4.33</td><td align=\"center\">4.60</td><td align=\"center\">8.1</td></tr><tr><td align=\"center\">851</td><td align=\"center\">P52904</td><td align=\"center\">Pyruvate dehydrogenase E1 component subunit β, mitochondrial precursor</td><td align=\"center\"><bold>PDHE1</bold></td><td align=\"center\">39.59</td><td align=\"center\">38.79</td><td align=\"center\">5.17</td><td align=\"center\">5.88</td><td align=\"center\">7.2</td></tr><tr><td align=\"center\">1109</td><td align=\"center\">CAN78176</td><td align=\"center\">Xyloglucan endotransglycosylase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>XET</bold></td><td align=\"center\">31.65</td><td align=\"center\">33.18</td><td align=\"center\">6.19</td><td align=\"center\">5.98</td><td align=\"center\">12.5</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Photosynthesis</bold></td></tr><tr><td align=\"center\">1088</td><td align=\"center\">CAN61828</td><td align=\"center\">Manganese-stablising protein/photosystem II polypeptide<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>MnSpPSII</bold></td><td align=\"center\">31.91</td><td align=\"center\">33.23</td><td align=\"center\">5.39</td><td align=\"center\">5.87</td><td align=\"center\">12.2</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Nucleobase metabolism</bold></td></tr><tr><td align=\"center\">844</td><td align=\"center\">AAU14832</td><td align=\"center\">Adenosine kinase isoform 1S</td><td align=\"center\"><bold>ADK</bold></td><td align=\"center\">40.04</td><td align=\"center\">37.44</td><td align=\"center\">5.60</td><td align=\"center\">5.07</td><td align=\"center\">19.7</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Amino acid metabolism</bold></td></tr><tr><td align=\"center\">172</td><td align=\"center\">CAN63089</td><td align=\"center\">Glycine cleavage system P-protein<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>GCPp</bold></td><td align=\"center\">109.41</td><td align=\"center\">112.81</td><td align=\"center\">6.37</td><td align=\"center\">6.99</td><td align=\"center\">4.9</td></tr><tr><td align=\"center\">270</td><td align=\"center\">CAN73135</td><td align=\"center\">Cobalamin-independent methionine synthase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>MetSy-1</bold></td><td align=\"center\">83.47</td><td align=\"center\">81.64</td><td align=\"center\">5.97</td><td align=\"center\">6.19</td><td align=\"center\">11.9</td></tr><tr><td align=\"center\">273</td><td align=\"center\">CAN73135</td><td align=\"center\">Cobalamin-independent methionine synthase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>MetSy-2</bold></td><td align=\"center\">82.38</td><td align=\"center\">81.64</td><td align=\"center\">5.98</td><td align=\"center\">6.19</td><td align=\"center\">14.4</td></tr><tr><td align=\"center\">572</td><td align=\"center\">NP_193129</td><td align=\"center\">Serine hydroxymethyltransferase 4</td><td align=\"center\"><bold>SHM4</bold></td><td align=\"center\">52.88</td><td align=\"center\">51.72</td><td align=\"center\">7.27</td><td align=\"center\">6.80</td><td align=\"center\">12.3</td></tr><tr><td align=\"center\">612</td><td align=\"center\">AAO92257</td><td align=\"center\">γ-aminobutyrate transaminase subunit precursor isozyme 3</td><td align=\"center\"><bold>ATpL3</bold></td><td align=\"center\">50.98</td><td align=\"center\">57.24</td><td align=\"center\">6.65</td><td align=\"center\">6.72</td><td align=\"center\">20.6</td></tr><tr><td align=\"center\">654</td><td align=\"center\">AAG09278</td><td align=\"center\">Ornithine aminotransferase</td><td align=\"center\"><bold>OAT</bold></td><td align=\"center\">48.56</td><td align=\"center\">51.32</td><td align=\"center\">6.21</td><td align=\"center\">6.44</td><td align=\"center\">9.4</td></tr><tr><td align=\"center\">815</td><td align=\"center\">P37833</td><td align=\"center\">Aspartate aminotransferase cytoplasmic</td><td align=\"center\"><bold>AsAT</bold></td><td align=\"center\">41.43</td><td align=\"center\">44.51</td><td align=\"center\">7.31</td><td align=\"center\">7.75</td><td align=\"center\">17.7</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Transcription</bold></td></tr><tr><td align=\"center\">1189</td><td align=\"center\">BAF46352</td><td align=\"center\">α chain of nascent polypeptide associated complex</td><td align=\"center\"><bold>PAC</bold></td><td align=\"center\">28.78</td><td align=\"center\">21.92</td><td align=\"center\">4.06</td><td align=\"center\">4.32</td><td align=\"center\">33.7</td></tr><tr><td align=\"center\">1511</td><td align=\"center\">ABE01085</td><td align=\"center\">BTF3</td><td align=\"center\"><bold>BTF3</bold></td><td align=\"center\">17.26</td><td align=\"center\">17.34</td><td align=\"center\">5.52</td><td align=\"center\">6.32</td><td align=\"center\">11.9</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Protein synthesis</bold></td></tr><tr><td align=\"center\">1606</td><td align=\"center\">AAL13082</td><td align=\"center\">Putative glycine-rich RNA-binding protein</td><td align=\"center\"><bold>GlyRp</bold></td><td align=\"center\">13.56</td><td align=\"center\">17.33</td><td align=\"center\">5.33</td><td align=\"center\">7.84</td><td align=\"center\">30.3</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Protein destination</bold></td></tr><tr><td align=\"center\">442</td><td align=\"center\">Q43116</td><td align=\"center\">Protein disulfide-isomerase precursor</td><td align=\"center\"><bold>PDIpr</bold></td><td align=\"center\">58.22</td><td align=\"center\">55.56</td><td align=\"center\">4.92</td><td align=\"center\">4.95</td><td align=\"center\">29.7</td></tr><tr><td align=\"center\">490</td><td align=\"center\">CAN68309</td><td align=\"center\">Heat shock chaperonin-binding motif<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>HSC</bold></td><td align=\"center\">56.04</td><td align=\"center\">41.04</td><td align=\"center\">4.94</td><td align=\"center\">4.94</td><td align=\"center\">17.1</td></tr><tr><td align=\"center\">1449</td><td align=\"center\">CAN60868</td><td align=\"center\">Molecular chaperone<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>MChap-1</bold></td><td align=\"center\">19.64</td><td align=\"center\">18.23</td><td align=\"center\">6.59</td><td align=\"center\">6.78</td><td align=\"center\">6.9</td></tr><tr><td align=\"center\">1513</td><td align=\"center\">CAN65631</td><td align=\"center\">Molecular chaperone<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>MChap-2</bold></td><td align=\"center\">17.26</td><td align=\"center\">18.15</td><td align=\"center\">5.73</td><td align=\"center\">6.17</td><td align=\"center\">8.8</td></tr><tr><td align=\"center\">1533</td><td align=\"center\">P27880</td><td align=\"center\">18.2 kDa class I heat shock protein</td><td align=\"center\"><bold>Hsp18.2</bold></td><td align=\"center\">16.56</td><td align=\"center\">18.17</td><td align=\"center\">6.85</td><td align=\"center\">5.81</td><td align=\"center\">12.0</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Cellular communication/signal transduction</bold></td></tr><tr><td align=\"center\">1016</td><td align=\"center\">CAN81470</td><td align=\"center\">Annexin<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>Annex</bold></td><td align=\"center\">34.86</td><td align=\"center\">35.19</td><td align=\"center\">6.92</td><td align=\"center\">7.13</td><td align=\"center\">29.4</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Secondary metabolism</bold></td></tr><tr><td align=\"center\">986</td><td align=\"center\">CAN60921</td><td align=\"center\">Kynurenine formamidase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>KF</bold></td><td align=\"center\">35.46</td><td align=\"center\">29.87</td><td align=\"center\">5.54</td><td align=\"center\">5.15</td><td align=\"center\">9.6</td></tr><tr><td align=\"center\">1008</td><td align=\"center\">CAI56335</td><td align=\"center\">Isoflavone reductase-like protein 6</td><td align=\"center\"><bold>IFRL6</bold></td><td align=\"center\">35.23</td><td align=\"center\">33.93</td><td align=\"center\">6.09</td><td align=\"center\">6.02</td><td align=\"center\">30.8</td></tr><tr><td align=\"center\">1028</td><td align=\"center\">CAI56334</td><td align=\"center\">Isoflavone reductase-like protein 5</td><td align=\"center\"><bold>IFRL5</bold></td><td align=\"center\">34.38</td><td align=\"center\">33.89</td><td align=\"center\">6.21</td><td align=\"center\">5.76</td><td align=\"center\">25.5</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Stress</bold></td></tr><tr><td align=\"center\">362</td><td align=\"center\">NP_001031620</td><td align=\"center\">Binding – stress inducible protein<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>BSP</bold></td><td align=\"center\">68.17</td><td align=\"center\">63.71</td><td align=\"center\">6.05</td><td align=\"center\">6.00</td><td align=\"center\">14.9</td></tr><tr><td align=\"center\">521</td><td align=\"center\">AAL83720</td><td align=\"center\">Catalase</td><td align=\"center\"><bold>CAT</bold></td><td align=\"center\">54.44</td><td align=\"center\">56.98</td><td align=\"center\">7.10</td><td align=\"center\">6.71</td><td align=\"center\">13.0</td></tr><tr><td align=\"center\">810</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-1</bold></td><td align=\"center\">41.26</td><td align=\"center\">67.39</td><td align=\"center\">6.88</td><td align=\"center\">6.39</td><td align=\"center\">8.4</td></tr><tr><td align=\"center\">819</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-2</bold></td><td align=\"center\">40.50</td><td align=\"center\">67.39</td><td align=\"center\">6.64</td><td align=\"center\">6.39</td><td align=\"center\">15.0</td></tr><tr><td align=\"center\">826</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-3</bold></td><td align=\"center\">41.09</td><td align=\"center\">67.39</td><td align=\"center\">6.81</td><td align=\"center\">6.39</td><td align=\"center\">6.1</td></tr><tr><td align=\"center\">843</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-4</bold></td><td align=\"center\">39.96</td><td align=\"center\">67.39</td><td align=\"center\">6.43</td><td align=\"center\">6.39</td><td align=\"center\">17.5</td></tr><tr><td align=\"center\">876</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-5</bold></td><td align=\"center\">38.98</td><td align=\"center\">67.39</td><td align=\"center\">5.99</td><td align=\"center\">6.39</td><td align=\"center\">9.6</td></tr><tr><td align=\"center\">906</td><td align=\"center\">CAN78553</td><td align=\"center\">Late embryogenesis abundant protein<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>LEA</bold></td><td align=\"center\">37.71</td><td align=\"center\">34.94</td><td align=\"center\">4.43</td><td align=\"center\">4.67</td><td align=\"center\">22.4</td></tr><tr><td align=\"center\">1071</td><td align=\"center\">CAB60154</td><td align=\"center\">1,3 β glucanase</td><td align=\"center\"><bold>Glucβ-1</bold></td><td align=\"center\">32.26</td><td align=\"center\">13.37</td><td align=\"center\">5.99</td><td align=\"center\">6.11</td><td align=\"center\">39.3</td></tr><tr><td align=\"center\">1075</td><td align=\"center\">CAB91554</td><td align=\"center\">1,3 β glucanase</td><td align=\"center\"><bold>Glucβ-2</bold></td><td align=\"center\">32.65</td><td align=\"center\">37.46</td><td align=\"center\">6.44</td><td align=\"center\">9.45</td><td align=\"center\">15.6</td></tr><tr><td align=\"center\">1148</td><td align=\"center\">AAQ10093</td><td align=\"center\">Class IV chitinase</td><td align=\"center\"><bold>Chit4-1</bold></td><td align=\"center\">30.19</td><td align=\"center\">27.53</td><td align=\"center\">4.57</td><td align=\"center\">5.38</td><td align=\"center\">9.1</td></tr><tr><td align=\"center\">1177</td><td align=\"center\">AAB65776</td><td align=\"center\">Class IV endochitinase</td><td align=\"center\"><bold>EnChit4</bold></td><td align=\"center\">28.50</td><td align=\"center\">27.24</td><td align=\"center\">4.93</td><td align=\"center\">5.38</td><td align=\"center\">21.1</td></tr><tr><td align=\"center\">1226</td><td align=\"center\">AAQ10093</td><td align=\"center\">Class IV chitinase</td><td align=\"center\"><bold>Chit4-2</bold></td><td align=\"center\">27.66</td><td align=\"center\">27.53</td><td align=\"center\">6.87</td><td align=\"center\">5.38</td><td align=\"center\">14.4</td></tr><tr><td align=\"center\">1240</td><td align=\"center\">AAQ10093</td><td align=\"center\">Class IV chitinase</td><td align=\"center\"><bold>Chit4-3</bold></td><td align=\"center\">26.99</td><td align=\"center\">27.53</td><td align=\"center\">7.35</td><td align=\"center\">5.38</td><td align=\"center\">14.4</td></tr><tr><td align=\"center\">1316</td><td align=\"center\">AAB61590</td><td align=\"center\">VVTL1</td><td align=\"center\"><bold>TLP</bold></td><td align=\"center\">24.62</td><td align=\"center\">23.97</td><td align=\"center\">4.69</td><td align=\"center\">5.09</td><td align=\"center\">9.0</td></tr><tr><td align=\"center\">1318</td><td align=\"center\">ABC86744</td><td align=\"center\">Abscisic stress ripening protein</td><td align=\"center\"><bold>ASR-1</bold></td><td align=\"center\">24.30</td><td align=\"center\">16.69</td><td align=\"center\">5.81</td><td align=\"center\">5.68</td><td align=\"center\">30.2</td></tr><tr><td align=\"center\">1358</td><td align=\"center\">ABC86744</td><td align=\"center\">Abscisic stress ripening protein</td><td align=\"center\"><bold>ASR-2</bold></td><td align=\"center\">23.94</td><td align=\"center\">16.69</td><td align=\"center\">5.77</td><td align=\"center\">5.68</td><td align=\"center\">30.2</td></tr><tr><td align=\"center\">1385</td><td align=\"center\">ABB02395</td><td align=\"center\">Temperature-induced lipocalin</td><td align=\"center\"><bold>TInLi</bold></td><td align=\"center\">22.87</td><td align=\"center\">21.54</td><td align=\"center\">6.42</td><td align=\"center\">6.63</td><td align=\"center\">13.0</td></tr><tr><td align=\"center\">1408</td><td align=\"center\">AAQ03092</td><td align=\"center\">Glutathione peroxidase</td><td align=\"center\"><bold>GPOX</bold></td><td align=\"center\">21.53</td><td align=\"center\">18.53</td><td align=\"center\">6.52</td><td align=\"center\">6.13</td><td align=\"center\">23.8</td></tr><tr><td align=\"center\">1417</td><td align=\"center\">ABC86744</td><td align=\"center\">Abscisic stress ripening protein</td><td align=\"center\"><bold>ASR-3</bold></td><td align=\"center\">21.22</td><td align=\"center\">16.69</td><td align=\"center\">5.73</td><td align=\"center\">5.68</td><td align=\"center\">26.2</td></tr><tr><td align=\"center\">1444</td><td align=\"center\">CAC16165</td><td align=\"center\">Pathogenesis-related protein 10</td><td align=\"center\"><bold>PR10-1</bold></td><td align=\"center\">19.76</td><td align=\"center\">17.13</td><td align=\"center\">6.11</td><td align=\"center\">5.96</td><td align=\"center\">22.8</td></tr><tr><td align=\"center\">1481</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-6</bold></td><td align=\"center\">18.50</td><td align=\"center\">67.39</td><td align=\"center\">4.91</td><td align=\"center\">6.39</td><td align=\"center\">5.9</td></tr><tr><td align=\"center\">1482</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-7</bold></td><td align=\"center\">18.41</td><td align=\"center\">67.39</td><td align=\"center\">4.99</td><td align=\"center\">6.39</td><td align=\"center\">10.7</td></tr><tr><td align=\"center\">1508</td><td align=\"center\">CAN83049</td><td align=\"center\">Pathogenesis-related protein Bet v I family<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>PRBetv1</bold></td><td align=\"center\">17.20</td><td align=\"center\">17.10</td><td align=\"center\">5.15</td><td align=\"center\">5.12</td><td align=\"center\">17.2</td></tr><tr><td align=\"center\">1524</td><td align=\"center\">ABD78554</td><td align=\"center\">Pathogenesis-related protein 10.1</td><td align=\"center\"><bold>PR10-2</bold></td><td align=\"center\">16.75</td><td align=\"center\">17.45</td><td align=\"center\">6.61</td><td align=\"center\">6.07</td><td align=\"center\">30.2</td></tr><tr><td align=\"center\">1768</td><td align=\"center\">AAB41022</td><td align=\"center\">Polyphenol oxidase</td><td align=\"center\"><bold>PPO-8</bold></td><td align=\"center\">18.45</td><td align=\"center\">67.39</td><td align=\"center\">4.79</td><td align=\"center\">6.39</td><td align=\"center\">6.4</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Unclassified</bold></td></tr><tr><td align=\"center\">476</td><td align=\"center\">CAN67811</td><td align=\"center\">Dihydrolipoamide dehydrogenase<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>Uncla-1</bold></td><td align=\"center\">56.94</td><td align=\"center\">49.57</td><td align=\"center\">6.13</td><td align=\"center\">7.18</td><td align=\"center\">9.6</td></tr><tr><td align=\"center\">1181</td><td align=\"center\">CAN64479</td><td align=\"center\">14-3-3 protein<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>Uncla-2</bold></td><td align=\"center\">28.29</td><td align=\"center\">28.78</td><td align=\"center\">4.67</td><td align=\"center\">4.78</td><td align=\"center\">16.1</td></tr><tr><td align=\"center\">1441</td><td align=\"center\">ABK64186</td><td align=\"center\">CBS domain-containing protein</td><td align=\"center\"><bold>Uncla-3</bold></td><td align=\"center\">19.84</td><td align=\"center\">22.25</td><td align=\"center\">6.95</td><td align=\"center\">9.24</td><td align=\"center\">25.2</td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Unknown</bold></td></tr><tr><td align=\"center\">1083</td><td align=\"center\">NP_001061484</td><td align=\"center\">Protein of unknown function DUF52 family<sup><bold>(<italic>d</italic>)</bold></sup></td><td align=\"center\"><bold>Unk</bold></td><td align=\"center\">32.53</td><td align=\"center\">33.55</td><td align=\"center\">6.22</td><td align=\"center\">6.11</td><td align=\"center\">16.4</td></tr></tbody></table></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Data on protein identification by LC-ESI-MS/MS and bioinformatic analysis. table shows the sequence of all the peptides identified by MS/MS fragmentation and the statistical information related to peptides, proteins and alignment analyses.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><sup><bold><italic>a</italic></bold></sup>: Experimental molecular weight (kDa) or isoelectric point</p><p><sup><bold><italic>b</italic></bold></sup>: Theoretical molecular weight (kDa) or isoelectric point.</p><p><sup><bold><italic>c</italic></bold></sup>: amino acid coverage (%)</p><p><sup><bold><italic>d</italic></bold></sup>: The protein was reported as a hypothetical protein. In the features, the similarity and function of the identified genes has been annotated by the authors according to Gene Ontology <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneontology.org\"/>.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2164-9-378-1\"/>", "<graphic xlink:href=\"1471-2164-9-378-2\"/>", "<graphic xlink:href=\"1471-2164-9-378-3\"/>", "<graphic xlink:href=\"1471-2164-9-378-4\"/>", "<graphic xlink:href=\"1471-2164-9-378-5\"/>", "<graphic xlink:href=\"1471-2164-9-378-6\"/>", "<graphic xlink:href=\"1471-2164-9-378-7\"/>", "<graphic xlink:href=\"1471-2164-9-378-8\"/>" ]
[ "<media xlink:href=\"1471-2164-9-378-S1.xls\" mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Kanellis", "Roubelakis-Angelakis", "Seymour G, Taylor J, Tucker G"], "given-names": ["AK", "KA"], "article-title": ["Grape"], "source": ["Biochemistry of Fruit Ripening"], "year": ["1993"], "publisher-name": ["London: Chapman and Hall"], "fpage": ["189"], "lpage": ["234"]}, {"surname": ["Coombe", "McCarthy"], "given-names": ["BG", "MG"], "article-title": ["Dynamics of grape berry growth and physiology of ripening"], "source": ["Aust J Grape Wine R"], "year": ["2000"], "volume": ["6"], "fpage": ["131"], "lpage": ["135"], "pub-id": ["10.1111/j.1755-0238.2000.tb00171.x"]}, {"surname": ["Possner", "Kliever"], "given-names": ["DRE", "WM"], "article-title": ["The localization of acids, sugars, potassium and calcium in developing grape berries"], "source": ["Vitis"], "year": ["1985"], "volume": ["24"], "fpage": ["229"], "lpage": ["240"]}, {"surname": ["Conde", "Silva", "Fontes", "Dias", "Tavares", "Sousa", "Agasse", "Delrot", "Ger\u00f3s"], "given-names": ["C", "P", "N", "ACP", "RM", "MJ", "A", "S", "H"], "article-title": ["Biochemical changes throughout grape berry development and fruit and wine quality"], "source": ["Food"], "year": ["2007"], "volume": ["1"], "fpage": ["1"], "lpage": ["22"]}, {"surname": ["Boss", "Davies", "Roubelakis-Angelakis KA"], "given-names": ["PK", "C"], "article-title": ["Molecular biology of sugar and anthocyanin accumulation in grape berries"], "source": ["Molecular Biology and Biotechnology of the Grapevine"], "year": ["2001"], "publisher-name": ["Dordrecht: Kluver Academic Publishers"], "fpage": ["1"], "lpage": ["33"]}, {"surname": ["Robinson", "Davies"], "given-names": ["SP", "C"], "article-title": ["Molecular biology of grape berry ripening"], "source": ["Aust J Grape Wine R"], "year": ["2000"], "volume": ["6"], "fpage": ["175"], "lpage": ["188"], "pub-id": ["10.1111/j.1755-0238.2000.tb00177.x"]}, {"surname": ["Adams"], "given-names": ["DO"], "article-title": ["Phenolics and ripening in grape berries"], "source": ["Am J Enol Viticult"], "year": ["2006"], "volume": ["57"], "fpage": ["249"], "lpage": ["256"]}, {"surname": ["Downey", "Dokoozlian", "Krstic"], "given-names": ["MO", "NK", "MP"], "article-title": ["Cultural practice and environmental impacts on the flavonoid composition of grapes and wine: a review of recent research"], "source": ["Am J Enol Vitic"], "year": ["2006"], "volume": ["57"], "fpage": ["257"], "lpage": ["268"]}, {"surname": ["Ortega-Regules", "Romero-Cascales", "L\u00f3pez-Roca", "Ros-Garc\u00eda", "G\u00f3mez-Plaza"], "given-names": ["A", "I", "JM", "JM", "E"], "article-title": ["Anthocyanin fingerprint of grapes: environmental and genetic variations"], "source": ["J Sci Food Agric"], "year": ["2006"], "volume": ["86"], "fpage": ["1460"], "lpage": ["1467"], "pub-id": ["10.1002/jsfa.2511"]}, {"surname": ["Derckel", "Audran", "Haye", "Lambert", "Legendre"], "given-names": ["JP", "J", "B", "B", "L"], "article-title": ["Characterization, induction by wounding and salicylic acid and activity against "], "italic": ["Botrytis cinerea "], "source": ["Physiol Plantarum"], "year": ["1998"], "volume": ["104"], "fpage": ["56"], "lpage": ["64"], "pub-id": ["10.1034/j.1399-3054.1998.1040108.x"]}, {"surname": ["Kraeva", "Andary", "Carbonneau", "Deloire"], "given-names": ["E", "C", "A", "A"], "article-title": ["Salicylic acid treatment of grape berries retards ripening"], "source": ["Vitis"], "year": ["1998"], "volume": ["37"], "fpage": ["143"], "lpage": ["144"]}, {"surname": ["Okuda", "Yokotsuka"], "given-names": ["T", "K"], "article-title": ["Levels of glutathione and activities of related enzymes during ripening of Koshu and Cabernet Sauvignon grapes and during winemaking"], "source": ["Am J Enol Viticult"], "year": ["1999"], "volume": ["50"], "fpage": ["264"], "lpage": ["270"]}, {"surname": ["Mayer", "Harel", "Fox PF"], "given-names": ["AM", "E"], "article-title": ["Phenoloxidases and their significance in fruit and vegetables"], "source": ["Food Enzymolog"], "year": ["1991"], "publisher-name": ["London: Elsevier"], "fpage": ["373"], "lpage": ["398"]}, {"surname": ["\u0141ata", "Przeradzka"], "given-names": ["B", "M"], "article-title": ["Changes of oxidant content in fruit peel and flesh of selected apple cultivars during storage"], "source": ["J Fruit Ornam Plant Res"], "year": ["2002"], "volume": ["10"], "fpage": ["5"], "lpage": ["13"]}, {"surname": ["R\u00fcffner"], "given-names": ["HP"], "article-title": ["Metabolism of tartaric and malic acids in "], "italic": ["Vitis"], "source": ["Vitis"], "year": ["1982"], "volume": ["21"], "fpage": ["346"], "lpage": ["358"]}, {"surname": ["R\u00fcffner", "Possner", "Brem", "Rast"], "given-names": ["HP", "D", "S", "DM"], "article-title": ["The physiological role of malic enzyme in grape ripening"], "source": ["Planta"], "year": ["1984"], "volume": ["160"], "fpage": ["444"], "lpage": ["448"], "pub-id": ["10.1007/BF00429761"]}, {"surname": ["Van Heeswijck", "Stines", "Grubb", "Skrumsager M\u00f8ller", "H\u00f8", "Roubelakis-Angelakis KA"], "given-names": ["RL", "AP", "J", "I", "PB"], "article-title": ["Molecular biology and biochemistry of proline accumulation in developing grape berries"], "source": ["Molecular Biology and Biotechnology of the Grapevine"], "year": ["2001"], "publisher-name": ["Dordrecht: Kluwer Academic Publishers"], "fpage": ["87"], "lpage": ["108"]}]
{ "acronym": [], "definition": [] }
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2022-01-12 14:47:26
BMC Genomics. 2008 Aug 8; 9:378
oa_package/eb/11/PMC2529320.tar.gz
PMC2529321
18687111
[ "<title>Background</title>", "<p>Automatic analysis of biological sequences is crucial for the treatment of massive genomic outputs. Our understanding of more than 90 % of protein sequences stored in public databases, deduced from automatic translation of gene sequences, will not result from direct experimentation, but from our ability to predict informative features using <italic>in silico </italic>workflows [##REF##16243268##1##,##UREF##0##2##]. An underlying postulate is that the molecular sequences determined in biological individuals or species, which have evolved from a common ancestor sequence and are therefore homologous, have conserved enough of the original features to be similar. Popular sequence alignment methods, such as Blast [##REF##2231712##3##] or Smith-Waterman [##REF##7265238##4##] algorithms are used as a starting point for homology searches. All these methods computes a score <italic>s</italic>(<italic>a</italic>, <italic>b</italic>) between two sequences <italic>a </italic>and <italic>b</italic>. They use scoring matrices to maximize the summed scores of compared residues and find optimal local alignments, computed with a dynamic programming procedure [##REF##2231712##3##,##REF##7265238##4##]. Scoring matrices have been found to be similarity matrices as well [##UREF##1##5##]. Many similarity matrices are available [##UREF##2##6##, ####REF##1438297##7##, ##REF##15948633##8####15948633##8##] and evaluation studies led to the conclusion that all can be considered as log-odds ratio matrices, including the BLOSUM family [##REF##1438297##7##] and the PAM family [##UREF##2##6##]. Log-odds ratio matrices are defined by where <italic>ω</italic>(<italic>i</italic>, <italic>j</italic>) is the joint probability of the amino acid pair (<italic>i</italic>, <italic>j</italic>), and <italic>ν</italic>(<italic>i</italic>) and <italic>ν</italic>(<italic>j</italic>) the probabilities of the amino acids <italic>i </italic>and <italic>j </italic>in the two aligned sequences.</p>", "<p>Because re-examination of alignments obtained after massive comparisons is not manageable, confidence in alignment score probabilities is critical for automatic sequence comparisons, clustering of orthologs and paralogs, homology-based annotations or phylogeny reconstructions based on pairwise alignments [##UREF##0##2##]. Assessing whether a computed alignment is evolutionarily relevant or whether it could have arisen simply by chance is therefore a question that has been extensively studied (for review: [##REF##14630646##9##]). Two major methods have been proposed.</p>", "<p>The first and oldest method, proposed by Lipman and Pearson [##REF##2983426##10##] and described extensively by Comet et al. [##REF##10627144##11##] and others [##REF##11459354##12##, ####REF##14990449##13##, ##REF##15757521##14####15757521##14##], uses Monte Carlo simulations to investigate the significance of a score, <italic>s </italic>calculated from the alignment of two real sequences <italic>a </italic>and <italic>b</italic>. This method consists in computing <italic>η </italic>alignments of <italic>a </italic>with sequences obtained after shuffling <italic>b </italic>[##REF##6842586##15##]. The random sequence corresponding to the shuffled sequence <italic>b </italic>is termed <italic>B</italic>. The <italic>η </italic>alignments allow an estimate of an empirical mean score () and standard deviation () from the distribution of the random variable <italic>S</italic>(<italic>a</italic>, <italic>B</italic>). A <italic>Z</italic>-<italic>value </italic>is then defined as:</p>", "<p></p>", "<p>where * indicates the sequence that was submitted to randomization.</p>", "<p>In practice, the computation of <italic>Z</italic>(<italic>a</italic>, <italic>b*</italic>) is known to be convergent and depends on the accuracy of the estimation of <italic>μ </italic>and <italic>σ</italic>, and therefore on <italic>η</italic>, ranging usually from 100 to 1000 [##REF##10627144##11##,##REF##12144171##16##]. Bacro and Comet [##REF##11459354##12##] showed that the asymptotic law of the <italic>Z-value </italic>(when <italic>η </italic>→ ∞) was independent of the length and composition of sequences. Bastien et al. [##REF##14990449##13##] further demonstrated that regardless of the distribution of the random variable <italic>S</italic>(<italic>a</italic>, <italic>B</italic>), the relation</p>", "<p></p>", "<p>is true. This relation, known as the TULIP theorem, shows that the <italic>Z</italic>-<italic>value </italic>computed for pairwise sequence alignments 1) provides an upper bound of alignment score probability [##REF##14990449##13##], 2) can be used to reconstruct molecular phylogenies [##REF##15757521##14##] and 3) is an accurate clustering criterion to reduce the diversity of protein sequence databases [##REF##15961444##17##]. Here we call <italic>T-value </italic>the upper bound deduced from the TULIP theorem, <italic>i.e. </italic>1/<italic>Z</italic>(<italic>a</italic>, <italic>b</italic>*)<sup>2</sup>.</p>", "<p>Simulations of <italic>Z</italic>-<italic>value </italic>distribution [##REF##10627144##11##,##REF##9514730##18##] shows that it fits a Gumbel distribution, suggesting that the distribution of alignment scores might follow a Gumbel distribution as well [##UREF##3##19##].</p>", "<p>The second and most popular method proposed by Karlin and Altschul [##REF##2315319##20##] is an estimate of the probability of an observed local ungapped alignment score according to an extreme value distribution (or EVD; for review: [##UREF##3##19##]), <italic>i.e. </italic>a Gumbel distribution, in the asymptotic limit of long sequences. The remarkable Karlin-Altschul formula is the consequence of interpreting the number of highest scoring matching regions above a threshold by a Poisson distribution. Briefly, considering <italic>A </italic>and <italic>B </italic>two random sequences, <italic>m </italic>and <italic>n </italic>their lengths, given the distribution of individual residues (<italic>i.e. </italic>amino acids), and given a scoring matrix, the number of distinct local alignments with score values of at least <italic>s </italic>is approximately Poisson distributed with mean</p>", "<p></p>", "<p>where <italic>λ </italic>and <italic>K </italic>can be calculated from the scoring matrix and average sequence compositions based on the Poisson distribution hypothesis. <italic>E</italic>(<italic>s</italic>) is known as the <italic>E-value</italic>. As a consequence, if <italic>s </italic>is the score obtained after aligning two real sequences <italic>a </italic>and <italic>b </italic>(with <italic>m </italic>and <italic>n </italic>their respective lengths), the probability of finding an ungapped segment pair with a score lower than or equal to <italic>s</italic>, follows a Gumbel distribution:</p>", "<p></p>", "<p>where <italic>S</italic>(<italic>A</italic>, <italic>B</italic>) is the random variable corresponding to the score of two random sequences. The <italic>P-value</italic>, defined as the probability of finding an ungapped segment pair with a score higher than <italic>s</italic>, is simply given by 1-<italic>P</italic>(S(<italic>A</italic>, <italic>B</italic>) ≤ <italic>s</italic>). Using the Taylor Expansion of equation (4), the <italic>P-value </italic>is approximated by the <italic>E-value </italic>when <italic>E</italic>(<italic>s</italic>) &lt; 0.01. The validity of the Karlin-Altschul model depends on restrictive conditions: firstly, the residue distributions in the compared sequences should not be \"too dissimilar\" and secondly, the sequence lengths (<italic>m </italic>an <italic>n</italic>) should \"grow at roughly equal rates\" [##REF##2315319##20##]. The length dependency of alignment scores has been discussed [##REF##2315319##20##,##REF##8289235##21##]. In particular, it has been demonstrated that the growth of the best matching score of gapped alignments was linear when gap penalties were small, becoming logarithmic when increasing sequence length and for larger gap penalties [##REF##8289235##21##]. Although the Karlin-Altschul formula given by equation (4) is not valid for gapped alignments and although no asymptotic score distribution has been analytically established for local alignments allowing gaps, simulations [##REF##10627144##11##,##REF##9514730##18##,##REF##11139604##22##,##REF##11751224##23##] showed that, for both local and global alignments, the Gumbel law was well-suited to the distribution of scores after pragmatic estimation of the <italic>λ </italic>and <italic>K </italic>parameters.</p>", "<p>Noticeably, this model relies on the fact that <italic>λ </italic>is the unique positive solution to the equation , for the 20 × 20 combinations of <italic>i </italic>and <italic>j </italic>amino acids, with <italic>ν</italic><sub><italic>a</italic></sub>(<italic>i</italic>) and <italic>ν</italic><sub><italic>b</italic></sub>(<italic>j</italic>) the probabilities of amino acids <italic>i </italic>and <italic>j </italic>in sequences <italic>a </italic>and <italic>b </italic>respectively and <italic>s</italic>(<italic>i</italic>, <italic>j</italic>) the score in the substitution matrix. From a theoretic point of view, and regardless of the practical performance of the Karlin and Altschul [##REF##2315319##20##] model, the fact that an observed distribution (the distribution of scores of real compared sequences) depends on a presupposed and pre-calculated parameter is not satisfactory. It would be more satisfactory if <italic>λ </italic>arose as a property of a biological process and/or features. We addressed therefore the question of the missing biological rationale to parameters, particularly <italic>λ </italic>and <italic>K</italic>, that proved to be valid in pragmatic terms.</p>", "<p>In this paper, we deduced biological rationale for the Gumbel-like distribution of sequence alignment scores and <italic>Z-values</italic>, based on a limited number of assumptions on sequences evolution. An ancestral sequence is the origin of a lineage of homologous sequences that are subjected to evolutionary mechanisms. We considered homologous sequences as entities sharing structural features, in particular some conserved or functionally similar amino acids detected by alignment methods. Features that are preserved in two homologous sequences are estimated by a shared amount of information (SAI). In this model, the amount of information shared between an initial sequence and the sequences in its lineage (<italic>i.e.</italic>, mutual information in Information Theory) is a decreasing function of time: over time, some substitutions of amino acids by others having redundant properties (SAI at the residue level) may be permitted without functional break down, but leads to a decrease of the SAI between the sequences. Classically, molecular evolution is formalized with Markovian models for residue substitutions, allowing the backward reconstruction of sequences' evolution with the assumption that the proteins have been selected for a functional conservation. Here, proteins were considered as systems, with a high level of structural redundancy, which components may \"age\" over evolution, and \"die\" in case of loss of the initial amount of information required to operate accurately for a given biological function. Assumptions are therefore generalist regarding the process of sequence evolution, should it be strictly Markovian or not, but they give a formalism to the reliability of the sequences reflecting the functional status of the folded and maturated protein, and being a criterion on which positive selection pressure might act. We introduced therefore principles of the <italic>reliability theory of aging and longevity </italic>[##REF##11742523##24##], that apply to a wide range of other systems, from artificial machines to biological population or organisms, applied here to molecular sequences. Based on the deduced model, we could provide biological basis for the <italic>Z-value </italic>Gumbel distribution, and significance for the corresponding Gumbel parameters (termed <italic>K' </italic>and <italic>λ'</italic>). Moreover, the assumption that the score between two sequences <italic>a </italic>and <italic>b </italic>should be the highest possible score between <italic>a </italic>and <italic>b </italic>is not necessary to observe an extreme values distribution for sequence alignment scores.</p>", "<p>Major points of the following demonstration are:</p>", "<p>i. The evolution of biological sequence is formalized by the evolution of the SAI between an initial sequence and sequences of its lineage. It is known that for two sequences <italic>a </italic>and <italic>b</italic>, this is measured by the mutual information I(<italic>a</italic>; <italic>b</italic>), based on Information Theory and is exactly the score s(<italic>a</italic>, <italic>b</italic>) computed with standard methods in sequences comparisons [##REF##15757521##14##].</p>", "<p>ii. If a sequence evolves, the probability that it stays near its \"last\" position in the sequence space is low and the longest the sequence, the lowest this probability (consequence of the concentration in a high dimensionality space [##UREF##4##25##]). The amount of information shared between an initial sequence and the sequences in its lineage decreases with time: as a consequence, one can indifferently use I(<italic>a</italic>; <italic>b</italic>) as a measure of the divergence time.</p>" ]
[ "<title>Methods</title>", "<p>Mathematical demonstrations are detailed in the Results section. Histograms and curves were built using the R package software (Statistics Department of the University of Auckland).</p>" ]
[ "<title>Results and discussion</title>", "<title>Assumptions for a model of sequences' evolution</title>", "<p>A basic process in the evolution of proteins is the change of amino acids over time. In the simplest view, these changes lead to amino acid substitutions, insertions or deletions. Dayhoff et al. [##UREF##2##6##] introduced the description of this process as a continuous-time Markov chain with a matrix of transition probabilities for the substitutions of any amino acid into another through time. This model allows forward and backward expressions of sequence evolution, under time homogeneity assumption, and is therefore an important tool for phylogeny reconstructions. Given a transition matrix and an equilibrium distribution of amino acids, then a matrix of amino acid substitution scores, in the sense of sequences' comparison, can be deduced [##REF##11382360##26##,##REF##11752185##27##].</p>", "<p>In the generalist model described here, assumptions regarding the process of sequence evolution were not formalized, should this process be strictly Markovian or not. Given two sequences, one can, one the one hand, compute a score using dynamic algorithms [##REF##2231712##3##,##REF##7265238##4##] and deduce the distribution of random scores from transition matrices under the hypothesis that the two sequences have evolved according to a continuous-time Markov chains process. On the other hand, Henikoff et al. [##REF##1438297##7##] demonstrated the possibility to calculate efficient log-odd matrices without the need of this assumption. Altschul [##REF##2051488##28##] and Bastien et al. [##REF##15757521##14##] demonstrated that log-odd matrices could be reformulated in the Information Theory framework. In particular, a score between two amino acids <italic>i </italic>and <italic>j </italic>can be interpreted as the mutual information between these two residues. At the 3D folded protein level, a molecular function emerges from the information encrypted in the amino acid sequence, and positive selection pressure acts therefore at the sequence level, maintaining a sufficient portion of the initial information, and consequently the functional status of the folded and maturated protein. We therefore focused on the evolution of the information shared between an initial sequence and the sequences of its lineage through time.</p>", "<title>Reliability theory and biological sequences evolution</title>", "<p>The Reliability Theory is a general theory about systems aging, in which the failure rate (the rate by which systems deteriorate) is related to the systems longevity (For review, [##REF##11742523##24##]). The system can be a machine with structured components, or a living entity or population. \"Reliability\" of a system (or of one of its components) refers to its ability to operate properly according to a standard [##UREF##5##29##]. The relation between the age of a system and its failure rate shows that aging is a direct consequence of redundancies within the system. For instance, when applied to a biological system in which redundant vital structures ensure a function, damage of a component that is compensated by another redundant intact one, does not lead to a complete impairment of the system. Defects do accumulate, resulting in redundancy exhaustion and giving rise to the phenomenon of aging. As the system (or one of its components) degenerates into a system with no redundancy, new defects can eventually lead to death. Reliability of the system (or component) is described by the \"reliability function\" <italic>R</italic>(<italic>x</italic>), also named \"survival function\", which is the probability that the system (or component) will carry out its mission through time <italic>x </italic>[##UREF##6##30##], expressed as the probability that the failure time <italic>X </italic>is beyond time <italic>x</italic>:</p>", "<p></p>", "<p>where <italic>F</italic>(<italic>x</italic>) = <italic>P</italic>(<italic>X </italic>≤ <italic>x</italic>) is a cumulative distribution function [##REF##11742523##24##] reflecting the resistance of the system to failures (at time <italic>x</italic>, distribution of the probability that the system could have failed previously). <italic>R</italic>(<italic>x</italic>) evaluates therefore the probability that the systems becomes completely defective after a time <italic>x </italic>(<italic>x </italic>can be a direct measure of time <italic>t </italic>or an increasing function of time).</p>", "<p>The \"hazard rate\"<italic>h</italic>(<italic>x</italic>), also called \"failure rate\", is defined as the relative rate for reliability function decline:</p>", "<p></p>", "<p>Hazard rate is equivalent to mortality force in demography [##UREF##7##31##,##REF##18202874##32##]. When <italic>h</italic>(<italic>x</italic>) is a constant <italic>h</italic>, the system does not deteriorate more often with age, and is therefore a <italic>non-aging </italic>system. In this case, a simple integration of equation (6) leads to</p>", "<p></p>", "<p>which is the exponential distribution that characterizes non-aging systems. Interestingly, a system with redundant <italic>non-aging components </italic>can be an <italic>aging system</italic>. That is to say the hazard rate of a system of components depends can depends of time whereas the hazard rate of components do not</p>", "<p>As discussed by Gavrilov and Gravrilova [##REF##11742523##24##], the \"reliability theory\" provided explanations for some fundamental problems regarding aging, longevity, death of organisms within populations. Organisms or populations are considered as systems in which categories of components (molecules, biological processes, cells, individuals, etc.) can be highly redundant, and be key elements for the system longevity.</p>", "<p>Here, we propose to consider the particular case of <italic>protein sequences as a system</italic>, in which redundancy is ensured:</p>", "<p>i. by the number of residue positions involved in the evolution process.</p>", "<p>ii. at the residue level by the existence of functionally redundant amino acids (<italic>e.g. </italic>after a DNA damage that leads to a genetic mutation, an aspartic acid may be substituted by a functionally redundant glutamic acid), <italic>i.e. </italic>the existence of a SAI for all amino acids pairs.</p>", "<p>In this model, evolutionary time is negatively correlated to the amount of information shared between an initial sequence and sequences in its lineage (SAI decreases with time, see below).</p>", "<title>The conservation rate: a mathematical tool to study the evolution of the information shared by biological sequences</title>", "<p>To measure the rate of conservation of a shared structure/function relationship at time <italic>x </italic>within a system of homologous proteins (<italic>i.e. </italic>the time of observation), we considered that the decay of information shared between an original sequence and sequences of its lineage was a function of time, and therefore a mean to measure time. Evolutionary time is therefore measured here in information units. We defined an <italic>information conservation rate </italic>Ψ as follows:</p>", "<title>Definition</title>", "<p>Given the cumulative distribution function <italic>F</italic>(<italic>x</italic>) = <italic>P</italic>(<italic>X </italic>≤ <italic>x</italic>) (Probability that the system shared less than <italic>x </italic>information units with a reference), supposed continuously differentiable, the <italic>conservation rate </italic>Ψ is given by:</p>", "<p></p>", "<p>The <italic>conservation rate </italic>is simply related to the hazard function, measuring a quantity that decreases over time (shared information) instead of a quantity that increases over time (age). Given <italic>f</italic>(<italic>x</italic>) = <italic>dF</italic>(<italic>x</italic>)/<italic>dx </italic>the density function of <italic>x</italic>, this conservation rate has the following properties.</p>", "<p></p>", "<p>and as corollaries:</p>", "<p></p>", "<p></p>", "<p></p>", "<title>Derivation of the distribution of sequence alignment scores based on the distribution of mutual information between amino acids</title>", "<p>Dobzhansky [##UREF##8##33##] and Wu et al. [##REF##4369316##34##] established that <italic>information </italic>harbored by a protein 1) emerged from the three-dimensional self organization of its residues (<italic>i.e. </italic>the sequence of amino acids) and had to do with information harbored by amino acids, and 2) was submitted through time to evolutionary pressure (achievement of a minimal functional level fitting environmental and species survival conditions). Using previous empirical results [##UREF##2##6##,##REF##1438297##7##,##REF##3221397##35##], Bastien et al. [##REF##15757521##14##] have shown that the alignment score of two homologous sequences <italic>a </italic>and <italic>b </italic>was proportional to the estimate of the SAI due to their common origin and parallel evolution under similar conservative pressure, <italic>i.e. </italic>the <italic>mutual information I</italic>(<italic>a</italic>; <italic>b</italic>) between the two events <italic>a </italic>and <italic>b </italic>in the sense of Hartley [##UREF##9##36##,##UREF##10##37##]:</p>", "<p></p>", "<p>with <italic>ξ </italic>a constant defining the unity (<italic>ξ </italic>= 1, in bits) and <italic>s</italic>(<italic>a</italic>, <italic>b</italic>) the sum of the elementary scores for all aligned positions (including gap opening and gap extension penalties). Mutual information between two events <italic>a </italic>and <italic>b </italic>(differing from the mutual information defined between random variables, see [##REF##15757521##14##,##UREF##11##38##]) measures the information gained by the knowledge of event <italic>a </italic>on the occurrence of event <italic>b</italic>. The mutual information being additive, <italic>I</italic>(<italic>a</italic>; <italic>b</italic>) is the sum of the mutual information of aligned residues, <italic>reflecting the magnitude of the redundancy between the sequences at the amino acid level</italic>. Mutual information between residues is therefore simply deduced from the 20 × 20 amino acid substitution matrix [##UREF##2##6##, ####REF##1438297##7##, ##REF##15948633##8####15948633##8##,##REF##3221397##35##] used to compute the alignment.</p>", "<p>Inside a given sequence, mutual information was also shown to <italic>reflect the dependency of close or remote amino acids</italic>, a phenomenon known as the residue co-evolution, due to their co-contribution to the sequence function [##REF##16254389##39##,##REF##16254390##40##].</p>", "<p>Considering a <italic>protein </italic>as a <italic>system</italic>, which <italic>components </italic>are <italic>amino acids</italic>, we examined the mutual information between the original components and their descendants, and how amino acid mutation affected the evolution of mutual information between proteins. We simply hypothesized that an amino acid may mutate over time following random DNA mutations and look at the behavior of the entire system, namely the protein which can be measured here by the mutual information between the initial residues and the new ones, <italic>i.e. </italic>the corresponding substitution scores in a 20 × 20 substitution matrix. The substitution matrix is considered as an estimate of the mutual information between residues because it was computed from real sequences' data [##UREF##2##6##, ####REF##1438297##7##, ##REF##15948633##8####15948633##8##,##REF##3221397##35##].</p>", "<p>Over time, an amino acid <italic>i </italic>is either conserved or substituted. The similarity of <italic>i </italic>in an initial sequence compared with residues at the same position in protein descendants is therefore either that of identity (the diagonal term in the scoring matrix) or a lower value(no score is higher than that of identity). In average, the magnitude of the similarity of <italic>i </italic>compared with its descendants, related to mutual information following equation (13), is therefore a decreasing function of elapsed time. On a functional point of view, the probability that <italic>i </italic>was mutated into a residue with a score <italic>S</italic><sub><italic>i </italic></sub>lower than a threshold <italic>s</italic><sub><italic>i </italic></sub>defined to allow the component to operate like <italic>i</italic>, can be deduced from the distribution of substitution scores. For most amino acids (F, P, W, Y, V, E, G, H, I, L, K, R, N, D and C), the distribution of scores deduced from BLOSUM 62 fits an exponential distribution (see the case of valine in Figure ##FIG##0##1A##. For five amino acids (M, S, T, A and Q), the distribution of scores does not fit an exponential distribution (see the case of Threonine in Figure ##FIG##0##1B##). Taking the average situation, the distribution of scores deduced from the BLOSUM 62 matrix is exponential-like (Figure ##FIG##0##1C##) supporting a general model for amino acids mutual information distribution: The probability <italic>P</italic><sub><italic>r </italic></sub>that a residue <italic>i </italic>is mutated into a residue with mutual information below <italic>s</italic><sub><italic>i </italic></sub>is:</p>", "<p></p>", "<p>where <italic>λ</italic><sub><italic>i </italic></sub>is the <italic>constant information hazard rate, or failure rate, for reliability function decline of the amino acid mutual information</italic>.</p>", "<p>Given a sequence <italic>a</italic>, what is the probability that any of its <italic>m </italic>residues (termed <italic>i</italic>) had previously mutated into the <italic>n </italic>residues (termed <italic>j</italic>) of a sequence <italic>b </italic>and leads to the observed mutual information between sequence <italic>a </italic>and sequence <italic>b</italic>? We can consider <italic>m </italic>≠ <italic>n </italic>due to insertion or deletion events. If <italic>m </italic>and <italic>n </italic>are large, we can state the following asymptotic approximations: <italic>S </italic>≈ <italic>m </italic>⟨<italic>S</italic><sub><italic>i</italic></sub>⟩, with and <italic>s </italic>≈ <italic>m </italic>⟨<italic>s</italic><sub><italic>i</italic></sub>⟩, with where <italic>s </italic>(respectively <italic>S</italic>) is the score between the sequence <italic>a </italic>(respectively <italic>A</italic>) and the sequence <italic>b </italic>(respectively <italic>B</italic>) (for discussion of these approximations, see [##UREF##12##41##]). In the asymptotic limit of long sequences, we can envisage different scenarios for the evolution of <italic>a </italic>into <italic>b</italic>:</p>", "<p>In a first step (Figure ##FIG##1##2##, step 1), the probability that one residue <italic>a</italic><sub>1 </sub>is mutated into a residue <italic>b</italic><sub>1 </sub>with mutual information below <italic>s</italic><sub><italic>i </italic></sub>is given:</p>", "<p></p>", "<p>Considering one possible evolutionary scenario, <italic>i.e. </italic>one alignment (Figure ##FIG##1##2##, step 2), residues are considered as independent and the probability is the product of elementary probabilities for each positions aligned in this scenario. For the alignment of the <italic>m </italic>amino acids of sequence <italic>a</italic>, we obtain the following probability:</p>", "<p></p>", "<p>Alternative scenarios are also possible (Figure ##FIG##1##2##, step 3). The final probability is therefore computed taking into account all possible evolutionary paths (all possible alignments, Figure ##FIG##1##2##, step 3) and using <italic>K'</italic>&lt;1 a correcting factor for edge effects, deletion and insertion points:</p>", "<p></p>", "<p>Considering the approximation of ⟨<italic>S</italic><sub><italic>i</italic></sub>⟩ and ⟨<italic>s</italic><sub><italic>i</italic></sub>⟩ respectively by <italic>S</italic>/<italic>m </italic>and <italic>s</italic>/<italic>m</italic>, we deduce the final formula:</p>", "<p></p>", "<p>The density function <italic>f</italic>(<italic>s</italic>) is therefore given by:</p>", "<p></p>", "<p>with the density of the probability <italic>P</italic><sub><italic>r</italic></sub>(<italic>S </italic>≤ <italic>s</italic>) that a residue is mutated into another</p>", "<p>with mutual information below <italic>s</italic>. We can then deduce the <italic>homology longevity rate </italic>Ψ, defined earlier as a function of the pairwise alignment score:</p>", "<p></p>", "<p>Using the expression of <italic>P</italic><sub><italic>i</italic></sub>(<italic>S</italic><sub><italic>i </italic></sub>≤ <italic>s</italic><sub><italic>i</italic></sub>) given by Equation (14) implies that:</p>", "<p></p>", "<p>Asymptotically, the information conservation rate is therefore given by</p>", "<p></p>", "<p>Using equation (12), we deduce that the distribution of alignment scores should respect the general form of the Karlin-Altschul formula:</p>", "<p></p>", "<title>Applications and Conclusion</title>", "<p>We built a model of evolution of the information shared between an initial molecular sequence and the sequences of its lineage (<italic>i.e. </italic>homologous sequences). Sequences were considered as systems, which components are the amino acids that can independently be damaged by random DNA mutations. Residues harbor a functional redundancy reflected by the amino acid substitution scores.</p>", "<p>From these assumptions, we deduced that the pairwise sequence alignment score should follow a Gumbel distribution (equation (22)). The <italic>λ' </italic>parameter is the information hazard rate for the reliability of amino acids' mutual information: it depends 1) on the distribution of the amino acids and 2) on the distribution of amino acid similarities deduced from a substitution matrix. The <italic>K</italic>' parameter has a more complex meaning, because it depends on likelihood of an alignment of two sequences, with edge effects, gaps, length difference and repartition of the information (the local score) in the alignment. It reflects therefore internal structural constraints on the evolution of sequences.</p>", "<p>The Gumbel parameters for score alignments can be estimated by two kinds of simulations. First is by adjusting EVD to the simulated distribution of scores [##UREF##3##19##,##REF##11139604##22##]. In that case, it is simpler to express the Gumbel law as</p>", "<p></p>", "<p>with and . The estimate of Gumbel parameters is achieved by determining <italic>β </italic>and <italic>θ</italic>, allowing an easy estimate of the <italic>λ' </italic>and <italic>K</italic>' parameters of equation (23). Second estimation of the Gumbel parameters is by computing the <italic>Z-value </italic>corresponding to the simulation of score distribution. Using the fact that for a Gumbel distribution, <italic>μ </italic>= <italic>θ </italic>+ <italic>γβ </italic>and , then the <italic>Z-value </italic>allows a computation of the <italic>β </italic>and <italic>θ </italic>constants.</p>", "<p>Simulations of <italic>Z</italic>-<italic>value </italic>distribution [##REF##10627144##11##,##REF##9514730##18##] showed that it fitted with a Gumbel law. Based on the Gumbel distribution of scores (equations (24) and (25)) and by an appropriate change of variable with equation (1), then the distribution of <italic>Z</italic>-<italic>values </italic>should respect the following equality:</p>", "<p></p>", "<p>with <italic>γ </italic>the Euler-Mascheroni constant (<italic>γ </italic>≈ 0.5772). Equation (25) is the precise expression of the distribution of <italic>Z-values </italic>deduced by Pearson [##REF##9514730##18##] from simulations. It is important to note that this expression of the <italic>Z-value </italic>distribution is independent of sequence lengths and amino acid distributions.</p>", "<p>This consideration has practical implications, since it allows a refined estimate of the <italic>P-value </italic>based on <italic>Z-value </italic>computation, and a real gain over available methods, particularly in some documented cases where the Karlin-Altschul formula failed to assess the significance of an alignment. Table ##TAB##0##1## shows for instance the different statistical estimates for the alignment of two homologous TFIIA gamma sequences from <italic>Plasmodium falciparum </italic>and <italic>Arabidopsis thaliana</italic>. The compositional bias in the proteome of <italic>Plasmodium falciparum</italic>, the malarial parasite, is known to limit the use of Karlin-Altschul statistics for pairwise comparisons with unbiased proteins such as those of <italic>Arabidopsis thaliana </italic>[##REF##15246528##42##]. The TFIIA gamma subunit sequence of <italic>Plasmodium </italic>could not be deduced from BLASTP-based homology searches [##REF##16042788##43##]. The Blastp apparent search failure was due to the overestimate of the <italic>P-value </italic>following the Karlin-Altschul formula (0.008, using unfiltered BLASTP, see Table ##TAB##0##1##). Alignment score <italic>Z-value</italic>, computed with either Blastp (P. Ortet, unpublished algorithm) or Smith-Waterman was above 10. The upper bound for the <italic>P-value </italic>based on the TULIP theorem, given by the formula <italic>T-value </italic>= 1/<italic>Z-value</italic><sup>2 </sup>[##REF##14990449##13##], was therefore below 10<sup>-2</sup>. Eventually, the <italic>P-value </italic>deduced from the <italic>Z-value </italic>Gumbel distribution was below 10<sup>-6 </sup>(see Table ##TAB##0##1##) indicating that, for both the Blastp and Smith-Waterman methods, the homology could be statistically assessed, even in the limit case of unbiased vs biased sequence comparisons. We noticed that the asymmetric DirAtPf100 matrix specified for <italic>Plasmodium </italic>vs. <italic>Arabidopsis </italic>comparisons that we developed earlier [##REF##15948633##8##] allowed an additional gain in estimating this missed homology.</p>", "<p>Besides a theoretical support for pragmatic observations, this report shows therefore that the alignment score Gumbel distribution is a particular and general evolutionary law for molecular sequences taken as dynamical systems. This model can be parameterized using the Karlin-Altschul or the <italic>Z-Value </italic>form. If Karlin-Altschul model parameters are well-estimated (using simulations for example), both forms are equivalent in practice as reported by Hulsen et al. [##REF##17038163##44##]. This model shows that an extreme value distribution of alignment scores can arise not only by considering high scoring segments pairs. Indeed, derivation of a Gumbel distribution from maximum independent random variables is a well-known technique [##UREF##3##19##] and the Karlin-Altschul theorem was first demonstrated, based on this consideration [##REF##2315319##20##]. We can now state that this distribution allows a different interpretation in the light of the Reliability Theory, reflecting the redundancy of information between sequences due to both the number of residues and the shared information between these residues. The model elaboration described here additionally provides a link between concepts of biological sequence analysis and the emerging field of systems biology, with a generalization of the aging concepts to all scales of the living world.</p>" ]
[ "<title>Results and discussion</title>", "<title>Assumptions for a model of sequences' evolution</title>", "<p>A basic process in the evolution of proteins is the change of amino acids over time. In the simplest view, these changes lead to amino acid substitutions, insertions or deletions. Dayhoff et al. [##UREF##2##6##] introduced the description of this process as a continuous-time Markov chain with a matrix of transition probabilities for the substitutions of any amino acid into another through time. This model allows forward and backward expressions of sequence evolution, under time homogeneity assumption, and is therefore an important tool for phylogeny reconstructions. Given a transition matrix and an equilibrium distribution of amino acids, then a matrix of amino acid substitution scores, in the sense of sequences' comparison, can be deduced [##REF##11382360##26##,##REF##11752185##27##].</p>", "<p>In the generalist model described here, assumptions regarding the process of sequence evolution were not formalized, should this process be strictly Markovian or not. Given two sequences, one can, one the one hand, compute a score using dynamic algorithms [##REF##2231712##3##,##REF##7265238##4##] and deduce the distribution of random scores from transition matrices under the hypothesis that the two sequences have evolved according to a continuous-time Markov chains process. On the other hand, Henikoff et al. [##REF##1438297##7##] demonstrated the possibility to calculate efficient log-odd matrices without the need of this assumption. Altschul [##REF##2051488##28##] and Bastien et al. [##REF##15757521##14##] demonstrated that log-odd matrices could be reformulated in the Information Theory framework. In particular, a score between two amino acids <italic>i </italic>and <italic>j </italic>can be interpreted as the mutual information between these two residues. At the 3D folded protein level, a molecular function emerges from the information encrypted in the amino acid sequence, and positive selection pressure acts therefore at the sequence level, maintaining a sufficient portion of the initial information, and consequently the functional status of the folded and maturated protein. We therefore focused on the evolution of the information shared between an initial sequence and the sequences of its lineage through time.</p>", "<title>Reliability theory and biological sequences evolution</title>", "<p>The Reliability Theory is a general theory about systems aging, in which the failure rate (the rate by which systems deteriorate) is related to the systems longevity (For review, [##REF##11742523##24##]). The system can be a machine with structured components, or a living entity or population. \"Reliability\" of a system (or of one of its components) refers to its ability to operate properly according to a standard [##UREF##5##29##]. The relation between the age of a system and its failure rate shows that aging is a direct consequence of redundancies within the system. For instance, when applied to a biological system in which redundant vital structures ensure a function, damage of a component that is compensated by another redundant intact one, does not lead to a complete impairment of the system. Defects do accumulate, resulting in redundancy exhaustion and giving rise to the phenomenon of aging. As the system (or one of its components) degenerates into a system with no redundancy, new defects can eventually lead to death. Reliability of the system (or component) is described by the \"reliability function\" <italic>R</italic>(<italic>x</italic>), also named \"survival function\", which is the probability that the system (or component) will carry out its mission through time <italic>x </italic>[##UREF##6##30##], expressed as the probability that the failure time <italic>X </italic>is beyond time <italic>x</italic>:</p>", "<p></p>", "<p>where <italic>F</italic>(<italic>x</italic>) = <italic>P</italic>(<italic>X </italic>≤ <italic>x</italic>) is a cumulative distribution function [##REF##11742523##24##] reflecting the resistance of the system to failures (at time <italic>x</italic>, distribution of the probability that the system could have failed previously). <italic>R</italic>(<italic>x</italic>) evaluates therefore the probability that the systems becomes completely defective after a time <italic>x </italic>(<italic>x </italic>can be a direct measure of time <italic>t </italic>or an increasing function of time).</p>", "<p>The \"hazard rate\"<italic>h</italic>(<italic>x</italic>), also called \"failure rate\", is defined as the relative rate for reliability function decline:</p>", "<p></p>", "<p>Hazard rate is equivalent to mortality force in demography [##UREF##7##31##,##REF##18202874##32##]. When <italic>h</italic>(<italic>x</italic>) is a constant <italic>h</italic>, the system does not deteriorate more often with age, and is therefore a <italic>non-aging </italic>system. In this case, a simple integration of equation (6) leads to</p>", "<p></p>", "<p>which is the exponential distribution that characterizes non-aging systems. Interestingly, a system with redundant <italic>non-aging components </italic>can be an <italic>aging system</italic>. That is to say the hazard rate of a system of components depends can depends of time whereas the hazard rate of components do not</p>", "<p>As discussed by Gavrilov and Gravrilova [##REF##11742523##24##], the \"reliability theory\" provided explanations for some fundamental problems regarding aging, longevity, death of organisms within populations. Organisms or populations are considered as systems in which categories of components (molecules, biological processes, cells, individuals, etc.) can be highly redundant, and be key elements for the system longevity.</p>", "<p>Here, we propose to consider the particular case of <italic>protein sequences as a system</italic>, in which redundancy is ensured:</p>", "<p>i. by the number of residue positions involved in the evolution process.</p>", "<p>ii. at the residue level by the existence of functionally redundant amino acids (<italic>e.g. </italic>after a DNA damage that leads to a genetic mutation, an aspartic acid may be substituted by a functionally redundant glutamic acid), <italic>i.e. </italic>the existence of a SAI for all amino acids pairs.</p>", "<p>In this model, evolutionary time is negatively correlated to the amount of information shared between an initial sequence and sequences in its lineage (SAI decreases with time, see below).</p>", "<title>The conservation rate: a mathematical tool to study the evolution of the information shared by biological sequences</title>", "<p>To measure the rate of conservation of a shared structure/function relationship at time <italic>x </italic>within a system of homologous proteins (<italic>i.e. </italic>the time of observation), we considered that the decay of information shared between an original sequence and sequences of its lineage was a function of time, and therefore a mean to measure time. Evolutionary time is therefore measured here in information units. We defined an <italic>information conservation rate </italic>Ψ as follows:</p>", "<title>Definition</title>", "<p>Given the cumulative distribution function <italic>F</italic>(<italic>x</italic>) = <italic>P</italic>(<italic>X </italic>≤ <italic>x</italic>) (Probability that the system shared less than <italic>x </italic>information units with a reference), supposed continuously differentiable, the <italic>conservation rate </italic>Ψ is given by:</p>", "<p></p>", "<p>The <italic>conservation rate </italic>is simply related to the hazard function, measuring a quantity that decreases over time (shared information) instead of a quantity that increases over time (age). Given <italic>f</italic>(<italic>x</italic>) = <italic>dF</italic>(<italic>x</italic>)/<italic>dx </italic>the density function of <italic>x</italic>, this conservation rate has the following properties.</p>", "<p></p>", "<p>and as corollaries:</p>", "<p></p>", "<p></p>", "<p></p>", "<title>Derivation of the distribution of sequence alignment scores based on the distribution of mutual information between amino acids</title>", "<p>Dobzhansky [##UREF##8##33##] and Wu et al. [##REF##4369316##34##] established that <italic>information </italic>harbored by a protein 1) emerged from the three-dimensional self organization of its residues (<italic>i.e. </italic>the sequence of amino acids) and had to do with information harbored by amino acids, and 2) was submitted through time to evolutionary pressure (achievement of a minimal functional level fitting environmental and species survival conditions). Using previous empirical results [##UREF##2##6##,##REF##1438297##7##,##REF##3221397##35##], Bastien et al. [##REF##15757521##14##] have shown that the alignment score of two homologous sequences <italic>a </italic>and <italic>b </italic>was proportional to the estimate of the SAI due to their common origin and parallel evolution under similar conservative pressure, <italic>i.e. </italic>the <italic>mutual information I</italic>(<italic>a</italic>; <italic>b</italic>) between the two events <italic>a </italic>and <italic>b </italic>in the sense of Hartley [##UREF##9##36##,##UREF##10##37##]:</p>", "<p></p>", "<p>with <italic>ξ </italic>a constant defining the unity (<italic>ξ </italic>= 1, in bits) and <italic>s</italic>(<italic>a</italic>, <italic>b</italic>) the sum of the elementary scores for all aligned positions (including gap opening and gap extension penalties). Mutual information between two events <italic>a </italic>and <italic>b </italic>(differing from the mutual information defined between random variables, see [##REF##15757521##14##,##UREF##11##38##]) measures the information gained by the knowledge of event <italic>a </italic>on the occurrence of event <italic>b</italic>. The mutual information being additive, <italic>I</italic>(<italic>a</italic>; <italic>b</italic>) is the sum of the mutual information of aligned residues, <italic>reflecting the magnitude of the redundancy between the sequences at the amino acid level</italic>. Mutual information between residues is therefore simply deduced from the 20 × 20 amino acid substitution matrix [##UREF##2##6##, ####REF##1438297##7##, ##REF##15948633##8####15948633##8##,##REF##3221397##35##] used to compute the alignment.</p>", "<p>Inside a given sequence, mutual information was also shown to <italic>reflect the dependency of close or remote amino acids</italic>, a phenomenon known as the residue co-evolution, due to their co-contribution to the sequence function [##REF##16254389##39##,##REF##16254390##40##].</p>", "<p>Considering a <italic>protein </italic>as a <italic>system</italic>, which <italic>components </italic>are <italic>amino acids</italic>, we examined the mutual information between the original components and their descendants, and how amino acid mutation affected the evolution of mutual information between proteins. We simply hypothesized that an amino acid may mutate over time following random DNA mutations and look at the behavior of the entire system, namely the protein which can be measured here by the mutual information between the initial residues and the new ones, <italic>i.e. </italic>the corresponding substitution scores in a 20 × 20 substitution matrix. The substitution matrix is considered as an estimate of the mutual information between residues because it was computed from real sequences' data [##UREF##2##6##, ####REF##1438297##7##, ##REF##15948633##8####15948633##8##,##REF##3221397##35##].</p>", "<p>Over time, an amino acid <italic>i </italic>is either conserved or substituted. The similarity of <italic>i </italic>in an initial sequence compared with residues at the same position in protein descendants is therefore either that of identity (the diagonal term in the scoring matrix) or a lower value(no score is higher than that of identity). In average, the magnitude of the similarity of <italic>i </italic>compared with its descendants, related to mutual information following equation (13), is therefore a decreasing function of elapsed time. On a functional point of view, the probability that <italic>i </italic>was mutated into a residue with a score <italic>S</italic><sub><italic>i </italic></sub>lower than a threshold <italic>s</italic><sub><italic>i </italic></sub>defined to allow the component to operate like <italic>i</italic>, can be deduced from the distribution of substitution scores. For most amino acids (F, P, W, Y, V, E, G, H, I, L, K, R, N, D and C), the distribution of scores deduced from BLOSUM 62 fits an exponential distribution (see the case of valine in Figure ##FIG##0##1A##. For five amino acids (M, S, T, A and Q), the distribution of scores does not fit an exponential distribution (see the case of Threonine in Figure ##FIG##0##1B##). Taking the average situation, the distribution of scores deduced from the BLOSUM 62 matrix is exponential-like (Figure ##FIG##0##1C##) supporting a general model for amino acids mutual information distribution: The probability <italic>P</italic><sub><italic>r </italic></sub>that a residue <italic>i </italic>is mutated into a residue with mutual information below <italic>s</italic><sub><italic>i </italic></sub>is:</p>", "<p></p>", "<p>where <italic>λ</italic><sub><italic>i </italic></sub>is the <italic>constant information hazard rate, or failure rate, for reliability function decline of the amino acid mutual information</italic>.</p>", "<p>Given a sequence <italic>a</italic>, what is the probability that any of its <italic>m </italic>residues (termed <italic>i</italic>) had previously mutated into the <italic>n </italic>residues (termed <italic>j</italic>) of a sequence <italic>b </italic>and leads to the observed mutual information between sequence <italic>a </italic>and sequence <italic>b</italic>? We can consider <italic>m </italic>≠ <italic>n </italic>due to insertion or deletion events. If <italic>m </italic>and <italic>n </italic>are large, we can state the following asymptotic approximations: <italic>S </italic>≈ <italic>m </italic>⟨<italic>S</italic><sub><italic>i</italic></sub>⟩, with and <italic>s </italic>≈ <italic>m </italic>⟨<italic>s</italic><sub><italic>i</italic></sub>⟩, with where <italic>s </italic>(respectively <italic>S</italic>) is the score between the sequence <italic>a </italic>(respectively <italic>A</italic>) and the sequence <italic>b </italic>(respectively <italic>B</italic>) (for discussion of these approximations, see [##UREF##12##41##]). In the asymptotic limit of long sequences, we can envisage different scenarios for the evolution of <italic>a </italic>into <italic>b</italic>:</p>", "<p>In a first step (Figure ##FIG##1##2##, step 1), the probability that one residue <italic>a</italic><sub>1 </sub>is mutated into a residue <italic>b</italic><sub>1 </sub>with mutual information below <italic>s</italic><sub><italic>i </italic></sub>is given:</p>", "<p></p>", "<p>Considering one possible evolutionary scenario, <italic>i.e. </italic>one alignment (Figure ##FIG##1##2##, step 2), residues are considered as independent and the probability is the product of elementary probabilities for each positions aligned in this scenario. For the alignment of the <italic>m </italic>amino acids of sequence <italic>a</italic>, we obtain the following probability:</p>", "<p></p>", "<p>Alternative scenarios are also possible (Figure ##FIG##1##2##, step 3). The final probability is therefore computed taking into account all possible evolutionary paths (all possible alignments, Figure ##FIG##1##2##, step 3) and using <italic>K'</italic>&lt;1 a correcting factor for edge effects, deletion and insertion points:</p>", "<p></p>", "<p>Considering the approximation of ⟨<italic>S</italic><sub><italic>i</italic></sub>⟩ and ⟨<italic>s</italic><sub><italic>i</italic></sub>⟩ respectively by <italic>S</italic>/<italic>m </italic>and <italic>s</italic>/<italic>m</italic>, we deduce the final formula:</p>", "<p></p>", "<p>The density function <italic>f</italic>(<italic>s</italic>) is therefore given by:</p>", "<p></p>", "<p>with the density of the probability <italic>P</italic><sub><italic>r</italic></sub>(<italic>S </italic>≤ <italic>s</italic>) that a residue is mutated into another</p>", "<p>with mutual information below <italic>s</italic>. We can then deduce the <italic>homology longevity rate </italic>Ψ, defined earlier as a function of the pairwise alignment score:</p>", "<p></p>", "<p>Using the expression of <italic>P</italic><sub><italic>i</italic></sub>(<italic>S</italic><sub><italic>i </italic></sub>≤ <italic>s</italic><sub><italic>i</italic></sub>) given by Equation (14) implies that:</p>", "<p></p>", "<p>Asymptotically, the information conservation rate is therefore given by</p>", "<p></p>", "<p>Using equation (12), we deduce that the distribution of alignment scores should respect the general form of the Karlin-Altschul formula:</p>", "<p></p>", "<title>Applications and Conclusion</title>", "<p>We built a model of evolution of the information shared between an initial molecular sequence and the sequences of its lineage (<italic>i.e. </italic>homologous sequences). Sequences were considered as systems, which components are the amino acids that can independently be damaged by random DNA mutations. Residues harbor a functional redundancy reflected by the amino acid substitution scores.</p>", "<p>From these assumptions, we deduced that the pairwise sequence alignment score should follow a Gumbel distribution (equation (22)). The <italic>λ' </italic>parameter is the information hazard rate for the reliability of amino acids' mutual information: it depends 1) on the distribution of the amino acids and 2) on the distribution of amino acid similarities deduced from a substitution matrix. The <italic>K</italic>' parameter has a more complex meaning, because it depends on likelihood of an alignment of two sequences, with edge effects, gaps, length difference and repartition of the information (the local score) in the alignment. It reflects therefore internal structural constraints on the evolution of sequences.</p>", "<p>The Gumbel parameters for score alignments can be estimated by two kinds of simulations. First is by adjusting EVD to the simulated distribution of scores [##UREF##3##19##,##REF##11139604##22##]. In that case, it is simpler to express the Gumbel law as</p>", "<p></p>", "<p>with and . The estimate of Gumbel parameters is achieved by determining <italic>β </italic>and <italic>θ</italic>, allowing an easy estimate of the <italic>λ' </italic>and <italic>K</italic>' parameters of equation (23). Second estimation of the Gumbel parameters is by computing the <italic>Z-value </italic>corresponding to the simulation of score distribution. Using the fact that for a Gumbel distribution, <italic>μ </italic>= <italic>θ </italic>+ <italic>γβ </italic>and , then the <italic>Z-value </italic>allows a computation of the <italic>β </italic>and <italic>θ </italic>constants.</p>", "<p>Simulations of <italic>Z</italic>-<italic>value </italic>distribution [##REF##10627144##11##,##REF##9514730##18##] showed that it fitted with a Gumbel law. Based on the Gumbel distribution of scores (equations (24) and (25)) and by an appropriate change of variable with equation (1), then the distribution of <italic>Z</italic>-<italic>values </italic>should respect the following equality:</p>", "<p></p>", "<p>with <italic>γ </italic>the Euler-Mascheroni constant (<italic>γ </italic>≈ 0.5772). Equation (25) is the precise expression of the distribution of <italic>Z-values </italic>deduced by Pearson [##REF##9514730##18##] from simulations. It is important to note that this expression of the <italic>Z-value </italic>distribution is independent of sequence lengths and amino acid distributions.</p>", "<p>This consideration has practical implications, since it allows a refined estimate of the <italic>P-value </italic>based on <italic>Z-value </italic>computation, and a real gain over available methods, particularly in some documented cases where the Karlin-Altschul formula failed to assess the significance of an alignment. Table ##TAB##0##1## shows for instance the different statistical estimates for the alignment of two homologous TFIIA gamma sequences from <italic>Plasmodium falciparum </italic>and <italic>Arabidopsis thaliana</italic>. The compositional bias in the proteome of <italic>Plasmodium falciparum</italic>, the malarial parasite, is known to limit the use of Karlin-Altschul statistics for pairwise comparisons with unbiased proteins such as those of <italic>Arabidopsis thaliana </italic>[##REF##15246528##42##]. The TFIIA gamma subunit sequence of <italic>Plasmodium </italic>could not be deduced from BLASTP-based homology searches [##REF##16042788##43##]. The Blastp apparent search failure was due to the overestimate of the <italic>P-value </italic>following the Karlin-Altschul formula (0.008, using unfiltered BLASTP, see Table ##TAB##0##1##). Alignment score <italic>Z-value</italic>, computed with either Blastp (P. Ortet, unpublished algorithm) or Smith-Waterman was above 10. The upper bound for the <italic>P-value </italic>based on the TULIP theorem, given by the formula <italic>T-value </italic>= 1/<italic>Z-value</italic><sup>2 </sup>[##REF##14990449##13##], was therefore below 10<sup>-2</sup>. Eventually, the <italic>P-value </italic>deduced from the <italic>Z-value </italic>Gumbel distribution was below 10<sup>-6 </sup>(see Table ##TAB##0##1##) indicating that, for both the Blastp and Smith-Waterman methods, the homology could be statistically assessed, even in the limit case of unbiased vs biased sequence comparisons. We noticed that the asymmetric DirAtPf100 matrix specified for <italic>Plasmodium </italic>vs. <italic>Arabidopsis </italic>comparisons that we developed earlier [##REF##15948633##8##] allowed an additional gain in estimating this missed homology.</p>", "<p>Besides a theoretical support for pragmatic observations, this report shows therefore that the alignment score Gumbel distribution is a particular and general evolutionary law for molecular sequences taken as dynamical systems. This model can be parameterized using the Karlin-Altschul or the <italic>Z-Value </italic>form. If Karlin-Altschul model parameters are well-estimated (using simulations for example), both forms are equivalent in practice as reported by Hulsen et al. [##REF##17038163##44##]. This model shows that an extreme value distribution of alignment scores can arise not only by considering high scoring segments pairs. Indeed, derivation of a Gumbel distribution from maximum independent random variables is a well-known technique [##UREF##3##19##] and the Karlin-Altschul theorem was first demonstrated, based on this consideration [##REF##2315319##20##]. We can now state that this distribution allows a different interpretation in the light of the Reliability Theory, reflecting the redundancy of information between sequences due to both the number of residues and the shared information between these residues. The model elaboration described here additionally provides a link between concepts of biological sequence analysis and the emerging field of systems biology, with a generalization of the aging concepts to all scales of the living world.</p>" ]
[ "<title>Applications and Conclusion</title>", "<p>We built a model of evolution of the information shared between an initial molecular sequence and the sequences of its lineage (<italic>i.e. </italic>homologous sequences). Sequences were considered as systems, which components are the amino acids that can independently be damaged by random DNA mutations. Residues harbor a functional redundancy reflected by the amino acid substitution scores.</p>", "<p>From these assumptions, we deduced that the pairwise sequence alignment score should follow a Gumbel distribution (equation (22)). The <italic>λ' </italic>parameter is the information hazard rate for the reliability of amino acids' mutual information: it depends 1) on the distribution of the amino acids and 2) on the distribution of amino acid similarities deduced from a substitution matrix. The <italic>K</italic>' parameter has a more complex meaning, because it depends on likelihood of an alignment of two sequences, with edge effects, gaps, length difference and repartition of the information (the local score) in the alignment. It reflects therefore internal structural constraints on the evolution of sequences.</p>", "<p>The Gumbel parameters for score alignments can be estimated by two kinds of simulations. First is by adjusting EVD to the simulated distribution of scores [##UREF##3##19##,##REF##11139604##22##]. In that case, it is simpler to express the Gumbel law as</p>", "<p></p>", "<p>with and . The estimate of Gumbel parameters is achieved by determining <italic>β </italic>and <italic>θ</italic>, allowing an easy estimate of the <italic>λ' </italic>and <italic>K</italic>' parameters of equation (23). Second estimation of the Gumbel parameters is by computing the <italic>Z-value </italic>corresponding to the simulation of score distribution. Using the fact that for a Gumbel distribution, <italic>μ </italic>= <italic>θ </italic>+ <italic>γβ </italic>and , then the <italic>Z-value </italic>allows a computation of the <italic>β </italic>and <italic>θ </italic>constants.</p>", "<p>Simulations of <italic>Z</italic>-<italic>value </italic>distribution [##REF##10627144##11##,##REF##9514730##18##] showed that it fitted with a Gumbel law. Based on the Gumbel distribution of scores (equations (24) and (25)) and by an appropriate change of variable with equation (1), then the distribution of <italic>Z</italic>-<italic>values </italic>should respect the following equality:</p>", "<p></p>", "<p>with <italic>γ </italic>the Euler-Mascheroni constant (<italic>γ </italic>≈ 0.5772). Equation (25) is the precise expression of the distribution of <italic>Z-values </italic>deduced by Pearson [##REF##9514730##18##] from simulations. It is important to note that this expression of the <italic>Z-value </italic>distribution is independent of sequence lengths and amino acid distributions.</p>", "<p>This consideration has practical implications, since it allows a refined estimate of the <italic>P-value </italic>based on <italic>Z-value </italic>computation, and a real gain over available methods, particularly in some documented cases where the Karlin-Altschul formula failed to assess the significance of an alignment. Table ##TAB##0##1## shows for instance the different statistical estimates for the alignment of two homologous TFIIA gamma sequences from <italic>Plasmodium falciparum </italic>and <italic>Arabidopsis thaliana</italic>. The compositional bias in the proteome of <italic>Plasmodium falciparum</italic>, the malarial parasite, is known to limit the use of Karlin-Altschul statistics for pairwise comparisons with unbiased proteins such as those of <italic>Arabidopsis thaliana </italic>[##REF##15246528##42##]. The TFIIA gamma subunit sequence of <italic>Plasmodium </italic>could not be deduced from BLASTP-based homology searches [##REF##16042788##43##]. The Blastp apparent search failure was due to the overestimate of the <italic>P-value </italic>following the Karlin-Altschul formula (0.008, using unfiltered BLASTP, see Table ##TAB##0##1##). Alignment score <italic>Z-value</italic>, computed with either Blastp (P. Ortet, unpublished algorithm) or Smith-Waterman was above 10. The upper bound for the <italic>P-value </italic>based on the TULIP theorem, given by the formula <italic>T-value </italic>= 1/<italic>Z-value</italic><sup>2 </sup>[##REF##14990449##13##], was therefore below 10<sup>-2</sup>. Eventually, the <italic>P-value </italic>deduced from the <italic>Z-value </italic>Gumbel distribution was below 10<sup>-6 </sup>(see Table ##TAB##0##1##) indicating that, for both the Blastp and Smith-Waterman methods, the homology could be statistically assessed, even in the limit case of unbiased vs biased sequence comparisons. We noticed that the asymmetric DirAtPf100 matrix specified for <italic>Plasmodium </italic>vs. <italic>Arabidopsis </italic>comparisons that we developed earlier [##REF##15948633##8##] allowed an additional gain in estimating this missed homology.</p>", "<p>Besides a theoretical support for pragmatic observations, this report shows therefore that the alignment score Gumbel distribution is a particular and general evolutionary law for molecular sequences taken as dynamical systems. This model can be parameterized using the Karlin-Altschul or the <italic>Z-Value </italic>form. If Karlin-Altschul model parameters are well-estimated (using simulations for example), both forms are equivalent in practice as reported by Hulsen et al. [##REF##17038163##44##]. This model shows that an extreme value distribution of alignment scores can arise not only by considering high scoring segments pairs. Indeed, derivation of a Gumbel distribution from maximum independent random variables is a well-known technique [##UREF##3##19##] and the Karlin-Altschul theorem was first demonstrated, based on this consideration [##REF##2315319##20##]. We can now state that this distribution allows a different interpretation in the light of the Reliability Theory, reflecting the redundancy of information between sequences due to both the number of residues and the shared information between these residues. The model elaboration described here additionally provides a link between concepts of biological sequence analysis and the emerging field of systems biology, with a generalization of the aging concepts to all scales of the living world.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a <italic>P-value</italic>, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a <italic>Z-value </italic>from a random score distribution obtained by a Monte-Carlo simulation. <italic>Z-values </italic>allow the deduction of an upper bound of the <italic>P-value </italic>(1/<italic>Z-value</italic><sup>2</sup>) following the TULIP theorem. Simulations of <italic>Z</italic>-<italic>value </italic>distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support.</p>", "<title>Results</title>", "<p>We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (<italic>i.e.</italic>, mutual information in Information Theory) is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. \"Reliability\" refers to the ability to operate properly according to a standard. Here, the \"reliability\" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure). Homologous sequences were considered as systems 1) having a high redundancy of information reflected by the magnitude of their alignment scores, 2) which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a constant rate, corresponding to the information hazard rate, and that pairwise sequence alignment scores should follow a Gumbel distribution, which parameters could find some theoretical rationale. In particular, one parameter corresponds to the information hazard rate.</p>", "<title>Conclusion</title>", "<p>Extreme value distribution of alignment scores, assessed from high scoring segments pairs following the Karlin-Altschul model, can also be deduced from the Reliability Theory applied to molecular sequences. It reflects the redundancy of information between homologous sequences, under functional conservative pressure. This model also provides a link between concepts of biological sequence analysis and of systems biology.</p>" ]
[ "<title>Abbreviations</title>", "<p>SAI: shared amount of information; TULIP: theorem of the upper limit of a score probability; EVD: extreme values distribution</p>", "<title>Authors' contributions</title>", "<p>OB conceived the main theoretical model, designed and developed all demonstrations and drafted the manuscript. EM participated in the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>Authors whish to thank Philippe Ortet for computing facilities, particularly for the use of an unpublished implementation of the <italic>Z-value </italic>statistical model using Blastp. This work was made possible by the financial support of Agence Nationale de la Recherche (Plasmoexplore project ANR-06-CIS6-MDCA-14-01) and the French Ministry of Foreign Affairs (SAFE-ITC program).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Aging properties of amino acids</bold>. Protein sequences are considered as systems, which components are amino acids. Over time, either amino acids are conserved (similarity of a residue with its descendant is that of identity, diagonal term of a substitution matrix) or modified due to random DNA mutations. Similarity decreases therefore with time, since no similarity is higher than that of identity. When the similarity falls below a threshold that is necessary for the residue to operate according to a standard (functional conservation), the component is damaged. <bold>(A) Score distribution corresponding to valine substitution. </bold>In this case, the score distribution is exponential, suggesting that valine (V) is a non-aging component. Based on BLOSUM62, residues of this type are V, F, P, W, Y, E, G, H, I, L, K, R, N, D and C <bold>(B) Score distribution corresponding to threonine substitution. </bold>The score distribution shows a peak, indicating a probable accelerated process of aging (functional damage) when the residue is substituted by random mutation in some other amino acids. Based on BLOSUM62, residues of this type are T, S, M, A and Q. <bold>(C) Score distribution in the BLOSUM62 similarity matrix. </bold>The complete distribution in the BLOSUM62 matrix is exponential (0.287.exp(-0.287.(<italic>s</italic>+4))), supporting a general model of amino acids as nonaging components. The exponential law for positive scores is characterized by the same parameter (<italic>λ' </italic>= 0.287). The original residue is termed <italic>i</italic>; its descent is termed <italic>j</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Computing of the probability that the amount of information shared by two sequences, <italic>S</italic>, is lower than a threshold <italic>s</italic></bold>. Given an initial sequence <italic>a</italic>, we can envisage different scenarios for its evolution into another sequence <italic>b</italic>. In a first step (<bold>Step 1</bold>), an elementary probability is computed by taking into account the evolution of just one residue (here <italic>a</italic><sub>1 </sub>into <italic>b</italic><sub>1</sub>). Considering one possible evolutionary scenario (<bold>Step 2</bold>), residues are considered as independent and the probability is the product of elementary probabilities for each positions aligned in this scenario, with approximations in the asymptotic limit of long sequences. The final probability (<bold>Step 3</bold>) is then estimated by taking into account all the possible evolutionary scenarios.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Alignment statistics of the homologous Transcription initiation factor IIA (TFIIA) gamma chain sequences from <italic>Plasmodium falciparum </italic>and <italic>Arabidopsis thaliana</italic>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Alignment method</td><td align=\"center\">Blastp</td><td align=\"center\" colspan=\"2\">Smith-Waterman</td></tr></thead><tbody><tr><td align=\"left\">Substitution matrix</td><td align=\"center\">BLOSUM62</td><td align=\"center\">BLOSUM62</td><td align=\"center\">DirAtPf100</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Statistics</td><td/><td/><td/></tr><tr><td align=\"right\"><italic>P-value </italic>(Karlin-Altschul)</td><td align=\"center\">0.008</td><td align=\"center\">NA</td><td align=\"center\">NA</td></tr><tr><td align=\"right\"><italic>Z-value </italic>(Pearson-Lipman)</td><td align=\"center\">10</td><td align=\"center\">11</td><td align=\"center\">12</td></tr><tr><td align=\"right\"><italic>T-value </italic>(TULIP theorem)</td><td align=\"center\">0.01</td><td align=\"center\">8.10<sup>-3</sup></td><td align=\"center\">7.10<sup>-3</sup></td></tr><tr><td align=\"right\"><italic>P-value </italic>(this work)</td><td align=\"center\">1.5.10<sup>-6</sup></td><td align=\"center\">3.7.10<sup>-7</sup></td><td align=\"center\">1.10<sup>-7</sup></td></tr></tbody></table></table-wrap>" ]
[ "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1471-2105-9-332-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mfrac><mml:mrow><mml:mi>ω</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>ν</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>ν</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1471-2105-9-332-i2\" overflow=\"scroll\"><mml:semantics><mml:mover accent=\"true\"><mml:mi>μ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1471-2105-9-332-i3\" overflow=\"scroll\"><mml:semantics><mml:mover accent=\"true\"><mml:mi>σ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM1\"><label>(1)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1471-2105-9-332-i4\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>Z</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mi>b</mml:mi><mml:mo>∗</mml:mo></mml:msup><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>s</mml:mi><mml:mo>−</mml:mo><mml:mover accent=\"true\"><mml:mi>μ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:mrow><mml:mover accent=\"true\"><mml:mi>σ</mml:mi><mml:mo>^</mml:mo></mml:mover></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\" name=\"1471-2105-9-332-i5\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>≤</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>Z</mml:mi><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mi>b</mml:mi><mml:mo>∗</mml:mo></mml:msup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM3\"><label>(3)</label><italic>E</italic>(<italic>s</italic>) ≈ <italic>K</italic>.<italic>m</italic>.<italic>n</italic>.exp(-<italic>λ</italic>.<italic>s</italic>)</disp-formula>", "<disp-formula id=\"bmcM4\"><label>(4)</label><italic>P</italic>(<italic>S</italic>(<italic>A</italic>, <italic>B</italic>) ≤ <italic>s</italic>) ≈ exp(-<italic>K</italic>.<italic>m</italic>.<italic>n</italic>.exp(-<italic>λ</italic>.<italic>s</italic>))</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M6\" name=\"1471-2105-9-332-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:munder><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:munder><mml:mrow><mml:msub><mml:mi>ν</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:msub><mml:mi>ν</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mo>{</mml:mo><mml:mi>λ</mml:mi><mml:mo>.</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>}</mml:mo><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM5\"><label>(5)</label><italic>R</italic>(<italic>x</italic>) = <italic>P</italic>(<italic>X </italic>&gt; <italic>x</italic>) = 1 - <italic>P</italic>(<italic>X </italic>≤ <italic>x</italic>) = 1 - <italic>F</italic>(<italic>x</italic>)</disp-formula>", "<disp-formula id=\"bmcM6\"><label>(6)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M7\" name=\"1471-2105-9-332-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>h</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM7\"><label>(7)</label><italic>R</italic>(<italic>x</italic>) = <italic>R</italic>(0)exp(-<italic>h</italic>.<italic>x</italic>)</disp-formula>", "<disp-formula id=\"bmcM8\"><label>(8)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M8\" name=\"1471-2105-9-332-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mrow><mml:mi>lim</mml:mi><mml:mo>⁡</mml:mo></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mo>→</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:munder><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>−</mml:mo><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mo>&lt;</mml:mo><mml:mi>X</mml:mi><mml:mo>≤</mml:mo><mml:mi>x</mml:mi><mml:mo>/</mml:mo><mml:mi>X</mml:mi><mml:mo>≤</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM9\"><label>(9)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M9\" name=\"1471-2105-9-332-i9\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>F</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>X</mml:mi><mml:mo>≤</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM10\"><label>(10)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M10\" name=\"1471-2105-9-332-i10\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>F</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM11\"><label>(11)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M11\" name=\"1471-2105-9-332-i11\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>X</mml:mi><mml:mo>≤</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>−</mml:mo><mml:mstyle displaystyle=\"true\"><mml:mrow><mml:munderover><mml:mo>∫</mml:mo><mml:mi>x</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:munderover><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>d</mml:mi><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM12\"><label>(12)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M12\" name=\"1471-2105-9-332-i12\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>−</mml:mo><mml:mstyle displaystyle=\"true\"><mml:mrow><mml:munderover><mml:mo>∫</mml:mo><mml:mi>x</mml:mi><mml:mrow><mml:mo>+</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:munderover><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>d</mml:mi><mml:mi>u</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM13\"><label>(13)</label><italic>s</italic>(<italic>a</italic>, <italic>b</italic>) = <italic>ξ</italic>.<italic>I </italic>(<italic>a</italic>; <italic>b</italic>)</disp-formula>", "<disp-formula id=\"bmcM14\"><label>(14)</label><italic>P</italic><sub><italic>r</italic></sub>(<italic>S</italic><sub><italic>i </italic></sub>≤ <italic>s</italic><sub><italic>i</italic></sub>) = 1-exp(-<italic>λ</italic><sub><italic>i</italic></sub>.<italic>s</italic><sub><italic>i</italic></sub>)</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M13\" name=\"1471-2105-9-332-i13\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mo>〈</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>〉</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mrow><mml:mi>lim</mml:mi><mml:mo>⁡</mml:mo></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mo>→</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:munder><mml:mfrac><mml:mi>S</mml:mi><mml:mi>m</mml:mi></mml:mfrac></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M14\" name=\"1471-2105-9-332-i14\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mo>〈</mml:mo><mml:msub><mml:mi>s</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>〉</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mrow><mml:mi>lim</mml:mi><mml:mo>⁡</mml:mo></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mo>→</mml:mo><mml:mo>+</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:munder><mml:mfrac><mml:mi>s</mml:mi><mml:mi>m</mml:mi></mml:mfrac></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM15\"><label>(15)</label><italic>P</italic><sub><italic>r</italic></sub>(<italic>S</italic><sub><italic>i </italic></sub>≤ <italic>s</italic><sub><italic>i</italic></sub>) = <italic>P</italic><sub><italic>r</italic></sub>(<italic>S</italic><sub><italic>i </italic></sub>≤ <italic>s</italic>(<italic>a</italic><sub>1</sub>,<italic>b</italic><sub>1</sub>)) ≈ <italic>P</italic><sub><italic>r</italic></sub>(⟨<italic>S</italic><sub><italic>i</italic></sub>⟩ ≤ ⟨<italic>s</italic><sub><italic>i</italic></sub>⟩)</disp-formula>", "<disp-formula id=\"bmcM16\"><label>(16)</label><italic>P</italic><sub><italic>scenario</italic>1 </sub>(<italic>S </italic>≤ <italic>s</italic>) = (<italic>P</italic><sub><italic>r</italic></sub>(⟨<italic>S</italic><sub><italic>i</italic></sub>⟩ ≤ ⟨<italic>s</italic><sub><italic>i</italic></sub>⟩))<sup><italic>m</italic></sup></disp-formula>", "<disp-formula id=\"bmcM17\"><label>(17)</label><italic>P</italic>(<italic>S </italic>≤ <italic>s</italic>) = (<italic>P</italic><sub><italic>r</italic></sub>(⟨<italic>S</italic><sub><italic>i</italic></sub>⟩ ≤ ⟨<italic>s</italic><sub><italic>i</italic></sub>⟩))<sup><italic>K</italic>'<italic>mn</italic></sup></disp-formula>", "<disp-formula id=\"bmcM18\"><label>(18)</label><italic>P</italic>(<italic>S </italic>≤ <italic>s</italic>) = (<italic>P</italic><sub><italic>r</italic></sub>(<italic>S </italic>≤ <italic>s</italic>))<sup><italic>K</italic>'.<italic>m</italic>.<italic>n</italic></sup></disp-formula>", "<disp-formula id=\"bmcM19\"><label>(19)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M15\" name=\"1471-2105-9-332-i15\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msup><mml:mi>K</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>.</mml:mo><mml:mi>m</mml:mi><mml:mo>.</mml:mo><mml:mi>n</mml:mi><mml:mo>.</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msup><mml:mi>K</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>.</mml:mo><mml:mi>m</mml:mi><mml:mo>.</mml:mo><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M16\" name=\"1471-2105-9-332-i16\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>P</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM20\"><label>(20)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M17\" name=\"1471-2105-9-332-i17\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>ψ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mi>K</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>.</mml:mo><mml:mi>m</mml:mi><mml:mo>.</mml:mo><mml:mi>n</mml:mi><mml:mo>.</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>.</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msup><mml:mi>K</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>.</mml:mo><mml:mi>m</mml:mi><mml:mo>.</mml:mo><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>S</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi><mml:mo 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[]
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[]
[ "<table-wrap-foot><p>TFIIA gamma sequences from <italic>Plasmodium </italic>(UniProtKB Q8I4S7_PLAF7) and <italic>Arabidopsis </italic>(UniProtKB T2AG_ARATH) were aligned with Blastp and Smith-Waterman methods. Statistics were computed following the Karlin-Altschul model (as implemented in the Blastp algorithm) or the Lipman-Pearson <italic>Z-value </italic>model. The upper bound for the <italic>P-value </italic>based on the TULIP theorem is given following the formula: <italic>T-value </italic>= 1/<italic>Z-value</italic><sup>2</sup>. The <italic>P-value </italic>deduced from the <italic>Z-value </italic>Gumbel distribution was computed following the model presented here. Substitution matrices were either BLOSUM62, or the asymmetric DirAtPf100 matrix specified for <italic>Plasmodium vs. Arabidopsis </italic>comparisons. NA: not applicable.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2105-9-332-1\"/>", "<graphic xlink:href=\"1471-2105-9-332-2\"/>" ]
[]
[{"surname": ["Bastien", "Ortet", "Roy", "Marechal"], "given-names": ["O", "P", "S", "E"], "article-title": ["The configuration space of homologous proteins: A theoretical and practical framework to reduce the diversity of the protein sequence space after massive all-by-all sequence comparisons"], "source": ["Fut Gen Comput Syst"], "year": ["2007"], "volume": ["23"], "fpage": ["410"], "lpage": ["427"], "pub-id": ["10.1016/j.future.2006.07.016"]}, {"surname": ["Setubal", "Meidanis"], "given-names": ["J", "J"], "source": ["Introduction to Computational Molecular Biology"], "year": ["1997"], "publisher-name": ["Boston : Pws Publishing Company"]}, {"surname": ["Dayhoff", "Schwartz", "Orcutt"], "given-names": ["MO", "RM", "BC"], "article-title": ["A Model of Evolutionary Change in Proteins"], "source": ["Atlas Prot Seq Struct"], "year": ["1978"], "volume": ["5"], "fpage": ["345"], "lpage": ["352"]}, {"surname": ["Coles"], "given-names": ["S"], "source": ["An introduction to Statistical Modeling of Extreme Values"], "year": ["2001"], "publisher-name": ["New York: Springer-Verlag"]}, {"surname": ["Lespinats"], "given-names": ["S"], "article-title": ["Style du genome explore par analyse textuelle de l'ADN"], "source": ["PhD thesis"], "year": ["2006"], "publisher-name": ["Paris VI University, Department of Epidemiology and Health Informatics"]}, {"surname": ["Crowder", "Kimber", "Smith", "Sweeting"], "given-names": ["MJ", "AC", "RL", "TJ"], "source": ["Statistical analysis of reliability data"], "year": ["1991"], "publisher-name": ["London: Chapman and Hall"]}, {"surname": ["Rigdon", "Basu"], "given-names": ["SE", "AP"], "source": ["Statistical methods for the reliability of repairable systems"], "year": ["2000"], "publisher-name": ["New-York: Wiley and Son"]}, {"surname": ["Valleron"], "given-names": ["AJ"], "source": ["Introduction \u00e0 la Biostatistique"], "year": ["1998"], "publisher-name": ["Paris: Masson"]}, {"surname": ["Dobzhansky"], "given-names": ["T"], "source": ["Studies in the Philosophy of Biology: Reduction and Related Problems"], "year": ["1974"], "publisher-name": ["Los Angeles: University of California Press"]}, {"surname": ["Hartley"], "given-names": ["RVL"], "article-title": ["Transmission of Information"], "source": ["Bell System Technical Journal"], "year": ["1928"], "volume": ["3"], "fpage": ["535"], "lpage": ["564"]}, {"surname": ["Shannon"], "given-names": ["CE"], "article-title": ["A Mathematical Theory of Communication"], "source": ["Bell System Technical Journal"], "year": ["1948"], "volume": ["27"], "fpage": ["379"], "lpage": ["423"], "comment": ["623\u2013656."]}, {"surname": ["Cover", "Thomas"], "given-names": ["TM", "JA"], "source": ["Elements of Information Theory"], "year": ["1991"], "publisher-name": ["New-York: Wiley and Son"]}, {"surname": ["Waterman"], "given-names": ["MS"], "source": ["Introduction to computational biology"], "year": ["1995"], "publisher-name": ["London: Chapman and Hall"]}]
{ "acronym": [], "definition": [] }
44
CC BY
no
2022-01-12 14:47:26
BMC Bioinformatics. 2008 Aug 7; 9:332
oa_package/b1/ce/PMC2529321.tar.gz
PMC2529322
18691414
[ "<title>Background</title>", "<p>DNA methylation frequently occurs in mammalian DNA at the 5 position of cytosine in CpG dinucleotides. It has been estimated that over 70% of cytosines of CpG dinucleotides are methylated in the human genome. CpG dinucleotides are under-represented in the genome and methylated CpG dinucleotides predominantly occur within repetitive elements [##REF##2777259##2##]. However, there are CpG rich regions of the genome which generally remain unmethylated [##REF##2423876##3##]. These CpG rich regions are known as CpG islands and are frequently located in the promoter or the first exon regions of approximately 60% of all genes [##REF##7505451##4##]. The unmethylated status of CpG islands is thought to be a prerequisite state to maintain the linked gene in an active transcribed and transcriptional permissive state.</p>", "<p>Differential Methylation Hybridisation (DMH) is one of several techniques for examining CpG island methylation at a genome-wide scale that has been applied to the identification of aberrantly methylated gene promoters in various cancers [##REF##9949205##5##, ####REF##11731411##6##, ##REF##12114427##7##, ##REF##12163706##8##, ##REF##12095276##9##, ##REF##11951657##10##, ##REF##16900213##11##, ##REF##17504987##12####17504987##12##]. Nouzova <italic>et al</italic>[##REF##15302897##13##] modified the original method by using digestion with a methylation-dependent enzyme, M<italic>cr</italic>BC. This enzyme only cleaves methylated CpG DNA sequences. Within-sample comparison is applied after competitive hybridisation with M<italic>cr</italic>BC digested DNA and undigested (mock digested) DNA labelled with Cy3 and Cy5. If a locus is unmethylated the signal intensities of Cy3 and Cy5 are equivalent, while if methylated the Cy5/Cy3 (undigested/digested) ratio is greater than one. However, no common reference is generally used in the modified DMH method, and the unequal representation of methylated and unmethylated sequences due to competitive hybridisation may reduce sensitivity and specificity to detect differential methylation.</p>", "<p>Currently, Significance Analysis of Microarrays (SAM) [##REF##11309499##14##] and Prediction Analysis for Microarrays (PAM) [##REF##12011421##15##] are commonly applied in DNA methylation analysis. Based on the change in hybridisation relative to the standard deviation of repeated measurements, SAM assigns each gene a score that is an extension of the t-statistic. For significant genes with a score over a certain threshold, SAM uses permutations to estimate the false discovery rate (FDR). It has been implemented in many studies of gene expression data [##REF##17478416##16##, ####REF##17720884##17##, ##REF##18065728##18##, ##REF##17875689##19##, ##REF##17599952##20##, ##REF##17591626##21####17591626##21##] as well as DMH data, e.g. Wei <italic>et al</italic>. [##REF##16675572##22##] applied SAM to find the differential methylation of CpG island loci between ovarian caner patient groups with short and long progression-free survival (PFS). However, SAM assumes that the microarray data conform to approximate normality and symmetry, leading to the loss of power in the analysis of DMH data that are inherently skewed due to the biological features of DNA methylation in cancer and competitive hybridisation on DMH arrays (Figure ##FIG##0##1##).</p>", "<p>In the modified DMH method, the ratios of raw signal intensities (undigested/digested) greater than 1 reflect the various methylation levels [##REF##15302897##13##]. A ratio cut-off is generally used to identify the hypermethylated loci [##REF##12114427##7##]. However, this is an arbitrary value and does not necessarily accurately reflect the various sources of variation in the experiment. It is therefore desirable to develop an algorithm to more objectively assess the methylation status of loci from DMH data.</p>", "<p>PAM is a nearest centroid shrinkage method that identifies those genes that discriminate best between classes. This technique shrinks the class gene centroid towards the overall centroid by a \"threshold\" amount after standardizing each gene by its within class standard deviation. The \"threshold\" is identified by cross-validation. This approach was applied in the study by Wei <italic>et al</italic>. [##REF##16675572##22##] and showed certain power in the identification of differentially methylated loci, but PAM is designed for class prediction rather than class comparison. Although the class predictor used in PAM can reflect the difference between classes, a large number of loci actually differentially methylated between the classes are excluded to improve the accuracy of prediction.</p>", "<p>Although normalisation has become a standard procedure for the study of microarray data and is necessary for SAM and PAM analysis, unbalanced shifts in methylation status between class samples in DMH limit the use of between-class normalisation which assumes the changes are roughly symmetric. Thus, the differential methylation can be masked by the over-correction of normalisation and it would be preferable to use a method of analysis that does not require normalisation of the data.</p>", "<p>Since PAM and SAM may have limitations for analysing DMH data, we have developed an alternative approach based on the specific features and known biological properties of the arrays used for DMH analysis. The algorithm is named as Methylation Linear Discriminant Analysis (MLDA) and has been applied to identify a set of loci differentially methylated between ovarian cisplatin sensitive and resistant cancer cell lines.</p>" ]
[ "<title>Methods</title>", "<p>First, all intensity values were log transformed. A multiplicative background correction was applied to correct signal intensities for the background noise in each array. After background correction, the log-transformed digested and undigested intensities show three approximately parallel linear patterns (Figure ##FIG##1##2a##). The first pattern (digested/undigested is close to 1) represents the unmethylated sequences. The second pattern represents either hemi-methylated sequences or the unmethylated sequences cross-hybridised with the methylated ones on the panel. The third pattern represents the methylated sequences in target DNA. The methylated and unmethylated loci in target DNA can be characterised by a linear regression model for each pattern. The distance of each spot to the methylated and unmethylated lines respectively can then be estimated by standardised residuals. The log likelihood ratio of a locus being methylated is then proportional to the difference between the squared standardised residual from the methylated line and that from the unmethylated one. The algorithm based around this regression approach is named Methylation Linear Discriminant Analysis (MLDA) and was programmed in R version 2.7.0.</p>", "<title>Log-likelihood ratio transformation</title>", "<p>a An univariate linear regression model was constructed for the unmethylated probes (e.g. mitochondrial derived features) using formula (1) where <italic>α </italic>is the intercept, <italic>β </italic>is the slope of the model, and <italic>ξ </italic>is the error representing the unpredicted or unexplained variation in the model (Figure ##FIG##1##2b##). The parameters of regression line were estimated by the method of least squares (formula 2 and 3).</p>", "<p></p>", "<p></p>", "<p></p>", "<p><italic>k </italic>is the number of unmethylated controls on DMH array. <italic>G</italic><sub><italic>i </italic></sub>and <italic>R</italic><sub><italic>i </italic></sub>are the logarithmic-transformed digested and undigested intensities of microarray probes for mitochondrial sequences, respectively. and are the averaged logarithmic-transformed undigested and digested intensities of the k unmethylated controls.</p>", "<p>b. The scale estimate <italic>σ</italic><sub>mito </sub>associated with the error term in the linear regression model was estimated from the residuals from the observed <italic>k </italic>points to the fitted line. The most extreme 10% of residuals was omitted from either end of the distribution to minimise the impact of extreme residuals on this estimate.</p>", "<p>c. The standardised residuals of all the microarray probes to the unmethylation regression line were calculated as formula (4).</p>", "<p></p>", "<p>d. The point corresponding to the 97.5-quantiles residual below the unmethylation line is represented as X (R.975, G.975). The intermediate linear model (Figure ##FIG##1##2c##) was constructed through point X with a slope assumed to be 1 and the intercept estimated as formula (5).</p>", "<p></p>", "<p>e. The standardised residuals of all the microarray probes to the line with slope 1 and intercept estimated from (5) were calculated as formula (6). The variance of the residuals to the intermediate model was assumed to be similar as that in the mitochondrial model.</p>", "<p></p>", "<p>f. The microarray probes with standardised residuals less than 2 were included for later robust regression analysis. The line estimated from this regression analysis represents the methylation regression line (Figure ##FIG##1##2d##).</p>", "<p>g. The scale estimate <italic>σ</italic><sub>meth </sub>of the methylation regression line was estimated using only those microarray probes below the line, with the most extreme 5% removed.</p>", "<p>h. The standardised residuals of all the microarray probes to the methylated regression line were calculated as formula (7). The log likelihood ratio (LR) of all the microarray probes was estimated by formula (8) for further analysis.</p>", "<p></p>", "<p></p>", "<title>Determination of log likelihood ratio cut-offs</title>", "<p>Two inconsistency rates (IR<sub>meth </sub>and IR<sub>unmeth</sub>) and two consistency rates (CR<sub>meth </sub>and CR<sub>unmeth</sub>) between dye-swap arrays were used to determine the log like likelihood ratio threshold. IR<sub>meth </sub>(formula 9) represents the rate of the microarray probes identified as methylated in one array but as unmethylated in the other one, while IR<sub>unmeth </sub>(formula 10) is the rate of the microarray probes identified as unmethylated in one array but as methylated in the other one. CR<sub>meth </sub>(formula 11) and CR<sub>unmeth </sub>(formula 12) are the rates for the spots identified as methylated (CR<sub>meth</sub>) and unmethylated (CR<sub>unmeth</sub>) in both dye-swap arrays (Figure ##FIG##6##7##).</p>", "<p></p>", "<p></p>", "<p></p>", "<p></p>", "<p>b The log likelihood ratio thresholds (LR<sub>meth </sub>and LR<sub>unmeth</sub>) for methylated and unmethylated microarray probes, which kept the IR rates low (at or close to 1%) and the CR rates high (at or close to 140%), were used as the cut-offs for methylated and unmethylated loci. IR tends to rise with the increase of CR slowly, but starts to increase dramatically when the CR goes above 140%, at which point the inconsistency rate is generally about 1%. We have therefore used CR &gt; 140% and IR &lt; 1% as the criteria for determining the methylation cut-offs.</p>", "<title>Identification of robust regression outliers</title>", "<p>Each microarray probe was scored based on the cut-offs of likelihood ratios for methylation and unmethylation on dye-swap arrays using the weighted methylation scoring scheme shown in Figure ##FIG##2##3##. The microarray probes consistently identified as methylated candidates on dye-swap arrays were scored of 1; similarly unmethylated microarray probes were scored of -1. The rest of the microarray probes were assigned a weighted score based on their location on the plot.</p>", "<p>A robust regression model [##UREF##2##28##] was constructed with the averaged scores in one class of samples as the explanatory variable, and the corresponding scores in the other class of samples as the dependent variable. The degree of trimming was determined according to Barnett <italic>et al</italic>. [##UREF##3##29##] when estimating the variance of residuals to the robust linear regression model.</p>", "<p>It was assumed that the standardised residuals (SRs) from the robust regression line followed a normal distribution <italic>N</italic>(<italic>μ</italic>, <italic>σ</italic><sup>2</sup>). <italic>μ </italic>and <italic>σ </italic>were estimated excluding outliers using the MAD-Median Rule [##UREF##4##30##]. The p value for each SR cut-off was calculated as described by Simon <italic>et al </italic>[##UREF##1##25##]. This p-value reflects the probability of observing a group of more extreme residuals from the fitted normal distribution. Microarray probes were identified as outliers if their SRs were larger than the cut-off for which the p-value was less than 0.01.</p>", "<title>Estimation of misclassification rate</title>", "<p>The misclassification rate was estimated by drawing bootstrap samples 500 times with replacement from the two classes (sensitive and resistant) and carrying out hierarchical clustering based on the loci identified as differentially methylated using weighted scores for MLDA and log ratios without between-group normalisation for SAM and PAM, respectively. Clustering was carried out using Euclidean distance as the distance metric, and clusters were agglomerated using the average linkage criterion. The clustering tree was cut into two groups and the number of misclassified cell lines was counted. The misclassification rate was obtained from the averaged number of misclassified samples in 500 bootstraps divided by the total number of samples.</p>", "<title>SAM and PAM analysis</title>", "<p>The raw signal intensities of each channel were subtracted by the median signal intensities of corresponding channel of controls on HCGI12K array. After this correction, SAM in samr package and PAM in pamr package were applied using log ratios (digested/undigested) in R version 2.7.0. Between-group normalisation was not used in SAM and PAM to avoid over-correction masking the differential methylation.</p>" ]
[ "<title>Results</title>", "<title>Outline of MLDA</title>", "<p>In this study, we have developed a novel approach, named MLDA, for analysing CpG island microarray hybridisation data that allows the identification of differentially methylated loci. MLDA was programmed in R (version 2.7.0) and the package is available at CRAN [##UREF##0##1##]. This approach uses three relatively simple linear regression models. The first one is constructed by the log-transformed signal intensities of unmethylated features and used as the reference for unmethylation (Figure ##FIG##1##2b##). The second one is the intermediate model constructed through the point corresponding to the 97.5-quantiles residual below the first linear regression line (Figure ##FIG##1##2c##). The features with a standardised residual less than 2 from this intermediate model are used to generate the third model which is used as the reference for methylation (Figure ##FIG##1##2d##). The log likelihood ratio of a locus being methylated is then proportional to the difference between the squared standardised residual from the methylated line and that from the unmethylated line. The log likelihood threshold of zero then provides a more rational basis for distinguishing between methylated and unmethylated loci than a robust undigested/digested ratio of 1.5, as it takes into account the observed variability in the experiment.</p>", "<p>In our approach the consistency and inconsistency rates of log likelihood ratios on dye-swapped/duplicate arrays are used to determine methylation and unmethylation cut-offs, which keep the consistency rate (CR) relatively high (about 140%) and the inconsistency rate (IR) low (about 1%). Each loci is assigned a score based on the cut-offs using the weighted methylation scoring scheme. The feature consistently identified as methylated candidates on dye-swapped/duplicate arrays are scored as 1; similarly unmethylated features are scored as -1; the rest of the feature are assigned a weighted score corresponding to their location on the plot of log-likelihood ratios (Figure ##FIG##2##3##).</p>", "<p>The averaged score for each locus is calculated in each sample class (e.g. resistant or sensitive) and plotted against each other. A robust regression model is then fitted to these data. The standardised residuals from the robust regression model are assumed to follow a normal distribution <italic>N</italic>(<italic>μ</italic>, <italic>σ</italic><sup>2</sup>). The outliers of the standardised residuals are identified as the differentially methylated loci between the class samples.</p>", "<title>DMH dataset</title>", "<p>MLDA was applied to identify the CGIs differentially methylated from DMH data derived from sensitive A2780 derivatives (A2780, A2780p3, A2780p5, A2780p6, A2780p13, A2780p14) and isogenically matched, resistant lines [##REF##8640828##23##] derived by multiple exposures to cytotoxic levels of cisplatin and which are 2–5 fold resistant to cisplatin in clonogenic assays (A2780cp70, A2780/MCP1, A2780/MCP2, A2780/MCP3, A2780/MCP4, A2780/MCP5, A2780/MCP6, A2780/MCP7, A2780/MCP8, A2780/MCP9). After background correction, the log-transformed digested and undigested intensities of the 13056 microarray probes show three approximately parallel linear patterns (Figure ##FIG##1##2a##). The first pattern (digested/undigested is close to 1) represents the unmethylated sequences. The second pattern represents either hemi-methylated sequences or the unmethylated sequences cross-hybridised with the methylated ones on the panel. The third pattern represents the methylated sequences in target DNA. The methylated and unmethylated loci in target DNA can be characterised by a linear regression model for each pattern. As previously mentioned, normalisation may not be appropriate for DMH data, so the log ratios of signal intensities in two classes of samples are not at the same level (Figure ##FIG##3##4##). Normalisation is not required for MLDA as the determination of the methylation score is based on the data within each experiment.</p>", "<p>Mitochondrial DNA is unmethylated [##REF##15277367##24##], therefore, the signal intensities of both channels of microarray probes for mitochondrial sequences are expected to be equal. However, a bi-modal distribution is observed in the log-transformed fluorescence ratios (digested/undigested) of 121 mitochondrial sequences. The first peak represents the unmethylated mitochondrial sequences and the second lower peak is assumed to be the mitochondrial sequences cross-hybridised with other methylated sequences on the panel. Thus, we selected 94 of 121 mitochondrial sequences that were consistently unmethylated through all the cell lines and used them as the unmethylation reference in target DNA.</p>", "<p>The parameters of those two models in all 16 cell lines were estimated (Table ##TAB##0##1##). The slope of the unmethylated regression line constructed by 94 mitochondrial sequences is indeed close to 1. After computing the log-likelihood ratios, the methylation and unmethylation cut-offs and associated IRs and CRs were determined from the dye-swapped array pairs (details in Method section). As shown in Figure ##FIG##4##5##, IR tends to rise with the increase of CR slowly, but starts to increase dramatically when the CR goes above 140%, at which point IR is generally about 1%. We have therefore used CR &gt; 140% and IR &lt; 1% as the criteria for determining the methylation and unmethylation cut-offs. Each locus was scored using the weighted scoring scheme based on those cut-offs. The averaged scores in 6 cisplatin-sensitive cell lines and 10 cisplatin-resistant cell lines were used to construct a robust regression model. Figure ##FIG##5##6a## shows that the standardised residuals (residual/<italic>σ</italic>) from the robust regression model roughly follow a normal distribution. The positive and negative outliers are determined as described in Method section.</p>", "<p>Finally, 115 loci were identified as candidates differentially methylated between A2780 sensitive and these resistant cell lines (additional file ##SUPPL##0##1##). Noticeably, 113 of 115 loci (<italic>p </italic>= 8.8 × 10<sup>-3</sup>, outlier detection test [##UREF##1##25##]) were hypermethylated, but only 2 loci (<italic>p </italic>&lt; 0.001, outlier detection test) lost methylation in the resistant cell lines (Figure ##FIG##5##6b##). This is consistent with the unbalanced shift in DMH data and indicates cisplatin treatment of cells selects preferentially for hypermethylation of loci, rather than hypomethylation in these tumor cells.</p>", "<title>Validation of differential methylation</title>", "<p>To confirm the differential methylation of loci identified in this study, we experimentally tested the methylation of 26 loci by methylation-specific PCR (MSP) and/or pyrosequencing of bisulphite modified DNA [##REF##8790415##26##] in sensitive A2780 derivatives and cisplatin resistant derivatives. Twenty-three out of the 26 loci have been confirmed as differentially methylated (additional file ##SUPPL##0##1##). It should be noted that MSP and pyrosequencing only examine methylation at a limited number of CpG sites of the sequence present on the DMH analysis. It is possible that the loci which were not confirmed as differentially methylated are methylated at other CpG sites which are detected by DMH but not targeted by MSP and/or pyrosequencing primers and so 23 out of 26 loci confirmed as differentially methylated is a minimum estimation.</p>", "<p>To compare the results from MLDA, SAM and PAM, we analysed the DMH dataset by all three methods. MLDA identified 115 loci (113 hypermethylated and 2 hypomethylated loci, misclassification error &lt; 0.001), SAM identified 152 loci (149 hypermethylated and 3 hypomethylated loci, misclassification error = 0.227, FDR = 6.17 × 10<sup>-3</sup>), and PAM found 24 hypermethylated loci (misclassification error = 0.084, FDR &lt; 0.001) in the resistant cell lines. Twenty-four loci identified by all three methods are listed in Table ##TAB##1##2##.</p>" ]
[ "<title>Discussion</title>", "<p>Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state and is a potential rich source of biomarkers of cancer. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA). MLDA utilises log likelihood ratios representing the relative probability that loci are methylated instead of log ratios of signal intensities used in previous studies [##REF##11731411##6##, ####REF##12114427##7##, ##REF##12163706##8##, ##REF##12095276##9##, ##REF##11951657##10####11951657##10##,##REF##12724229##27##]. Validation of 23/26 identified loci using independent methods of methylation analysis shows that MLDA can robustly identify differential methylated loci between ovarian cancer sensitive and resistant cell lines without requiring the data to be normalised.</p>", "<p>Although a log likelihood ratio above zero means that the locus tends to be methylated, we did not use zero as the cut-off to determine the number of methylated and unmethylated sequences, as the existence of cross-hybridisation and measurement errors in the DMH assay makes this unreliable. To increase the precision of the methylation classification, we used the inconsistency (IR) and consistency (CR) rates between the dye-swap arrays to determine likelihood ratio cut-offs for methylation and unmethylation and assigned each locus a methylation score based on the position relative to these cut-offs. As shown in Figure ##FIG##4##5##, not all cell lines can reach the point that CR is around 140% and IR is about 1%. IR and CR need to be carefully selected as the methylation scores of loci are consequently influenced by the change of IR and CR. We also observed a lower CR (about 120%) and a higher IR (about 2%) in another CpG island array using DMH (data not shown), therefore, further examination of what factors influence the achievable CR and IR rates may improve the utility of the MLDA approach.</p>", "<p>Data on methylation status for 121 mitochondrial derived sequences were available in this study. Mitochondrial sequences would be expected to be unmethylated. We used 94 mitochondrial sequences to construct unmethylated linear model at the beginning of the study, and indeed, 93 of 121 mitochondrial loci were defined as unmethylated and 25 loci being of uncertain methylation status by MLDA. However, three mitochondrial loci were identified as hypermethylated candidates in the resistant ovarian carcinoma cell lines by both MLDA and SAM. One explanation of this discrepancy is that all these three loci have more than one BLAT hit indicating the existence of homology with nuclear DNA sequences, raising the possibility of hybridisation with these nuclear DNA sequences which may be differentially methylated. As shown in Figure ##FIG##1##2a##, the loci in the middle pattern represent either hemi-methylated sequences or the unmethylated sequences cross-hybridised with the methylated ones on the panel. No specific allowance is made for these intermediate points in analysis by SAM and PAM, whereas MLDA attempts specifically to down-weight these points in the identification of the methylation regression line. By giving a lower weighted score (close to 0) (Figure ##FIG##2##3##) to those loci, MLDA reduces the influence of cross-hybridisation among this group of sequences. Of course cross-hybridisation may also occur in the loci in the other two patterns (methylated and unmethylated patterns), but it is not possible for any mathematical approach to identify this.</p>", "<p>The misclassification error of MLDA based on the methylation score is much lower than that for either SAM or PAM based on the log ratios, indicating the potential of MLDA methylation scores to be used as a reliable discriminator between classes of samples.</p>" ]
[ "<title>Conclusion</title>", "<p>We have developed a novel method, named MLDA, for genome-wide DNA methylation studies. MLDA can transform the signal intensities to log-likelihood ratios through three linear regression models. Using this approach MLDA allows determination of the methylation status of a locus based on dye-swapped/duplicate arrays. The method has been applied to assess the methylation status of each locus and identified 115 loci that exhibit differential methylation between A2780 sensitive and resistant cell lines. A minimum of 23 out of 26 loci have been confirmed by independent methods as differentially methylated.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).</p>", "<title>Results</title>", "<p>MLDA was programmed in R (version 2.7.0) and the package is available at CRAN [##UREF##0##1##]. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.</p>", "<title>Conclusion</title>", "<p>MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.</p>" ]
[ "<title>Authors' contributions</title>", "<p>WD conducted the statistical analysis and algorithm development supervised by JP and RB. The DMH data was produced by JMT in collaboration with TH and PY. RB, JP and KV conceived the study. JMT, JG and CZ conducted validation by MSP or pyrosequencing in RB's lab. WD, JP and RB prepared the manuscript with review by all authors. Funding was obtained by RB and TH.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported by a CR-UK programme grant to RB, NIH R21 grant CA110475 to RB and TH, and an Ovarian Cancer Action project grant to RB, JMT and JG.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Distribution of log-transformed ratio of gene expression data in breast cancer and DMH data in A2780 cell line</bold>. The left histogram shows the distribution of log-transformed ratios (cy3/cy5) in gene expression profiling data from a previous study of breast cancer 36] which is symmetric, while the right histogram shows the log-transformed ratios (undigested/digested) of DMH data from the present study which is skewed.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>An illustration of unmethylated and methylated model construction in MLDA in A2780 cell line</bold>. a: Three patterns can be observed on the scatter plot of log-transformed Cy3 (undigested) against log-transformed Cy5 (digested) intensities. b: The unmethylated model constructed using 94 mitochondrial sequences as a unmethylation reference. c: The intermediate model constructed through the 97.5 quantile residual. The point X is the 97.5 quantile residual. The microarray probes colored in blue (standardised residual to the intermediate model is less than 2) are selected to construct the methylated model. d: Methylated (in blue) and unmethylated (in red) models in A2780 cell line.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Weighted scoring scheme</bold>. The microarray probes consistently identified as methylated candidates on dye-swap arrays were scored 1; similarly unmethylated microarray probes were scored -1. The rest of the microarray probes were assigned a weighted score based on their location on the plot. LRmeth: log likelihood ratio cut-off for methylated loci; LRunmeth: log likelihood ratio cut-off for unmethylated loci. LR: log likelihood ratio on dye-swapped arrays.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Box plot of log ratios of undigested signal intensities against digested signal intensities in 16 cell lines (dye-swapped arrays)</bold>. The boxes colored in red are the A2780 sensitive cell lines; in blue are the A2780 resistant cell lines. As normalisation is not applied, the center and scale of log ratios for the 16 cell lines are not at the same level.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>CR against IR in 16 cell lines</bold>. X axis is the consistency rate (CR) and y axis is the inconsistency rate (IR). IR tends to rise with the increase of CR slowly, but starts to increase dramatically when the CR goes above 140%, at which point the inconsistency rate is generally about 1%. Not all cell lines could reach this point e.g. MCP3.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Outliers identifications</bold>. a: Distribution of the observed (histogram) standardised residuals and the theoretical distribution based on the fitted model (dashed smooth line in red). The red and blue solid line are the positive and negative cut-offs, respectively. b: Scatter plot of sensitive scores against resistant scores in A2780 series cell lines. The hypermethylated loci are colored in red and hypomethylated loci are in blue. The robust regression model is Y = 0.9956X + 0.0019.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Determination of methylation and unmethylation cut-offs of likelihood ratios on dye-swapped arrays</bold>. LRmeth: log likelihood ratio cut-off for methylated spots; LRunmeth: log likelihood ratio cut-off for unmethylated spots.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Parameters of linear models in MLDA for 16 cell lines in DMH dataset I</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\" colspan=\"9\"><bold>Unmethylation linear regression model</bold></td></tr><tr><td align=\"left\">cell line</td><td align=\"left\">intercetp (<italic>α</italic>)</td><td align=\"left\">slope (<italic>β</italic>)</td><td align=\"left\"><italic>σ</italic></td><td align=\"left\">R2</td><td align=\"left\">intercept<sub>ds </sub>(<italic>α</italic>)</td><td align=\"left\">slope<sub>ds </sub>(<italic>β</italic>)</td><td align=\"left\"><italic>σ</italic><sub>ds</sub></td><td align=\"left\">R2<sub>ds</sub></td></tr></thead><tbody><tr><td align=\"left\">A2780</td><td align=\"left\">-0.0003</td><td align=\"left\">1.0122</td><td align=\"left\">0.1727</td><td align=\"left\">0.9829</td><td align=\"left\">0.0005</td><td align=\"left\">1.0574</td><td align=\"left\">0.1897</td><td align=\"left\">0.978</td></tr><tr><td align=\"left\">A2780p3</td><td align=\"left\">-0.0003</td><td align=\"left\">1.0343</td><td align=\"left\">0.1212</td><td align=\"left\">0.9897</td><td align=\"left\">0.0018</td><td align=\"left\">1.1065</td><td align=\"left\">0.1425</td><td align=\"left\">0.9882</td></tr><tr><td align=\"left\">A2780p5</td><td align=\"left\">-0.0003</td><td align=\"left\">1.0138</td><td align=\"left\">0.1684</td><td align=\"left\">0.984</td><td align=\"left\">-0.0002</td><td align=\"left\">1.0728</td><td align=\"left\">0.1425</td><td align=\"left\">0.9883</td></tr><tr><td align=\"left\">A2780p6</td><td align=\"left\">0.0002</td><td align=\"left\">0.9914</td><td align=\"left\">0.1605</td><td align=\"left\">0.9778</td><td align=\"left\">-0.0001</td><td align=\"left\">1.0012</td><td align=\"left\">0.1638</td><td align=\"left\">0.9747</td></tr><tr><td align=\"left\">A2780p13</td><td align=\"left\">-0.0012</td><td align=\"left\">1.024</td><td align=\"left\">0.1628</td><td align=\"left\">0.9786</td><td align=\"left\">-0.0005</td><td align=\"left\">1.0744</td><td align=\"left\">0.1436</td><td align=\"left\">0.9852</td></tr><tr><td align=\"left\">A2780p14</td><td align=\"left\">-0.0022</td><td align=\"left\">1.0499</td><td align=\"left\">0.1523</td><td align=\"left\">0.9809</td><td align=\"left\">-0.0009</td><td align=\"left\">1.034</td><td align=\"left\">0.2069</td><td align=\"left\">0.9691</td></tr><tr><td align=\"left\">A2780cp70</td><td align=\"left\">0.0013</td><td align=\"left\">0.9604</td><td align=\"left\">0.2532</td><td align=\"left\">0.9524</td><td align=\"left\">-0.0002</td><td align=\"left\">1.0119</td><td align=\"left\">0.2402</td><td align=\"left\">0.9479</td></tr><tr><td align=\"left\">MCP1</td><td align=\"left\">0.0002</td><td align=\"left\">0.9946</td><td align=\"left\">0.145</td><td align=\"left\">0.9803</td><td align=\"left\">-0.0023</td><td align=\"left\">1.112</td><td align=\"left\">0.1452</td><td align=\"left\">0.9836</td></tr><tr><td align=\"left\">MCP2</td><td align=\"left\">0.0000</td><td align=\"left\">0.998</td><td align=\"left\">0.137</td><td align=\"left\">0.9727</td><td align=\"left\">-0.0028</td><td align=\"left\">1.0475</td><td align=\"left\">0.1719</td><td align=\"left\">0.9653</td></tr><tr><td align=\"left\">MCP3</td><td align=\"left\">0.0004</td><td align=\"left\">0.9932</td><td align=\"left\">0.2253</td><td align=\"left\">0.9183</td><td align=\"left\">-0.0023</td><td align=\"left\">1.0517</td><td align=\"left\">0.2795</td><td align=\"left\">0.8978</td></tr><tr><td align=\"left\">MCP4</td><td align=\"left\">0.0006</td><td align=\"left\">0.9838</td><td align=\"left\">0.1739</td><td align=\"left\">0.9718</td><td align=\"left\">-0.0028</td><td align=\"left\">1.077</td><td align=\"left\">0.1947</td><td align=\"left\">0.9751</td></tr><tr><td align=\"left\">MCP5</td><td align=\"left\">0.0009</td><td align=\"left\">0.9857</td><td align=\"left\">0.2464</td><td align=\"left\">0.9639</td><td align=\"left\">-0.0008</td><td align=\"left\">1.017</td><td align=\"left\">0.2166</td><td align=\"left\">0.9692</td></tr><tr><td align=\"left\">MCP6</td><td align=\"left\">-0.0022</td><td align=\"left\">1.0352</td><td align=\"left\">0.122</td><td align=\"left\">0.9751</td><td align=\"left\">-0.0068</td><td align=\"left\">1.1283</td><td align=\"left\">0.154</td><td align=\"left\">0.9752</td></tr><tr><td align=\"left\">MCP7</td><td align=\"left\">-0.0005</td><td align=\"left\">1.0079</td><td align=\"left\">0.1379</td><td align=\"left\">0.9791</td><td align=\"left\">-0.0045</td><td align=\"left\">1.1529</td><td align=\"left\">0.1588</td><td align=\"left\">0.9764</td></tr><tr><td align=\"left\">MCP8</td><td align=\"left\">-0.0028</td><td align=\"left\">1.0578</td><td align=\"left\">0.1903</td><td align=\"left\">0.9431</td><td align=\"left\">-0.0068</td><td align=\"left\">1.1193</td><td align=\"left\">0.1885</td><td align=\"left\">0.9575</td></tr><tr><td align=\"left\">MCP9</td><td align=\"left\">-0.0017</td><td align=\"left\">1.0331</td><td align=\"left\">0.1834</td><td align=\"left\">0.9614</td><td align=\"left\">-0.0091</td><td align=\"left\">1.1538</td><td align=\"left\">0.1691</td><td align=\"left\">0.9674</td></tr><tr><td colspan=\"9\"><hr/></td></tr><tr><td align=\"left\" colspan=\"9\"><bold>Methylation linear regression model</bold></td></tr><tr><td align=\"left\">cell line</td><td align=\"left\">intercetp (<italic>α</italic>)</td><td align=\"left\">slope (<italic>β</italic>)</td><td align=\"left\"><italic>σ</italic></td><td align=\"left\">R2</td><td align=\"left\">intercept<sub>ds </sub>(<italic>α</italic>)</td><td align=\"left\">slope<sub>ds </sub>(<italic>β</italic>)</td><td align=\"left\"><italic>σ</italic><sub>ds</sub></td><td align=\"left\">R2<sub>ds</sub></td></tr><tr><td colspan=\"9\"><hr/></td></tr><tr><td align=\"left\">A2780</td><td align=\"left\">-0.8839</td><td align=\"left\">0.8917</td><td align=\"left\">0.1491</td><td align=\"left\">0.9438</td><td align=\"left\">-0.8055</td><td align=\"left\">0.9086</td><td align=\"left\">0.1706</td><td align=\"left\">0.926</td></tr><tr><td align=\"left\">A2780p3</td><td align=\"left\">-1.1672</td><td align=\"left\">0.9797</td><td align=\"left\">0.1518</td><td align=\"left\">0.9553</td><td align=\"left\">-0.7414</td><td align=\"left\">0.9774</td><td align=\"left\">0.179</td><td align=\"left\">0.9438</td></tr><tr><td align=\"left\">A2780p5</td><td align=\"left\">-0.8991</td><td align=\"left\">0.8978</td><td align=\"left\">0.1515</td><td align=\"left\">0.9476</td><td align=\"left\">-0.9246</td><td align=\"left\">0.9766</td><td align=\"left\">0.1673</td><td align=\"left\">0.9523</td></tr><tr><td align=\"left\">A2780p6</td><td align=\"left\">-0.9455</td><td align=\"left\">0.9562</td><td align=\"left\">0.1838</td><td align=\"left\">0.9378</td><td align=\"left\">-1.1995</td><td align=\"left\">0.9641</td><td align=\"left\">0.1788</td><td align=\"left\">0.9324</td></tr><tr><td align=\"left\">A2780p13</td><td align=\"left\">-1.8918</td><td align=\"left\">0.9807</td><td align=\"left\">0.2535</td><td align=\"left\">0.8962</td><td align=\"left\">-1.8049</td><td align=\"left\">0.9652</td><td align=\"left\">0.2936</td><td align=\"left\">0.8512</td></tr><tr><td align=\"left\">A2780p14</td><td align=\"left\">-1.5637</td><td align=\"left\">0.9142</td><td align=\"left\">0.2549</td><td align=\"left\">0.8857</td><td align=\"left\">-1.4468</td><td align=\"left\">0.9066</td><td align=\"left\">0.2128</td><td align=\"left\">0.8837</td></tr><tr><td align=\"left\">A2780cp70</td><td align=\"left\">-1.0317</td><td align=\"left\">0.8501</td><td align=\"left\">0.1581</td><td align=\"left\">0.9115</td><td align=\"left\">-1.3074</td><td align=\"left\">0.8967</td><td align=\"left\">0.1541</td><td align=\"left\">0.9265</td></tr><tr><td align=\"left\">MCP1</td><td align=\"left\">-1.199</td><td align=\"left\">0.9781</td><td align=\"left\">0.1692</td><td align=\"left\">0.9467</td><td align=\"left\">-1.0935</td><td align=\"left\">1.0384</td><td align=\"left\">0.1775</td><td align=\"left\">0.9525</td></tr><tr><td align=\"left\">MCP2</td><td align=\"left\">-0.8037</td><td align=\"left\">0.9292</td><td align=\"left\">0.1486</td><td align=\"left\">0.9557</td><td align=\"left\">-0.9738</td><td align=\"left\">0.9381</td><td align=\"left\">0.2176</td><td align=\"left\">0.8848</td></tr><tr><td align=\"left\">MCP3</td><td align=\"left\">-1.1244</td><td align=\"left\">0.9151</td><td align=\"left\">0.1755</td><td align=\"left\">0.9482</td><td align=\"left\">-0.9303</td><td align=\"left\">0.9205</td><td align=\"left\">0.2599</td><td align=\"left\">0.8098</td></tr><tr><td align=\"left\">MCP4</td><td align=\"left\">-1.4326</td><td align=\"left\">0.9171</td><td align=\"left\">0.1418</td><td align=\"left\">0.966</td><td align=\"left\">-1.6205</td><td align=\"left\">0.961</td><td align=\"left\">0.2348</td><td align=\"left\">0.8323</td></tr><tr><td align=\"left\">MCP5</td><td align=\"left\">-1.1187</td><td align=\"left\">0.9425</td><td align=\"left\">0.1839</td><td align=\"left\">0.9404</td><td align=\"left\">-1.2007</td><td align=\"left\">0.9546</td><td align=\"left\">0.1757</td><td align=\"left\">0.9295</td></tr><tr><td align=\"left\">MCP6</td><td align=\"left\">-1.246</td><td align=\"left\">0.925</td><td align=\"left\">0.1966</td><td align=\"left\">0.9294</td><td align=\"left\">-1.2182</td><td align=\"left\">0.9826</td><td align=\"left\">0.2248</td><td align=\"left\">0.8989</td></tr><tr><td align=\"left\">MCP7</td><td align=\"left\">-1.8972</td><td align=\"left\">0.9909</td><td align=\"left\">0.1977</td><td align=\"left\">0.9442</td><td align=\"left\">-1.4894</td><td align=\"left\">1.0139</td><td align=\"left\">0.2458</td><td align=\"left\">0.8886</td></tr><tr><td align=\"left\">MCP8</td><td align=\"left\">-1.0219</td><td align=\"left\">0.9905</td><td align=\"left\">0.1975</td><td align=\"left\">0.9468</td><td align=\"left\">-0.4735</td><td align=\"left\">0.9421</td><td align=\"left\">0.228</td><td align=\"left\">0.8761</td></tr><tr><td align=\"left\">MCP9</td><td align=\"left\">-1.3399</td><td align=\"left\">0.9837</td><td align=\"left\">0.2073</td><td align=\"left\">0.9352</td><td align=\"left\">-1.1497</td><td align=\"left\">1.0078</td><td align=\"left\">0.1967</td><td align=\"left\">0.9115</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>24 loci identified by MLDA, PAM and SAM as differentially methylated candidates in the comparison between A2780 cisplatin sensitive and cisplatin multiple-selected resistant cell lines.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>microarray ID</bold></td><td align=\"center\"><bold>status</bold></td><td align=\"center\"><bold>validation</bold></td><td align=\"center\"><bold>MLDA rank*</bold></td><td align=\"center\"><bold>PAM rank</bold></td><td align=\"center\"><bold>SAM rank</bold></td><td align=\"center\"><bold>CGI***</bold></td><td align=\"center\"><bold>gene symbol**</bold></td><td align=\"center\"><bold>GenBank Accession</bold></td><td align=\"center\"><bold>Chromosome</bold></td></tr></thead><tbody><tr><td align=\"center\">66_G_6</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">Yes</td><td/><td/><td/></tr><tr><td align=\"center\">121_D_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">2</td><td align=\"center\">5</td><td align=\"center\">6</td><td align=\"center\">Yes</td><td align=\"center\">CRABP1</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_004378\">NM_004378</ext-link></td><td align=\"center\">15</td></tr><tr><td align=\"center\">39_E_1</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">122_D_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">No</td><td align=\"center\">4</td><td align=\"center\">11</td><td align=\"center\">11</td><td align=\"center\">Yes</td><td align=\"center\">SOX12</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_006943\">NM_006943</ext-link></td><td align=\"center\">20</td></tr><tr><td align=\"center\">123_D_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">No</td><td align=\"center\">5</td><td align=\"center\">10</td><td align=\"center\">10</td><td align=\"center\">Yes</td><td align=\"center\">SOX12</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_006943\">NM_006943</ext-link></td><td align=\"center\">20</td></tr><tr><td align=\"center\">51_H_8</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">6</td><td align=\"center\">18</td><td align=\"center\">19</td><td align=\"center\">No</td><td align=\"center\">FEZF2</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_018008\">NM_018008</ext-link></td><td align=\"center\">3</td></tr><tr><td align=\"center\">58_A_1</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">7</td><td align=\"center\">22</td><td align=\"center\">22</td><td align=\"center\">Yes</td><td/><td/><td/></tr><tr><td align=\"center\">80_H_5</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">8</td><td align=\"center\">14</td><td align=\"center\">16</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">21_A_11</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">9</td><td align=\"center\">17</td><td align=\"center\">18</td><td align=\"center\">Yes</td><td align=\"center\">NTN4</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_021229\">NM_021229</ext-link></td><td align=\"center\">12</td></tr><tr><td align=\"center\">38_D_7</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">11</td><td align=\"center\">23</td><td align=\"center\">24</td><td align=\"center\">Yes</td><td align=\"center\">AGBL2</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_024783\">NM_024783</ext-link></td><td align=\"center\">11</td></tr><tr><td align=\"center\">40_E_1</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">12</td><td align=\"center\">19</td><td align=\"center\">17</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">18_A_7</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">13</td><td align=\"center\">20</td><td align=\"center\">20</td><td align=\"center\">No</td><td align=\"center\">EDIL3</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_005711\">NM_005711</ext-link></td><td align=\"center\">5</td></tr><tr><td align=\"center\">55_F_8</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">14</td><td align=\"center\">9</td><td align=\"center\">9</td><td align=\"center\">Yes</td><td align=\"center\">BC127881</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"BC127881\">BC127881</ext-link></td><td align=\"center\">7</td></tr><tr><td align=\"center\">122_B_1</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">15</td><td align=\"center\">16</td><td align=\"center\">14</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">109_A_6</td><td align=\"center\">hypermethylated</td><td align=\"center\">No</td><td align=\"center\">18</td><td align=\"center\">12</td><td align=\"center\">13</td><td align=\"center\">Yes</td><td/><td/><td/></tr><tr><td align=\"center\">41_D_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">22</td><td align=\"center\">8</td><td align=\"center\">8</td><td align=\"center\">Yes</td><td align=\"center\">WNT1</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_005430\">NM_005430</ext-link></td><td align=\"center\">12</td></tr><tr><td align=\"center\">42_D_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">23</td><td align=\"center\">4</td><td align=\"center\">4</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">119_A_6</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">24</td><td align=\"center\">6</td><td align=\"center\">5</td><td align=\"center\">Yes</td><td align=\"center\">NR2E1</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_003269\">NM_003269</ext-link></td><td align=\"center\">6</td></tr><tr><td align=\"center\">63_A_8</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">26</td><td align=\"center\">15</td><td align=\"center\">15</td><td align=\"center\">No</td><td/><td/><td/></tr><tr><td align=\"center\">6_D_4</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">31</td><td align=\"center\">3</td><td align=\"center\">3</td><td align=\"center\">Yes</td><td align=\"center\">LMX1A</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_177398\">NM_177398</ext-link></td><td align=\"center\">1</td></tr><tr><td align=\"center\">17_H_9</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">34</td><td align=\"center\">13</td><td align=\"center\">12</td><td align=\"center\">Yes</td><td align=\"center\">HRASLS3</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_006290\">NM_006290</ext-link></td><td align=\"center\">6</td></tr><tr><td align=\"center\">5_D_4</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">35</td><td align=\"center\">7</td><td align=\"center\">7</td><td align=\"center\">Yes</td><td align=\"center\">LMX1A</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_177398\">NM_177398</ext-link></td><td align=\"center\">1</td></tr><tr><td align=\"center\">24_D_3</td><td align=\"center\">hypermethylated</td><td align=\"center\">Yes</td><td align=\"center\">75</td><td align=\"center\">24</td><td align=\"center\">23</td><td align=\"center\">Yes</td><td align=\"center\">SP5</td><td align=\"center\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_001003845\">NM_001003845</ext-link></td><td align=\"center\">2</td></tr><tr><td align=\"center\">122_G_1</td><td align=\"center\">hypermethylated</td><td align=\"center\">ND</td><td align=\"center\">101</td><td align=\"center\">21</td><td align=\"center\">21</td><td align=\"center\">Yes</td><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Classification of loci</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">Unmethylated</td><td align=\"center\">uncertain</td><td align=\"center\">methylated</td></tr></thead><tbody><tr><td align=\"left\">Unmethylated</td><td align=\"right\">a</td><td align=\"right\">b</td><td align=\"right\">c</td></tr><tr><td align=\"left\">Uncertain</td><td align=\"right\">d</td><td align=\"right\">e</td><td align=\"right\">f</td></tr><tr><td align=\"left\">Methylated</td><td align=\"right\">g</td><td align=\"right\">h</td><td align=\"right\">i</td></tr></tbody></table></table-wrap>" ]
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[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>115 differential methylated candidates identified by MLDA in A2780 series cell lines.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>ds: dye swap</p><p><italic>σ</italic>: standard deviation</p><p>R<sup>2</sup>: coefficient of determination</p></table-wrap-foot>", "<table-wrap-foot><p>ND: not done. Yes: validated. No: not validated</p><p>MLDA rank*: the rank of standardised residuals to the robust regression line constructed by the averaged sensitive scores against averaged resistant scores</p><p>Gene symbol**: only the gene of which transcription start site (TSS) is within 5 kb span of the loci</p><p>CGI***: CpG island defined by Gardiner-Garden and Frommer [##REF##3656447##31##].</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2105-9-337-1\"/>", "<graphic xlink:href=\"1471-2105-9-337-2\"/>", "<graphic xlink:href=\"1471-2105-9-337-3\"/>", "<graphic xlink:href=\"1471-2105-9-337-4\"/>", "<graphic xlink:href=\"1471-2105-9-337-5\"/>", "<graphic xlink:href=\"1471-2105-9-337-6\"/>", "<graphic xlink:href=\"1471-2105-9-337-7\"/>" ]
[ "<media xlink:href=\"1471-2105-9-337-S1.xls\" mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"><caption><p>Click here for file</p></caption></media>" ]
[{}, {"surname": ["Simon", "Korn", "McShane", "Radmacher", "Wright", "Zhao"], "given-names": ["RM", "EL", "LM", "MD", "GW", "Y"], "article-title": ["Design and Analysis of DNA Microarray Investigations"], "source": ["Statistics for Biology and Health"], "year": ["2003"], "publisher-name": ["New York , Springer"]}, {"surname": ["Tukey"], "given-names": ["JW"], "source": ["Exploratory Data Analysis"], "year": ["1977"], "publisher-name": ["Rading Massachusetts , Addison-Wesley"]}, {"surname": ["Wilcox", "Holland BA"], "given-names": ["RR"], "article-title": ["Robust Regression"], "source": ["Robust Esitmation and Hypothesis Testing"], "year": ["2005"], "publisher-name": ["London , Elsevier Academic Press"]}, {"surname": ["Wilcox", "Holland BA"], "given-names": ["RR"], "article-title": ["Estimating Measures of Location and Scale"], "source": ["Robust Esitmation and Hypothesis Testing"], "year": ["2005"], "publisher-name": ["London , Elsevier Academic Press"]}]
{ "acronym": [], "definition": [] }
31
CC BY
no
2022-01-12 14:47:26
BMC Bioinformatics. 2008 Aug 8; 9:337
oa_package/99/a2/PMC2529322.tar.gz
PMC2529323
18702831
[ "<title>Background</title>", "<p>The long branch attraction (LBA) artifact [##UREF##0##1##] still remains one of important causes of biases and mistakes in phylogenetic analyses of sequence data [##REF##16243762##2##]. LBA causes taxa with long branches to be artifactually grouped with or attracted to other long branched taxa (i.e., fast evolving taxa or taxa evolving for a long time separate from other groups, e.g. outgroups). An important source of LBA is substitution saturation of positions in alignment (the term \"mutational saturation\" is also used, although it is not correct in this context). It would be ideal to have positions that underwent a single or a few changes during evolution, but many positions in real alignments are subject to multiple substitutions. This subset of rapidly evolving positions is the source of stochastic noise rather than useful signal. However, these saturated positions are responsible for a major part of information used in phylogenetic analyses [##UREF##1##3##], which could confuse most of the tree-reconstructing methods. Because there are only four possible states for nucleic acid data (20 for amino acids), it is probable that a part of saturated positions will evolve randomly – convergently into the same state. It could then be erroneously judged as a synapomorphy. LBA can thus be a major problem especially in maximum parsimony, but occurs also in other analyses [##UREF##2##4##]. Maximum likelihood can, under an appropriate model of evolution, deal better with saturated positions, but datasets containing sites with different rates of substitution across the tree (covarion-like) may still be problematic [##REF##15034136##5##]. Besides LBA, a high level of saturation in the dataset may cause signal simply to be overwhelmed by noise at least at some points of the tree topology. Such nodes could be resolved incorrectly or (at least) with a low statistical support.</p>", "<p>It has been shown that in real alignments, LBA can be a major problem [##REF##16243762##2##]. An effective way to estimate and reduce the effect of substitution saturation and LBA is removal of fast-evolving data. One such method is slow-fast analysis of the dataset [##REF##10368959##6##]. The positions of the alignment are divided into several classes according to their substitution rate (estimated within <italic>a priori </italic>defined monophyletic groups). Several new alignments are then created, which contain only positions with a substitution rate lower than several thresholds, ranging from maximum to minimum rate. Thus the signal/noise ratio of the alignments successively increases, however, on the expense of amount of positions included in the alignment. Technically, the Slow-Fast method needs some input tree topology to work with. The topology must be provided by primary phylogenetic analysis of the dataset or by another independent method. This topology is needed for recognition of some monophyletic groups (whose relative positions on the tree is not necessary to know before slow-fast analysis). Maximum parsimony is then used to determine the number of changes for each position within the monophyletic subgroups. Substitution rates assigned to positions are thus independent from interrelationships among the monophyletic groups, and therefore, these interrelationships may in turn be investigated without the fear of circularity. When each position is assigned its number of changes, those with the highest substitution rate are gradually omitted from new alignments. The following phylogenetic analyses of these new datasets (starting from the dataset containing the positions with the highest substitution rate) then provide results based on decreasing number of sequence data, however, with decreasing risk of artefactual groupings of long branches. There are several good examples of successful use of slow-fast analysis, see e.g. [##REF##10368959##6##, ####REF##10939675##7##, ##UREF##3##8##, ##REF##15288049##9##, ##REF##16403903##10##, ##UREF##4##11####4##11##].</p>", "<p>Although the slow-fast analysis is relatively powerful and very simple in principle, it is quite demanding when one wants to determine the number of changes for individual alignment positions (e.g., with the help of PAUP [##REF##12504223##12##], using the \"describetree\" command) and the manual procedure of deleting of positions by editing the dataset is especially very time consuming. We believe that this is one of the most important reasons why this method is used relatively scarcely. Clearly, a computer program that provides this evaluation of positions and which produces new alignments would be handy. To our knowledge, the only software providing slow-fast analysis have been MUST [##REF##8255784##13##]. MUST is a complex package, yet it still does not provide a quick and easily operated tool for this type of analysis. This is what our program SlowFaster does. It is a user-friendly tool to conduct slow-fast analysis and produce a set of new alignments without fast evolving positions. It have several additional functions. Note that another program for slow-fast analysis was presented recently [##UREF##4##11##].</p>" ]
[]
[ "<title>Results and discussion</title>", "<title>Sample Data</title>", "<p>As an example, we analysed an alignment of 34 SSU rDNA sequences of 31 isolates of <italic>Blastocystis </italic>+ 3 outgroups. <italic>Blastocystis </italic>is an unusual protist, a sister group of slopalinids (used as the outgroup) within the group of stramenopiles. See e.g. [##REF##8894352##15##,##REF##12062550##16##] for a review of <italic>Blastocystis</italic>. Although these nonflagellated, multinucleated gut commensals comprise a single genus, their SSU rDNA phylogeny shows clearly that they are rather long branched taxa in comparison to other stramenopiles. Their branches are even longer than, for example, branches separating classes of autotrophic stramenopiles. This group is therefore suspected of a high level of substitution saturation. We sequenced SSU rRNA genes of five <italic>Blastocystis </italic>isolated from tortoises to improve taxon sampling by increasing the number of non-mammalian and non-bird isolates in the analysis (the vast majority of <italic>Blastocystis </italic>sequences available in GenBank are from bird or mammalian isolates). The accession numbers of the five new sequences (GERA3b, GERA3a, GECA2, KINIX2 and GEPA2) are [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209016\">EF209016</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209017\">EF209017</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209018\">EF209018</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209019\">EF209019</ext-link>] and [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209020\">EF209020</ext-link>], respectively.</p>", "<p>The alignment was prepared with ClustalX [##REF##9396791##17##] and ambiguous parts with many indels were deleted from the alignment in the program BioEdit [##UREF##5##18##]. The resulting alignment contained 1471 positions. PAUP 4.0β10 [##REF##12504223##12##] was used to analyse the dataset employing maximum likelihood (ML), maximum parsimony (MP), the Fitch-Margoliash method with LogDet distances (LD) and maximum likelihood distances (MD). Appropriate models for maximum likelihood were chosen with the help of Modeltest [##UREF##6##19##]. The robustness of each obtained topology was tested by bootstrapping (1000 replicates for all methods except for ML, for which 100 replicates were used). Phylogenetic analyses resulted in the tree shown in fig. ##FIG##0##1##. Two deep nodes of the phylogeny were resolved with low bootstrap support and/or resolved differently by different methods and were therefore depicted and treated as unresolved trichotomies.</p>", "<title>Use of SlowFaster</title>", "<p>At this time, our SlowFaster program was employed to perform the slow-fast analysis. First, the alignment used in our analyses was loaded via the \"Load alignment\" button. Then the tree topology shown in fig. ##FIG##0##1## was loaded via the \"Load tree\" button. In typical slow-fast analyses, several monophyletic subgroups are chosen in this step. We decided to select the single subtree of all <italic>Blastocystis </italic>isolates. This arrangement was enabled by the fact that we were mostly interested in resolving the two nodes represented in the input tree by trichotomies. Assigning substitution rates to alignment positions was thus independent from the true topology of these nodes. When the <italic>Blastocystis</italic>-containing subgroup was chosen in the tree window of SlowFaster program, new datasets in NEXUS format were created by clicking the \"New alignments\" button. Also, alignments of the same length as these new datasets, but shortened by random deletion of positions, were prepared by checking the \"jackknives\" checkbox on the program screen. These were used to test whether the loss of informative positions influences decrease of bootstrap support of the resulting tree topology more than shortening the datasets itself. We did not use the \"Weights\" feature of the program. When this checkbox is checked, the algorithm will assign different weights to changes within different chosen monophyletic groups. Changes within smaller groups would have assigned greater weight (if group A is twice as taxon-rich as group B, changes within it will have half the weight of the weight of changes in group B). The impact of large monophyla is then not dominant just because they contain more taxa.</p>", "<p>The maximum number of observed changes in a position of our alignment was 9. Thus, nine new alignments were created. They were labeled BlastoS8 down to BlastoS0, where the number is the threshold. BlastoS0 alignment was of course of no use in this particular case (the analysis with just one monophyletic group) as it contained only those positions that did not change during the evolution of <italic>Blastocystis</italic>. All other alignments were analysed phylogenetically by all four methods (ML, MP, LD, MD) and topologies of the 32 resulting trees were bootstrapped.</p>", "<p>It is highly probable that in some point of the slow-fast analysis, the profit from diminishing noise is lower than the loss from diminishing information. To roughly estimate the effect of the lack of information, we used average values of bootstraps as a measure of reliability of the alignments [##REF##16403903##10##]. We found that this average value drops suddenly for the alignment BlastoS1 which is therefore likely to suffer from lack of information and the resulting trees obtained from this dataset were not taken into account. To further prove this decision, \"jackknifed\" datasets of the same length but shortened by random deletion of position were also analysed. For each of eight datasets (Blasto_S1 to S8), ten of these randomly shortened datasets were analysed (80 alignments on the whole: Blasto_J1_1 to J1_10, J2_1 to J2_10, ... J8_1 to Blasto_J8_10). Within each dataset, the average value of bootstraps was determined and average of these averages for ten dataset of the same length were compared to average bootstrap value of the respective dataset resulting from slow-fast analysis. This comparison showed that the bootstrap values does not change much when analysing J8_x down-to J1_x datasets (e.g. all these average values ranged from 84.76 to 86.36 in ML analyses or from 90.15 to 91.5 in LD). On the contrary, the downfall of bootstraps was much more prominent in Blasto_S1 dataset when compared to Blasto_S2 – BlastoS8 datasets (e.g. 87.19 for original dataset, 86.90 for Blasto_S2, but 81.13 for Blasto_S1 in ML analyses, or 91.29 and 88.03 vs. 79.13, respectively, for LD).</p>", "<p>Results concerning the two unresolved trichotomies are shown in Table ##TAB##0##1##. The isolate GERA3b grouped either with the basal branch of three reptile/amphibian isolates (1a, in fig. ##FIG##0##1##) or with the rest of <italic>Blastocystis </italic>(1b). In the original alignment, the former topology was very well supported by MP and LD, the latter was weakly supported by ML and MD. As the most saturated positions were deleted from alignment, the bootstrap support for topology \"1a\" decreased slightly in MP, but increased strikingly in MD and slightly in ML analysis (BlastoS1 not taken into account). The slow-fast analysis thus supports the \"1a\" topology. The second unresolved node concerned a branch of four reptile/amphibian isolates. Either it was basal to two major branches of mostly mammal/bird isolates (2b; weakly supported by ML and MP in the original alignment), or it grouped with one of them (2a; weakly supported by LD and MD). After the slow-fast method was applied, both LD and MD favored the first possibility with reasonable bootstrap support for S3 and S2 datasets. However, MP and ML were unable to decide on the two possibilities. We conclude that the \"2b\" topology is probably correct, although the certainty is not high. For other nodes, decrease/increase of their bootstrap support from datasets S3 and S2 is marked in fig. ##FIG##0##1##.</p>" ]
[ "<title>Results and discussion</title>", "<title>Sample Data</title>", "<p>As an example, we analysed an alignment of 34 SSU rDNA sequences of 31 isolates of <italic>Blastocystis </italic>+ 3 outgroups. <italic>Blastocystis </italic>is an unusual protist, a sister group of slopalinids (used as the outgroup) within the group of stramenopiles. See e.g. [##REF##8894352##15##,##REF##12062550##16##] for a review of <italic>Blastocystis</italic>. Although these nonflagellated, multinucleated gut commensals comprise a single genus, their SSU rDNA phylogeny shows clearly that they are rather long branched taxa in comparison to other stramenopiles. Their branches are even longer than, for example, branches separating classes of autotrophic stramenopiles. This group is therefore suspected of a high level of substitution saturation. We sequenced SSU rRNA genes of five <italic>Blastocystis </italic>isolated from tortoises to improve taxon sampling by increasing the number of non-mammalian and non-bird isolates in the analysis (the vast majority of <italic>Blastocystis </italic>sequences available in GenBank are from bird or mammalian isolates). The accession numbers of the five new sequences (GERA3b, GERA3a, GECA2, KINIX2 and GEPA2) are [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209016\">EF209016</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209017\">EF209017</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209018\">EF209018</ext-link>], [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209019\">EF209019</ext-link>] and [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EF209020\">EF209020</ext-link>], respectively.</p>", "<p>The alignment was prepared with ClustalX [##REF##9396791##17##] and ambiguous parts with many indels were deleted from the alignment in the program BioEdit [##UREF##5##18##]. The resulting alignment contained 1471 positions. PAUP 4.0β10 [##REF##12504223##12##] was used to analyse the dataset employing maximum likelihood (ML), maximum parsimony (MP), the Fitch-Margoliash method with LogDet distances (LD) and maximum likelihood distances (MD). Appropriate models for maximum likelihood were chosen with the help of Modeltest [##UREF##6##19##]. The robustness of each obtained topology was tested by bootstrapping (1000 replicates for all methods except for ML, for which 100 replicates were used). Phylogenetic analyses resulted in the tree shown in fig. ##FIG##0##1##. Two deep nodes of the phylogeny were resolved with low bootstrap support and/or resolved differently by different methods and were therefore depicted and treated as unresolved trichotomies.</p>", "<title>Use of SlowFaster</title>", "<p>At this time, our SlowFaster program was employed to perform the slow-fast analysis. First, the alignment used in our analyses was loaded via the \"Load alignment\" button. Then the tree topology shown in fig. ##FIG##0##1## was loaded via the \"Load tree\" button. In typical slow-fast analyses, several monophyletic subgroups are chosen in this step. We decided to select the single subtree of all <italic>Blastocystis </italic>isolates. This arrangement was enabled by the fact that we were mostly interested in resolving the two nodes represented in the input tree by trichotomies. Assigning substitution rates to alignment positions was thus independent from the true topology of these nodes. When the <italic>Blastocystis</italic>-containing subgroup was chosen in the tree window of SlowFaster program, new datasets in NEXUS format were created by clicking the \"New alignments\" button. Also, alignments of the same length as these new datasets, but shortened by random deletion of positions, were prepared by checking the \"jackknives\" checkbox on the program screen. These were used to test whether the loss of informative positions influences decrease of bootstrap support of the resulting tree topology more than shortening the datasets itself. We did not use the \"Weights\" feature of the program. When this checkbox is checked, the algorithm will assign different weights to changes within different chosen monophyletic groups. Changes within smaller groups would have assigned greater weight (if group A is twice as taxon-rich as group B, changes within it will have half the weight of the weight of changes in group B). The impact of large monophyla is then not dominant just because they contain more taxa.</p>", "<p>The maximum number of observed changes in a position of our alignment was 9. Thus, nine new alignments were created. They were labeled BlastoS8 down to BlastoS0, where the number is the threshold. BlastoS0 alignment was of course of no use in this particular case (the analysis with just one monophyletic group) as it contained only those positions that did not change during the evolution of <italic>Blastocystis</italic>. All other alignments were analysed phylogenetically by all four methods (ML, MP, LD, MD) and topologies of the 32 resulting trees were bootstrapped.</p>", "<p>It is highly probable that in some point of the slow-fast analysis, the profit from diminishing noise is lower than the loss from diminishing information. To roughly estimate the effect of the lack of information, we used average values of bootstraps as a measure of reliability of the alignments [##REF##16403903##10##]. We found that this average value drops suddenly for the alignment BlastoS1 which is therefore likely to suffer from lack of information and the resulting trees obtained from this dataset were not taken into account. To further prove this decision, \"jackknifed\" datasets of the same length but shortened by random deletion of position were also analysed. For each of eight datasets (Blasto_S1 to S8), ten of these randomly shortened datasets were analysed (80 alignments on the whole: Blasto_J1_1 to J1_10, J2_1 to J2_10, ... J8_1 to Blasto_J8_10). Within each dataset, the average value of bootstraps was determined and average of these averages for ten dataset of the same length were compared to average bootstrap value of the respective dataset resulting from slow-fast analysis. This comparison showed that the bootstrap values does not change much when analysing J8_x down-to J1_x datasets (e.g. all these average values ranged from 84.76 to 86.36 in ML analyses or from 90.15 to 91.5 in LD). On the contrary, the downfall of bootstraps was much more prominent in Blasto_S1 dataset when compared to Blasto_S2 – BlastoS8 datasets (e.g. 87.19 for original dataset, 86.90 for Blasto_S2, but 81.13 for Blasto_S1 in ML analyses, or 91.29 and 88.03 vs. 79.13, respectively, for LD).</p>", "<p>Results concerning the two unresolved trichotomies are shown in Table ##TAB##0##1##. The isolate GERA3b grouped either with the basal branch of three reptile/amphibian isolates (1a, in fig. ##FIG##0##1##) or with the rest of <italic>Blastocystis </italic>(1b). In the original alignment, the former topology was very well supported by MP and LD, the latter was weakly supported by ML and MD. As the most saturated positions were deleted from alignment, the bootstrap support for topology \"1a\" decreased slightly in MP, but increased strikingly in MD and slightly in ML analysis (BlastoS1 not taken into account). The slow-fast analysis thus supports the \"1a\" topology. The second unresolved node concerned a branch of four reptile/amphibian isolates. Either it was basal to two major branches of mostly mammal/bird isolates (2b; weakly supported by ML and MP in the original alignment), or it grouped with one of them (2a; weakly supported by LD and MD). After the slow-fast method was applied, both LD and MD favored the first possibility with reasonable bootstrap support for S3 and S2 datasets. However, MP and ML were unable to decide on the two possibilities. We conclude that the \"2b\" topology is probably correct, although the certainty is not high. For other nodes, decrease/increase of their bootstrap support from datasets S3 and S2 is marked in fig. ##FIG##0##1##.</p>" ]
[ "<title>Conclusion</title>", "<p>Overall, the slow-fast analysis, provided by the program SlowFaster, proved to be a useful tool to solve uncertain phylogenies by increasing the signal/noise ratio. In the <italic>Blastocystis </italic>SSU rDNA tree it was able to make a choice among competing hypotheses and add more confidence in some other cases. Our software automates quite time-consuming slow-fast analysis.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Slow-fast analysis is a simple and effective method to reduce the influence of substitution saturation, one of the causes of phylogenetic noise and long branch attraction (LBA) artifacts. In several steps of increasing stringency, the slow-fast analysis omits the fastest substituting alignment positions from the analysed dataset and thus increases its signal/noise ratio.</p>", "<title>Results</title>", "<p>Our program SlowFaster automates the process of assessing the substitution rate of the alignment positions and the process of producing new alignments by deleting the saturated positions. Its use is very simple. It goes through the whole process in several steps: data input – necessary choices – production of new alignments.</p>", "<title>Conclusion</title>", "<p>SlowFaster is a user-friendly tool providing new alignments prepared with slow-fast analysis. These data can be used for further phylogenetic analyses with lower risk of long branch attraction artifacts.</p>" ]
[ "<title>Implementation</title>", "<p>SlowFaster was programmed in Borland Delphi and runs under MS Windows. Both the executable file [see Additional file ##SUPPL##0##1##] and the source code [see Additional file ##SUPPL##1##2##] are available as supplements. The program leads the user in several steps through the process of generating new datasets. Original alignment is loaded in FASTA, Phylip or NEXUS format. The program works with both nucleic acid and amino acid alignments and supports usual ambiguity coding. The topology needed for the recognition of monophyla is loaded as a tree in the Newick (\"bracketed\") format (PAUP users can use \"savetree format = phylip\" command to obtain tree in Newick format). After choosing the monophyletic groups by simply clicking on the branches of the depicted tree, parsimony is used to count the number of changes of every alignment position within the selected groups. Finally, new alignments are produced (in FASTA, Phylip or NEXUS format). Each of the new datasets has a number which is a threshold: positions with greater number of changes were omitted from this dataset. As the threshold gets lower and lower, the datasets contain fewer and fewer data because the more saturated positions were deleted from them. These datasets can be then further analysed to obtain phylogenies with a lower risk of LBA. During the whole process, there are hints shown in a window, telling the user what to do in the given step.</p>", "<p>The software was tested thoroughly on several model datasets [see Additional file ##SUPPL##2##3##] and also on dataset of Hampl <italic>et al</italic>. [##REF##16403903##10##]. In this latter case, we obtained the same new datasets with our program (Hampl <italic>et al</italic>. obtained them with the help of PAUP and through careful manual deletion of positions).</p>", "<p>An interesting alternative to slow-fast method is using substitution rates estimated with maximum likelihood (ML). Although ML estimates are not implemented in SlowFaster, this program enables production of alignments without positions with high rates through the \"Load changes\" button. The rates can be counted in another software. E.g. Tree-Puzzle [##REF##11934758##14##], if rate heterogeneity is selected, gives information on the rate category of each position in its outfile under \"Combination of categories that contributes the most to the likelihood\". These data can be simply copied in a file which is then loaded in SlowFaster. New alignments are then produced directly from these data. More generally, any sequence of any (even real) numbers can be loaded and the software will divide positions in rate categories (their number is specified by the user) based on these values.</p>", "<p>The program also creates a log file which contains useful information, most notably groups used for changes counting, list of positions with certain number of changes and number of changes for all positions from the first one to the last.</p>", "<title>Availability and requirements</title>", "<p><bold>Project name</bold>: SlowFaster</p>", "<p><bold>Project home page</bold>: <ext-link ext-link-type=\"uri\" xlink:href=\"http://natur.cuni.cz/flegr/programs/slowfaster.htm\"/></p>", "<p><bold>Operating system</bold>: MS Windows</p>", "<p><bold>Programming language</bold>: Borland Delphi</p>", "<p><bold>Any restrictions to use by non-academics</bold>: none</p>", "<p>The software can be accessed through the project home page and its current version is included with the manuscript as an additional file.</p>", "<title>Authors' contributions</title>", "<p>MK and JF designed the program and contributed bug fixes. MK developed the source code. IC and MU collected the data used as example and analysed them together with MK. These three authors contributed to writing the manuscript. All authors read and approved the final manuscript.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported by Czech Ministry of Education (projects MSM 0021620828 and MSM 6007665806) and Czech Science Foundation (project 206/05/0371). We also want to thank Vladimir Hampl for providing us with his data for software testing. Lastly, we owe a huge thank to four anonymous reviewers. Without them the SlowFaster would not be what it is.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>MP tree of 31 <italic>Blastocystis </italic>isolates and 3 outgroups based on SSU rDNA sequences</bold>. MP tree of 31 <italic>Blastocystis </italic>isolates (host in brackets) and 3 outgroups, based on SSU rDNA sequences. Bootstrap support values for four tree-reconstructing methods – ML, MP, LD and MD, respectively – are shown at the nodes. The symbol \"+\" is used for bootstrap support 99 and higher (in case only one \"+\" symbol is present, all methods scored such a high support). The effect of slow-fast analysis on nodes is represented by arrow symbols in the figure. Increase of an average bootstrap support by more than 10% of one and more than one tree-reconstructing method in two datasets (BlastS3 and S2) is marked with \"↑\" and \"↑↑\", respectively. Similarly, the decrease of bootstrap support is marked with \"↓\" and \"↓↓\" at the particular nodes. Bootstraps of other nodes did not change dramatically. Except for our five new isolates (GERA3A, GERA3B, GEPA2, GECA2, KINIX2), <italic>Blastocystis </italic>isolates are labeled with accession numbers of their SSU rRNA gene sequences.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Overview of results from slow-fast analysis of <italic>Blastocystis </italic>alignment</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Dataset</td><td align=\"center\">Posit.</td><td align=\"center\">Length</td><td align=\"center\" colspan=\"4\">1a</td><td align=\"center\" colspan=\"4\">1b</td><td align=\"center\" colspan=\"4\">2a</td><td align=\"center\" colspan=\"4\">2b</td></tr><tr><td/><td/><td/><td colspan=\"16\"><hr/></td></tr><tr><td/><td/><td/><td align=\"center\">ML</td><td align=\"center\">MP</td><td align=\"center\">LD</td><td align=\"center\">MD</td><td align=\"center\">ML</td><td align=\"center\">MP</td><td align=\"center\">LD</td><td align=\"center\">MD</td><td align=\"center\">ML</td><td align=\"center\">MP</td><td align=\"center\">LD</td><td align=\"center\">MD</td><td align=\"center\">ML</td><td align=\"center\">MP</td><td align=\"center\">LD</td><td align=\"center\">MD</td></tr></thead><tbody><tr><td align=\"center\"><bold>Untr</bold>.</td><td align=\"center\">1471</td><td align=\"center\">1289</td><td/><td align=\"center\">92</td><td align=\"center\">99</td><td/><td align=\"center\">54</td><td/><td/><td align=\"center\">58</td><td align=\"center\">50</td><td align=\"center\">33</td><td/><td/><td/><td/><td align=\"center\">46</td><td align=\"center\">35</td></tr><tr><td align=\"center\"><bold>S8</bold></td><td align=\"center\">1467</td><td align=\"center\">1250</td><td/><td align=\"center\">93</td><td align=\"center\">99</td><td/><td align=\"center\">51</td><td/><td/><td align=\"center\">54</td><td align=\"center\">45</td><td/><td/><td align=\"center\">30</td><td/><td align=\"center\">34</td><td align=\"center\">43</td><td/></tr><tr><td align=\"center\"><bold>S7</bold></td><td align=\"center\">1460</td><td align=\"center\">1187</td><td align=\"center\">57</td><td align=\"center\">96</td><td align=\"center\">99</td><td align=\"center\">54</td><td/><td/><td/><td/><td align=\"center\">42</td><td align=\"center\">34</td><td/><td align=\"center\">36</td><td/><td/><td align=\"center\">37</td><td/></tr><tr><td align=\"center\"><bold>S6</bold></td><td align=\"center\">1452</td><td align=\"center\">1121</td><td align=\"center\">62</td><td align=\"center\">96</td><td align=\"center\">99</td><td align=\"center\">48</td><td/><td/><td/><td/><td align=\"center\">48</td><td align=\"center\">35</td><td/><td align=\"center\">38</td><td/><td/><td align=\"center\">38</td><td/></tr><tr><td align=\"center\"><bold>S5</bold></td><td align=\"center\">1438</td><td align=\"center\">1026</td><td align=\"center\">61</td><td align=\"center\">91</td><td align=\"center\">97</td><td align=\"center\">55</td><td/><td/><td/><td/><td align=\"center\">54</td><td align=\"center\">42</td><td/><td align=\"center\">34</td><td/><td/><td align=\"center\">35</td><td/></tr><tr><td align=\"center\"><bold>S4</bold></td><td align=\"center\">1407</td><td align=\"center\">844</td><td align=\"center\">63</td><td align=\"center\">87</td><td align=\"center\">99</td><td align=\"center\">73</td><td/><td/><td/><td/><td align=\"center\">57</td><td/><td/><td align=\"center\">36</td><td/><td align=\"center\">45</td><td align=\"center\">36</td><td/></tr><tr><td align=\"center\"><bold>S3</bold></td><td align=\"center\">1371</td><td align=\"center\">674</td><td align=\"center\">59</td><td align=\"center\">82</td><td align=\"center\">97</td><td align=\"center\">85</td><td/><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">38</td><td align=\"center\">67</td><td align=\"center\">73</td><td align=\"center\">-</td><td/><td/><td/></tr><tr><td align=\"center\"><bold>S2</bold></td><td align=\"center\">1330</td><td align=\"center\">522</td><td align=\"center\">68</td><td align=\"center\">75</td><td align=\"center\">98</td><td align=\"center\">97</td><td/><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">30</td><td align=\"center\">64</td><td align=\"center\">64</td><td align=\"center\">-</td><td/><td/><td/></tr><tr><td align=\"center\"><bold>S1</bold></td><td align=\"center\">1258</td><td align=\"center\">343</td><td/><td align=\"center\">49</td><td align=\"center\">92</td><td align=\"center\">90</td><td align=\"center\">71</td><td/><td/><td/><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">90</td><td align=\"center\">76</td><td align=\"center\">-</td><td align=\"center\">-</td><td/><td/></tr><tr><td align=\"center\"><bold>S0</bold></td><td align=\"center\">1097</td><td align=\"center\">124</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p><bold>SlowFaster</bold>. This is the executable file of the application.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold>Source code</bold>. Zip archive containing Delphi source code of the program and additional Delphi files.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p><bold>Sample data</bold>. Zip archive containing sample data – alignments in Phylip, FASTA and NEXUS format and tree files in Newick format.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>For each dataset (the first column) ranging from untreated initial alignment (Untr.) to alignment BlastoS0, the number of alignment positions (Posit.) and the length of the most parsimonious tree (Length) are noted in the second and third columns, respectively. In the remaining columns is given the bootstrap support from the four tree reconstructing methods for four topologies of interest. In some cases (marked with a dash) the method was unable to decide between the given node and its alternative.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2105-9-341-1\"/>" ]
[ "<media xlink:href=\"1471-2105-9-341-S1.exe\" mimetype=\"application\" mime-subtype=\"x-msdownload\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2105-9-341-S2.zip\" mimetype=\"application\" mime-subtype=\"x-zip-compressed\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2105-9-341-S3.zip\" mimetype=\"application\" mime-subtype=\"x-zip-compressed\"><caption><p>Click here for file</p></caption></media>" ]
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{ "acronym": [], "definition": [] }
19
CC BY
no
2022-01-12 14:47:26
BMC Bioinformatics. 2008 Aug 15; 9:341
oa_package/80/c3/PMC2529323.tar.gz
PMC2529324
18721472
[ "<title>Background</title>", "<p>Genome sequencing projects continue to produce amino acid sequences; however, understanding the biological roles played by these putative proteins requires knowledge of their structure and function [##REF##11588250##1##]. Despite that empirical structure determination methods have provided structural information for some proteins, computational methods are still required for the large number of proteins whose structures are difficult to determine experimentally. And while the primary sequence should contain the folding guide for a given protein, our ability to predict the three-dimensional (3D) structure from the primary sequence alone remains limited. Some <italic>ab initio </italic>methods do not require such information, but the application of these methods is often limited to small proteins [##REF##10526364##2##,##REF##11746709##3##].</p>", "<p>Structure alignment research has led to the discovery of homologues of novel protein structures. And, although many structure alignment tools have been developed, such as CE [##REF##9796821##4##], DALI [##REF##8377180##5##], VAST [##REF##8710828##6##], MAMMOTH [##REF##12381844##7##], FATCAT [##REF##14534198##8##], and Vorolign [##REF##17237093##9##], we wanted to provide a different perspective on protein structure analysis. Previous studies of protein structures have shown the importance of repetitive secondary structures, particularly <italic>α</italic>-helices and <italic>β</italic>-sheets, in overall structure determination. Together with variable coils, these structures constitute a basic three-letter structural alphabet that has been used in the development of early-generation secondary structure prediction algorithms (such as GOR [##REF##642007##10##]) as well as more recent-generation algorithms. These newer algorithms have been applied to neural networks, homology sequences, and discriminative models [##REF##10944389##11##, ####REF##11551180##12##, ##REF##17570843##13##, ##REF##16405736##14####16405736##14##], and their accuracy in predicting secondary structure approaches 80%. However, despite this predictive accuracy, the three-letter alphabet does not contain the information necessary to approximate more refined 3D reconstructions.</p>", "<p>The recent rapid increase in the number of available protein structures has allowed more precise and thorough studies of protein structures. Several authors have developed more complex structural alphabets that incorporate information about the heterogeneity of backbone protein structures by using subsets of small protein fragments that are observed frequently in different protein structure databases [##REF##11025540##15##, ####REF##12441385##16##, ##REF##15996119##17####15996119##17##]. The alphabet size varies from several letters to about 100 letters [##REF##16385557##18##]. For example, Unger <italic>et al</italic>. [##REF##2798411##19##] and Schuchhardt <italic>et al</italic>. [##REF##8931122##20##] used k-means methods and self-organizing maps (SOMs), respectively, to identify the most common folds, but the number of clusters generated was too large to have substantial predictive value. By applying autoassociative neural networks, Fetrow <italic>et al</italic>. defined six clusters that represent super-secondary structures which subsume the classic secondary structures [##REF##9061789##21##]. Bystroff and Baker produced similar short folds of different lengths and grouped them into 13 clusters that they used to predict 3D structure [##UREF##0##22##]. Camproux <italic>et al</italic>. developed a hidden Markov model (HMM) approach that accounted for the Markovian dependence to learn the geometry of the structural alphabet letters and the local rules for the assembly process [##REF##10611400##23##]. Fixing the alphabet size to 23 letters, Yang &amp; Tung applied a nearest-neighbor algorithm on a (<italic>κ</italic>, <italic>α</italic>)-map of structural segments to identify the 23 groups of segments used in their alphabet [##REF##16885238##24##]. More details about these local structures can be found in a recent review [##UREF##1##25##].</p>", "<p>In this study, we developed a flexible pipeline for protein structural alphabet design based on a combinatorial, multi-strategy approach. Instead of applying cross-validation [##UREF##0##22##] or Markovian processes [##REF##11025540##15##] to refine the clusters directly, we used SOMs and Bayesian Information Criterion (BIC) to determine the optimum size of structural alphabet. We then applied the k-means algorithm [##UREF##2##26##] to group protein fragments into clusters, forming the bases of our structural alphabet. Moreover, unlike most other works that built substitution matrices for alphabets based on known blocks of aligned proteins, we used a matrix training framework that generated matrices automatically without depending on known alignments. An expressive structural alphabet allows us to quantify the similarities among proteins encoded in the appropriate letters. It also enables the primary representation of 3D structures using standard 1D amino acid sequence alignment methods. To demonstrate the feasibility of our new method, we verified the application of the alphabet produced by our pipeline and the trained substitution matrix to a widely used 1D alignment tool, FASTA [##REF##10547837##27##]. We conducted several experiments using the same datasets used in other recently published works and evaluated the performance of our tool in database-scale searches. In addition to investigating whether our alphabet and matrix worked well with 1D alignment tools in database searches, we evaluated the ability of our structural alphabet to characterize local structural features.</p>" ]
[ "<title>Methods</title>", "<p>The use of frequent local structural motifs embedded in a polypeptide backbone has recently been shown to improve protein structure prediction [##REF##11588250##1##,##UREF##0##22##]. The success of this strategy has paved the way for further studies of structural alphabets and has enabled the application of standard 1D sequence alignment methods to 3D protein structural searches. In this study, we combined several computational methods into a new approach to the design of a protein structural alphabet. We then developed an automatic matrix training framework that could generate appropriate substitution matrices for new alphabets when applied in standard 1D sequence alignment methods, such as FASTA [##REF##10547837##27##].</p>", "<title>Structural alphabet design</title>", "<p>We used proteins from the nrPDB [##REF##10592235##44##] in our study with the aim of building a structural alphabet suitable for all proteins. The same approach could easily be applied to other databanks as well. We transformed each protein backbone into a series of dihedral angles (<italic>ϕ </italic>and <italic>ψ</italic>, neglecting <italic>ω</italic>) [##REF##11025540##15##,##UREF##0##22##]. Following de Brevern <italic>et al</italic>. [##REF##11025540##15##], our analysis was limited to fragments of five residues because this number of residues is sufficient for describing a short <italic>α </italic>helix and a minimal <italic>β </italic>structure. Fixing the window size at five residues, we applied a sliding-window approach to each protein sequence in nrPDB and gathered 20,953,584 fragment vectors. Each protein fragment, associated with <italic>α</italic>-carbons <italic>C</italic><sub><italic>α</italic>(<italic>i</italic>-2)</sub>, <italic>C</italic><sub><italic>α</italic>(<italic>i</italic>-1)</sub>, <italic>C</italic><sub><italic>α</italic>(<italic>i</italic>)</sub>, <italic>C</italic><sub><italic>α</italic>(<italic>i</italic>+1)</sub>, and <italic>C</italic><sub><italic>α</italic>(<italic>i</italic>+2)</sub>, was represented by a vector of eight dihedral angles [<italic>ψ</italic><sub><italic>i</italic>-2</sub>, <italic>ϕ</italic><sub><italic>i</italic>-1</sub>, <italic>ψ</italic><sub><italic>i</italic>-1</sub>, <italic>ϕ</italic><sub><italic>i</italic></sub>, <italic>ψ</italic><sub><italic>i</italic></sub>, <italic>ϕ</italic><sub><italic>i</italic>+1</sub>, <italic>ψ</italic><sub><italic>i</italic>+1</sub>, <italic>ϕ</italic><sub><italic>i</italic>+2</sub>] Unlike previous works that directly applied SOMs to obtain clusters of backbone fragments as the basis of the structural alphabet [##UREF##3##28##], in our approach the SOM was only part of the process that determined the number of letters required for the alphabet. We did not build our alphabet directly from the clusters found by SOM.</p>", "<p>The U-matrix is one of the most widely used methods for visualizing the clustering results of the SOM. The U-matrix shows the distances between neighboring reference vectors and can be visualized efficiently using the greyscale [##REF##16894618##45##]. We conducted a post-process on the U-matrix using a minimum spanning tree algorithm. Based on the grey levels in the U-matrix, all of the map units were linked in the minimum spanning tree. Given a threshold <italic>θ </italic>determined by BIC, we partitioned the entire tree into several disconnected subtrees by removing the links between map units with grey levels below <italic>θ</italic>.</p>", "<p>Let <italic>S </italic>= {<italic>s</italic><sub><italic>i </italic></sub>| <italic>i </italic>= 1...<italic>M</italic>} be the set of map units we wished to cluster. Each map unit <italic>s</italic><sub><italic>i </italic></sub>is associated with a collection of input data points, , mapped to the map unit <italic>s</italic><sub><italic>i</italic></sub>. Let <italic>C</italic><sub><italic>k </italic></sub>= {<italic>c</italic><sub><italic>i </italic></sub>| <italic>i </italic>= 1...<italic>k</italic>} be the clustering of map units <italic>S </italic>with <italic>k </italic>clusters. We modeled each cluster <italic>c</italic><sub><italic>i </italic></sub>as a multivariate Gaussian distribution <italic>N</italic>(<italic>μ</italic><sub><italic>i</italic></sub>, <italic>Σ</italic><sub><italic>i</italic></sub>), where <italic>μ</italic><sub><italic>i </italic></sub>and <italic>Σ</italic><sub><italic>i </italic></sub>were estimated as the sample mean and the sample covariance from <italic>X</italic><sup><italic>i</italic></sup>, respectively. The number of parameters for each cluster was thus , where <italic>d </italic>= 8 in our case. We defined <italic>BIC</italic>(<italic>C</italic><sub><italic>k</italic></sub>) as:</p>", "<p></p>", "<p>where and <italic>λ</italic>, the penalty weight, was set to 1.</p>", "<p>We chose the threshold <italic>θ </italic>that maximized <italic>BIC</italic>(<italic>C</italic><sub><italic>k</italic></sub>). For example, for an SOM with 200 × 200 map units, the threshold <italic>θ </italic>that maximized <italic>BIC</italic>(<italic>C</italic><sub><italic>k</italic></sub>) was 21. The number of subtrees becomes the structural alphabet size. Because the SOM can be viewed as a topology preserving mapping from input space onto the 2D grid of map units, the number of map units can affect the clustering result. We systematically varied the number of units and repeated the above process. We selected the most frequent number of clusters as the alphabet size. After a series of systematic tests, we found that 18 was the most frequent number of clusters; therefore, 18 letters became size of our structural alphabet.</p>", "<p>Rather than adopt the two-level approach that first trains the SOM then performs clustering on the trained SOM after determining the alphabet size [##UREF##3##28##], we applied the k-means algorithm to the input data vectors directly to obtain the clusters. The SOM established a local order among the set of reference vectors such that the closeness between two reference vectors in the <italic>R</italic><sup><italic>d </italic></sup>space was dependent on how close the corresponding map units were in the 2D array. Nevertheless, an inductive bias of this kind might not be appropriate for structural alphabets since the local order does not always faithfully characterize the relationship between structural building blocks and can sometimes be misleading. For example, forcing the topology to preserve mapping from the input space of <italic>α</italic>-helix and <italic>β</italic>-strand to a 2D grid of units could be harmful to clustering. Therefore, we used the SOM only to visualize the alphabet size and relied on the k-means algorithm to extract the local features directly from the input data that actually reflected the characteristics of the clusters. The centroid of each cluster forms the prototypical representation of each alphabet letter. We performed k-means clustering 50 times, starting with different random seeds, all using k = 18. We computed the Euclidean distances from each fragment in each cluster to its centroid as the intra-cluster distance; we also calculated the centroid-to-centroid distance. We kept the clustering result that had the minimum ratio of the average intra-cluster distance to the centroid-to-centroid distance. Given this result as the basis for the structural alphabet, we could transform a protein into a series of alphabet letters by matching each of its fragments against our alphabet prototypes.</p>", "<title>Automatic substitution matrix training</title>", "<p>The substitution matrix used to align proteins represented by structural alphabets affects the alignment accuracy. The matrix is a crucial factor in the success of applying a 1D sequence alignment tool to search for similar 3D structures. The simplest matrix that can be used is the identity matrix. Some authors have applied an HMM approach to define the matrix [##REF##15215446##33##], while others have adopted approaches similar to the development of BLOSUM matrices [##REF##16885238##24##,##REF##1438297##31##,##REF##16894618##45##]. The identity matrix ignores possible acceptable alphabet letter substitutions, significantly limiting its applicability. The BLOSUM-like approach requires alignments of homologous proteins before calculating the log-odd ratios as the entries in the matrix; however, reliably aligned protein blocks might not always be available for log-odd ratio estimation. To avoid these drawbacks, we trained the substitution matrix without using the known blocks of protein alignments. This matrix training procedure can be applied regardless of how the alphabet is derived.</p>", "<p>There are three components in the matrix training framework: an alignment tool with a substitution matrix, training data, and a matrix trainer. We used FASTA as the alignment tool and the non-redundant proteins in SCOP1.69 with sequence similarity less than 40%, excluding the families with less than five proteins and those proteins used for validation, as the training dataset. Note that the training dataset was only 9.62% of the entire SCOP1.69. The test data we used in the later experiments (see Results section) did not overlap with our training examples. We started by using the identity matrix as the initial substitution matrix where the score for a match was 1, and for a mismatch, 0. Each protein in the training dataset was iterated as a query for FASTA to search the rest of the dataset for similar proteins. If a protein returned by FASTA belonged to the same family as the query, we considered the case a positive hit; otherwise it was a negative hit. Those proteins not returned by FASTA but in the same family as the query were considered misses. We gathered the alignments of all positive hits and misses and computed the log-odd ratios to build the <italic>positive matrix </italic>based on the alignments. Similarly, we constructed the <italic>negative matrix </italic>using the alignments of negative hits, The matrix trainer updated the current substitution matrix <italic>S</italic><sup>(<italic>t</italic>) </sup>to <italic>S</italic><sup>(<italic>t</italic>+1) </sup>as follows:</p>", "<p></p>", "<p></p>", "<p></p>", "<p></p>", "<p>where <italic>P </italic>and <italic>N </italic>are the positive and the negative matrix, respectively, <italic>τ </italic>is the learning rate (similar to the learning rate in neural networks), and <italic>W</italic><sub><italic>p </italic></sub>and <italic>W</italic><sub><italic>n </italic></sub>are the weights. The weights were defined as the proportion of the total number of positive hits and misses to the training data size and the ratio of the number of negative hits to the training data size, respectively. We repeated the update process to train the substitution matrix until there were no changes in the matrix, that is, the number of both the positive and the negative hits remained constant. This converged matrix was the final substitution matrix that we combined with FASTA to become a new alignment tool named SA-FAST. SA-FAST was used to demonstrate the applicability of our new alphabet and matrix. The training framework appears in Figure ##FIG##6##7##.</p>" ]
[ "<title>Results</title>", "<title>Structural alphabet</title>", "<p>By combining SOMs, minimum spanning trees, and k-means clustering, we developed a multi-strategy approach to designing a protein structural alphabet. To derive an appropriate substitution matrix for the new alphabet, we developed a matrix training framework that would automatically refine an initial matrix repeatedly until it converged. Unlike some previous works that presumed the size of the alphabet [##REF##10611400##23##], our method determined the alphabet size autonomously and statistically. Various experiments were conducted to evaluate our methodology.</p>", "<p>The SOM is an unsupervised inductive learner and can be viewed as topology preserving mapping from input space onto the 2D grid of map units [##UREF##3##28##]. The number of map units in SOMs defines an inductive bias [##UREF##4##29##], as does the number of hidden units for the feedforward artificial neural networks, and it affects the clustering results. By systematically varying the number of SOM map units and applying BIC, we identified the most frequent number of clusters that maximized the BIC and used this number to define the size of the alphabet. We tested SOMs ranging in size from 10 × 10 to 200 × 200, ultimately defining the size of our alphabet at 18 letters. The relationship between number of clusters found and number of SOM map units used is summarized in Table ##TAB##0##1##.</p>", "<p>To verify whether fragments were assigned to the same cluster by the various SOMs, we analyzed those SOMs (with varying numbers of map units) that produced 18 clusters, including SOMs sized 80 × 80, 90 × 90, 190 × 190 units, etc. We calculated the overlap level between any two of the SOMs, defined as percentage of fragments that belonged to the same cluster. The average overlap between all pairs of SOMs for each of the 18 clusters was over 90%, indicating that these clusters were very consistent (Table ##TAB##1##2##). Table ##TAB##2##3## and ##TAB##3##4## display the within-cluster Euclidean distance, defined as the average distance of each segment to the center, and the center-to-center Euclidean distance for the 18 protein fragment clusters found by our method and by SOM alone, respectively. The average Phi/Psi angles (i.e. the Phi/Psi angles of the centroid) for the 18 clusters are presented in Table ##TAB##4##5##. As indicated in Table ##TAB##2##3## and ##TAB##3##4##, the within-cluster Euclidean distances for our clusters were smaller than those of the SOM clusters, which suggested that our 18 clusters were more coherent. On the other hand, the center-to-center distances for our clusters were larger than those of the SOM clusters, indicating that our clusters were better separated from each other. The 3D conformation of the representative segment for each alphabet letter is illustrated in Figure ##FIG##0##1## and the superimposition of protein segments is shown in Figure ##FIG##1##2##. To verify that these representative segments could be the building blocks for protein structures, we analyzed the frequency of their occurrence in four major structural classes according to the Structural Classification of Proteins (SCOP): all-alpha, all-beta, alpha/beta, and alpha+beta [##REF##14681400##30##]. The frequency of each category of segments is presented in Table ##TAB##5##6##. The alpha helix segments represented by alphabet letters T, P, and R occurred more often in the all-alpha class than did the other segments. Similarly, more beta sheet segments, such as N, E, and A, were found in the all-beta class. In both the alpha/beta and alpha+beta classes, most of the segments were found to be either alpha helices or beta sheets.</p>", "<title>TRISUM – Substitution matrix</title>", "<p>Most approaches to constructing substitution matrices require the alignment of known proteins [##REF##16885238##24##,##REF##1438297##31##,##UREF##5##32##]. Because alignments are not always available and their validity can be dubious, we used a self-training strategy to build the substitution matrix for our new structural alphabet. This training framework had a flexible and modular design, and unlike most other approaches, it did not rely on the pre-alignment of protein sequences or structures. Different training data or alignment tools can be incorporated into this framework to generate appropriate matrices under various circumstances. In this study, we used the non-redundant proteins contained in SCOP1.69 with sequence similarity of less than 40% for training, excluding those proteins in SCOP-894 and the 50 test proteins (see details below) to ensure that the training data and the testing data did not overlap. We defined the positive hit rate of a query as the ratio of the number of positive hits to the size of the family the query belonged to. As we iterated each training protein (as a query), we refined the matrix until we could no longer increase the average positive hit rate of all the proteins. We tried different learning rates ranging from 0.25 to 1.00. The final average positive hit rates under different learning rates were similar, ranging between 0.9112 and 0.9153. An example of the learning curve of matrix training is presented in Figure ##FIG##2##3##. We selected the converged matrix with the maximum positive hit rate with the learning rate set to 0.50. We named this matrix TRISUM-169 (<underline>TR</underline>ained <underline>I</underline>teratively for <underline>SU</underline>bstitution <underline>M</underline>atrix-SCOP<underline>1.69</underline>), as shown in Figure ##FIG##3##4##.</p>", "<title>Comparison with other tools</title>", "<p>Several protein structure search tools based on 1D alignment algorithms have been developed, including SA-Search [##REF##15215446##33##], YAKUSA [##REF##16049912##34##], and 3D-BLAST [##REF##16885238##24##]. Yang and Tung tested 3D-BLAST on the SCOP database scan task [##REF##16885238##24##]. They prepared a protein query dataset named SCOP-894 from SCOP 1.67 and 1.69; this dataset contains 894 proteins with &lt;95% sequence similarity. We tested SA-FAST on the same dataset in order to allow direct comparison (Table ##TAB##6##7##). The results indicated that SA-FAST outperformed 3D-BLAST and PSI-BLAST in the test of the SCOP-894 query dataset.</p>", "<p>We also used the same 50 proteins selected from SCOP95-1.69 that were used by Yang &amp; Tung to compare SA-FAST with 3D-BLAST, PSI-BLAST, YAKUSA, MAMMOTH, and CE, in search time, predictive accuracy, and precision. Other search tools exist, such as PBE [##REF##16844973##35##], SA-Search [##REF##15215446##33##], and Vorolign [##REF##17237093##9##], but because they either could not be tested on the SCOP database directly or the versions of their databases provided were too old (e.g. ASTRAL in PBE derived from SCOP-1.65, Vorolign server only scans SCOP40-1.69), these tools were not used in the comparisons. The results showed that SA-FAST outperformed the other two BLAST-based search tools (i.e. 3D-BLAST and PSI-BLAST) and another structure search tool that describes structures as 1D sequences (YAKUSA) in both predictive accuracy and precision (Table ##TAB##7##8##). Additionally, SA-FAST was comparably accurate and precise as the structural alignment tools MAMMOTH and CE. Regarding search time (using one Intel Pentium 2.8 GHz processor and 512 Mbytes of memory), Table ##TAB##7##8## clearly indicates that SA-FAST was far more efficient than were the structural alignment tools MAMMOTH and CE.</p>", "<p>To further evaluate the predictive validity of our alphabet, we examined pairwise alignment of difficult cases based on the number of residues aligned and the superposition root mean square deviation (RMSD). To avoid alignment process bias and to maintain consistency in our analysis of various structural alphabets, we applied the same FASTA-based alignment algorithm [##REF##10547837##27##] in the alignment tests. We tested the alphabets and substitution matrices used in PBE-align, 3D-BLAST, and SA-FAST on ten difficult cases of previously studied pairwise alignments and compared the results with those produced using VAST, DALI, CE, and FATCAT [##REF##14534198##8##,##UREF##6##36##]. Based on the alignments obtained using different alphabets and matrices, we used VMD [##REF##8744570##37##] to calculate the superposition RMSD for PBE-align, 3D-BLAST, and SA-FAST. Table ##TAB##8##9## shows that our alphabet had the lowest average RMSD per aligned residue among the three structural alphabets in the ten difficult alignment tests. Figure ##FIG##4##5## shows four superimposition examples based on our structural alphabet.</p>", "<p>Local structure conservation in putative active sites can reflect biological meaning and these types of structural patterns can be used to predict protein function [##REF##2798411##19##], e.g., the binding sites for metal-binding proteins [##UREF##7##38##]. Conserved local structural features can be identified in various ways and described using different representations. Because of the aforementioned advantages to 1D representation, we wanted to evaluate the feasibility of describing structural domains/sub-domains using our structural alphabet. Because there is no motif finding tool specifically designed for protein structural alphabets, we applied the motif finding programs available to evaluate the feasibility of using structural alphabets to characterize local structure features. Currently, we use the motif finding program, MEME [##UREF##8##39##] to identify common structural motifs in protein families. We tested our method on a well-known protein family, the epidermal growth factor (EGF)/EGF-like family. Based on the information published in literature or recorded in databases, we could verify whether the protein domains/sub-domains in EGF/EGF-like proteins could be described accurately using structural alphabets. EGF domains comprise extracellular protein modules described by 30–40 amino acids primarily stabilized by three disulfide bonds. Homologies and functional data suggest that these domains share some common functional features. If we number the cysteine residues as Cys1 to Cys6, where Cys1 is the closest to the N-terminus, the regularity of cysteine spacing defines three regions: A, B, and C. Based on the conservation in sequence and length of these regions, the homologies have been classified into three different categories [##REF##3282918##40##]. We first described the 227 proteins in the EGF-type module family of SCOP 1.69 using our alphabet and the alphabets of Yang &amp; Tung's [##REF##16885238##24##] and de Brevern et al. [##REF##12441385##16##,##REF##16844973##35##]. We then used MEME to identify the common motifs corresponding to the A, B, and C sub-domains. According to InterPro [##REF##17202162##41##], 24 of these proteins were exclusively of <italic>EGF Type-1</italic>, 74 were of <italic>EGF-like Type-2</italic>, and 117 belonged to <italic>EGF-like Type-3 </italic>only. We classified the remaining 12 proteins as <italic>Others</italic>. Sub-domain A was typically composed of five to six residues in Types 1 and 2, sub-domain B usually contained 10–11 residues in Type-1 but was consistently three residues shorter than in Type-2. Sub-domain C was conserved in length and contained four or five specific residues in Type-1 and Type-2 [##REF##3282918##40##]. The sub-domains in <italic>EGF-like Type-3 </italic>were less conserved. A found motif was considered to correspond to a sub-domain if more than one-half of the residues in the sub-domain were included in the motif. If any single motif correctly corresponded to a sub-domain, we claimed that this sub-domain was recovered successfully (that is, a hit). The results of the motifs found are summarized in Table ##TAB##9##10## and ##TAB##10##11##. They show that MEME was able to identify more EGF sub-domains using our structural alphabet than using the alphabets of Yang &amp; Tung or de Brevern <italic>et al</italic>. One example of each EGF group is shown in Figure ##FIG##5##6##, including the structures with highlighted sub-domains. Using our alphabet, MEME identified meaningful motifs that covered all three sub-domains in the EGF examples (Figure ##FIG##5##6##); however, using Yang &amp; Tung's or de Brevern <italic>et al</italic>.'s alphabets, the motifs found covered only one or two sub-domains.</p>" ]
[ "<title>Discussion</title>", "<p>This study aimed to: (1) introduce a systematic and modular pipeline for protein structural alphabet design, and (2) analyze the potential of our new alphabet to characterize local protein properties. There are two features that distinguish our method from the others. First, we took a multi-strategy approach to structural alphabet design. The alphabet size was automatically and statistically determined based on BIC and was visualized using a unified distance matrix (U-matrix). We did not pre-specify the alphabet size [##REF##16885238##24##] or use an ad hoc procedure, such as iterative shrinking, to find the optimal size [##REF##11025540##15##]. And, unlike other methods that use specialized databases, e.g. Pair Database [##REF##16885238##24##] and PDB-SELECT [##UREF##5##32##,##REF##8019422##42##], the protein structure data used to build the alphabet were obtained from the non-redundant PDB (nrPDB) database and were not pre-processed for any particular purpose, ensuring the generality of our alphabet. Second, we proposed a novel automatic matrix training framework to construct an appropriate substitution matrix for the alphabet. This training strategy did not need any information about known alignments, e.g. PALI [##REF##12520058##43##], that most previous strategies have required. Using different training data and update rules, the self-training methodology can be applied to various alphabets. For example, instead of protein classifications, we could consider RMSD in the update rules to tune the matrix. In Table ##TAB##11##12##, we summarize the properties of the structural alphabets and design methods evaluated in this study.</p>", "<p>We demonstrated that our pipeline could produce a biologically meaningful structural alphabet. We compared SA-FAST, a search tool based on FASTA combined with our alphabet and substitution matrix, with other search tools. The results showed that SA-FAST was very competitive in its predictive accuracy and alignment efficiency for database-scale searches. In addition, we compared our alphabet with others in difficult cases of pairwise alignment. The number of residues aligned and the RMSD superpositions indicated that our structural alphabet was not only comparable to other alphabets but also performed competitively with structural alignment tools.</p>", "<p>We found several advantages to using a 1D structural alphabet. First, 1D representations of protein structures are easier to compare and more economical to store. Second, previously designed and widely used 1D sequence alignment tools can be applied directly to protein structure and sequence analysis. Third, 1D-based approaches can serve as pre-processors to filter out irrelevant proteins prior to the application of more computationally intensive structural analysis tools.</p>" ]
[ "<title>Conclusion</title>", "<p>These results are encouraging and we can extend this work in several directions. Firstly, we can use more complete datasets for substitution matrix training to increase the sensitivity and selectivity of future database searches. Secondly, we can combine other alignment tools, in addition to FASTA, with our substitution matrix and evaluate the performance of these different combinations. Thirdly, to increase the performance of MEME in structural motif detection, we could modify MEME or develop a new motif-finding tool specifically for our structural alphabet. MEME was originally designed to find motifs in amino acid and nucleic acid sequences. Currently, we use MEME to detect protein motifs and we have demonstrated that it can recover some of the structural sub-domains described by our structural alphabet. Finally, several structural alphabets have been developed based on different protein structural characteristics. It would be worthwhile to conduct a thorough comparative study and evaluate the feasibility of combining different alphabets. The combination of complementary structural alphabets would increase their overall applicability and characterize 3D protein structures more completely.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p>", "<title>Results</title>", "<p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"http://140.113.166.178/safast/\"/>.</p>", "<title>Conclusion</title>", "<p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p>" ]
[ "<title>Authors' contributions</title>", "<p>S–YK implemented the structural alphabet design pipeline and conducted the experiments. Y–JH designed the BIC procedure, the matrix training framework and experiments, and supervised this study. Both authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>Thanks go to Jinn-Moon Yang and Chi-Hua Tung for their assistance in using 3D-BLAST. This work was supported in part by National Science Council, Taiwan (NSC 96-2221-E-009-042; 96-2627-B-009-003) and Institute of Statistics, Academia Sinica, Taiwan.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>The 3D conformation of the representative segment for each alphabet letter.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Superimposition of protein segments in the 18 clusters.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Example learning curve of matrix training</bold>. The average positive hit rate converged at 0.9153 with the learning rate set to 0.5.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>The substitution matrix TRISUM-169.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Superimposition examples based on alignments identified by SA-FAST</bold>. (a) 1fxiA &amp; 1ubq_ (b) 2azaA &amp; 1paz_ (c) 1cewI &amp; 1molA (d) 1cid_ &amp; 2rhe.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Examples of structural motifs corresponding to EGF sub-domains</bold>. We colored the sub-domains A, B, and C in blue, green, and red, respectively. The motifs that corresponded to EGF sub-domains, using our structural alphabet and those of Yang &amp; Tung and de Brevern <italic>et al</italic>., were also highlighted in blue, green, and red. The overlapping region between motifs was colored purple. In the sequence view, the first three sequences are EGF protein represented by our structural alphabet, the alphabet of Yang &amp; Tung, and the alphabet of de Brevern <italic>et al</italic>., respectively. The fourth is the amino acid sequence with the cysteines highlighted in orange. The sub-domains are marked at the bottom.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>System architecture of the matrix training framework.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Relationship between the number of clusters found and the number of SOM map units used</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">SOM map size</td><td align=\"center\">Number of clusters</td><td align=\"center\">SOM map size</td><td align=\"center\">Number of clusters</td></tr></thead><tbody><tr><td align=\"center\">10 × 10</td><td align=\"center\">6</td><td align=\"center\">110 × 110</td><td align=\"center\">24</td></tr><tr><td align=\"center\">20 × 20</td><td align=\"center\">9</td><td align=\"center\">120 × 120</td><td align=\"center\">19</td></tr><tr><td align=\"center\">30 × 30</td><td align=\"center\">10</td><td align=\"center\">130 × 130</td><td align=\"center\">21</td></tr><tr><td align=\"center\">40 × 40</td><td align=\"center\">12</td><td align=\"center\">140 × 140</td><td align=\"center\">22</td></tr><tr><td align=\"center\">50 × 50</td><td align=\"center\">15</td><td align=\"center\">150 × 150</td><td align=\"center\">18</td></tr><tr><td align=\"center\">60 × 60</td><td align=\"center\">13</td><td align=\"center\">160 × 160</td><td align=\"center\">15</td></tr><tr><td align=\"center\">70 × 70</td><td align=\"center\">14</td><td align=\"center\">170 × 170</td><td align=\"center\">21</td></tr><tr><td align=\"center\">80 × 80</td><td align=\"center\">18</td><td align=\"center\">180 × 180</td><td align=\"center\">18</td></tr><tr><td align=\"center\">90 × 90</td><td align=\"center\">18</td><td align=\"center\">190 × 190</td><td align=\"center\">18</td></tr><tr><td align=\"center\">100 × 100</td><td align=\"center\">20</td><td align=\"center\">200 × 200</td><td align=\"center\">18</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>The average overlap between all pairs of SOMs that produced 18 clusters of fragments</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Cluster</bold></td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">4</td><td align=\"left\">5</td><td align=\"left\">6</td><td align=\"left\">7</td><td align=\"left\">8</td><td align=\"left\">9</td><td align=\"left\">10</td><td align=\"left\">11</td><td align=\"left\">12</td><td align=\"left\">13</td><td align=\"left\">14</td><td align=\"left\">15</td><td align=\"left\">16</td><td align=\"left\">17</td><td align=\"left\">18</td></tr></thead><tbody><tr><td align=\"left\"><bold>Overlap</bold></td><td align=\"left\">99.8</td><td align=\"left\">98.4</td><td align=\"left\">96.7</td><td align=\"left\">97.4</td><td align=\"left\">97.4</td><td align=\"left\">94.3</td><td align=\"left\">99.1</td><td align=\"left\">95.0</td><td align=\"left\">97.8</td><td align=\"left\">94.6</td><td align=\"left\">99.8</td><td align=\"left\">95.6</td><td align=\"left\">96.7</td><td align=\"left\">95.3</td><td align=\"left\">95.7</td><td align=\"left\">98.2</td><td align=\"left\">96.3</td><td align=\"left\">95.5</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Summary of the within-cluster Euclidean distance and the center-to-center Euclidean distance for 18 protein fragment clusters found by our alphabet design pipeline</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"2\"><bold>Within-Cluster</bold></td><td align=\"center\" colspan=\"18\"><bold>Center-to-Center</bold></td></tr><tr><td/><td align=\"center\"><bold>Mean</bold></td><td align=\"center\"><bold>SD</bold></td><td align=\"center\">18</td><td align=\"center\">17</td><td align=\"center\">16</td><td align=\"center\">15</td><td align=\"center\">14</td><td align=\"center\">13</td><td align=\"center\">12</td><td align=\"center\">11</td><td align=\"center\">10</td><td align=\"center\">9</td><td align=\"center\">8</td><td align=\"center\">7</td><td align=\"center\">6</td><td align=\"center\">5</td><td align=\"center\">4</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">1</td></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"center\">116.6</td><td align=\"center\">37.2</td><td align=\"center\">252.3</td><td align=\"center\">300.4</td><td align=\"center\">330.1</td><td align=\"center\">242.8</td><td align=\"center\">181.7</td><td align=\"center\">182.1</td><td align=\"center\">317.6</td><td align=\"center\">327.7</td><td align=\"center\">415.4</td><td align=\"center\">266.3</td><td align=\"center\">329.0</td><td align=\"center\">181.7</td><td align=\"center\">242.5</td><td align=\"center\">262.2</td><td align=\"center\">273.6</td><td align=\"center\">253.4</td><td align=\"center\">193.2</td><td align=\"center\">0</td></tr><tr><td align=\"center\">2</td><td align=\"center\">238.7</td><td align=\"center\">38.5</td><td align=\"center\">315.8</td><td align=\"center\">226.6</td><td align=\"center\">272.7</td><td align=\"center\">197.4</td><td align=\"center\">243.3</td><td align=\"center\">227.2</td><td align=\"center\">285.3</td><td align=\"center\">270.5</td><td align=\"center\">346.1</td><td align=\"center\">283.9</td><td align=\"center\">285.4</td><td align=\"center\">261.3</td><td align=\"center\">189.5</td><td align=\"center\">182.3</td><td align=\"center\">215.0</td><td align=\"center\">296.0</td><td align=\"center\">0</td><td/></tr><tr><td align=\"center\">3</td><td align=\"center\">264.7</td><td align=\"center\">29.8</td><td align=\"center\">219.7</td><td align=\"center\">279.8</td><td align=\"center\">193.6</td><td align=\"center\">220.6</td><td align=\"center\">190.4</td><td align=\"center\">284.1</td><td align=\"center\">251.1</td><td align=\"center\">292.9</td><td align=\"center\">413.2</td><td align=\"center\">195.1</td><td align=\"center\">237.6</td><td align=\"center\">181.4</td><td align=\"center\">324.4</td><td align=\"center\">234.1</td><td align=\"center\">285.9</td><td align=\"center\">0</td><td/><td/></tr><tr><td align=\"center\">4</td><td align=\"center\">319.3</td><td align=\"center\">41.5</td><td align=\"center\">297.8</td><td align=\"center\">297.0</td><td align=\"center\">270.7</td><td align=\"center\">285.5</td><td align=\"center\">311.5</td><td align=\"center\">288.6</td><td align=\"center\">286.9</td><td align=\"center\">317.1</td><td align=\"center\">352.2</td><td align=\"center\">302.9</td><td align=\"center\">184.3</td><td align=\"center\">250.7</td><td align=\"center\">256.2</td><td align=\"center\">193.3</td><td align=\"center\">0</td><td/><td/><td/></tr><tr><td align=\"center\">5</td><td align=\"center\">250.4</td><td align=\"center\">39.7</td><td align=\"center\">248.6</td><td align=\"center\">268.9</td><td align=\"center\">190.2</td><td align=\"center\">238.1</td><td align=\"center\">302.2</td><td align=\"center\">280.1</td><td align=\"center\">258.5</td><td align=\"center\">287.2</td><td align=\"center\">406.6</td><td align=\"center\">267.2</td><td align=\"center\">258.8</td><td align=\"center\">192.8</td><td align=\"center\">229.0</td><td align=\"center\">0</td><td/><td/><td/><td/></tr><tr><td align=\"center\">6</td><td align=\"center\">257.5</td><td align=\"center\">28.0</td><td align=\"center\">220.4</td><td align=\"center\">174.2</td><td align=\"center\">242.3</td><td align=\"center\">180.4</td><td align=\"center\">262.8</td><td align=\"center\">266.2</td><td align=\"center\">264.4</td><td align=\"center\">229.1</td><td align=\"center\">310.3</td><td align=\"center\">322.3</td><td align=\"center\">270.9</td><td align=\"center\">308.6</td><td align=\"center\">0</td><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">7</td><td align=\"center\">72.2</td><td align=\"center\">20.4</td><td align=\"center\">220.8</td><td align=\"center\">356.7</td><td align=\"center\">289.2</td><td align=\"center\">297.5</td><td align=\"center\">266.1</td><td align=\"center\">244.8</td><td align=\"center\">307.2</td><td align=\"center\">361.1</td><td align=\"center\">478.3</td><td align=\"center\">248.9</td><td align=\"center\">316.8</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">8</td><td align=\"center\">282.2</td><td align=\"center\">31.0</td><td align=\"center\">275.3</td><td align=\"center\">214.2</td><td align=\"center\">186.1</td><td align=\"center\">218.9</td><td align=\"center\">259.1</td><td align=\"center\">335.6</td><td align=\"center\">258.2</td><td align=\"center\">253.8</td><td align=\"center\">286.9</td><td align=\"center\">273.9</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">9</td><td align=\"center\">320.9</td><td align=\"center\">27.9</td><td align=\"center\">275.8</td><td align=\"center\">287.6</td><td align=\"center\">250.7</td><td align=\"center\">244.5</td><td align=\"center\">222.6</td><td align=\"center\">292.2</td><td align=\"center\">286.7</td><td align=\"center\">307.3</td><td align=\"center\">354.3</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">10</td><td align=\"center\">148.8</td><td align=\"center\">26.1</td><td align=\"center\">406.3</td><td align=\"center\">243.1</td><td align=\"center\">334.4</td><td align=\"center\">286.3</td><td align=\"center\">333.5</td><td align=\"center\">361.8</td><td align=\"center\">293.2</td><td align=\"center\">240.8</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">11</td><td align=\"center\">97.1</td><td align=\"center\">43.4</td><td align=\"center\">290.4</td><td align=\"center\">169.5</td><td align=\"center\">214.8</td><td align=\"center\">178.9</td><td align=\"center\">248.7</td><td align=\"center\">238.3</td><td align=\"center\">270.4</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">12</td><td align=\"center\">272.0</td><td align=\"center\">32.7</td><td align=\"center\">259.7</td><td align=\"center\">226.6</td><td align=\"center\">200.7</td><td align=\"center\">218.7</td><td align=\"center\">269.1</td><td align=\"center\">325.6</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">13</td><td align=\"center\">133.6</td><td align=\"center\">33.2</td><td align=\"center\">291.2</td><td align=\"center\">309.3</td><td align=\"center\">334.3</td><td align=\"center\">267.6</td><td align=\"center\">230.5</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">14</td><td align=\"center\">272.8</td><td align=\"center\">31.4</td><td align=\"center\">255.5</td><td align=\"center\">206.2</td><td align=\"center\">258.7</td><td align=\"center\">145.3</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">15</td><td align=\"center\">106.2</td><td align=\"center\">32.3</td><td align=\"center\">241.1</td><td align=\"center\">76.8</td><td align=\"center\">162.1</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">16</td><td align=\"center\">109.0</td><td align=\"center\">39.1</td><td align=\"center\">221.8</td><td align=\"center\">172.6</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">17</td><td align=\"center\">33.2</td><td align=\"center\">23.2</td><td align=\"center\">272.9</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">18</td><td align=\"center\">146.2</td><td align=\"center\">38.2</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Summary of the within-cluster Euclidean distance and the center-to-center Euclidean distance for 18 protein fragment clusters found by the SOM alone</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"2\"><bold>Within-Cluster</bold></td><td align=\"center\" colspan=\"18\"><bold>Center-to-Center</bold></td></tr><tr><td/><td align=\"center\"><bold>Mean</bold></td><td align=\"center\"><bold>SD</bold></td><td align=\"center\">18</td><td align=\"center\">17</td><td align=\"center\">16</td><td align=\"center\">15</td><td align=\"center\">14</td><td align=\"center\">13</td><td align=\"center\">12</td><td align=\"center\">11</td><td align=\"center\">10</td><td align=\"center\">9</td><td align=\"center\">8</td><td align=\"center\">7</td><td align=\"center\">6</td><td align=\"center\">5</td><td align=\"center\">4</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">1</td></tr></thead><tbody><tr><td align=\"center\">1</td><td align=\"center\">129.1</td><td align=\"center\">38.9</td><td align=\"center\">220.9</td><td align=\"center\">270.9</td><td align=\"center\">302.4</td><td align=\"center\">202.7</td><td align=\"center\">175.7</td><td align=\"center\">161.9</td><td align=\"center\">277.2</td><td align=\"center\">295.9</td><td align=\"center\">381.4</td><td align=\"center\">233.8</td><td align=\"center\">307.6</td><td align=\"center\">181.6</td><td align=\"center\">234.8</td><td align=\"center\">223.0</td><td align=\"center\">263.6</td><td align=\"center\">250.5</td><td align=\"center\">164.4</td><td align=\"center\">0</td></tr><tr><td align=\"center\">2</td><td align=\"center\">242.7</td><td align=\"center\">39.4</td><td align=\"center\">304.8</td><td align=\"center\">198.1</td><td align=\"center\">265.0</td><td align=\"center\">192.0</td><td align=\"center\">201.2</td><td align=\"center\">217.5</td><td align=\"center\">241.3</td><td align=\"center\">237.1</td><td align=\"center\">309.9</td><td align=\"center\">244.0</td><td align=\"center\">247.9</td><td align=\"center\">259.6</td><td align=\"center\">165.5</td><td align=\"center\">169.5</td><td align=\"center\">189.0</td><td align=\"center\">273.5</td><td align=\"center\">0</td><td/></tr><tr><td align=\"center\">3</td><td align=\"center\">265.8</td><td align=\"center\">29.8</td><td align=\"center\">180.3</td><td align=\"center\">276.0</td><td align=\"center\">168.3</td><td align=\"center\">177.9</td><td align=\"center\">169.5</td><td align=\"center\">266.4</td><td align=\"center\">237.3</td><td align=\"center\">256.2</td><td align=\"center\">397.2</td><td align=\"center\">185.0</td><td align=\"center\">218.6</td><td align=\"center\">156.4</td><td align=\"center\">298.5</td><td align=\"center\">224.5</td><td align=\"center\">280.4</td><td align=\"center\">0</td><td/><td/></tr><tr><td align=\"center\">4</td><td align=\"center\">327.1</td><td align=\"center\">41.4</td><td align=\"center\">261.7</td><td align=\"center\">275.8</td><td align=\"center\">265.8</td><td align=\"center\">241.5</td><td align=\"center\">298.8</td><td align=\"center\">273.8</td><td align=\"center\">250.9</td><td align=\"center\">297.5</td><td align=\"center\">321.2</td><td align=\"center\">266.9</td><td align=\"center\">182.9</td><td align=\"center\">215.2</td><td align=\"center\">250.4</td><td align=\"center\">156.8</td><td align=\"center\">0</td><td/><td/><td/></tr><tr><td align=\"center\">5</td><td align=\"center\">251.9</td><td align=\"center\">39.6</td><td align=\"center\">206.8</td><td align=\"center\">223.7</td><td align=\"center\">150.2</td><td align=\"center\">207.1</td><td align=\"center\">300.9</td><td align=\"center\">258.4</td><td align=\"center\">217.8</td><td align=\"center\">274.8</td><td align=\"center\">400.7</td><td align=\"center\">227.2</td><td align=\"center\">243.6</td><td align=\"center\">167.1</td><td align=\"center\">227.6</td><td align=\"center\">0</td><td/><td/><td/><td/></tr><tr><td align=\"center\">6</td><td align=\"center\">260.7</td><td align=\"center\">29.2</td><td align=\"center\">202.0</td><td align=\"center\">158.5</td><td align=\"center\">235.8</td><td align=\"center\">137.3</td><td align=\"center\">248.4</td><td align=\"center\">225.3</td><td align=\"center\">258.8</td><td align=\"center\">205.8</td><td align=\"center\">304.7</td><td align=\"center\">300.23</td><td align=\"center\">235.0</td><td align=\"center\">297.0</td><td align=\"center\">0</td><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">7</td><td align=\"center\">75.7</td><td align=\"center\">20.5</td><td align=\"center\">191.9</td><td align=\"center\">323.9</td><td align=\"center\">243.6</td><td align=\"center\">280.5</td><td align=\"center\">238.4</td><td align=\"center\">199.0</td><td align=\"center\">292.7</td><td align=\"center\">346.6</td><td align=\"center\">463.2</td><td align=\"center\">247.1</td><td align=\"center\">291.3</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">8</td><td align=\"center\">291.4</td><td align=\"center\">30.8</td><td align=\"center\">250.4</td><td align=\"center\">196.2</td><td align=\"center\">144.8</td><td align=\"center\">203.1</td><td align=\"center\">245.8</td><td align=\"center\">322.9</td><td align=\"center\">245.4</td><td align=\"center\">226.8</td><td align=\"center\">265.6</td><td align=\"center\">272.7</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">9</td><td align=\"center\">329.1</td><td align=\"center\">27.9</td><td align=\"center\">275.3</td><td align=\"center\">251.6</td><td align=\"center\">219.3</td><td align=\"center\">200.6</td><td align=\"center\">197.1</td><td align=\"center\">263.8</td><td align=\"center\">278.1</td><td align=\"center\">272.5</td><td align=\"center\">342.3</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">10</td><td align=\"center\">157.9</td><td align=\"center\">27.4</td><td align=\"center\">364.8</td><td align=\"center\">240.4</td><td align=\"center\">310.3</td><td align=\"center\">262.0</td><td align=\"center\">292.9</td><td align=\"center\">329.8</td><td align=\"center\">266.4</td><td align=\"center\">234.3</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">11</td><td align=\"center\">113.8</td><td align=\"center\">45.8</td><td align=\"center\">244.9</td><td align=\"center\">156.7</td><td align=\"center\">190.2</td><td align=\"center\">167.5</td><td align=\"center\">224.8</td><td align=\"center\">213.6</td><td align=\"center\">254.9</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">12</td><td align=\"center\">283.0</td><td align=\"center\">32.4</td><td align=\"center\">215.7</td><td align=\"center\">205.4</td><td align=\"center\">197.6</td><td align=\"center\">191.5</td><td align=\"center\">239.0</td><td align=\"center\">299.2</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">13</td><td align=\"center\">170.3</td><td align=\"center\">29.5</td><td align=\"center\">277.6</td><td align=\"center\">272.6</td><td align=\"center\">322.4</td><td align=\"center\">252.2</td><td align=\"center\">188.8</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">14</td><td align=\"center\">277.8</td><td align=\"center\">32.6</td><td align=\"center\">238.5</td><td align=\"center\">179.7</td><td align=\"center\">239.8</td><td align=\"center\">99.5</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">15</td><td align=\"center\">111.2</td><td align=\"center\">33.1</td><td align=\"center\">210.6</td><td align=\"center\">59.5</td><td align=\"center\">161.6</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">16</td><td align=\"center\">114.05</td><td align=\"center\">38.4</td><td align=\"center\">219.4</td><td align=\"center\">146.8</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">17</td><td align=\"center\">36.2</td><td align=\"center\">24.8</td><td align=\"center\">228.6</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"center\">18</td><td align=\"center\">158.5</td><td align=\"center\">37.4</td><td align=\"center\">0</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>The average Phi/Psi angles (i.e. the Phi/Psi angles of the centroid) for the 18 clusters found by our alphabet design pipeline</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Φ(i)</bold></td><td align=\"center\"><bold>Φ(i+1)</bold></td><td align=\"center\"><bold>Φ(i-1)</bold></td><td align=\"center\"><bold>Φ(i+2)</bold></td><td align=\"center\"><bold>ψ(i)</bold></td><td align=\"center\"><bold>ψ(i-1)</bold></td><td align=\"center\"><bold>ψ(i-2)</bold></td><td align=\"center\"><bold>ψ(i+1)</bold></td></tr></thead><tbody><tr><td align=\"center\"><bold>1(A)</bold></td><td align=\"center\">-97.99</td><td align=\"center\">-70.43</td><td align=\"center\">-104.52</td><td align=\"center\">-79.77</td><td align=\"center\">132.99</td><td align=\"center\">118.98</td><td align=\"center\">132.37</td><td align=\"center\">-44.26</td></tr><tr><td align=\"center\"><bold>2(R)</bold></td><td align=\"center\">-67.81</td><td align=\"center\">-67.48</td><td align=\"center\">-92.52</td><td align=\"center\">-69.17</td><td align=\"center\">-52.78</td><td align=\"center\">134.75</td><td align=\"center\">96.12</td><td align=\"center\">-35.69</td></tr><tr><td align=\"center\"><bold>3(N)</bold></td><td align=\"center\">-98.66</td><td align=\"center\">-99.17</td><td align=\"center\">-83.46</td><td align=\"center\">-104.16</td><td align=\"center\">132.56</td><td align=\"center\">75.64</td><td align=\"center\">-36.97</td><td align=\"center\">134.01</td></tr><tr><td align=\"center\"><bold>4(D)</bold></td><td align=\"center\">90.39</td><td align=\"center\">-63.35</td><td align=\"center\">-93.54</td><td align=\"center\">-84.31</td><td align=\"center\">-5.43</td><td align=\"center\">97.71</td><td align=\"center\">115.22</td><td align=\"center\">94.64</td></tr><tr><td align=\"center\"><bold>5(C)</bold></td><td align=\"center\">-88.09</td><td align=\"center\">-102.50</td><td align=\"center\">-93.58</td><td align=\"center\">-97.49</td><td align=\"center\">-51.56</td><td align=\"center\">88.66</td><td align=\"center\">106.12</td><td align=\"center\">133.27</td></tr><tr><td align=\"center\"><bold>6(Q)</bold></td><td align=\"center\">-65.87</td><td align=\"center\">-69.19</td><td align=\"center\">-85.50</td><td align=\"center\">-59.89</td><td align=\"center\">-35.12</td><td align=\"center\">-50.41</td><td align=\"center\">129.98</td><td align=\"center\">-37.57</td></tr><tr><td align=\"center\"><bold>7(E)</bold></td><td align=\"center\">-107.28</td><td align=\"center\">-96.08</td><td align=\"center\">-107.66</td><td align=\"center\">-105.96</td><td align=\"center\">132.71</td><td align=\"center\">130.92</td><td align=\"center\">133.88</td><td align=\"center\">133.06</td></tr><tr><td align=\"center\"><bold>8(G)</bold></td><td align=\"center\">89.16</td><td align=\"center\">-93.43</td><td align=\"center\">-62.92</td><td align=\"center\">-90.25</td><td align=\"center\">20.65</td><td align=\"center\">0.22</td><td align=\"center\">-32.50</td><td align=\"center\">85.94</td></tr><tr><td align=\"center\"><bold>9(H)</bold></td><td align=\"center\">-91.05</td><td align=\"center\">-90.16</td><td align=\"center\">91.91</td><td align=\"center\">-91.53</td><td align=\"center\">100.48</td><td align=\"center\">103.36</td><td align=\"center\">5.40</td><td align=\"center\">75.56</td></tr><tr><td align=\"center\"><bold>10(I)</bold></td><td align=\"center\">58.59</td><td align=\"center\">56.79</td><td align=\"center\">55.50</td><td align=\"center\">54.75</td><td align=\"center\">-42.38</td><td align=\"center\">-38.76</td><td align=\"center\">-47.77</td><td align=\"center\">-48.46</td></tr><tr><td align=\"center\"><bold>11(L)</bold></td><td align=\"center\">-71.08</td><td align=\"center\">-84.21</td><td align=\"center\">-65.92</td><td align=\"center\">87.57</td><td align=\"center\">-21.11</td><td align=\"center\">-29.95</td><td align=\"center\">-31.80</td><td align=\"center\">20.00</td></tr><tr><td align=\"center\"><bold>12(K)</bold></td><td align=\"center\">-83.07</td><td align=\"center\">95.78</td><td align=\"center\">-69.02</td><td align=\"center\">-91.34</td><td align=\"center\">9.50</td><td align=\"center\">-9.18</td><td align=\"center\">-5.50</td><td align=\"center\">100.52</td></tr><tr><td align=\"center\"><bold>13(M)</bold></td><td align=\"center\">-88.72</td><td align=\"center\">-64.82</td><td align=\"center\">-95.72</td><td align=\"center\">91.27</td><td align=\"center\">100.65</td><td align=\"center\">113.69</td><td align=\"center\">107.43</td><td align=\"center\">0.70</td></tr><tr><td align=\"center\"><bold>14(F)</bold></td><td align=\"center\">-87.36</td><td align=\"center\">-71.63</td><td align=\"center\">-75.80</td><td align=\"center\">-68.31</td><td align=\"center\">134.69</td><td align=\"center\">58.97</td><td align=\"center\">-35.87</td><td align=\"center\">-49.72</td></tr><tr><td align=\"center\"><bold>15(P)</bold></td><td align=\"center\">-96.95</td><td align=\"center\">-78.84</td><td align=\"center\">-75.71</td><td align=\"center\">-78.03</td><td align=\"center\">4.07</td><td align=\"center\">2.17</td><td align=\"center\">-33.25</td><td align=\"center\">-25.92</td></tr><tr><td align=\"center\"><bold>16(S)</bold></td><td align=\"center\">-83.07</td><td align=\"center\">-95.71</td><td align=\"center\">-63.62</td><td align=\"center\">-97.87</td><td align=\"center\">-28.27</td><td align=\"center\">-28.59</td><td align=\"center\">-38.35</td><td align=\"center\">126.57</td></tr><tr><td align=\"center\"><bold>17(T)</bold></td><td align=\"center\">-63.55</td><td align=\"center\">-65.43</td><td align=\"center\">-62.97</td><td align=\"center\">-68.03</td><td align=\"center\">-42.53</td><td align=\"center\">-41.88</td><td align=\"center\">-42.16</td><td align=\"center\">-38.34</td></tr><tr><td align=\"center\"><bold>18(W)</bold></td><td align=\"center\">-105.06</td><td align=\"center\">-91.96</td><td align=\"center\">-78.47</td><td align=\"center\">-94.14</td><td align=\"center\">122.89</td><td align=\"center\">-83.40</td><td align=\"center\">109.64</td><td align=\"center\">99.64</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T6\"><label>Table 6</label><caption><p>Frequency of occurrence of the protein segments defined by our alphabet in four main SCOP classes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"2\">All alpha</td><td align=\"center\" colspan=\"2\">All beta</td><td align=\"center\" colspan=\"2\">alpha/beta</td><td align=\"center\" colspan=\"2\">alpha+beta</td></tr><tr><td align=\"center\">Letter</td><td align=\"center\">Count</td><td align=\"center\">Percentage</td><td align=\"center\">Count</td><td align=\"center\">Percentage</td><td align=\"center\">Count</td><td align=\"center\">Percentage</td><td align=\"center\">Count</td><td align=\"center\">Percentage</td></tr></thead><tbody><tr><td align=\"center\">A</td><td align=\"center\">54859</td><td align=\"center\">2.95</td><td align=\"center\">255473</td><td align=\"center\">8.83</td><td align=\"center\">278238</td><td align=\"center\">5.46</td><td align=\"center\">161041</td><td align=\"center\">6.07</td></tr><tr><td align=\"center\">R</td><td align=\"center\">91363</td><td align=\"center\">4.92</td><td align=\"center\">148361</td><td align=\"center\">5.13</td><td align=\"center\">270345</td><td align=\"center\">5.31</td><td align=\"center\">145619</td><td align=\"center\">5.49</td></tr><tr><td align=\"center\">N</td><td align=\"center\">76176</td><td align=\"center\">4.10</td><td align=\"center\">309834</td><td align=\"center\">10.71</td><td align=\"center\">340682</td><td align=\"center\">6.69</td><td align=\"center\">202555</td><td align=\"center\">7.64</td></tr><tr><td align=\"center\">D</td><td align=\"center\">21055</td><td align=\"center\">1.13</td><td align=\"center\">127159</td><td align=\"center\">4.39</td><td align=\"center\">112078</td><td align=\"center\">2.20</td><td align=\"center\">66959</td><td align=\"center\">2.53</td></tr><tr><td align=\"center\">C</td><td align=\"center\">34856</td><td align=\"center\">1.88</td><td align=\"center\">172334</td><td align=\"center\">5.96</td><td align=\"center\">193952</td><td align=\"center\">3.81</td><td align=\"center\">102632</td><td align=\"center\">3.87</td></tr><tr><td align=\"center\">Q</td><td align=\"center\">102444</td><td align=\"center\">5.51</td><td align=\"center\">111333</td><td align=\"center\">3.85</td><td align=\"center\">271893</td><td align=\"center\">5.34</td><td align=\"center\">138081</td><td align=\"center\">5.21</td></tr><tr><td align=\"center\">E</td><td align=\"center\">58672</td><td align=\"center\">3.16</td><td align=\"center\">782607</td><td align=\"center\">27.06</td><td align=\"center\">620717</td><td align=\"center\">12.18</td><td align=\"center\">427778</td><td align=\"center\">16.14</td></tr><tr><td align=\"center\">G</td><td align=\"center\">42350</td><td align=\"center\">2.28</td><td align=\"center\">72105</td><td align=\"center\">2.49</td><td align=\"center\">147390</td><td align=\"center\">2.89</td><td align=\"center\">76968</td><td align=\"center\">2.90</td></tr><tr><td align=\"center\">H</td><td align=\"center\">39017</td><td align=\"center\">2.10</td><td align=\"center\">115542</td><td align=\"center\">3.99</td><td align=\"center\">163319</td><td align=\"center\">3.21</td><td align=\"center\">89203</td><td align=\"center\">3.37</td></tr><tr><td align=\"center\">I</td><td align=\"center\">3547</td><td align=\"center\">0.19</td><td align=\"center\">6607</td><td align=\"center\">0.23</td><td align=\"center\">9449</td><td align=\"center\">0.19</td><td align=\"center\">5739</td><td align=\"center\">0.22</td></tr><tr><td align=\"center\">L</td><td align=\"center\">49312</td><td align=\"center\">2.65</td><td align=\"center\">40909</td><td align=\"center\">1.41</td><td align=\"center\">141605</td><td align=\"center\">2.78</td><td align=\"center\">65856</td><td align=\"center\">2.48</td></tr><tr><td align=\"center\">K</td><td align=\"center\">43582</td><td align=\"center\">2.35</td><td align=\"center\">58687</td><td align=\"center\">2.04</td><td align=\"center\">146869</td><td align=\"center\">2.88</td><td align=\"center\">70549</td><td align=\"center\">2.66</td></tr><tr><td align=\"center\">M</td><td align=\"center\">16727</td><td align=\"center\">0.90</td><td align=\"center\">127070</td><td align=\"center\">4.39</td><td align=\"center\">110318</td><td align=\"center\">2.17</td><td align=\"center\">67912</td><td align=\"center\">2.56</td></tr><tr><td align=\"center\">F</td><td align=\"center\">70718</td><td align=\"center\">3.81</td><td align=\"center\">89366</td><td align=\"center\">3.09</td><td align=\"center\">179145</td><td align=\"center\">3.52</td><td align=\"center\">91702</td><td align=\"center\">3.46</td></tr><tr><td align=\"center\">P</td><td align=\"center\">104364</td><td align=\"center\">5.62</td><td align=\"center\">54939</td><td align=\"center\">1.91</td><td align=\"center\">192654</td><td align=\"center\">3.78</td><td align=\"center\">87149</td><td align=\"center\">3.29</td></tr><tr><td align=\"center\">S</td><td align=\"center\">76080</td><td align=\"center\">4.10</td><td align=\"center\">83725</td><td align=\"center\">2.89</td><td align=\"center\">173935</td><td align=\"center\">3.41</td><td align=\"center\">91160</td><td align=\"center\">3.44</td></tr><tr><td align=\"center\">T</td><td align=\"center\">937938</td><td align=\"center\">50.49</td><td align=\"center\">149259</td><td align=\"center\">5.17</td><td align=\"center\">1551585</td><td align=\"center\">30.46</td><td align=\"center\">651525</td><td align=\"center\">24.58</td></tr><tr><td align=\"center\">W</td><td align=\"center\">34533</td><td align=\"center\">1.86</td><td align=\"center\">186476</td><td align=\"center\">6.46</td><td align=\"center\">190001</td><td align=\"center\">3.72</td><td align=\"center\">108460</td><td align=\"center\">4.09</td></tr><tr><td align=\"center\"><bold>Total</bold></td><td align=\"center\"><bold>1857593</bold></td><td align=\"center\"><bold>100.00</bold></td><td align=\"center\"><bold>2891786</bold></td><td align=\"center\"><bold>100.00</bold></td><td align=\"center\"><bold>5094175</bold></td><td align=\"center\"><bold>100.00</bold></td><td align=\"center\"><bold>2650888</bold></td><td align=\"center\"><bold>100.00</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T7\"><label>Table 7</label><caption><p>SA-FAST versus 3D-BLAST and PSI-BLAST in SCOP structural function assignment accuracy for the SCOP-894 protein dataset</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Class</td><td align=\"center\">894 proteins </td><td align=\"center\" colspan=\"3\">Accuracy<sup>a </sup>(894 proteins)</td><td align=\"center\" colspan=\"3\">Accuracy (sequence identity &lt;25%)</td></tr><tr><td/><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\">Number of queries</td><td align=\"center\">SA-FAST</td><td align=\"center\">3D-BLAST</td><td align=\"center\">PSI-BLAST</td><td align=\"center\">SA-FAST</td><td align=\"center\">3D-BLAST</td><td align=\"center\">PSI-BLAST</td></tr></thead><tbody><tr><td align=\"center\">All alpha</td><td align=\"center\">161</td><td align=\"center\">99.27</td><td align=\"center\">94.41</td><td align=\"center\">94.41</td><td align=\"center\">95.83</td><td align=\"center\">75.00</td><td align=\"center\">66.67</td></tr><tr><td align=\"center\">All beta</td><td align=\"center\">199</td><td align=\"center\">95.12</td><td align=\"center\">94.47</td><td align=\"center\">93.97</td><td align=\"center\">87.32</td><td align=\"center\">77.55</td><td align=\"center\">73.33</td></tr><tr><td align=\"center\"><italic>α</italic>/<italic>β</italic></td><td align=\"center\">292</td><td align=\"center\">97.58</td><td align=\"center\">97.26</td><td align=\"center\">91.44</td><td align=\"center\">95.68</td><td align=\"center\">87.88</td><td align=\"center\">65.77</td></tr><tr><td align=\"center\"><italic>α</italic>+<italic>β</italic></td><td align=\"center\">242</td><td align=\"center\">95.13</td><td align=\"center\">94.63</td><td align=\"center\">88.84</td><td align=\"center\">93.81</td><td align=\"center\">83.33</td><td align=\"center\">60.87</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T8\"><label>Table 8</label><caption><p>Comparison between SA-FAST, 3D-BLAST, PSI-BLAST, YAKUSA, MAMMOTH, and CE on 50 proteins selected from SCOP95-1.69</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Search tool</td><td align=\"center\">Average time required for a query (sec)</td><td align=\"center\">Relative to SA-FAST</td><td align=\"center\">Accuracy<sup>a </sup>(%)</td><td align=\"center\">Average precision<sup>b </sup>(%)</td></tr></thead><tbody><tr><td align=\"center\">SA-FAST</td><td align=\"center\">1.15</td><td align=\"center\">1.00</td><td align=\"center\">96</td><td align=\"center\">90.80</td></tr><tr><td align=\"center\">3D-BLAST</td><td align=\"center\">1.30</td><td align=\"center\">1.13</td><td align=\"center\">94</td><td align=\"center\">85.20</td></tr><tr><td align=\"center\">PSI-BLAST</td><td align=\"center\">0.48</td><td align=\"center\">0.42</td><td align=\"center\">84</td><td align=\"center\">68.16</td></tr><tr><td align=\"center\">YAKUSA</td><td align=\"center\">8.88</td><td align=\"center\">7.72</td><td align=\"center\">90</td><td align=\"center\">74.86</td></tr><tr><td align=\"center\">MAMMOTH</td><td align=\"center\">1834.18</td><td align=\"center\">1594.94</td><td align=\"center\">100</td><td align=\"center\">94.01</td></tr><tr><td align=\"center\">CE</td><td align=\"center\">22053.32</td><td align=\"center\">19176.80</td><td align=\"center\">98</td><td align=\"center\">90.78</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T9\"><label>Table 9</label><caption><p>Results of ten difficult cases of pairwise alignment</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Protein 1</bold></td><td align=\"center\"><bold>Protein 2</bold></td><td align=\"center\"><bold>VAST</bold></td><td align=\"center\"><bold>DALI</bold></td><td align=\"center\"><bold>CE</bold></td><td align=\"center\"><bold>FATCAT</bold></td><td align=\"center\"><bold>Yang &amp; Tung's</bold></td><td align=\"center\"><bold>de Brevern et al.'s</bold></td><td align=\"center\"><bold>Our SA</bold></td></tr></thead><tbody><tr><td align=\"center\">1fxia</td><td align=\"center\">1ubq</td><td align=\"center\">48(2.10)</td><td align=\"center\">60(2.60)</td><td align=\"center\">64(3.80)</td><td align=\"center\">63(3.01)</td><td align=\"center\">59(2.76)</td><td align=\"center\">76(2.89)</td><td align=\"center\">58(2.64)</td></tr><tr><td align=\"center\">1ten</td><td align=\"center\">3hhrb</td><td align=\"center\">78(1.60)</td><td align=\"center\">86(1.90)</td><td align=\"center\">87(1.90)</td><td align=\"center\">87(1.90)</td><td align=\"center\">57(2.57)</td><td align=\"center\">73(2.31)</td><td align=\"center\">90(2.24)</td></tr><tr><td align=\"center\">3hlab</td><td align=\"center\">2rhe_</td><td align=\"center\">-</td><td align=\"center\">63(2.50)</td><td align=\"center\">85(3.50)</td><td align=\"center\">79(2.81)</td><td align=\"center\">54(2.65)</td><td align=\"center\">78(3.01)</td><td align=\"center\">79(2.87)</td></tr><tr><td align=\"center\">2azaa</td><td align=\"center\">1paz_</td><td align=\"center\">74(2.20)</td><td align=\"center\">81(2.50)</td><td align=\"center\">85(2.90)</td><td align=\"center\">87(3.01)</td><td align=\"center\">70(2.34)</td><td align=\"center\">57(2.23)</td><td align=\"center\">87(2.40)</td></tr><tr><td align=\"center\">1cewi</td><td align=\"center\">1mola</td><td align=\"center\">71(1.9)</td><td align=\"center\">81(2.30)</td><td align=\"center\">69(1.90)</td><td align=\"center\">83(2.44)</td><td align=\"center\">52(2.37)</td><td align=\"center\">53(2.35)</td><td align=\"center\">61(1.83)</td></tr><tr><td align=\"center\">1cid_</td><td align=\"center\">2rhe_</td><td align=\"center\">85(2.20)</td><td align=\"center\">95(3.30)</td><td align=\"center\">94(2.70)</td><td align=\"center\">100(3.11)</td><td align=\"center\">54(2.75)</td><td align=\"center\">53(2.49)</td><td align=\"center\">55(2.08)</td></tr><tr><td align=\"center\">1crl_</td><td align=\"center\">1ede</td><td align=\"center\">-</td><td align=\"center\">211(3.40)</td><td align=\"center\">187(3.20)</td><td align=\"center\">269(3.55)</td><td align=\"center\">167(3.35)</td><td align=\"center\">120(3.47)</td><td align=\"center\">187(3.25)</td></tr><tr><td align=\"center\">2sim_</td><td align=\"center\">1nsba</td><td align=\"center\">284(3.80)</td><td align=\"center\">286(3.80)</td><td align=\"center\">264(3.00)</td><td align=\"center\">286(3.07)</td><td align=\"center\">121(2.75)</td><td align=\"center\">121(2.96)</td><td align=\"center\">137(3.2)</td></tr><tr><td align=\"center\">1bgea</td><td align=\"center\">2gmfa</td><td align=\"center\">74(2.50)</td><td align=\"center\">98(3.50)</td><td align=\"center\">94(4.10)</td><td align=\"center\">100(3.19)</td><td align=\"center\">27(3.34)</td><td align=\"center\">77(2.8)</td><td align=\"center\">78(2.72)</td></tr><tr><td align=\"center\">1tie_</td><td align=\"center\">4fgf_</td><td align=\"center\">82(1.70)</td><td align=\"center\">108(2.00)</td><td align=\"center\">116(2.90)</td><td align=\"center\">117(3.05)</td><td align=\"center\">91(3.15)</td><td align=\"center\">62(3.45)</td><td align=\"center\">115(3.05)</td></tr><tr><td align=\"center\"><bold>Average RMSD/aligned-residues</bold></td><td/><td align=\"center\"><bold>0.0226</bold></td><td align=\"center\"><bold>0.0238</bold></td><td align=\"center\"><bold>0.0261</bold></td><td align=\"center\"><bold>0.0229</bold></td><td align=\"center\"><bold>0.0373</bold></td><td align=\"center\"><bold>0.0363</bold></td><td align=\"center\"><bold>0.0278</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T10\"><label>Table 10</label><caption><p>Comparison between our structural alphabet (used in SA-FAST) and those of Yang &amp; Tung (used in 3D-BLAST) and de Brevern <italic>et al</italic>. (converted by PBE-T, a facility associated with PBE-align) for describing motifs found by MEME within the EGF family</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\" colspan=\"6\"><bold>Our SA</bold></td><td align=\"center\" colspan=\"6\"><bold>Yang &amp; Tung's</bold></td><td align=\"center\" colspan=\"6\"><bold>de Brevern <italic>et al</italic>.'s</bold></td></tr></thead><tbody><tr><td align=\"center\" colspan=\"2\"><bold>Sub-domain Type</bold></td><td align=\"center\" colspan=\"2\"><bold>A</bold></td><td align=\"center\" colspan=\"2\"><bold>B</bold></td><td align=\"center\" colspan=\"2\"><bold>C</bold></td><td align=\"center\" colspan=\"2\"><bold>A</bold></td><td align=\"center\" colspan=\"2\"><bold>B</bold></td><td align=\"center\" colspan=\"2\"><bold>C</bold></td><td align=\"center\" colspan=\"2\"><bold>A</bold></td><td align=\"center\" colspan=\"2\"><bold>B</bold></td><td align=\"center\" colspan=\"2\"><bold>C</bold></td></tr><tr><td colspan=\"20\"><hr/></td></tr><tr><td align=\"center\"><bold>EGF proteins</bold></td><td align=\"center\"><bold>No</bold>.<sup><bold>a</bold></sup></td><td align=\"center\"><bold>Hits</bold><sup><bold>b</bold></sup></td><td align=\"center\"><bold>Cov</bold><sup><bold>c</bold></sup></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td><td align=\"center\"><bold>Hits</bold></td><td align=\"center\"><bold>Cov</bold></td></tr><tr><td colspan=\"20\"><hr/></td></tr><tr><td align=\"center\"><bold>Type 1</bold></td><td align=\"center\">24</td><td align=\"center\">23</td><td align=\"center\">95.8</td><td align=\"center\">22</td><td align=\"center\">91.7</td><td align=\"center\">23</td><td align=\"center\">95.8</td><td align=\"center\">11</td><td align=\"center\">45.8</td><td align=\"center\">21</td><td align=\"center\">87.5</td><td align=\"center\">19</td><td align=\"center\">79.2</td><td align=\"center\">18</td><td align=\"center\">75.0</td><td align=\"center\">14</td><td align=\"center\">58.3</td><td align=\"center\">18</td><td align=\"center\">75.0</td></tr><tr><td align=\"center\"><bold>Type 2</bold></td><td align=\"center\">74</td><td align=\"center\">73</td><td align=\"center\">98.6</td><td align=\"center\">71</td><td align=\"center\">95.9</td><td align=\"center\">74</td><td align=\"center\">100.0</td><td align=\"center\">62</td><td align=\"center\">83.8</td><td align=\"center\">73</td><td align=\"center\">98.6</td><td align=\"center\">60</td><td align=\"center\">81.1</td><td align=\"center\">68</td><td align=\"center\">91.9</td><td align=\"center\">62</td><td align=\"center\">83.8</td><td align=\"center\">70</td><td align=\"center\">94.6</td></tr><tr><td align=\"center\"><bold>Type 3</bold></td><td align=\"center\">117</td><td align=\"center\">116</td><td align=\"center\">99.1</td><td align=\"center\">106</td><td align=\"center\">90.6</td><td align=\"center\">61</td><td align=\"center\">52.1</td><td align=\"center\">54</td><td align=\"center\">46.2</td><td align=\"center\">102</td><td align=\"center\">87.2</td><td align=\"center\">25</td><td align=\"center\">21.4</td><td align=\"center\">109</td><td align=\"center\">93.2</td><td align=\"center\">112</td><td align=\"center\">95.7</td><td align=\"center\">48</td><td align=\"center\">41.0</td></tr><tr><td align=\"center\"><bold>Others</bold></td><td align=\"center\">12</td><td align=\"center\">12</td><td align=\"center\">100.0</td><td align=\"center\">11</td><td align=\"center\">91.7</td><td align=\"center\">11</td><td align=\"center\">91.7</td><td align=\"center\">9</td><td align=\"center\">75.0</td><td align=\"center\">11</td><td align=\"center\">91.7</td><td align=\"center\">9</td><td align=\"center\">75.0</td><td align=\"center\">12</td><td align=\"center\">100.0</td><td align=\"center\">11</td><td align=\"center\">91.7</td><td align=\"center\">9</td><td align=\"center\">75.0</td></tr><tr><td align=\"center\"><bold>All</bold></td><td align=\"center\">227</td><td align=\"center\">224</td><td align=\"center\">98.6</td><td align=\"center\">210</td><td align=\"center\">92.5</td><td align=\"center\">169</td><td align=\"center\">74.4</td><td align=\"center\">136</td><td align=\"center\">59.9</td><td align=\"center\">207</td><td align=\"center\">91.2</td><td align=\"center\">113</td><td align=\"center\">49.8</td><td align=\"center\">207</td><td align=\"center\">91.2</td><td align=\"center\">199</td><td align=\"center\">87.7</td><td align=\"center\">145</td><td align=\"center\">63.9</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T11\"><label>Table 11</label><caption><p>Statistical analysis of EGF(EGF-like) proteins whose sub-domains were detected by MEME</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"6\"><bold>Structural Alphabet</bold></td></tr></thead><tbody><tr><td align=\"center\"><bold>EGF proteins</bold></td><td align=\"center\" colspan=\"2\"><bold>Our SA</bold></td><td align=\"center\" colspan=\"2\"><bold>Yang &amp; Tung's</bold></td><td align=\"center\" colspan=\"2\"><bold>de Brevern <italic>et al</italic>.'s</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\"><bold>Count</bold></td><td align=\"center\"><bold>Percentage</bold></td><td align=\"center\"><bold>Count</bold></td><td align=\"center\"><bold>Percentage</bold></td><td align=\"center\"><bold>Count</bold></td><td align=\"center\"><bold>Percentage</bold></td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"center\"><bold>Found 3</bold><sup><bold>a</bold></sup></td><td align=\"center\">151</td><td align=\"center\">66.52</td><td align=\"center\">79</td><td align=\"center\">34.80</td><td align=\"center\">104</td><td align=\"center\">45.81</td></tr><tr><td align=\"center\"><bold>Found 2</bold><sup><bold>b</bold></sup></td><td align=\"center\">74</td><td align=\"center\">32.60</td><td align=\"center\">78</td><td align=\"center\">34.36</td><td align=\"center\">116</td><td align=\"center\">51.10</td></tr><tr><td align=\"center\"><bold>Found 1</bold><sup><bold>c</bold></sup></td><td align=\"center\">2</td><td align=\"center\">0.88</td><td align=\"center\">63</td><td align=\"center\">27.75</td><td align=\"center\">7</td><td align=\"center\">3.08</td></tr><tr><td align=\"center\"><bold>Found 0</bold><sup><bold>d</bold></sup></td><td align=\"center\">0</td><td align=\"center\">0.00</td><td align=\"center\">7</td><td align=\"center\">3.08</td><td align=\"center\">0</td><td align=\"center\">0.00</td></tr><tr><td align=\"center\"><bold>Total</bold></td><td align=\"center\">227</td><td align=\"center\">100.00</td><td align=\"center\">227</td><td align=\"center\">100.00</td><td align=\"center\">227</td><td align=\"center\">100.00</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T12\"><label>Table 12</label><caption><p>Summary of properties of structural alphabets and alphabet designs</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Structural Alphabet</bold></td><td align=\"center\"><bold>Tung &amp; Yang</bold></td><td align=\"center\"><bold>de Brevern et al</bold>.</td><td align=\"center\"><bold>Our SA</bold></td></tr></thead><tbody><tr><td align=\"center\"><bold>Alphabet Size</bold></td><td align=\"center\">23</td><td align=\"center\">16</td><td align=\"center\">18</td></tr><tr><td align=\"center\"><bold>How the alphabet size was determined</bold></td><td align=\"center\">Prespecified</td><td align=\"center\">Iterative shrinking</td><td align=\"center\">BIC</td></tr><tr><td align=\"center\"><bold>Clustering</bold></td><td align=\"center\">k-means</td><td align=\"center\">SOM+HMM</td><td align=\"center\">SOM+k-means</td></tr><tr><td align=\"center\"><bold>Data Set</bold></td><td align=\"center\">Preprocessed (Pair Database)</td><td align=\"center\">Preprocessed (PBE-SELECT)</td><td align=\"center\">No preprocess (nrPDB)</td></tr><tr><td align=\"center\"><bold>Substitution Matrix</bold></td><td align=\"center\">BLOSUM-like</td><td align=\"center\">BLOSUM-like</td><td align=\"center\">Self-Training</td></tr><tr><td align=\"center\"><bold>Requires known alignments to build matrix</bold></td><td align=\"center\">Yes</td><td align=\"center\">Yes</td><td align=\"center\">No</td></tr><tr><td align=\"center\"><bold>Applicability</bold></td><td align=\"center\">Limited</td><td align=\"center\">Limited</td><td align=\"center\">Modular design More flexible</td></tr></tbody></table></table-wrap>" ]
[ "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1471-2105-9-349-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msup><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msup><mml:mo>=</mml:mo><mml:mo>{</mml:mo><mml:msubsup><mml:mi>x</mml:mi><mml:mi>j</mml:mi><mml:mi>i</mml:mi></mml:msubsup><mml:mo>|</mml:mo><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1...</mml:mn><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>}</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1471-2105-9-349-i2\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mstyle scriptlevel=\"+1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac></mml:mstyle><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1471-2105-9-349-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>B</mml:mi><mml:mi>I</mml:mi><mml:mi>C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>k</mml:mi></mml:msubsup><mml:mrow><mml:mo>{</mml:mo><mml:mo>−</mml:mo><mml:mstyle scriptlevel=\"+1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac></mml:mstyle><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:msub><mml:mo>∑</mml:mo><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mo>|</mml:mo></mml:mrow></mml:mrow></mml:mstyle><mml:mo>}</mml:mo><mml:mo>−</mml:mo><mml:mi>λ</mml:mi><mml:mstyle scriptlevel=\"+1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac></mml:mstyle><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mstyle scriptlevel=\"+1\"><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac></mml:mstyle><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>d</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>k</mml:mi><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1471-2105-9-349-i4\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>k</mml:mi></mml:msubsup><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><italic>S</italic><sup>(<italic>t</italic>+1) </sup>= <italic>S</italic><sup>(<italic>t</italic>) </sup>+ <italic>M</italic></disp-formula>", "<disp-formula><italic>M </italic>= [<italic>W</italic><sub><italic>p</italic></sub>·(<italic>P </italic>- <italic>S</italic><sup>(<italic>t</italic>)</sup>) - <italic>W</italic><sub><italic>n</italic></sub>·(<italic>N </italic>- <italic>S</italic><sup>(<italic>t</italic>)</sup>)]·<italic>τ</italic></disp-formula>", "<disp-formula><italic>W</italic><sub><italic>p </italic></sub>= (|<italic>positive_hits</italic>| + |<italic>misses</italic>|)/|<italic>taining_data</italic>|</disp-formula>", "<disp-formula><italic>W</italic><sub><italic>n </italic></sub>= |<italic>negative_hits</italic>|/|<italic>training_data</italic>|</disp-formula>" ]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>Our analysis determined that among the number of clusters that maximized the <italic>BIC</italic>, 18 clusters occurred most frequently. Thus, we assigned 18 letters to our alphabet.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a</sup>The top-ranked family in the hit list of a query was used as the predicted family. Accuracy is the percentage of times that the family was correctly predicted.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a</sup>The top-ranked family in the hit list of a query was used as the predicted family. Accuracy is the percentage of times that the family was correctly predicted.</p><p><sup>b</sup>The precision is defined as T/H, where T is the number of true hit structures in the hit list, and H is the total number of structures in the hit list.</p></table-wrap-foot>", "<table-wrap-foot><p>The number of residues aligned and the RMSD (in parentheses) are shown. The last row displays the average RMSD per aligned residue. Except for PBE-align, 3D-BLAST, and SA-FAST, the results of the methods were adopted from [##UREF##6##36##].</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a</sup>The number of EGF proteins of a specific type, <sup>b</sup>A hit for a sub-domain occurred when more than half of the sub-domain residues were contained in a given motif. We present the number of hits of different types, <sup>c</sup>Cov(Coverage) was defined as the ratio of the number of hits to the number of EGF proteins, e.g., if No. = 24 and Hits = 22, then Cov = 22/24 = 91.7%.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a</sup>EGF (EGF-like) proteins in which all three sub-domains (A, B and C) were found by MEME, <sup>b</sup>EGF (EGF-like) proteins in which two of the three sub-domains were found by MEME, <sup>c</sup>EGF (EGF-like) proteins in which only one sub-domain was found by MEME, <sup>d</sup>EGF (EGF-like) proteins in which MEME failed to identify any sub-domain.</p></table-wrap-foot>" ]
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[]
[{"surname": ["Bystroff", "Baker"], "given-names": ["C", "D"], "article-title": ["Prediction of local structure in proteins using a library of sequence-structure motif"], "source": ["J Molecular Biology"], "year": ["1998"], "volume": ["281"], "fpage": ["565"], "lpage": ["577"], "pub-id": ["10.1006/jmbi.1998.1943"]}, {"surname": ["Offmann", "Tyagi", "de Brevern"], "given-names": ["B", "M", "AG"], "article-title": ["Local Protein Structures"], "source": ["Current Bioinformatics"], "year": ["2007"], "volume": ["2"], "fpage": ["165"], "lpage": ["202"], "pub-id": ["10.2174/157489307781662105"]}, {"surname": ["Hartigan", "Wong"], "given-names": ["JA", "MA"], "article-title": ["A k-means clustering algorithm"], "source": ["Applied Statistics"], "year": ["1975"], "volume": ["28"], "fpage": ["100"], "lpage": ["108"], "pub-id": ["10.2307/2346830"]}, {"surname": ["Vesanto", "Alhoniemi"], "given-names": ["J", "E"], "article-title": ["Cluster of the self-organizing map"], "source": ["IEEE trans Neural Networks"], "year": ["2000"], "volume": ["11"], "fpage": ["586"], "lpage": ["600"], "pub-id": ["10.1109/72.846731"]}, {"surname": ["Mitchell"], "given-names": ["TM"], "source": ["Machine Learning"], "year": ["1997"], "publisher-name": ["McGraw-Hill"]}, {"surname": ["Zheng", "Liu"], "given-names": ["WM", "X"], "article-title": ["A protein structural alphabet and its substitution matrix CLESUM"], "source": ["LNCS"], "year": ["2005"], "volume": ["3680"], "fpage": ["59"], "lpage": ["67"]}, {"surname": ["Tyagi", "de Brevern", "Srinivasan", "Offmann"], "given-names": ["M", "AG", "N", "B"], "article-title": ["Protein structure mining using structural alphabet"], "source": ["Proteins: structure, function and bioinformatics"], "year": ["2007"]}, {"surname": ["Dudev", "Lim"], "given-names": ["M", "C"], "article-title": ["Discovering structural motifs using a structural alphabet: Applications to magnesium-binding sites"], "source": ["BMC Bioinformtics"], "year": ["2007"], "volume": ["8"], "fpage": ["106"], "pub-id": ["10.1186/1471-2105-8-106"]}, {"surname": ["Bailey", "Elkan"], "given-names": ["TL", "C"], "article-title": ["Unsupervised learning of multiple motifs in biopolymers using EM"], "source": ["Machine Learning"], "year": ["1995"], "volume": ["21"], "fpage": ["51"], "lpage": ["80"]}]
{ "acronym": [], "definition": [] }
45
CC BY
no
2022-01-12 14:53:33
BMC Bioinformatics. 2008 Aug 22; 9:349
oa_package/5f/c6/PMC2529324.tar.gz
PMC2529325
18706080
[ "<title>Background</title>", "<p>With growing interest in systems biology, mathematical models have been widely used to study metabolic networks, gene regulatory networks and cell signaling pathways [##REF##12424381##1##, ####REF##15364960##2##, ##REF##16025103##3##, ##REF##17024084##4##, ##REF##16980977##5##, ##REF##17625511##6####17625511##6##]. These mathematical models are used to reproduce experimental data and predict unobserved behaviors of the system. However, many sources of uncertainty including errors, inconsistency and noise of experimental data, absence of parameter information, incomplete representation of underlying process details, and poor understanding of the biological mechanisms impose a limit on model confidence. Furthermore, intrinsic variability or noise of the system such as the occurrence of stochastic events also affects the output of the model. Therefore, it is important not only to understand the dynamical properties of the model with particular parameter values, but also to further investigate the effect of their perturbations on the system [##REF##15710397##7##]. Sensitivity analysis is a powerful approach for investigating which parameters in a model have the strongest effect on overall behavior. In addition to identifying key parameters in a model, sensitivity analysis is valuable in pinpointing parameters, which should be in the focus of experimental perturbation [##REF##17060902##8##].</p>", "<p>Sensitivity analysis has been widely utilized for the systems biology research [##REF##15364960##2##,##REF##15710397##7##,##UREF##0##9##, ####REF##9078251##10##, ##REF##12242336##11##, ##UREF##1##12##, ##REF##15454422##13##, ##REF##16623463##14##, ##REF##16986622##15##, ##REF##17616983##16####17616983##16##]. However, it is time consuming for researchers to apply different algorithms to their specific models. In order to automate sensitivity analysis for different types of systems biology models, we developed a free software tool named SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool. SBML is a language developed by the systems biology community to represent and exchange models of biochemical reaction networks [##REF##14641091##17##]. SBML is being used by a large group of software developers and researchers. More than 120 software systems have so far been developed for supporting SBML <ext-link ext-link-type=\"uri\" xlink:href=\"http://sbml.org\"/>. Although a few existing software systems such as COPASI [##REF##17032683##18##] and SBToolbox [##REF##16317076##19##] incorporate local sensitivity analysis, a powerful, flexible and broadly applicable sensitivity analysis platform is still lacking. In particular, some important features missing from the existing software systems are described below.</p>", "<p>Firstly, some mathematical models of biological system include discontinuous events, such as the division of cells, removal of biological signal at a specific time or blocking protein synthesis during an experiment. Most existing SBML supported software systems (except for SBML-PET [##REF##16926221##20##], MathSBML [##REF##15087311##21##], SBTOOLBOX2 [##REF##16317076##19##], etc.) do not support models involving such discontinuous events. The broad applicability of these software systems is thus limited.</p>", "<p>Secondly, none of the existing SBML software packages allows for global sensitivity analysis. A few of the existing software systems can run local sensitivity analysis which introduces a small perturbation of one parameter for each simulation. Therefore, local sensitivity analysis investigates sensitivity of the model outputs with respect to a particular point in the parameter space. However, a single \"true\" point of parameter set may not occur in nature. It is likely that biological parameters such as rate constants and initial concentrations are variable in a large range depending on the specific cell types and cellular environments. For this reason, a global sensitivity analysis is valuable to explore sensitivities of model outputs to simultaneous variations of all the parameters over a large range and examine possible non-linear effects of the parameters as well as their interactions [##REF##15710397##7##].</p>", "<p>Thirdly, the results of sensitivity analysis correspond to specific model outputs. The specific model outputs of interest usually vary from case to case. In some cases, users may want to study the integrated or maximum response of certain species, while in other cases interest may be placed on particular time dependent or steady state responses of the system. Thus, a good sensitivity analysis software platform should provide various options for specifying model outputs.</p>", "<p>Here, we present the software system SBML-SAT that encompasses all of the above capabilities. It is worth pointing out that the purpose of this paper is not to explain the technical details of the software (described in the manual file) or the published algorithms, nor to present any particular biological findings. Rather, we provide an overview of the software, its validation with a variety of mathematical models for biological systems and demonstrate its broad applicability.</p>" ]
[]
[ "<title>Results</title>", "<p>In this section, we will demonstrate the functions and broad applicability of SBML-SAT using a variety of mathematical models for the biological systems. All of the models presented here are pre-encoded in SBML format and most of them are taken from the BioModels Database [##REF##16381960##39##]. At the start of each subsection, a brief description of the instructions to operate SBML-SAT for each function are provided to enable the reader to further envision the interaction with the software tool and facilitate its use.</p>", "<title>Simulation of SBML models</title>", "<p>To simulate a pre-constructed SBML model, the user loads the SBML model, sets the time course for simulation, and selects \"Run Simulation\".</p>", "<p>SBML-SAT provides an easy way to run a simulation and visualize the simulation results for SBML models. The output screen for SBML-SAT model simulation is shown in Figure ##FIG##1##2##. In order to test the wide applicability of SBML-SAT, we ran simulations for a variety of models from the BioModels Database, which include biophysical models, signaling pathways, gene expression and metabolic networks. The results shown in Figure ##FIG##2##3## demonstrates that SBML-SAT appropriately simulates both continuous SBML models (signaling pathway, gene expression and metabolic models), as well as those with discontinuous events (cell cycle model) with different degrees of complexity and nonlinearity.</p>", "<title>Sample local sensitivity analysis</title>", "<p>To conduct the local sensitivity analysis, the user</p>", "<p>• loads the SBML model,</p>", "<p>• sets the time course,</p>", "<p>• chooses the parameter(s),</p>", "<p>• defines the perturbation coefficient, and</p>", "<p>• selects the objects (ODE variables or reaction rates) and the model output operation for the analysis,</p>", "<p>• select the appropriate analysis approach to run.</p>", "<p>The result of a SBML-SAT normalized local sensitivity analysis on the MAPK cascade model (BioModels ID: BIOMD0000000010) is shown in Figure ##FIG##3##4##. For this analysis, the objects of the sensitivity analysis were the state variables associated with the various phosphorylated forms of the MAPK cascade elements and the model output analyzed were the integrated responses. The parameters perturbed were the initial concentrations of each form with the default perturbation coefficient. These results indicate that the integrated response of the MAPK concentration was the most sensitive to the initial concentration of MAPK.</p>", "<title>Sample global sensitivity analysis</title>", "<p>The user interface and operation for performing global sensitivity analyses is similar to that for the local sensitivity analysis: the user specifies the time course, object(s) and parameter(s) as well as the model output(s) for global sensitivity analysis. In addition, the user chooses the global sensitivity analysis method, and sets the variation range of the parameter values. The user must also define the number of Monte-Carlo simulations to be performed to base the analysis upon: this is highly dependent upon the nature of the model, the number of parameters (factors) to be analyzed, and the size of the parameter space (factor levels). The user needs to try different \"Number of Simulations\". If the analysis results are not significantly changed by the increasing of \"Number of Simulations\", then the results are assumed to be reliable and accurate enough. Once all these settings are done, SBML-SAT is ready to perform the specified global sensitivity analysis. The time required to complete the analysis varies from several minutes to several hours. It depends on the complexity of the model and the number of Monte-Carlo simulations.</p>", "<p>We use a model of the receptor trafficking network to demonstrate how to implement global sensitivity, steady state and robustness analyses in SBML-SAT. The general model of receptor trafficking networks is composed of the de novo production of surface receptor, ligand-receptor interaction, internalization, recycling and degradation of both empty and occupied receptors (Figure ##FIG##4##5##). The symbols of the parameters in the model and their corresponding biological processes are listed in Table ##TAB##0##1##. Detailed information about this model is available in our previous work [##REF##17825822##24##,##REF##18546478##40##].</p>", "<p>The results of global sensitivity analysis of the integrated response of the state variables in the receptor trafficking model using all four different methods are shown in Figure ##FIG##5##6A–D##. The exact values of the sensitivity indices obtained by different methods are not comparable because of their distinct definitions. However, the ranks or relative importance of the parameters to the model output are similar among different global sensitivity analysis methods. The results suggest that the rates of ligand-receptor complexes formation (parameters <italic>k</italic><sub>2 </sub>and <italic>k</italic><sub>3</sub>) are very important to the integrated response of ligand concentration (<italic>L</italic>). In contrast, the integrated response of ligand-receptor complexes (<italic>LRs </italic>and <italic>LRi</italic>) are shown to be mainly affected by the rates of the internalization, recycling and dephosphorylation of the occupied receptors (parameters <italic>k</italic><sub>6</sub>, <italic>k</italic><sub>7 </sub>and <italic>k</italic><sub>8</sub>). The MPSA global sensitivity analysis result of the time dependent response (Figure ##FIG##5##6E##) indicates that <italic>k</italic><sub>2 </sub>is the key regulator for <italic>R</italic><sub><italic>s </italic></sub>behavior at the early stage (before 20 minutes), but its effect is reduced significantly at a later stage. Upon further analysis, the MPSA global sensitivity analysis of the steady state response (Figure ##FIG##5##6F##) shows that the steady state of <italic>R</italic><sub><italic>s </italic></sub>is not very sensitive to <italic>k</italic><sub>2</sub>.</p>", "<title>Sample steady state analysis</title>", "<p>A steady state analysis of a user loaded SBML model simply requires to select such analysis from the icons or pull down menu. SBML-SAT initially tries to algebraically solve the system of ODEs for equilibrium solutions. If that fails, the model is simulated over an extended time period to approach the stable steady state related to the initial conditions provided.</p>", "<p>The results of the steady state analysis of the model of receptor trafficking network are provided in Figure ##FIG##6##7##. At steady state, all the ligand molecules are taken up by the receptors and eventually degraded, while the internalized and surface receptors that remain unbounded by ligand reach non-zero equilibriums. This information helps to interpret the steady-state global sensitivity analysis results shown in Figure ##FIG##5##6F##.</p>", "<title>Sample robustness analysis</title>", "<p>To conduct a robustness analysis, the user</p>", "<p>• loads the SBML model,</p>", "<p>• sets the time course,</p>", "<p>• chooses the parameter(s),</p>", "<p>• defines the variation range of the parameter(s), and</p>", "<p>• selects the objects (ODE variables or reaction rates) and the model output operation for the analysis and eventually</p>", "<p>• runs the analysis</p>", "<p>Figure ##FIG##7##8## shows the result of robustness analysis of the receptor trafficking model. The steady state concentrations of different forms of receptors are plotted as a function of the total parameter variation (TPV) and the quantitative robustness metric is provided in the subplot title. The results indicate that the steady state concentrations of unbound receptors are less robust to parameter perturbations than the internalized unbound receptor concentration. Not surprisingly, the ligand-bound receptors' concentrations are very robust to the parameter perturbations since their steady state solutions are zero.</p>" ]
[ "<title>Discussion and conclusion</title>", "<p>Currently, a SBML model editor module is not available in SBML-SAT. Fortunately, many existing free software packages such as CellDesigner, SBMLeditor and COPASI, share a common functionality for constructing and editing SBML models. The users can easily generate their models with these free software packages and then run a variety of analyses in SBML-SAT by importing the model in SBML format. Although SBML-SAT doesn't provide a SBML editor for model construction, it provides a convenient track for modifying the initial conditions of the state variables and parameter values in the model. Moreover, delay differential equation models are not supported in SBML-SAT, as in most existing software systems. In practice, delay differential equations can be solved in approximation by converting to ordinary differential equations using the linear chain transformation [##UREF##11##41##]. Therefore, users can still apply SBML-SAT to their delay differential equation models.</p>", "<p>There are more than 120 SBML-supporting software packages for kinetic analysis of biological models and this number continues to grow. However, a powerful, flexible and broadly applicable software package for global sensitivity analysis and robustness analysis has been lacking. In reality, it is difficult and time consuming to implement different sensitivity analysis algorithms especially the global sensitivity analysis methods. Here we introduced, a free Matlab-based software tool, SBML-SAT, for both local and global sensitivity analysis of SBML models. With a user-friendly graphic interface, this tool allows the user to run sensitivity analysis, steady state analysis and robustness analysis for a variety of model outputs. Models involving events are also supported in SBML-SAT. Furthermore, created in Matlab, the most popular software used in the community of systems biology [##REF##17420739##42##], SBML-SAT has a good cross-compatibility with different platforms. Taken all together, we can expect that SBML-SAT will have a broad applicability among systems biologists.</p>" ]
[ "<title>Discussion and conclusion</title>", "<p>Currently, a SBML model editor module is not available in SBML-SAT. Fortunately, many existing free software packages such as CellDesigner, SBMLeditor and COPASI, share a common functionality for constructing and editing SBML models. The users can easily generate their models with these free software packages and then run a variety of analyses in SBML-SAT by importing the model in SBML format. Although SBML-SAT doesn't provide a SBML editor for model construction, it provides a convenient track for modifying the initial conditions of the state variables and parameter values in the model. Moreover, delay differential equation models are not supported in SBML-SAT, as in most existing software systems. In practice, delay differential equations can be solved in approximation by converting to ordinary differential equations using the linear chain transformation [##UREF##11##41##]. Therefore, users can still apply SBML-SAT to their delay differential equation models.</p>", "<p>There are more than 120 SBML-supporting software packages for kinetic analysis of biological models and this number continues to grow. However, a powerful, flexible and broadly applicable software package for global sensitivity analysis and robustness analysis has been lacking. In reality, it is difficult and time consuming to implement different sensitivity analysis algorithms especially the global sensitivity analysis methods. Here we introduced, a free Matlab-based software tool, SBML-SAT, for both local and global sensitivity analysis of SBML models. With a user-friendly graphic interface, this tool allows the user to run sensitivity analysis, steady state analysis and robustness analysis for a variety of model outputs. Models involving events are also supported in SBML-SAT. Furthermore, created in Matlab, the most popular software used in the community of systems biology [##REF##17420739##42##], SBML-SAT has a good cross-compatibility with different platforms. Taken all together, we can expect that SBML-SAT will have a broad applicability among systems biologists.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.</p>", "<title>Results</title>", "<p>This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.</p>", "<title>Conclusion</title>", "<p>SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.</p>" ]
[ "<title>Implementation</title>", "<title>Overview of the software system</title>", "<p>SBML-SAT is designed to run simulation, steady state analysis, robustness analysis, as well as local and global sensitivity analysis for ordinary differential equations (ODE) based biological models. SBML-SAT meets the needs mentioned in the rational section with the following features:</p>", "<p>Inspired by our previous work in SBML-PET project [##REF##16926221##20##], we enabled SBML-SAT to support a variety of models including assignment rules and events, even for complicated event scenarios such as bisecting mass in case of cell division. Therefore, SBML-SAT will have a wide applicability for different types of models.</p>", "<p>In addition to the implementation of traditional local sensitivity analysis, SBML-SAT provides four different global sensitivity analysis methods, including multi-parametric sensitivity analysis [##REF##15710397##7##,##UREF##1##12##], partial rank correlation coefficient analysis [##UREF##0##9##,##UREF##2##22##], SOBOL's method [##REF##16986622##15##,##UREF##3##23##] and weighted average of local sensitivities [##REF##15364960##2##]. Furthermore, steady state analysis and robustness analysis are also available in this tool. The algorithms for these different types of analyses are briefly described in the following section.</p>", "<p>The sensitivity analysis can be performed with respect to any ODE model variable (species amount or concentration) and reaction rate; these quantities are referred to as the object of the sensitivity analysis in SBML-SAT. The model output, which the sensitivity analysis is performed on, is defined through a functional operation on the object. The predefined model outputs in SBML-SAT are: steady state response, maximum response, integrated response, and time dependent response. The steady state response is only applicable for model objects that are ODE variables as the sensitivity analysis is computed with respect to the equilibrium solution of the system (when all derivatives of the ODE variables are algebraically set to zero). The maximum response is the maximum value of the object <italic>X</italic><sub><italic>i </italic></sub>(state variable or reaction rate) over the time course simulated:</p>", "<p></p>", "<p>The integrated response corresponds to the area under the curve when plotting <italic>X</italic><sub><italic>i </italic></sub>versus time. SBML-SAT approximates the integrated response for object <italic>X</italic><sub><italic>i </italic></sub>by the discrete summation [##REF##17825822##24##]:</p>", "<p></p>", "<p>The time dependent response performs multiple sensitivity analyses based on the values of the object, <italic>X</italic><sub><italic>i</italic></sub>, at selected time points during the simulated time course.</p>", "<p>SBML-SAT for Windows, Mac, and Linux can be freely downloaded from its website <ext-link ext-link-type=\"uri\" xlink:href=\"http://sysbio.molgen.mpg.de/SBML-SAT\"/>. The manual documentation file including a detailed tutorial for the usage of SBML-SAT is also available in the website. The future updates of SBML-SAT will be released on the website as well. Like most other SBML supported software systems, SBML-SAT requires a link to libSBML and utilizes SBMLToolbox [##REF##16574696##25##], which allows us to import SBML into MATLAB [##UREF##4##26##]. Once the SBML model is imported into SBML-SAT, a MATLAB file will be automatically generated, which includes the ODEs of the model. This is very helpful for the user, who wants to code in MATLAB for other purposes. To speed up the process of solving the ODEs, we employed the CVODE module of SUNDIALS (Suite of Nonlinear and Differential/Algebraic Equation Solvers) as the ODE Solver [##UREF##5##27##]. An interface to setting the options of CVODE solver is also available in SBML-SAT. Both SBMLToolbox and SUNDIALS [##UREF##6##28##] can be freely downloaded.</p>", "<p>In order to run the analysis in SBML-SAT, the users need to represent their models in SBML format which can be easily done using the existing software systems such as CellDesigner [##UREF##7##29##], COPASI [##REF##17032683##18##] and SBMLeditor [##REF##17341299##30##]. Then, the users can load the SBML models to SBML-SAT and perform a variety of analyses.</p>", "<p>Simulation, robustness analysis and sensitivity analysis can be easily implemented using SBML-SAT's graphical user interface (Figure ##FIG##0##1##). SBML-SAT allows the user to browse the model information, to save the model as well as to simulate and analyze the model. Simulation and sensitivity analysis results can be exported as text files, making it convenient for post-processing. In addition to the export function, SBML-SAT provides automatic visualization of the results. Furthermore, SBML-SAT is smart in remembering the user's settings for the corresponding tasks. The user can save his/her project settings as a project file and load it again to SBML-SAT for further analysis later.</p>", "<title>Local sensitivity analysis</title>", "<p>Local sensitivity analysis is a study of the changes in the model outputs with respect to parameter (factor) variations around a local point in the parameter space, which are quantified by the sensitivity coefficients. Mathematically, the sensitivity coefficients are the first order derivatives of model outputs with respect to the model parameters:</p>", "<p></p>", "<p>where <italic>O</italic><sub><italic>i </italic></sub>is the <italic>i</italic>-th model output and <italic>p</italic><sub><italic>j </italic></sub>is the <italic>j</italic>-th parameter. This is called \"Unnormalized Sensitivity\" in SBML-SAT. SBML-SAT employed centered difference approximation to compute the sensitivity coefficients in the following way [##REF##15695639##31##]:</p>", "<p></p>", "<p>When the model output and parameters are non-zero, the normalized sensitivity coefficients are defined as:</p>", "<p></p>", "<p>The new model outputs are calculated by a small perturbation (Δ<italic>p</italic><sub><italic>j</italic></sub>) of parameter <italic>p</italic><sub><italic>j </italic></sub>while keeping all the other parameter values the same: SBML-SAT computes one-at-a-time (OAT) local sensitivity coefficients.</p>", "<p>The proper choice of perturbation size is a delicate issue as it depends on the nature of the model and the numerical solution method. The perturbation should be small enough to achieve a negligible error in the centered difference approximation, and large enough to be unaffected by the numerical inaccuracies of the ODE solver. Too large parameter perturbation violates the implied linearity of the approximations in (4) and (5) and will provide inaccurate results. The user can modify the perturbation coefficient in the \"Sensitivity Analysis\" panel of SBML-SAT. The default perturbation is 0.1% of the corresponding parameter value, ie. Δ<italic>p</italic><sub><italic>j </italic></sub>= 0.001 × <italic>p</italic><sub><italic>j</italic></sub>.</p>", "<title>Global sensitivity analysis</title>", "<p>As mentioned in the rationale section, there are many sources of uncertainty in the model parameter values. Global sensitivity analysis is a useful way to investigate the global effects of parameters on the model output by simultaneously perturbing all the parameters within a parameter space. In the SBML-SAT tool, four different global sensitivity analysis methods are available. Each method has a distinct mathematical rationale and can be used for different purposes.</p>", "<p>(1) Multi-Parametric Sensitivity Analysis (MPSA): This method was first proposed by Hornberger et al [##UREF##8##32##] in the field of hydrology and further applied to modeling of biological systems by Cho et al. [##UREF##1##12##] and Zi et al. [##REF##15710397##7##]. MPSA can be used to study the relative importance of the parameters with respect to the model output. The basic idea of MPSA is to map the uncertainty of the parameters into the model output by randomly generating parameter values from predefined distributions (without prior knowledge, uniform distributions are assumed). SBML-SAT uses Latin Hypercube Sampling (LHS) method to sample the parameter values under the given ranges of the parameters [##REF##15710397##7##]. The LHS method is an efficient method to sample random parameter vectors while guaranteeing that individual parameter ranges are evenly covered [##UREF##9##33##]. The ranges of the parameter distributions are usually determined from the available literature or guided by experience of the researchers.</p>", "<p>For each randomly generated parameter set, the objective function is computed by the sum of square errors between the model outputs from the random parameter set and the reference parameter set. The next step is to classify each parameter set as acceptable or unacceptable by comparing its objective function value to the average of all the objective function values. If the objective function value is smaller than the average, the parameter set is classified as \"acceptable\"; otherwise it is \"unacceptable\". Then, the cumulative frequency is calculated for both acceptable and unacceptable cases for each selected parameter with increasing parameter values. Finally, the sensitivity of the parameter is measured by the maximum vertical distance of the two cumulative frequency curves according to the Kolmogorov-Smirnov statistics [##REF##15710397##7##]. The calculated MPSA sensitivities are between 0 and 1, where a value closer to 1 indicates a relatively higher importance of the parameter variation to the overall corresponding model output.</p>", "<p>(2) Partial Rank Correlation Coefficient Analysis (PRCC): The PRCC method is a rank transformed linear regression analysis that is routinely used for analysis of systems with a nonlinear and monotonic relationship between the system inputs and outputs [##UREF##2##22##]. Linear regression analysis methods best fit a straight line to input and output values. When nonlinear, monotonic relationships exist between system input and outputs, poor linear regression fits can be alleviated by performing the linear regression analysis on a rank ordered list of the model output and input values. PRCC calculates the sensitivity indices from the Pearson correlation coefficients between the model output and input parameters as well as each pair of parameters after rank transformation [##UREF##0##9##]. Interactions among different parameters are eliminated by evaluating multiple regression models on a subset of parameters that excludes a single parameter. The calculated PRCC sensitivity indices are a standardized sensitivity measurement between -1 and 1 with 0 indicating an input to which the model output is completely insensitive. SBML-SAT computes PRCC as implemented in [##REF##16986622##15##] with LHS sampling of the parameter space.</p>", "<p>(3) SOBOL's Method: SOBOL's method is a variance based method that makes no assumptions on the relationship between the system inputs and outputs. It is computationally expensive since it utilizes a large number of model simulations with parameter values sampled from the parameter space by the winding stair algorithm. The variance of the numerous model outputs is estimated by Monte Carlo integrations. The model output variance is apportioned into summands of partial variances from combinations of input parameters with increasing dimensionality [##UREF##3##23##]. The total effects sensitivity indices quantify all of the effects that a parameter, in combination with any other parameter(s), has on the model output. They are defined as the ratio of the sum of the related partial variances to the overall variance of the model output. The larger the fraction, the higher is the corresponding sensitivity. SBML-SAT calculates the total effect sensitivity indices.</p>", "<p>(4) Weighted Average of Local Sensitivities: In this approach, local sensitivity indices are calculated at multiple random points within the parameter space; a weighted average of the local sensitivity indices serves to provide some approximation of the global parameter sensitivities. Bentele et al. [##REF##15364960##2##] proposed a Boltzmann-Distribution weighting function, exp(-E/k<sub>b</sub>T), where E is the error between the model simulation and experimental data and k<sub>b</sub>T is a customizable scaling factor. Herein we define E as the least squares error (LSE) between the perturbed model simulation and reference model simulation and k<sub>b</sub>T as the minimum LSE. Based on this weighting function, the random points in the parameter space with low LSE contribute the most to the calculated global sensitivity indices.</p>", "<title>Steady state analysis</title>", "<p>SBML-SAT uses two different methods to check the existence of a steady state for the SBML model. The first strategy is to set the ordinary differential equations to zero and solve the algebraic system by KINSOL, which is part of the software family called SUNDIALS and is an algebraic system solver based on Newton-Krylov method [##UREF##5##27##]. Another method is called quasi steady state method, which runs the simulation for a very long time and check the rate of change of the ODE variables (such as species and other state variables) at different time points. When the rates of change for all the variables are smaller than a certain threshold (1 × 10<sup>-10</sup>), a quasi steady state is reached. The latter method is useful for steady state analysis of models that include events and implicit mass conservation rules. These two methods will only find a single steady state to which the initial condition converges. Other existing steady states as well as the steady state of oscillatory and unstable system will not be detected. SBML-SAT automatically selects the method for steady state analysis. If the model doesn't have events, SBML-SAT will use the algebraic method to detect the steady state of the model. Otherwise the second quasi steady state method will be used.</p>", "<title>Robustness analysis</title>", "<p>Robustness is one of the fundamental properties of biological systems, which allows the system to maintain its behavior against random perturbations [##REF##15369668##34##,##REF##17882156##35##]. SBML-SAT employs a method proposed in previous studies to investigate the robustness of model output against the total parameter variation, <italic>TPV</italic>, which is defined as [##REF##9202124##36##, ####REF##14604583##37##, ##UREF##10##38####10##38##]:</p>", "<p></p>", "<p>where <italic>k</italic><sub><italic>n </italic></sub>are the perturbed model parameters randomly generated by the LHS method; are the corresponding reference parameter values in the model; <italic>L </italic>is the total number of parameters that are randomly varied.</p>", "<p>To measure robustness, we use the robustness metric , which quantifies the change in a function of the system (model output) induced by TPV:</p>", "<p></p>", "<p>where <italic>f</italic><sub>0 </sub>and <italic>f</italic><sub><italic>p </italic></sub>are the model output which describes the biological function under non-perturbed condition (reference model) and perturbed condition (parameters varied model), respectively. <italic>N </italic>is the total number of perturbations or model simulations. <italic>M </italic>denotes the model for the corresponding system. When the reference model output is zero, the following alternative definition is used:</p>", "<p></p>", "<p>According to the definition of (7) and (8), the robustness score of a biological system (model) usually assumes a negative value. The closer it is to zero, the more robust the system (model) is against the perturbations (parameter variations). When the robustness score of a system is zero, it means this system is absolutely robust against the imposed perturbations.</p>", "<p>The difference between the robustness scores of two systems (models) with respect to a certain model output against the perturbations can be evaluated as:</p>", "<p></p>", "<p>The comparison of the robustness scores of two systems/models is meaningful only when the evaluated model output of the two systems/models are the same and perturbations are operated in the same way.</p>", "<title>Availability and requirements</title>", "<p><bold>Project name: </bold>SBML-SAT: A Systems Biology Markup Language (SBML) based Sensitivity Analysis Tool</p>", "<p><bold>Project homepage: </bold><ext-link ext-link-type=\"uri\" xlink:href=\"http://sysbio.molgen.mpg.de/SBML-SAT/\"/></p>", "<p><bold>Operating system(s): </bold>Windows, Linux, Mac</p>", "<p><bold>Programming language:</bold> Matlab</p>", "<p><bold>Other requirements: </bold>SBMLToolbox, SUNDIALS TB</p>", "<p><bold>License:</bold> none</p>", "<p><bold>Any restrictions to use by non-academics:</bold> none</p>", "<title>Abbreviations</title>", "<p>SBML: Systems Biology Markup Language; SBML-SAT: Systems Biology Markup Language based Sensitivity Analysis Tool; MPSA: Multi-Parametric Sensitivity Analysis; PRCC: Partial Rank Correlation Coefficient; WALS: Weighted Average of Local Sensitivities; GUI: Graphic User Interface; LSE: Least Squares Error; TPV: Total Parameter Variation</p>", "<title>Authors' contributions</title>", "<p>ZZ proposed the project, designed the GUI interface and wrote all the source code of the software. AR and YZ contributed some algorithms for global sensitivity analysis methods. ZZ, YZ, AR and EK wrote the manuscript and tested the software. All authors have read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>Z. Zi is supported by PhD program of the IMPRS for Computational Biology and Scientific Computing. EK thanks the Yeast System Biology Network (YSBN, EU project: grant LSHG-CT-2005-018942) for support.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>GUI of SBML-SAT</bold>. The graphic user interface (GUI) of SBML-SAT provides an easy way for the user to run the simulation, steady state analysis, sensitivity analysis and robustness analysis for SBML models.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Plot of simulation result in SBML-SAT</bold>. The plot function enables the user to visualize the time course profiles of species and reaction rates. This graph shows the simulation result of the MAPK cascade model [##REF##10712587##43##] (BioModels ID: BIOMD0000000010).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Simulation of different types of models in SBML-SAT</bold>. (A) Simulation result of the fission yeast cell cycle model (events included, BioModels ID: BIOMD0000000111), identical to Fig. 4 of [##REF##12779461##44##]. (B) Simulation result of a NF-κB signalling pathway model (BioModels ID: BIOMD0000000140), identifical to Fig. 2F of [##REF##12424381##1##]. (C) Simulation result of a T cell gene expression model (BioModels ID: BIOMD0000000122), identical to Fig. 4a of [##REF##17031595##45##]. (D) Simulation result of a metabolic model (BioModels ID: BIOMD0000000106), identical to Fig. 2A of [##REF##17381237##46##].</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Local sensitivity analysis in SBML-SAT</bold>. Local sensitivity analysis of the integrated response of MAPK cascade model [##REF##10712587##43##] (BioModels ID: BIOMD0000000010) with respect to variation of initial conditions.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Scheme of receptor trafficking network model</bold>. Schematic description of the receptor trafficking network. The symbols <italic>L, Rs, LRs, Ri, LRi </italic>represent the ligand, unbound cell surface receptor, cell surface ligand-receptor complex, internalized unbound receptor and internalized ligand-receptor complex, respectively. The parameter information is listed in Table 1.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Global sensitivity analysis in SBML-SAT</bold>. Results of different types of global sensitivity analysis for the receptor trafficking model. (A) MPSA analysis, (B) PRCC analysis, (C) SOBOL's total effect sensitivity analysis, and (D) WALS analysis of integrated response. (E) MPSA analysis of the time dependent response. (F) MPSA analysis of the steady state response.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Steady state analysis in SBML-SAT</bold>. Steady state analysis of the receptor trafficking network model in SBML-SAT.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Robustness analysis in SBML-SAT</bold>. Robustness analysis of the steady state response of the receptor trafficking model against simultaneous variations of the parameter values. The red circles correspond to the reference model output. The blue points correspond to the model outputs under perturbed parameter values. (Specifications for SBML-SAT, \"Model Output\": \"Steady State Response\"). (A-B) Robustness of unbound receptor steady state concentration. (C-D) Robustness of ligand bound receptor steady state concentration.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Parameters for the model of the receptor trafficking network</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Symbols of Parameters</td><td align=\"left\">Corresponding Biological Processes</td></tr></thead><tbody><tr><td align=\"center\">k<sub>1</sub></td><td align=\"left\">de novo synthesis of surface receptor</td></tr><tr><td align=\"center\">k<sub>2</sub></td><td align=\"left\">formation of ligand-receptor complex</td></tr><tr><td align=\"center\">k<sub>3</sub></td><td align=\"left\">dissociation of ligand-receptor complex</td></tr><tr><td align=\"center\">k<sub>4</sub></td><td align=\"left\">recycling of internalized unbound receptor</td></tr><tr><td align=\"center\">k<sub>5</sub></td><td align=\"left\">internalization of unbound surface receptor</td></tr><tr><td align=\"center\">k<sub>6</sub></td><td align=\"left\">recycling of internalized ligand-receptor complex</td></tr><tr><td align=\"center\">k<sub>7</sub></td><td align=\"left\">internalization of surface ligand-receptor complex</td></tr><tr><td align=\"center\">k<sub>8</sub></td><td align=\"left\">dephosphorylation of ligand-receptor complex</td></tr><tr><td align=\"center\">k<sub>9</sub></td><td align=\"left\">degradation of unbound receptor</td></tr><tr><td align=\"center\">k<sub>10</sub></td><td align=\"left\">degradation of ligand-receptor complex</td></tr></tbody></table></table-wrap>" ]
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[]
[]
[]
[]
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[ "<graphic xlink:href=\"1471-2105-9-342-1\"/>", "<graphic xlink:href=\"1471-2105-9-342-2\"/>", "<graphic xlink:href=\"1471-2105-9-342-3\"/>", "<graphic xlink:href=\"1471-2105-9-342-4\"/>", "<graphic xlink:href=\"1471-2105-9-342-5\"/>", "<graphic xlink:href=\"1471-2105-9-342-6\"/>", "<graphic xlink:href=\"1471-2105-9-342-7\"/>", "<graphic xlink:href=\"1471-2105-9-342-8\"/>" ]
[]
[{"surname": ["Blower", "Dowlatabadi"], "given-names": ["SM", "H"], "article-title": ["Sensitivity and Uncertainty Analysis of Complex-Models of Disease Transmission - an Hiv Model, as an Example"], "source": ["International Statistical Review"], "year": ["1994"], "volume": ["62"], "fpage": ["229"], "lpage": ["243"]}, {"surname": ["Cho", "Shin", "Kolch", "Wolkenhauer"], "given-names": ["KH", "SY", "W", "O"], "article-title": ["Experimental design in systems biology based on parameter sensitivity analysis using a Monte Carlo method: a case study for the TNF alpha-mediated NF-kappaB signal transduction pathway"], "source": ["SIMULATION"], "year": ["2003"], "volume": ["79"], "fpage": ["726"], "lpage": ["739"]}, {"surname": ["Draper", "Smith"], "given-names": ["N", "H"], "source": ["Applied Regression Analysis"], "year": ["1981"], "edition": ["2nd"], "publisher-name": ["New York , Wiley"]}, {"surname": ["Sobol"], "given-names": ["IM"], "article-title": ["Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates"], "source": ["Mathematics and Computers in Simulation"], "year": ["2001"], "volume": ["55"], "fpage": ["271"], "lpage": ["280"]}, {"collab": ["Matlab"]}, {"surname": ["Hindmarsh", "Brown", "Grant", "Lee", "Serban", "Shumaker", "Woodward"], "given-names": ["AC", "PN", "KE", "SL", "R", "DE", "CS"], "article-title": ["SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers"], "source": ["Acm Transactions on Mathematical Software"], "year": ["2005"], "volume": ["31"], "fpage": ["363"], "lpage": ["396"]}, {"collab": ["SUNDIALS"]}, {"surname": ["Funahashi"], "given-names": ["A"], "suffix": ["Tanimura, N., Morohashi, M., and Kitano, H"], "article-title": ["CellDesigner: a process diagram editor for gene-regulatory and biochemical networks"], "source": ["BIOSILICO"], "year": ["2003"], "volume": ["1"], "fpage": ["159"]}, {"surname": ["Hornberger", "Spear"], "given-names": ["GM", "RC"], "article-title": ["An Approach to the Preliminary-Analysis of Environmental Systems"], "source": ["Journal of Environmental Management"], "year": ["1981"], "volume": ["12"], "fpage": ["7"], "lpage": ["18"]}, {"surname": ["Mckay", "Beckman", "Conover"], "given-names": ["MD", "RJ", "WJ"], "article-title": ["Comparison of 3 Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code"], "source": ["Technometrics"], "year": ["1979"], "volume": ["21"], "fpage": ["239"], "lpage": ["245"]}, {"surname": ["Zi", "Sun"], "given-names": ["ZK", "ZR"], "article-title": ["Robustness analysis of the IFN-gamma induced JAK-STAT signaling pathway"], "source": ["Journal of Computer Science and Technology"], "year": ["2005"], "volume": ["20"], "fpage": ["491"], "lpage": ["495"]}, {"surname": ["Fall;", "Marland;", "Wagner;", "Tyson"], "given-names": ["CP", "ES", "JM", "JJ"], "source": ["Computational Cell Biology"], "year": ["2002"], "publisher-name": ["New York , Springer"]}]
{ "acronym": [], "definition": [] }
46
CC BY
no
2022-01-12 14:47:26
BMC Bioinformatics. 2008 Aug 15; 9:342
oa_package/e9/9a/PMC2529325.tar.gz
PMC2529326
18664292
[ "<title>Background</title>", "<p>Mass spectrometry (MS) has emerged in recent years as one of the most powerful tools for protein analysis available to proteomics research. MS-based protein identification strategies typically involve the digestion of protein samples prior to introduction into the mass spectrometer by a site-specific protease such as trypsin. The derived peptides are subsequently ionized at entry into the mass spectrometer and measured as intact fragment (parent) ions. Subsets of these ions can then be selected on the basis of their mass-to-charge ratio (m/z) and subject to further fragmentation, most commonly using collision induced dissociation (CID), in a process known as tandem mass spectrometry (MS/MS). Under the conditions utilized in CID, peptides fragment in predictable patterns resulting in a series of signature spectra. Identification of the protein components in an analyzed sample can then be achieved by correlating the observed signature spectra of individual peptides with the predicted MS/MS spectra of the amino acid sequences derived from protein databases such as Swiss-Prot and TrEMBL <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/\"/>.</p>", "<p>Over the past few years, computer-assisted database searching using mass spectrometry data has become the standard method for high-throughput protein identification. Unsurprisingly, the performance of computer search algorithms, for example Sequest <ext-link ext-link-type=\"uri\" xlink:href=\"http://bart.scripps.edu/wiki/index.php/SEQUEST\"/>, Mascot <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.matrixscience.com/\"/> and others, has a dramatic influence on the accuracy and reliability of the protein identification process [##REF##14997485##1##]. In general terms, such algorithms use a built-in fragmentation model to construct theoretical fragmentation spectra for candidate peptides derived from databases, and then evaluate the match of these theoretical spectra with observed spectra from MS/MS experiments using defined scoring criteria. The candidate peptide whose predicted fragmentation spectra best matches the experimental MS/MS spectra is selected as representing the true identity of the analyzed peptide [##UREF##0##2##, ####REF##10612281##3##, ##REF##14572045##4##, ##REF##12622365##5####12622365##5##].</p>", "<p>Unfortunately, the performance of computer algorithms currently available is still less than ideal. Generally, these algorithms tend to only utilize the positional information (mass-to-charge ratios; m/z) contained in MS/MS spectra, whereas fail to systematically incorporate the additional intensity information available in the same spectra. The intensity information is usually applied in an indirect way, for example in Mascot, peaks are selected based on intensity for peptide matching, and in Sequest peaks for y/b ions are supposed to be higher than peaks for other ions. Previous published work indicates that some efforts have been made to try to utilize spectrum intensity information more effectively [##REF##12585468##6##, ####REF##15262780##7##, ##REF##16316165##8####16316165##8##], but they have predominantly focused on the design of better scoring methods. Furthermore, the application of these previous studies was limited by the oversimplification of the peptide fragmentation models which were applied to construct the theoretical spectra. This is probably due to our insufficient understanding of the complex mechanisms involved in peptide fragmentation during MS/MS analysis, which makes accurate prediction of spectra intensities in MS/MS very difficult.</p>", "<p>Recently, a number of research groups have proposed novel fragmentation models in attempts to better understand the mechanisms involved in MS/MS. For example, Wysocki, <italic>et al</italic>. proposed the \"Mobile Proton\" hypothesis in which protons added to a peptide can transfer along its backbone from the initial site of protonation and subsequently induce fragmentation [##REF##11180630##9##, ####UREF##1##10##, ##UREF##2##11####2##11##]. According to the hypothesis, peptides can be classified as \"mobile\" or \"non-mobile\" by the ratio of charge to Arginine number. They also statistically examined the effect of particular amino acid residues such as asparagine, proline and histidine on fragmentation patterns, with the aim of deducing rules for the influence of these residues on spectra intensities [##REF##11128940##12##, ####REF##15053674##13##, ##REF##12720328##14####12720328##14##]. The \"Mobile Proton\" model was later expanded by Kapp, <italic>et al</italic>. into the \"Relative Mobile Proton\" (RMP) model [##REF##14616009##15##], in which peptides are classified as \"mobile\", \"non-mobile\" and \"partial mobile\" based on their charge number and basic residue number. Schutz, <italic>et al</italic>. used a linear model based on RMP hypothesis to calculate the influence of sequence context effects on fragmentation [##REF##14641094##16##]. A kinetic model was developed by Zhang [##REF##15253624##17##,##REF##16194101##18##] to simulate the fragmentation process of a peptide undergoing low-energy CID, and further used to predict the spectra intensity patterns of given peptides. Machine-learning techniques such as Bayesian decision trees have also been used to investigate peptide fragmentation behaviour [##REF##14730315##19##] and from this work a group of features that may influence peptide fragmentation have been proposed. This was the first attempt to our knowledge to systematically utilize intensity information of peptide fragmentation. However, the machine learning approach used in [##REF##14730315##19##] discovered only a limited number of features to have significant effect on peptide fragmentation and many of these features have already been revealed by other researchers, for example, the presence of basic residues in a peptide sequence, the charge state of the peptide and the presence of proline residue in peptide sequence, etc [##UREF##1##10##,##UREF##2##11##,##REF##11875127##20##, ####REF##14987077##21##, ##REF##17211901##22####17211901##22##]. Whether many other putative determinants are of relevance, and the extent of their influence on fragmentation, is still in question.</p>", "<p>In this work, we present a probabilistic machine-learning approach designed to analyze the intensity information contained in MS/MS data, with the aim of developing a better understanding of the rules involved in peptide fragmentation events. A library of peptide-relevant features as listed in Table ##TAB##0##1## was examined and a score was assigned to each feature to represent the magnitude of its influence on peptide fragmentation. This information was then used to develop a more sophisticated model to predict the intensity patterns of spectra generated in MS/MS with the expectation that this will improve the reliability of peptide identification. Overall, we attempted to find answers for three basic questions: What factors influence peptide fragmentation during CID? What is the relationship between the features that influence peptide fragmentation and the resulting intensity pattern of fragmentation spectra? And finally, how can we accurately predict the spectrum intensity pattern of a given peptide and use this information to improve peptide identification?</p>" ]
[ "<title>Methods</title>", "<p>We proposed that ion intensities resulting from peptide fragmentation in MS/MS can be expressed as a complicated mathematical function of various features that reflect the physical, chemical and other characteristics of fragmented peptides. Accordingly, a Bayesian neural network was constructed in an attempt to link the spectrum intensity of a peptide and these features together. The Automatic Relevance Determination (ARD) technique [##UREF##3##27##,##UREF##4##28##] was applied to this network to distinguish features (input) that significantly influence intensity patterns of fragmentation spectra (output) from those that do not. The structure of the Bayesian neural network used in the study is shown in Figure ##FIG##0##1##. The network is a fully connected feed-forward neural network comprising three layers: an input layer, a hidden layer and an output layer. In our study, we used this network to analyse a library of features that were supposed to influence peptide fragmentation, distinguishing those with significant influence from non-influential or less-influential ones. The feature set used in our study is a modified version of what was used by Elias, <italic>et al</italic>. [##REF##14730315##19##]. The original feature set was reduced by eliminating features that unfit for our study, such as b/y ion type and identity of residues at N/C terminus of peptides. The descriptions and abbreviations used for the features applied in our study are listed in Table ##TAB##0##1##, and the values of relevant amino acid properties that were used for calculating these features can be found in Table ##TAB##1##2##.</p>", "<p>In this study, we considered all bonds within a given peptide to represent potential sites for fragmentation. As a consequence, the failure to observe peaks resulting from cleavage at these sites were not considered to denote that cleavage had not occurred at these peptide bonds, but was instead taken to indicate that cleavage at the sites concerned was poor, i.e. the relevant peaks were too low in intensity to be separated from the background noise. Accordingly, all bonds within a given peptide were coded separately into the network. One set of input corresponded to features derived from only one target peptide bond. We used 73 nodes in the input layer of the Bayesian neural network to represent the 35 features of target peptide/peptide bond as listed in Table ##TAB##0##1##. In order to represent the identification of the residue at N- or C- terminus of the target peptide bond, we used 20 nodes to cover all the 20 alternative amino acid identifications of one residue. Their values are binary such that only one of the 20 nodes that correspond to the identification of the target residue was set to 1 during training and all others were set to zero. Every node in the input layer has been given an independent coefficient to reveal its \"relevance\" to the network output. The hidden layer comprises 40 nodes, making the network less complicated while simultaneously maintaining enough computational power. The activation function of the hidden layer is sigmoidal: <italic>f </italic>(<italic>x</italic>) = 1+e<sup>-<italic>ωx</italic></sup>, where <italic>ω </italic>is the parameter used to control saturation. Finally, the neural network has only one output, defined to represent the quantity of ions generated from fragmentation at the specific peptide bond, i.e. it represents the unnormalized spectrum intensity of the particular target peptide bond. The network and all the other relevant programs were implemented using Matlab.</p>", "<p>Theoretically, one peptide can fragment into a complete series of ions of smaller mass, comprising those designated as x, y, z and a, b, c ions, among which b and y ions are usually the most prominent [##REF##11875127##20##,##REF##12641236##24##]. Typically, classical protein identification algorithms rank peptide candidates according to the number of matches for both the b and y ion series. However, it has been observed that b ions are more likely to degrade into a variety of ions with lower mass by losing CO, NH<sub>3 </sub>or other neutral components [##REF##12641236##24##], thus introducing difficulties in accurate evaluation of their original intensities. To allow us to take advantage of the intensity information of spectra, we made the assumption that a doubly charged peptide can only fragment into two singly charged ions rather than (albeit relatively rarely), a doubly charged ion and a neutral counterpart. Under this assumption, y/b ions would be generated at the same rate during fragmentation, and thus manifest the same ion intensities (before degradation) on MS/MS maps, enabling us to focus only on intensities of the y ions. In practice, however, doubly charged y ions were still taken into account. We recorded the ion intensity of a certain fragmentation site by summing up the intensities of all y-related ions, including singly and doubly charged y-ions and intensities of their degraded ions from losses of H<sub>2</sub>O and NH<sub>3</sub>, while disregarding the intensities of the complementary b-ion series. Recorded intensity values of a peptide were firstly normalized by dividing its intensity of total ion current and then times 100 to unify different intensity magnitude among peptides. They are subsequently subject to log transformation to reduce intensity-dependent variances [##UREF##5##29##] and finally normalized to [0, 1] scale.</p>", "<p>For the <italic>p</italic><sup><italic>th </italic></sup>peptide with <italic>n</italic><sub><italic>p </italic></sub>peptide bonds, we have an input set {<italic>B</italic><sub><italic>p</italic>1</sub>, <italic>B</italic><sub><italic>p</italic>2</sub>,..., } representing all bonds within the peptide. We obtain a set of output {<italic>O</italic><sub><italic>p</italic>1</sub>,<italic>O</italic><sub><italic>p</italic>2</sub>,..., } from the neural network that can be normalized to a relative scale, as shown in Eq. 2:</p>", "<p></p>", "<p>where <italic>O</italic><sub><italic>pmax </italic></sub>= <italic>max</italic>{<italic>O</italic><sub><italic>p</italic>1</sub>, <italic>O</italic><sub><italic>p</italic>2</sub>,..., }. The normalized output set <italic>I</italic><sub><italic>pk</italic>-<italic>predict </italic></sub>(<italic>k </italic>= 1,..., <italic>n</italic><sub><italic>p</italic></sub>) can hence be viewed as normalized spectra intensities to approximate the real spectra intensities <italic>I</italic><sub><italic>pk</italic>-<italic>real </italic></sub>(<italic>k </italic>= 1,..., <italic>n</italic><sub><italic>p</italic></sub>) observed from experimental MS/MS data.</p>", "<p>In contrast to the classical back-propagation algorithm [##UREF##3##27##], the normalization process used in the training of the neural network, leads to a unique way of updating network weights. Let <italic>E</italic><sub><italic>pk </italic></sub>be the error calculated at the <italic>k</italic><sup><italic>th </italic></sup>peptide bond of the <italic>p</italic><sup><italic>th </italic></sup>peptide,</p>", "<p></p>", "<p>the derivatives of <italic>E</italic><sub><italic>pk </italic></sub>can be evaluated using</p>", "<p></p>", "<p>where <italic>W</italic><sub><italic>ij </italic></sub>is the weight connecting node <italic>i </italic>and <italic>j</italic>.</p>", "<p>A Bayesian inference is applied with the neural network by assuming that weights of the neural network <italic>W </italic>= {<italic>W</italic><sub><italic>ij</italic></sub>} have a Gaussian prior distribution [##REF##17018520##26##,##UREF##3##27##]:</p>", "<p></p>", "<p>where <italic>α </italic>is the parameter controlling the distribution of weights, <italic>Z</italic><sub><italic>W</italic></sub>(<italic>α</italic>) is a normalization constant, and E<sub>W </sub>is the error function for the weights defined as: <italic>E</italic><sub><italic>W </italic></sub>= ||<italic>W</italic>||<sup>2</sup>/2. We have also assumed that the noise present in experimental MS/MS data also has a Gaussian prior distribution,</p>", "<p></p>", "<p>where <italic>D </italic>is the experimental data, <italic>β </italic>is the parameter controlling the distribution of noise, <italic>Z</italic><sub><italic>D</italic></sub>(<italic>β</italic>) is a normalization constant, <italic>E</italic><sub><italic>D </italic></sub>is the summation of error for all training peptide bonds:</p>", "<p></p>", "<p>where <italic>m </italic>is the total number of peptides used in network training and <italic>n</italic><sub><italic>p </italic></sub>is the number of bonds in the <italic>p</italic><sup><italic>th </italic></sup>peptide. By applying Bayesian theory on Eq. 4, Eq. 5, we have:</p>", "<p></p>", "<p>We need to maximize <italic>P(W</italic>|<italic>D) </italic>to train the neural network, which is equal to minimize</p>", "<p></p>", "<p>where <italic>E</italic><sub><italic>D</italic></sub>, <italic>E</italic><sub><italic>W </italic></sub>are error evaluated for network output and weights as defined before, parameters <italic>α</italic>, <italic>β </italic>are pre-set values donating our guess on distribution of weights and noise. The value of <italic>α </italic>and <italic>β </italic>are periodically re-evaluated during training by the equation:</p>", "<p></p>", "<p>thereby updating our initial estimates regarding the distribution of weights and noise, where <italic>Y </italic>is the dimension of weights in the network.</p>", "<p>In our network, every input node has a separate parameter a to control distribution of weights connecting to it. According to the ARD theory [##UREF##3##27##,##UREF##4##28##], <italic>α </italic>can also be used to evaluate the relevance of each input to output. In practice, we trained the neural network 100 times with random initial values of weights, and then evaluated <italic>α </italic>for each input feature by taking the normalized average over all training loops. The processed values of <italic>α</italic>, denoted as irrelevance scores, are illustrated in Figure ##FIG##1##2## and are discussed in the experimental section. A set of features that are most relevant to peptide fragmentation can be acquired by gradually reducing less important features in the input (Figure ##FIG##3##4##).</p>", "<p>Using selected features that have been proven to have significant influence on peptide fragmentation, another Bayesian neural network was constructed to predict the intensity of fragmentation spectra given the sequence of a peptide. Such prediction has long been recognized as a difficulty because MS/MS data typically contains a large volume of noise. This noise results from a variety of factors, most of which are not relevant to the fragmentation pathway followed by the peptide itself, including differences in sample preparation methodologies, the ionization method used, the type of mass spectrometer, etc. Even under identical experimental conditions, the ion intensities resulting from fragmentation of a particular peptide bond can vary considerably from experiment to experiment, making accurate prediction impossible. Accordingly, it is reasonable to take the fragmentation process as a random event, i.e. although the fragmentation pathway of a given peptide is invariable, the relative ion intensities generated along the pathway are not fixed. For each potential ion-type, the degree of fragmentation can fluctuate by a limited but nevertheless significant amount, whereas for its MS/MS map, the relative ion intensities values may vary considerably because of normalization. In this work, we assumed that the quantity of each ion species is Gaussian distributed. This assumption was applied by assigning variances to outputs of the new network with the reduced input feature set. For the <italic>k</italic><sup><italic>th </italic></sup>bond within the <italic>p</italic><sup><italic>th </italic></sup>unknown peptide, normalization process (Eq. 2) becomes a linear transformation. We have:</p>", "<p></p>", "<p>where <italic>P</italic>(<italic>W</italic>|<italic>D</italic>) is the posterior distribution of network weights defined by Eq. 6. By applying the Taylor expansion on training error (Eq. 7) around the weights whose values maximize (locally) <italic>P</italic>(<italic>W</italic>|<italic>D</italic>) and retain terms up to second order, we have</p>", "<p></p>", "<p>where <italic>W</italic><sub><italic>best </italic></sub>is the weights that maximize (locally) <italic>P</italic>(<italic>W</italic>|<italic>D</italic>) and <italic>H </italic>is the Hessian matrix of error function. By applying Eq. 5 and Eq. 10 on Eq. 9, we have</p>", "<p></p>", "<p>We then approximate <italic>I</italic><sub><italic>pk</italic>-<italic>predict </italic></sub>by its linear expansion around <italic>W</italic><sub><italic>best </italic></sub>(as defined before),</p>", "<p></p>", "<p>where <italic>g </italic>is the first derivative of <italic>I</italic><sub><italic>pk</italic>-<italic>predict</italic></sub>. By applying Eq. 12 on Eq. 11,</p>", "<p></p>", "<p>where <italic>β </italic>is the parameter defined in Eq. 5, Eq. 8. Eq. 13 informs us that the unnormalized spectra intensities predicted by our neural network are actually Gaussian distributed with mean values directly given by the output and variances given by Eq. 13 revealing that variances come from two factors: the average noise level contained in the MS/MS training data and the characteristics of the particular cleavage peptide bond.</p>" ]
[ "<title>Results</title>", "<p>The experimental design following development of our peptide fragmentation model comprised two phases: a feature selection stage for the determination of peptide characteristics that have significant influence on fragmentation, and a model development stage that trained a Bayesian neural network with features identified from the first stage. The performance of the model was then tested by using it to predict spectra intensity patterns for given peptides and subsequently compared with experimental data. Different data and data filtering algorithms were applied during the different phases.</p>", "<title>Experiment stage 1</title>", "<p>In this part of the study, MS/MS spectra data as described in [##REF##16159109##23##] were acquired from Wysocki VH. The intensity information contained within the spectra was then used to verify a library of features that are supposed to influence peptide fragmentation (Table ##TAB##0##1##). The values of relevant amino acid properties that were used for calculating these features can be found in Table ##TAB##1##2##. This feature set is a modified version of what was used by Elias, <italic>et al</italic>. [##REF##14730315##19##]. We aimed to determine a group of features that genuinely influence the intensity patterns of MS/MS spectra. For this purpose, a Bayesian neural network model was developed. The structure of the network model is illustrated in Figure ##FIG##0##1## and more details can be found in the method section of the paper.</p>", "<p>In brief the data comprised peptide MS/MS spectra from two micro organisms, <italic>Shewanella oneidensis </italic>and <italic>Deinococcus radiodurans</italic>. The datasets were derived using LC/MS analysis with ion trap instrumentation (further details can be found in the original paper [##REF##16159109##23##]). Peptide sequences were assigned to these spectra using the Sequest algorithm with a minimum XCorr score of 1.5 for peptides with molecular weight &lt; 1000, and 2.0 for all other peptides. Using the same chromatographic conditions, accurate masses of the precursor ions detected at the same retention time by FT-ICR were used to confirm the assigned sequences. Finally, a total of 28330 spectra of unique sequence and charge state (16008 from <italic>Shewanella </italic>and 12322 from <italic>Deinococcus</italic>) were acquired and subject to further analyses.</p>", "<p>In our work, we wished to analyse only spectra with non-biased peptide intensities so that the genuine influences of all the features can be determined. For this purpose, the following filtering criteria were applied to the available 28330 spectra:</p>", "<p>1. Only doubly charged peptide spectra were retained for the study.</p>", "<p>2. For a given peptide, the intensities of detected b/y ions (plus ions resulting from degradation events) according to the assigned sequence, should be no less than 25% of the total intensities of all peaks within the particular spectrum. This criterion came from our belief that a correctly identified peptide should be able to explain all peaks in the corresponding spectra reasonably well. Accordingly all spectra with this correlation lower than an arbitrary threshold were considered to be either mismatches or biased spectra due to undetected degradation/modification events.</p>", "<p>3. For a given peptide, the total intensities of the detected b/y ions should be no less than the intensity of the parent ion of the peptide. Application of this criterion was intended to ensure that all selected peptides are fully fragmented.</p>", "<p>4. Finally, all candidate peptides were classified according to the \"Relative Mobile Proton\" (RMP) hypothesis [##REF##14616009##15##]. Applying the RMP model as a classification criterion enables us to analyze peptides with different relative mobility separately, and also makes it easier for the machine learning algorithm to identify correct rules involved in peptide fragmentation.</p>", "<p>As a result, a total number of 13878 spectra were analysed in this study, comprising 5768 mobile peptides, 7154 partially mobile peptides and 956 non-mobile peptides. The length of these peptides ranges from 5 to 40 with a mean value of 16. The data provided 208563 input patterns (peptide bonds) for the training of our network model.</p>", "<p>The first stage of our experiment, a feature selection stage as described above, began with training the Bayesian neural network 100 epochs using the features listed in Table ##TAB##0##1##. Details of network structure and training method can be found in the method section. The importance of each individual feature was evaluated by updating its \"relevance coefficient\" <italic>α </italic>as in Eq. 8. The results of coefficients were ranked and normalized, with their mean values defined as 'irrelevance' scores. The greater an irrelevance score is, the less significant the influence of the corresponding feature is. The irrelevance scores of each feature of different peptide mobility status are compared in Figure ##FIG##1##2##, and the values of the original scores can be found in Additional file ##SUPPL##0##1##.</p>", "<p>As shown in Figure ##FIG##1##2##, the features that influence the fragmentation pathway of peptides vary considerably depending on peptide mobility status. Peptides of mobile or partial-mobile status generally share similar influential feature sets, but for peptides of non-mobile status, the features that influence fragmentation appear to be completely different. Such an observation implies that peptides of mobile- and partial-mobile status do not have fundamental differences in their fragmentation mechanism, whereas non-mobile peptides appear to possess their own unique method of fragmentation.</p>", "<p>The results in Figure ##FIG##1##2## indicate that the ion intensity pattern under non-mobile status depends highly on the sequence context of the fragmented peptide. It is well known that the identities of residues at the either side of a cleavage site play a very important role in determining whether cleavage can occur at this site, and the extent of this cleavage; but their influence are especially prominent for non-mobile peptides, who's spectra are often observed to be dominated by a limited number of ions. For mobile and partial-mobile peptides, however, fragmentation pathways appear to be determined by a mixture of factors including sequence context, position of cleavage site, mass and length of fragmented peptide, and many others.</p>", "<p>The results show that cleavage is more likely to occur at the middle of a peptide rather than at the two ends, as mentioned before by Kapp, et al. [##REF##14616009##15##,##REF##14641094##16##]. We speculate that the specificity of tryptic digestion may contribute to this. It is also conceivable that the low mass cut-off inherent in ion trap mass spectrometers play a role in this position-selective phenomenon. It is observed that this phenomenon is less significant for non-mobile peptides, most likely because of the dominant residue-specific fragmentation pathway. Our analysis also reveals that the presence of basic residues can hinder fragmentation at peptide bonds close to them, as reported in other publications [##REF##12720328##14##]. The influence of individual residue will be discussed in details in the next section.</p>", "<p>It does not appear from our results that the basic nature of specific residues can influence the fragmentation pathway directly. Although the presence of basic residues within a peptide can result in marked changes in spectra intensity patterns, the basic nature of a particular residue (BaRB_N/C/A/D) appears to have little relevance to the fragmentation pattern (Figure ##FIG##1##2##). However, the basic characteristic of the whole peptide (BaP) does appear to play an important role in fragmentation irrespective of peptide mobility status, and the basic characteristics of fragmented y ions (BaYI) can influence peptides of mobile and partial-mobile status.</p>", "<p>In general, the tendency of amino acid residues to contribute to the helicity nature of a peptide correlates with medium to high irrelevance scores, indicating that these characteristics do not have significant influence on peptide fragmentation, especially for non-mobile peptides (HeRB_N/C/A/D). Specific hydrophobicity-related features (HyRB_N, HyRB_C, HyYI), however, appear to be important in the fragmentation of both mobile and partial-mobile peptides, but they show little influence on peptides of non-mobile status. To the best of our knowledge there is no published theory suggesting a mechanism to explain how peptide hydrophobicity may influence fragmentation events in MS/MS, and, we are unsure whether our results stem from a causal relationship or simply a numerical correspondence. Indeed, this may be a topic worthy of future study. The PI values of residues show little direct influence on fragmentation of peptides (PIRB_N/C/A/D). The features \"number of basic residues in the whole peptide\" (NBaR_P) and \"number of basic residues in the fragmentation ion\" (NBaR_YI) were unsurprisingly ranked as having little influence on mobile peptides, because in the great majority of cases doubly charged mobile peptides contain only one single basic residue, which will be located at the C-terminus given the sequence specificity of trypsin. Accordingly these features are of little relevance for mobile peptides. In contrast, these two features do appear to be influential on peptides under other mobility status because variable numbers of basic residues are usually present in those cases. It is also apparent that the distance from the fragmented bond to basic residues has little influence on fragmentation pathways (DBBa). This feature indeed appears to influence mobile peptides, but such an effect is more likely to be a numerical correspondence only, because the sole basic residue in a doubly charged mobile peptide is located at its C-terminus, making this feature effectively synonymous with the feature \"distance from fragmented bond to peptide C-terminus\" (DB_C) that has been shown to influence peptide fragmentation. Finally, we find that the mass and length of fragmented ions/whole peptide can influence the overall fragmentation pattern (LP, LYI, MP, MYI). Comparatively, the ratio of mass/length are more influential (RLIP, RMIP) than absolute values of the two. This result agrees with findings reported elsewhere [##REF##12641236##24##].</p>", "<p>Many studies have been conducted to find out how the presence of a particular residue influences the subsequent fragmentation pathway of a whole peptide. A series of rules has been derived from both statistical analysis and manual interpretation of MS/MS spectra [##REF##11180630##9##,##REF##11128940##12##,##REF##12720328##14##,##REF##14641094##16##,##REF##15253624##17##,##REF##14730315##19##,##REF##16159109##23##,##REF##12641236##24##]. In our model, every residue has 2 separate nodes which represent its presence on the N- or C-terminus of a peptide bond. We are able to determine the influence of each residue by evaluating the weight values assigned to these nodes. The results are illustrated in Figure ##FIG##2##3##. As can be seen, many of the defined features appear to influence fragmentation, and most of them conform to the established rules. This correlation lends credence to the effectiveness of our approach, and supports the validity of the influence of the features as we suggest above.</p>", "<p>When a free proton is available within a peptide (i.e. in a \"mobile\" peptide according to the RMP model), we unsurprisingly find that proline (P) has a significant influence on fragmentation. As has previously been extensively documented [##REF##11180630##9##,##REF##12720328##14##,##REF##14641094##16##,##REF##15253624##17##,##REF##14730315##19##,##REF##16159109##23##,##REF##12641236##24##], proline markedly enhances cleavage at its N-terminal peptide bond while greatly inhibiting C-terminal cleavage. Conversely, aspartic acid (D) and glutamic acid (E) residues appear to inhibit cleavage at their N-termini, and similarly, asparagine (N) is found to have the same inhibitory effect on peptide fragmentation but to a less significant degree. Isoleucine (I) and valine (V) are found to promote C-terminal cleavage most, whereas glycine (G) and asparagine (N) residues have the greatest inhibitory effect (besides proline) on cleavage at the C-terminus.</p>", "<p>However with non-mobile peptides, for example those containing multiple arginine (R) residues, protons are sequestered by the basic amino acids, and as a result the peptide fragments in a totally different manner (Figure ##FIG##2##3-C##). In this situation proline still has the greatest influence on cleavage on N- terminal cleavage, but in comparison to the situation in mobile peptides, this effect is much reduced. Aspartic acid is now the most influential residue in respect to enhanced C-terminal cleavage (as has been reported by many other researchers [##REF##11128940##12##,##REF##14641094##16##,##REF##15253624##17##,##REF##16159109##23##,##REF##12641236##24##]), although its ability to inhibit cleavage at its N-terminal peptide bond is reduced. It is clear from the figure that the influence of aspartic acid is almost twice as much as that of proline, so even if they appear in the same peptide, the resulting spectra will be dominated by ions derived from aspartic acid-derived fragmentation. Glutamic acid (E) favours peptide cleavage at its C-terminus, a characteristic which probably results from the presence of a similar side chain to that of aspartic acid. Glycine-dependent inhibition of cleavage at its C-terminus is observed in all mobility status. Arginine (R) is observed to strongly promote cleavage at its C-terminus, and the other two basic residues Lysine (K) and Histidine (H) also present the same favour but in a less significant way. The rules defined above have also been reported previously by Wysocki group in work using a statistical method and the same MS/MS spectra dataset [##REF##16159109##23##], and by Zhang, using his kinetic model [##REF##15253624##17##,##REF##16194101##18##].</p>", "<p>We also observed a number of novel peptide sequence-context effects. Firstly arginine (R) residues show a markedly inhibited cleavage at their N-termini in non-mobile peptides. Secondly Histidine (H) appears to favour cleavage at its N-terminus, and such effect is observed in all mobility status. Besides these, previous studies have proposed that leucine (L) residues promote cleavage to their C-terminal peptide bonds, irrespective of the mobility status of the peptide [##REF##16159109##23##]. This effect is not apparent from our study, with the presence of leucine only having a relatively minor effect (enhancement) on C-terminal cleavage (Figure ##FIG##2##3##.)</p>", "<p>In the classical proton mobility theory peptides are classified into 2 distinct groups, designated as either mobile or non-mobile, according to the number of arginine residues present within the peptide. In addition, Kapp, <italic>et al</italic>. [##REF##14616009##15##] have since proposed another class: an intermediate or 'partial' mobility state. We have also analyzed the fragmentation behaviour of peptides belonging to this third mobility class, and the results are indicated in Figure ##FIG##2##3-B##. We find that peptides falling into this notional group fragment according to a combination of rules predominant only to either mobile or non-mobile peptides. Effectively the mechanism of fragmentation in partially mobile peptides appears to obey a hybrid rule set. In this rule set, the influence of residues on fragmentation at their N-terminal peptide bonds is similar to that for peptides of mobile status, in which proline has a dominant enhancing effect, and aspartic acid and glutamic acid inhibit cleavage most. In marked contrast, the influence of amino acid residues on peptide bonds at their C-terminus more closely resembles that occurring in peptides of non-mobile status, where aspartic acid has the most profound effect. At the same time, isoleucine and valine enhance cleavage at their C-terminal peptide bonds in partial mobility peptides, as they do in mobile status peptides. Exceptionally, Lysine (K) is observed to enhance C-terminal cleavage in partially mobile peptides. Such an effect was not observed under any other mobility status. Glycine and proline have the most marked inhibitory effect on C-terminal cleavage as they do in both other peptide mobility groups.</p>", "<p>It is worth noting that in the earlier work of Elias, <italic>et al</italic>. [##REF##14730315##19##] using a Bayesian decision tree method, a similar feature set was examined to analyse the influence of each component on peptide fragmentation. In that study however, only the 'proline effect' was observed, and the influences of other residues were suggested to be insignificant. By revealing a considerably larger set of valid fragmentation rules using a similar feature set, it appears that our machine learning model has abetter learning capacity and is capable of identifying more subtle, yet nevertheless significant differences, in the contribution of different amino acid residues to peptide fragmentation during CID.</p>", "<p>Having determined the irrelevance scores for all features examined, a new feature set can be defined containing only those found to markedly influence peptide fragmentation. To this end, we sequentially discarded the features with highest scores as listed in Figure ##FIG##1##2##, and then retrained the network with the reduced feature set. Comparison of the training results for all networks is illustrated in Figure ##FIG##3##4##. Taking non-mobile peptides as an example, the training error increases significantly when 23 less relevant features are removed, indicating that at most 22 features can be removed. The remaining features are indicated with filled circles in Figure ##FIG##1##2##.</p>", "<title>Experiment stage 2</title>", "<p>With the reduced feature set derived in the first stage, a new network was trained to predict the intensity patterns of fragmentation spectra for given peptides. Benefiting from the application of the Bayesian theory, the network can not only predict the absolute values of spectra intensities, but also assign variances for the predictions. The obtained results are thus more robust against noise and system errors that unavoidably appear in the experimental MS/MS data. Details of the prediction method used can be found in the methods section.</p>", "<p>A new MS/MS dataset was applied to evaluate the performance of the Bayesian intensity model. The dataset is a controlled dataset containing 18 different proteins as described in [##REF##12143966##25##]. The details of how the dataset is generated can be found in the original paper. There are in total 1656 doubly charged spectra that have been verified to be correctly identified. Applying the same filtering method as described in experimental stage 1, we finally obtained 1607 doubly charged peptides for model testing. The theoretical spectra of these peptides were predicted by the Bayesian intensity model and then compared with the experimental counterparts to evaluate the accuracy of the model.</p>", "<p>In order to compare an experimental spectrum with its predicted counterpart, a score capable of evaluating the similarity of two spectra has to be defined. As described in the method section, it is assumed that the log-transformed intensities of a given spectrum are Gaussian distributed with mean values and variances as predicted by our model. Accordingly, experimental spectra were normalized using the following method:</p>", "<p>1. All peaks related to parent ions (for example 2+ parent ion and its degradations) are removed.</p>", "<p>2. Divide each spectrum with its intensity of total ion current (TIC normalization) and then times 100.</p>", "<p>3. Log-transformed and then normalized to [0, 1] scale.</p>", "<p>It is then straightforward to design the following scoring system (Eq. 1) to measure the degree of similarity between the two spectra:</p>", "<p></p>", "<p>where <italic>n</italic><sub><italic>p </italic></sub>is the number of peptide bonds within peptide p, <italic>I</italic><sub><italic>pk</italic>-<italic>predict</italic></sub>(<italic>w</italic><sub><italic>best</italic></sub>) is the predicted mean intensity value of the peak at peptide bond <italic>k</italic>, <italic>I</italic><sub><italic>pk</italic>-<italic>real </italic></sub>is the observed intensity of the peak at peptide bond <italic>k </italic>in the experimental spectrum, and <italic>σ</italic><sub><italic>pk </italic></sub>is the standard deviation (SD) of the peak intensity at peptide bond <italic>k </italic>predicted by the intensity model. The more similar the two spectra, the higher the resulting score is.</p>", "<p>An example of spectrum predicted by the Bayesian intensity model can be found in Figure ##FIG##4##5## for the peptide GYSFVTTAER. The prediction for this peptide achieves one of the highest scores (best fit). It can be seen that the predicted spectrum matches its experimental counterpart very well, and the small differences between the two spectra are well within the variance. Further examples using peptides of different mobility status can be found in Additional file ##SUPPL##1##2##.</p>", "<p>Similarly, the prediction for peptide VLYPNDNFFEGK is illustrated in Figure ##FIG##5##6##. This peptide attained one of the lowest scores (worst fit), indicating a probable failure of spectrum prediction. Indeed, as can be seen in Figure ##FIG##5##6-C##, none of the peaks lie within the expected ranges. It is apparent from Figure ##FIG##5##6-A## that the experimental spectrum of the peptide is dominated by the y9 ion resulting from cleavage at the Y-P bond of the peptide, the other ions of the expected y-ion series are either very low or even below the level of detection. This pattern is characteristic of the type of spectrum that often leads to random (false) matches in database searching using current m/z based peptide identification algorithms. However, as shown in Figure ##FIG##5##6-B##, our model did correctly predict the general pattern of the spectrum, i.e. that y9 and y10 are the highest two peaks and the others peaks are lower and of relatively equal height. The experimental spectrum therefore represents a greatly exaggerated version of the predicted pattern.</p>", "<p>The similarity scores were firstly calculated for spectra predicted by the Bayesian intensity model as illustrated in Figure ##FIG##6##7##. The same scores were subsequently recalculated with intensity information excluded, i.e. after assigning the same intensity value to each peak within a spectrum. Such an approach, using intensity-free spectra, is typical of most peptide identification algorithms in current use, e.g. Sequest. In order to compare spectra with/without intensity information on an impartial basis, the similarity scores for intensity-free spectra were firstly calculated using the same variance values as used for scores with intensity information, and then recalculated with the influence of variance eliminated (set <italic>σ</italic><sub>pk </sub>= 1). The former case was illustrated in Figure ##FIG##6##7-A## and the latter one in Figure ##FIG##6##7-B##. It can be clearly seen that the scores derived using intensity information are consistently higher than those derived without. This result indicates that our network model can accurately predict fragmentation spectra for given peptides, and the predicted spectra fit experimental spectra much better than those generated using intensity-free information.</p>", "<p>In order to further validate our Bayesian intensity model, programs for kinetic intensity model published by Zhang [##REF##15253624##17##] were used to predict the intensity patterns of the same test dataset as mentioned above. The differences between experimental spectra and predicted counterparts are calculated and compared with those from our Bayesian intensity model. It should be noted that Zhang's kinetic model is able to predict intensity of b, y ions and their degradations. So only y related ions were pick out for comparison. At the same time, the variance information predicted by the Bayesian model was also ignored. As listed in Table ##TAB##2##3##, the Bayesian model has a smaller prediction error in 897 spectra out of the total 1607, showing a slightly higher accuracy than the kinetic model. However, the mean values and SD values of prediction errors from the two approaches are rather close. It is reasonable to conclude that the two approaches have similar performance in predicting spectra intensity values for given peptides, and our Bayesian intensity model can potentially be more informative because extra variances can be assigned to the predictions to tolerate the prediction error.</p>" ]
[ "<title>Discussion</title>", "<p>In this work, a novel Bayesian neural network approach was applied to examine features that were thought to influence peptide fragmentation. The benefit of this approach includes making the features numerically analysable so that large number of features regarding various characteristics of fragmented peptides can be compared directly at one time. In the experiment stage 2, a new network was trained to predict intensity patterns of fragmentation spectra for given peptides. Only a limited number of features with significant influence were applied in this stage, and the others were discarded. It is worth noting that the discarded features are not necessarily irrelevant to peptide fragmentation. Indeed, they may still influence fragmentation pathways but in a less significant or indirect way. However they were discarded given that results indicated that the accuracy of spectra intensity predictions was not significantly affected by the elimination of these features.</p>", "<p>The MS/MS data used in this work is dominated by spectra from mobile and partial-mobile peptides, whereas the number of non-mobile data is relatively small. This is mainly because these peptides were identified by Sequest, who rely heavily on m/z information to make identification. Non-mobile peptides, unfortunately, usually fail to present enough amounts of ions in the spectra, and therefore fail to be identified. It is worth noting that although many reasonable rules in fragmentation have been derived for non-mobile peptides, it is very likely that some important fragmentation rules are missing.</p>", "<p>To the best of our knowledge, most algorithms available for peptide identification to date make their identification based on spectra m/z information only. The importance of spectra intensity information on peptide identification have been realised by many researchers, but successful applications in published literature are still rare: [##REF##17018520##26##] is the only one to the best of our knowledge, and it is simply an application of Zhang's kinetic model. This phenomenon is partially because it is difficult to predict intensity patterns of fragmentation spectra accurately, and even if intensity patterns are successfully predicted, how to compare the predicted spectra with experimental ones still remains a problem. The large volumes of noise and system errors inevitably contained in experimental spectra make it difficult to apply conventional comparison methods to evaluate the true degree of similarity between spectra. Our network model provided a good way to solve the problem by assigning variances to the predictions to obtain certain degree of tolerance to the fluctuation of spectra intensities. In this study, we proposed a scoring method (Eq. 1) that combined the predicted variance information to compare two spectra under total ion current normalization. This method worked well in validating the intensity model and could be used for peptide identification. However, there were still cases in which the predicted spectra failed to match the experimental counterpart even if the general pattern of spectra intensity was predicted correctly (Figure ##FIG##5##6##). Future work will involve development of more robust scoring methods to further improve the performance of our intensity model in peptide identification, and allow for the effect of post-translational modifications (PTMs) on peptide fragmentation pathways. The influence of PTMs is unpredictable at present, as modified peptides may fragment in a different manner to unmodified ones, thereby making predictions using current fragmentation models less reliable.</p>", "<p>It is important to note that the intensity pattern of MS/MS spectra for the same peptide can differ depending on the nature of the mass spectrometer platform used for analysis, e.g. whether it is an ion trap or a Q-ToF system etc., and the method of ionisation employed (e.g. electrospray or MALDI etc.). In this situation, the intensity model needs to be retrained each time to adapt to the different machine types and peptide dissociation methods (e.g. CID, electron capture dissociation or electron transfer dissociation)</p>" ]
[ "<title>Conclusion</title>", "<p>In this work, we have shown that the intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities given the sequences of peptides. It has been demonstrated that the intensity pattern of fragmentation spectra predicted by our model fits experimental data reasonably well. It is suggested that such intensity predictions can be used with current peptide and protein identification algorithms to make them more reliable in high-throughput proteomics experiments.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.</p>", "<title>Results</title>", "<p>In this work, a Bayesian neural network approach is employed to analyze ion intensity information present in 13878 different MS/MS spectra. The influence of a library of 35 features on peptide fragmentation is examined under different proton mobility conditions. Useful rules involved in peptide fragmentation are found and subsets of features which have significant influence on fragmentation pathway of peptides are characterised. An intensity model is built based on the selected features and the model can make an accurate prediction of the intensity patterns for given MS/MS spectra. The predictions include not only the mean values of spectra intensity but also the variances that can be used to tolerate noises and system biases within experimental MS/MS spectra.</p>", "<title>Conclusion</title>", "<p>The intensity patterns of fragmentation spectra are informative and can be used to analyze the influence of various characteristics of fragmented peptides on their fragmentation pathway. The features with significant influence can be used in turn to predict spectra intensities. Such information can help develop more reliable algorithms for peptide and protein identification.</p>" ]
[ "<title>Authors' contributions</title>", "<p>CZ: Carried out the main data analysis work and the writing of the manuscript. LDB: Mass spectrometry: Generation of MS/MS data and participation in writing the manuscript. JF: Study design and overall supervision of the project.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Andrew Keller and Vicki H. Wysocki for providing tandem MS data and Zhongqi Zhang for providing a copy of MassAnalyzer software. We also thank the reviewers for pointing out several missing references [##REF##17211901##22##,##REF##12143966##25##,##REF##17018520##26##] and flaws in the paper. The algorithm program and other relevant material for this paper can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.informatics.sussex.ac.uk/users/cz22\"/></p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Structure of the Bayesian neural network used to explore the mechanism of gas-phase fragmentation of peptides</bold>. The network is fully connected and feed-forward with three layers including one hidden layer. 73 nodes are used in the input layer representing 35 features. 40 nodes in binary are used to represent the presence of 20 different residues at N and C terminus to the target peptide bond. Every node in the input layer has an independent coefficient to reveal its \"relevance\" to the network output. The hidden layer has 40 nodes and the activation function of the hidden layer is sigmoidal.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Verification of the features that potentially influence peptide fragmentation</bold>. The importance of the features listed in Table 1 is evaluated by the Bayesian neural network and the results are shown: Red circles: normalized irrelevance scores of the features under non-mobile status. Blue squares: normalized irrelevance scores of the features under partial-mobile status. Green triangles: normalized irrelevance scores of the features under mobile status. The higher an irrelevance score is, the less important the corresponding feature is. The threshold of each mobility status is shown in dashed line and the features proven to be influential on peptides' fragmentation (below threshold) are highlighted with filled circles/squares/triangles.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Influence of each residue on fragmentation at its N/C terminal peptide bond</bold>. The influence of each residue on cleavage at its N-terminus is illustrated in the left panel (blue dots), and the influence on cleavage at its C-terminus is illustrated in the right panel (red dots). The most influential residues are marked with arrows. Down arrows indicate inhibition whereas up arrows indicate enhancement. Figure 3-A: Mobile status. Figure 3-B: Partial-mobile status. Figure 3-C: Non-mobile status.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Reduction of training errors in the feature selection phase</bold>. Features are reduced according to their relevance to the fragmentation process (Figure 2). The X-axis represents the number of features being reduced and the Y-axis represents the average training error in percentage over 100 training times counted in percentage. The training error increases significantly when 23 less relevant features are removed, as indicated by the red arrow. It is then suggested that at most 22 features could be eliminated.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Predicting spectra intensity pattern: on peptide GYSFVTTAER</bold>. Figure 5-A: The raw MS/MS data of peptide GYSFVTTAER. Unlabeled green peaks are ions degraded from labeled b/y ions by losing H<sub>2</sub>O, NH<sub>3</sub>, etc. Figure 5-B: The comparison of the experimental spectrum (red) versus the spectrum predicted by the network model (blue). The experimental spectrum is the y-ions extracted from the raw data (Figure 5-A) with intensities log-transformed. Figure 5-C: The effect of using probability theory. Blue dots indicate the interval [mean intensity - SD, mean intensity + SD] within which intensities of the ions are supposed to lie.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Predicting spectra intensity pattern: on peptide VLYPNDNFFEGK</bold>. Figure 6-A: The raw MS/MS data of peptide VLYPNDNFFEGK. Unlabeled green peaks are ions degraded from labeled b/y ions by losing H<sub>2</sub>O, NH<sub>3</sub>, etc. Figure 6-B: The comparison of the experimental spectrum (red) versus the spectrum predicted by the network model (blue). The experimental spectrum is the y-ions extracted from the raw data (Figure 6-A) with intensities log-transformed. Figure 6-C: The effect of using probability theory. Blue dots indicate the interval [mean intensity - SD, mean intensity + SD] within which intensities of the ions are supposed to lie.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Comparison of scores with/without intensity information on all test peptides</bold>. Similarity scores are computed using Eq. 1. The higher a score, the more similar the predicted spectrum is to the experimental counterpart. Figure 7-A: The red line represents the sorted scores calculated with the predicted intensity information. The blue line represents the corresponding scores calculated without intensity information. The two score use the same variances predicted by the Bayesian neural network model. Figure 7-B: The red line represents the sorted scores calculated with the predicted intensity information. The blue line represents the corresponding scores calculated without intensity information. Variances for intensity-free scores are set to 1.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Features that potentially influence peptide fragmentation.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold><italic>ID</italic></bold></td><td align=\"left\"><bold><italic>Features</italic></bold></td><td align=\"left\"><bold><italic>Abbreviation</italic></bold></td></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Identity of residue C-terminal to fragmentation site</td><td align=\"left\">RB_C</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Identity of residue N-terminal to fragmentation site</td><td align=\"left\">RB_N</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Distance from fragmentation site to peptide N-terminus</td><td align=\"left\">DB_N</td></tr><tr><td align=\"left\">4</td><td align=\"left\">Distance from fragmentation site to peptide C-terminus</td><td align=\"left\">DB_C</td></tr><tr><td align=\"left\">5</td><td align=\"left\">Distance from fragmentation site to peptide center</td><td align=\"left\">DB_M</td></tr><tr><td align=\"left\">6</td><td align=\"left\">Whether fragmentation site is at either end of peptide</td><td align=\"left\">B_E</td></tr><tr><td align=\"left\">7</td><td align=\"left\">Basicity of residue N-terminal to fragmentation site</td><td align=\"left\">BaRB_N</td></tr><tr><td align=\"left\">8</td><td align=\"left\">Basicity of residue C-terminal to fragmentation site</td><td align=\"left\">BaRB_C</td></tr><tr><td align=\"left\">9</td><td align=\"left\">Average basicity of residues N/C terminal to fragmentation site</td><td align=\"left\">BaRB_A</td></tr><tr><td align=\"left\">10</td><td align=\"left\">Difference in basicity of residues N/C terminal to fragmentation site</td><td align=\"left\">BaRB_D</td></tr><tr><td align=\"left\">11</td><td align=\"left\">Basicity of fragmented y-ion</td><td align=\"left\">BaYI</td></tr><tr><td align=\"left\">12</td><td align=\"left\">Basicity of whole peptide</td><td align=\"left\">BaP</td></tr><tr><td align=\"left\">13</td><td align=\"left\">Helicity of residue N-terminal to fragmentation site</td><td align=\"left\">HeRB_N</td></tr><tr><td align=\"left\">14</td><td align=\"left\">Helicity of residue C-terminal to fragmentation site</td><td align=\"left\">HeRB_C</td></tr><tr><td align=\"left\">15</td><td align=\"left\">Average helicity of residues N/C terminal to fragmentation site</td><td align=\"left\">HeRB_A</td></tr><tr><td align=\"left\">16</td><td align=\"left\">Difference in helicity of residues N/C terminal to fragmentation site</td><td align=\"left\">HeRB_D</td></tr><tr><td align=\"left\">17</td><td align=\"left\">Hydrophobicity of residue N-terminal to fragmentation site</td><td align=\"left\">HyRB_N</td></tr><tr><td align=\"left\">18</td><td align=\"left\">Hydrophobicity of residue C-terminal to fragmentation site</td><td align=\"left\">HyRB_C</td></tr><tr><td align=\"left\">19</td><td align=\"left\">Average Hydrophobicity of residues N/C terminal to fragmentation site</td><td align=\"left\">HyRB_A</td></tr><tr><td align=\"left\">20</td><td align=\"left\">Difference in Hydrophobicity of residues N/C terminal to fragmentation site</td><td align=\"left\">HyRB_D</td></tr><tr><td align=\"left\">21</td><td align=\"left\">Hydrophobicity of fragmented y-ion</td><td align=\"left\">HyYI</td></tr><tr><td align=\"left\">22</td><td align=\"left\">Hydrophobicity of whole peptide</td><td align=\"left\">HyP</td></tr><tr><td align=\"left\">23</td><td align=\"left\">pI value of residue N-terminal to fragmentation site</td><td align=\"left\">PIRB_N</td></tr><tr><td align=\"left\">24</td><td align=\"left\">pI value of residue C-terminal to fragmentation site</td><td align=\"left\">PIRB_C</td></tr><tr><td align=\"left\">25</td><td align=\"left\">Average pI of residues N/C terminal to fragmentation site</td><td align=\"left\">PIRB_A</td></tr><tr><td align=\"left\">26</td><td align=\"left\">Difference in pI of residues N/C terminal to fragmentation site</td><td align=\"left\">PIRB_D</td></tr><tr><td align=\"left\">27</td><td align=\"left\">Length of whole peptide</td><td align=\"left\">LP</td></tr><tr><td align=\"left\">28</td><td align=\"left\">Length of fragmented y-ion</td><td align=\"left\">LYI</td></tr><tr><td align=\"left\">29</td><td align=\"left\">Ratio of length of fragmented y-ion and peptide</td><td align=\"left\">RLIP</td></tr><tr><td align=\"left\">30</td><td align=\"left\">Number of basic residues in whole peptide</td><td align=\"left\">NBaR_P</td></tr><tr><td align=\"left\">31</td><td align=\"left\">Number of basic residues in fragmented y-ion</td><td align=\"left\">NBaR_YI</td></tr><tr><td align=\"left\">32</td><td align=\"left\">Mass of whole peptide</td><td align=\"left\">MP</td></tr><tr><td align=\"left\">33</td><td align=\"left\">Mass of fragmented y-ion</td><td align=\"left\">MYI</td></tr><tr><td align=\"left\">34</td><td align=\"left\">Ratio of mass of fragmentated y-ion and peptide</td><td align=\"left\">RMIP</td></tr><tr><td align=\"left\">35</td><td align=\"left\">Distance from fragmentation site to basic residues</td><td align=\"left\">DBBa</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Values of amino acid property used in the study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Residue</bold></td><td align=\"center\"><bold>Mass</bold></td><td align=\"center\"><bold>Hydrophobicity</bold></td><td align=\"center\"><bold>Helicity</bold></td><td align=\"center\"><bold>Basicity</bold></td><td align=\"center\"><bold>PI value</bold></td></tr></thead><tbody><tr><td align=\"center\"><bold>A</bold></td><td align=\"center\">71.0788</td><td align=\"center\">0.16</td><td align=\"center\">1.24</td><td align=\"center\">206.4</td><td align=\"center\">6</td></tr><tr><td align=\"center\"><bold>C</bold></td><td align=\"center\">103.1388</td><td align=\"center\">2.5</td><td align=\"center\">0.79</td><td align=\"center\">206.2</td><td align=\"center\">5.02</td></tr><tr><td align=\"center\"><bold>D</bold></td><td align=\"center\">115.0886</td><td align=\"center\">-2.49</td><td align=\"center\">0.89</td><td align=\"center\">208.6</td><td align=\"center\">2.77</td></tr><tr><td align=\"center\"><bold>E</bold></td><td align=\"center\">129.1155</td><td align=\"center\">-1.5</td><td align=\"center\">0.85</td><td align=\"center\">215.6</td><td align=\"center\">3.22</td></tr><tr><td align=\"center\"><bold>F</bold></td><td align=\"center\">147.1766</td><td align=\"center\">5</td><td align=\"center\">1.26</td><td align=\"center\">212.1</td><td align=\"center\">5.48</td></tr><tr><td align=\"center\"><bold>G</bold></td><td align=\"center\">57.0519</td><td align=\"center\">-3.31</td><td align=\"center\">1.15</td><td align=\"center\">202.7</td><td align=\"center\">5.97</td></tr><tr><td align=\"center\"><bold>H</bold></td><td align=\"center\">137.1411</td><td align=\"center\">-4.63</td><td align=\"center\">0.97</td><td align=\"center\">223.7</td><td align=\"center\">7.47</td></tr><tr><td align=\"center\"><bold>I</bold></td><td align=\"center\">113.1594</td><td align=\"center\">4.76</td><td align=\"center\">1.28</td><td align=\"center\">209.6</td><td align=\"center\">5.94</td></tr><tr><td align=\"center\"><bold>K</bold></td><td align=\"center\">128.1741</td><td align=\"center\">-5</td><td align=\"center\">0.88</td><td align=\"center\">221.8</td><td align=\"center\">9.59</td></tr><tr><td align=\"center\"><bold>L</bold></td><td align=\"center\">113.1594</td><td align=\"center\">4.76</td><td align=\"center\">1.28</td><td align=\"center\">209.6</td><td align=\"center\">5.98</td></tr><tr><td align=\"center\"><bold>M</bold></td><td align=\"center\">131.1926</td><td align=\"center\">3.23</td><td align=\"center\">1.22</td><td align=\"center\">213.3</td><td align=\"center\">5.74</td></tr><tr><td align=\"center\"><bold>N</bold></td><td align=\"center\">114.1038</td><td align=\"center\">-3.79</td><td align=\"center\">0.94</td><td align=\"center\">212.8</td><td align=\"center\">5.41</td></tr><tr><td align=\"center\"><bold>P</bold></td><td align=\"center\">97.1167</td><td align=\"center\">-4.92</td><td align=\"center\">0.57</td><td align=\"center\">214.4</td><td align=\"center\">6.3</td></tr><tr><td align=\"center\"><bold>Q</bold></td><td align=\"center\">128.1307</td><td align=\"center\">-2.76</td><td align=\"center\">0.96</td><td align=\"center\">214.2</td><td align=\"center\">5.65</td></tr><tr><td align=\"center\"><bold>R</bold></td><td align=\"center\">156.1875</td><td align=\"center\">-2.77</td><td align=\"center\">0.95</td><td align=\"center\">237</td><td align=\"center\">11.15</td></tr><tr><td align=\"center\"><bold>S</bold></td><td align=\"center\">87.0782</td><td align=\"center\">-2.85</td><td align=\"center\">1</td><td align=\"center\">207.6</td><td align=\"center\">5.68</td></tr><tr><td align=\"center\"><bold>T</bold></td><td align=\"center\">101.1051</td><td align=\"center\">-1.08</td><td align=\"center\">1.09</td><td align=\"center\">211.7</td><td align=\"center\">5.64</td></tr><tr><td align=\"center\"><bold>V</bold></td><td align=\"center\">99.1326</td><td align=\"center\">3.02</td><td align=\"center\">1.27</td><td align=\"center\">208.7</td><td align=\"center\">5.96</td></tr><tr><td align=\"center\"><bold>W</bold></td><td align=\"center\">186.2132</td><td align=\"center\">4.88</td><td align=\"center\">1.07</td><td align=\"center\">216.1</td><td align=\"center\">5.89</td></tr><tr><td align=\"center\"><bold>Y</bold></td><td align=\"center\">163.176</td><td align=\"center\">2</td><td align=\"center\">1.11</td><td align=\"center\">213.1</td><td align=\"center\">5.66</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Prediction error of the kinetic model and the Bayesian intensity model.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Number of spectra with higher accuracy</bold></td><td align=\"center\"><bold>Mean error</bold></td><td align=\"center\"><bold>SD error</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Kinetic model</bold></td><td align=\"center\">710</td><td align=\"center\">0.4294</td><td align=\"center\">0.1769</td></tr><tr><td align=\"left\"><bold>Bayesian intensity model</bold></td><td align=\"center\">897</td><td align=\"center\">0.4094</td><td align=\"center\">0.1650</td></tr></tbody></table></table-wrap>" ]
[ "<disp-formula id=\"bmcM1\"><label>(1)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1471-2105-9-325-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>S</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:munderover><mml:mrow><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>w</mml:mi><mml:mrow><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M2\" name=\"1471-2105-9-325-i2\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M3\" name=\"1471-2105-9-325-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula id=\"bmcM2\"><label>(2)</label><italic>I</italic><sub><italic>pk</italic>-<italic>predict </italic></sub>= 100·<italic>O</italic><sub><italic>pk</italic></sub>/O<sub><italic>pmax</italic></sub>   (k = 1,⋯, n<sub>p</sub>)</disp-formula>", "<inline-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M4\" name=\"1471-2105-9-325-i3\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:semantics></mml:math></inline-formula>", "<disp-formula><italic>E</italic><sub><italic>pk </italic></sub>= (<italic>I</italic><sub><italic>pk</italic>-<italic>predict </italic></sub>- <italic>I</italic><sub><italic>pk</italic>-<italic>real</italic></sub>)<sup>2</sup>/2</disp-formula>", "<disp-formula id=\"bmcM3\"><label>(3)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M5\" name=\"1471-2105-9-325-i4\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>⋅</mml:mo><mml:mfrac><mml:mrow><mml:mn>100</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>⋅</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>max</mml:mi><mml:mo>⁡</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>⋅</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>O</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM4\"><label>(4)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M6\" name=\"1471-2105-9-325-i5\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>Z</mml:mi><mml:mi>W</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>α</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>⋅</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mi>α</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>W</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM5\"><label>(5)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M7\" name=\"1471-2105-9-325-i6\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>D</mml:mi><mml:mo>|</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>Z</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>⋅</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mi>β</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M8\" name=\"1471-2105-9-325-i7\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:munderover><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:munderover><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:mrow></mml:mstyle></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM6\"><label>(6)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M9\" name=\"1471-2105-9-325-i8\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>W</mml:mi><mml:mo>|</mml:mo><mml:mi>D</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>D</mml:mi><mml:mo>|</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>⋅</mml:mo><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>W</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>D</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>∝</mml:mo><mml:mi>exp</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mi>β</mml:mi><mml:msub><mml:mi>E</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>⋅</mml:mo><mml:mi>α</mml:mi><mml:msub><mml:mi>E</mml:mi><mml:mi>W</mml:mi></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM7\"><label>(7)</label><italic>E </italic>= <italic>β</italic>·<italic>E</italic><sub><italic>D </italic></sub>+ <italic>α</italic>·<italic>E</italic><sub><italic>W </italic></sub></disp-formula>", "<disp-formula id=\"bmcM8\"><label>(8)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M10\" name=\"1471-2105-9-325-i9\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:mi>α</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mi>Y</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>⋅</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>W</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd><mml:mrow><mml:mi>β</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mstyle displaystyle=\"true\"><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>m</mml:mi></mml:munderover><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:mrow></mml:mstyle></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>⋅</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mi>D</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:semantics></mml:math></disp-formula>", "<disp-formula id=\"bmcM9\"><label>(9)</label><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M11\" name=\"1471-2105-9-325-i10\" 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stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>β</mml:mi></mml:mfrac><mml:mo>+</mml:mo><mml:msup><mml:mi>g</mml:mi><mml:mi>T</mml:mi></mml:msup><mml:mo>⋅</mml:mo><mml:msup><mml:mi>H</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:mo>⋅</mml:mo><mml:mi>g</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:semantics></mml:math></disp-formula>" ]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Original Values of irrelevance score for all features. The original values of irrelevance scores for all features are listed in the file.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p>Examples of spectra predicted by the Bayesian neural network model. Spectra with different peptide mobilities are predicted by the Bayesian neural network model and illustrated in the file.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>All features listed above are supposed to exert influences on the gas-phase fragmentation of peptides. They are subject to further examination by the Bayesian neural network model.</p></table-wrap-foot>", "<table-wrap-foot><p>The values of different peptide property used in the study are listed in the table. Values for all the features listed in Table 1 are calculated with these property values during network training. The values for mass, hydrophobicity, helicity and basicity are cited from [##REF##14730315##19##] and the values for PI are cited from <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioscience.org/urllists/aminacid.htm\"/></p></table-wrap-foot>", "<table-wrap-foot><p>The intensity patterns of 1607 test data are predicted with Zhang's kinetic model and our Bayesian intensity model. The accuracy of predictions and mean/SD of prediction errors are listed in the table.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2105-9-325-1\"/>", "<graphic xlink:href=\"1471-2105-9-325-2\"/>", "<graphic xlink:href=\"1471-2105-9-325-3\"/>", "<graphic xlink:href=\"1471-2105-9-325-4\"/>", "<graphic xlink:href=\"1471-2105-9-325-5\"/>", "<graphic xlink:href=\"1471-2105-9-325-6\"/>", "<graphic xlink:href=\"1471-2105-9-325-7\"/>" ]
[ "<media xlink:href=\"1471-2105-9-325-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>", "<media xlink:href=\"1471-2105-9-325-S2.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Eng", "McCormack", "Yates"], "given-names": ["JK", "AL", "JR"], "article-title": ["An Approach to Correlate Tandem Mass Spectra Data of Peptides with Amino Acid Sequences in a Protein Database"], "source": ["J Am Soc Mass Spectrom"], "year": ["1994"], "volume": ["5"], "fpage": ["976"], "pub-id": ["10.1016/1044-0305(94)80016-2"]}, {"surname": ["Dongr\u00e9", "Jones", "Somogyi", "Wysocki"], "given-names": ["AR", "JL", "\u00c1", "VH"], "article-title": ["Influence of peptide composition, gas-phase basicity, and chemical modification on fragmentation efficiency: evidence for the mobile proton model"], "source": ["J Am Soc Mass Spectrom"], "year": ["1996"], "volume": ["118"], "fpage": ["8365"], "lpage": ["8374"]}, {"surname": ["Tsaprailis", "Nair", "Somogyi", "Wysocki", "Zhong", "Futrell", "Summerfield", "Gaskell"], "given-names": ["G", "H", "\u00c1", "VH", "W", "JH", "SG", "SJ"], "article-title": ["Influence of secondary structure on the fragmentation of protonated peptides"], "source": ["J Am Chem Soc"], "year": ["1999"], "volume": ["121"], "fpage": ["5142"], "lpage": ["5154"], "pub-id": ["10.1021/ja982980h"]}, {"surname": ["Bishop"], "given-names": ["CM"], "article-title": ["Neural networks for pattern recognition"], "source": ["Clarendon Press/OUP"], "year": ["1995"]}, {"surname": ["MacKay"], "given-names": ["D"], "article-title": ["Bayesian methods for neural networks: theory and applications"], "source": ["Course notes for Neural Networks Summer School"], "year": ["1995"]}, {"surname": ["Sauve", "Speed"], "given-names": ["AC", "TP"], "article-title": ["Normalization, Baseline Correction and Alignment of High-throughput Mass Spectrometry Data"], "source": ["Data proceedings Gensips"], "year": ["2004"]}]
{ "acronym": [], "definition": [] }
29
CC BY
no
2022-01-12 14:47:26
BMC Bioinformatics. 2008 Jul 30; 9:325
oa_package/cd/35/PMC2529326.tar.gz
PMC2529327
18718006
[ "<title>Background</title>", "<p>Constipation accounts for 3% of general pediatric and 10–20% of pediatric gastroenterology outpatient visits [##REF##1161398##1##]. A majority of these patients have functional constipation and the symptoms improve following behavioral modification and laxative treatment [##REF##11063041##2##]. Almost 30% of children with chronic constipation will have persistent symptoms or relapses that can persist into adult life [##REF##12891536##3##]. Children with chronic intractable constipation, who do not respond to conventional medical therapy, require manometric evaluation to exclude an underlying colon neuromuscular abnormality.</p>", "<p>Motilin is a 22-amino acid peptide hormone secreted by the enterochromaffin cells of the small intestine [##REF##9863486##4##,##REF##5011531##5##]. It exerts profound effect on gastric and small bowel motility by inducing the inter-digestive phase 3 of the migrating motor complex (MMC) [##REF##7409389##6##]. Peak plasma concentration of motilin is associated with MMC, both in animals and humans [##REF##11328252##7##, ####REF##3672037##8##, ##REF##456236##9####456236##9##].</p>", "<p>Conflicting results exists regarding the erythromycin effect on circular smooth muscle strips derived from the human colon. Few studies have reported the stimulatory effect of erythromycin in human smooth muscle contractions [##REF##11169123##10##,##REF##10620004##11##] and Nissan have reported lack of any excitatory effect on human colon [##REF##11975929##12##]. Studies have shown that motilin receptor is expressed in the enteric neurons of the colon [##REF##11169123##10##,##REF##10764957##13##] and plasma motilin concentration is reduced in adults with chronic constipation [##REF##9347469##14##]. Oral and intravenous erythromycin has no effect on distal colon contraction or transit in healthy human volunteers [##REF##1420750##15##].</p>", "<p>Erythromycin is a non-peptide motilin receptor agonist which induces phase 3 of the migrating motor complex in the antro-duodenum. Several studies have reported that erythromycin is safe and effective in improving feeding intolerance in preterm infants and children [##REF##11753159##16##, ####REF##11641030##17##, ##REF##11320044##18##, ##REF##15755792##19####15755792##19##]. The prokinetic effect of erythromycin has also been reported in older children with motility disorders [##REF##9144124##20##]. The data regarding the colon prokinetic effect of erythromycin is controversial. Oral erythromycin has been shown to reduce the colonic transit time assessed using radio opaque markers in adults with chronic constipation [##REF##7587829##21##]. However, another adult study using colon manometry reported no significant improvement in colon motility with erythromycin compared to a placebo [##REF##9577904##22##]. To date, no studies have evaluated the effect of erythromycin on colon motility in children.</p>", "<p>The aim of our study was to evaluate the effect of intravenous erythromycin lactobionate (1 mg/kg) on colon motility in children with chronic constipation and fecal incontinence using colon manometry.</p>" ]
[ "<title>Methods</title>", "<p>We retrospectively evaluated 10 simultaneously performed antro-duodenal manometry (ADM) and colon manometry (CM) studies performed at the Children's Hospital of Wisconsin between June 2000 and June 2005. These studies were performed to exclude an underlying small bowel and/or colon motility disorder. Only patients with normal antro-duodenal and colon motility studies were included. The presenting symptoms were chronic constipation in 8 patients, abdominal pain and fecal incontinence in 1 and abdominal distension and pain in 1 patient. Hirschsprung's disease was excluded either by anorectal manometry or rectal biopsies in all subjects. This retrospective chart review study was approved by the Children's Hospital of Wisconsin Human Research Review Board (Protocol number: CHW 05/187, GC 31). Informed consent was not obtained from the patients as this was a retrospective chart review study.</p>", "<p>All drugs known to affect the gastrointestinal motility were discontinued at least 72 hours before the motility studies. Patients fasted for at least 8 hours before the study. All children were anesthetized without a muscle relaxant. We waited for the child to recover completely from the effects of the drug before starting the motility tests [##REF##7562282##23##]. Colonoscopy was performed to assist colon manometry catheter placement. The tip of the colon motility catheter was positioned in the cecum/ascending colon in all subjects and the position was confirmed by fluoroscopy. A water perfused manometry catheter with 8 recording sites was used for ADM and CM. Catheter position was checked using fluoroscopy. The catheter was perfused with 0.45% sodium chloride solution at the rate of 0.4 ml per minute per recording site, using a pneumo-hydraulic infusion system. The pressures were transmitted to a transducer and recorded on a computer with specialized motility software (Medical Measurements System, Amsterdam). We performed at least 2 hours fasting recording of both ADM and CM studies, following which we administered intravenous erythromycin lactobionate 1 mg/kg and performed another 60 minute of recording. Next, the patients ate a meal appropriate for their age (meal provided &gt;30% of daily caloric requirement) and the recording was continued for another 60 minutes [##REF##12213110##24##]. Bisacodyl (5–10 mg) was administered through the central lumen of the manometry catheter, directly into the colon to induce high amplitude colon contractions (HAPCs) and recording was continued for at least 30 minutes (Figure ##FIG##0##1##). This is a standard protocol for simultaneously performed ADM and CM studies performed at our center.</p>", "<p>Antro-duodenal manometry was considered normal if i) phase 3 of the MMC, as defined by repetitive antral contractions occurring at a rate of 2–3 per minute and 10–12 per minute small bowel contractions, lasted for more than 2 minutes with normal antegrade propagation ii) there was normal stomach antrum and small bowel response to a meal [##REF##8026249##25##]. Colon manometry studies were considered normal if there was a gastro-colonic response and spontaneous or bisacodyl stimulated antegrade propagating HAPCs [##REF##1578302##26##]. We defined HAPCs as colon contractions with amplitude of at least 60 mmHg, and propagating over at least 30 cm of colon.</p>", "<p>All colon manometry studies were reviewed by an experienced gastroenterologist and artifact was removed. The area under the curve was calculated by measuring the area under the pressure line for one 60 minutes period during fasting, following erythromycin administration and after a meal. Increase in motility index following a meal is considered a gastro-colonic response. The CM recordings were also evaluated for HAPCs.</p>", "<p>We used SPSS software, version 10 for Windows (SPSS Inc., Chicago, Illinois, USA). We compared the mean (SE of mean) AUC using Student's t test. To compare the effect of erythromycin and bisacodyl on colon contractions, we used McNemar's test for disagreement.</p>" ]
[ "<title>Results</title>", "<p>The mean age of the patients at the time of the study was 9.6 years (range 4–12 years); there were five females. In all patients, we recorded phase 3 of the MMC following intravenous erythromycin and a normal postprandial antro-duodenal manometry.</p>", "<p>The mean (SE of mean) AUC in the colon during the fasting, post erythromycin and postprandial phases of the study was 2.1 mmHg/sec (0.35), 0.99 mmHg/sec (0.17) and 3.05 mmHg/sec (0.70), respectively (Table ##TAB##0##1##). The AUC following erythromycin was significantly less compared to the fasting phase of the study (p &lt; 0.01), suggesting that colon motor activity was reduced following erythromycin administration.</p>", "<p>All patients had a normal gastrocolonic response. Four patients had spontaneous HAPCs during the fasting period, one following erythromycin stimulation and none following the meal. All patients had HAPCs with bisacodyl stimulation, the mean number of HAPCs was 8.9 (range 1–16). McNemar's test for disagreement between erythromycin and bisacodyl induced HAPC was statistically significant (p = 0.004). The mean interval of first HAPC after bisacodyl was 7.18 minutes (range 1–10 minutes).</p>" ]
[ "<title>Discussion</title>", "<p>This retrospective study evaluates the effect of erythromycin on colon motility in children with chronic constipation using colon manometry. All our patients had normal antro-duodenal and colon manometric studies. The two recognizable features of normal colon motility in children are an increase in colon contractions following a meal (gastrocolonic response) and HAPCs[##REF##1578302##26##]. If spontaneous HAPCs are not recorded during fasting and postprandial period, bisacodyl is used to stimulate HAPCs [##REF##9779966##27##]. All patients in our study had a normal gastrocolonic response and bisacodyl induced HAPCs. This suggests that none of our patients had intestinal pseudo-obstruction or colon neuromuscular abnormality. In our opinion, these patients had functional constipation and/or fecal incontinence.</p>", "<p>All patients showed normal phase 3 MMC activity following intravenous erythromycin lactobionate (1 mg/kg dose). This shows that the dose of erythromycin lactobionate used was adequate to stimulate the motilin receptors in the foregut. In the colon there was a significant decrease in the frequency and amplitude of contractions following erythromycin lactobionate when compared to the fasting period. This may be because the motilin receptors in the colon have a higher threshold of activation compared to small bowel. An adult study reported no prokinetic effect of erythromycin on colon motility as determined by colon manometry [##REF##9577904##22##]. This suggests that unlike the foregut, erythromycin lactobionate in a dose of 1 mg/kg does not have a prokinetic effect on the colon and probably a higher concentration may be necessary to stimulate the motilin receptors in the colon.</p>", "<p>There is heterogeneity in the motilin receptor affinity for erythromycin in the gastrointestinal tract [##REF##11753157##28##]. It is possible that the colon motilin receptors may have reduced affinity for erythromycin compared to the antral nerves or the expression of motilin receptor may be reduced in the colon. The limitation of our study is that we only evaluated the effect of a single dose of erythromycin (1 mg/kg) given intravenously. It is possible that a higher dose may be necessary to induce a prokinetic effect on the colon. An adult study using a higher oral dose of erythromycin (1 g/day), reported improvement in segmental and colon transit time assessed using radio-opaque markers [##REF##7587829##21##].</p>" ]
[ "<title>Conclusion</title>", "<p>Our study suggests that erythromycin lactobionate at 1 mg/kg does not have a colon prokinetic effect in children with chronic intractable constipation. Further studies are needed, using a higher dose of erythromycin, to evaluate the dose response curve and affinity of the colon motilin receptors to erythromycin.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Motilin, a peptide hormone has a direct excitatory effect on circular smooth muscle strips derived from the human colon. Reduced plasma motilin concentration has been reported in adults with chronic constipation. Erythromycin, a non-peptide motilin receptor agonist, induces phase 3 of the migrating motor complex (MMC) in the antro-duodenum and also reduces oro-cecal transit time. A pediatric study has reported an improvement in clinical symptoms of constipation following erythromycin administration, but the effect on colon motility in children has not been formally evaluated. We used colon manometry to study the effect of intravenous erythromycin lactobionate at 1 mg/kg on colon motiltiy in ten children.</p>", "<title>Methods</title>", "<p>We selected patients with normal antroduodenal and colon manometry studies that were performed simultaneously. All studies were performed for clinically indicated reasons. We quantified the effect of erythromycin on colon contraction by calculating the area under the curve (AUC).</p>", "<title>Results</title>", "<p>The mean (SE of mean) AUC in the colon during the fasting, post-erythromycin and postprandial phases of the study was 2.1 mmHg/sec (0.35), 0.99 mmHg/sec (0.17) and 3.05 mmHg/sec (0.70) respectively. The AUC following erythromycin was significantly less compared to the fasting phase of the study (p &lt; 0.01).</p>", "<title>Conclusion</title>", "<p>Erythromycin lacks colon prokinetic effect in children with chronic constipation evaluated by colon manometry.</p>" ]
[ "<title>Abbreviations</title>", "<p>MMC: migrating motor complex, AUC: area under the curve, ADM: antroduodenal manometry, CM: colon manometry, HAPCs: high amplitude colon contractions</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>All authors read and approved the final manuscript.</p>", "<p>NVS: Data analysis and manuscript writing. CR: Helped with manuscript writing and providing patients. MS: Helped with manuscript writing, data analysis and providing patients.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-230X/8/38/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>none</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>HAPCs in a patient with chronic constipation</bold>. High amplitude propagating contractions (HAPCs) following bisacodyl stimulation in a 10 year old boy with chronic constipation. The colon contractions are propagating from the cecum to the sigmoid colon.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>AUC and HAPCs during fasting, following a meal and erythromycin</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Fasting Period</td><td align=\"left\">Postprandial period</td><td align=\"left\">Post-erythromycin period</td><td align=\"left\">Bisacodyl stimulation</td></tr></thead><tbody><tr><td align=\"left\">Mean area under the curve in mm/sec (SE of mean)</td><td align=\"left\">2.1(0.35)</td><td align=\"left\">3.05 (0.70)</td><td align=\"left\">0.9 (0.17)</td><td align=\"left\">NA</td></tr><tr><td align=\"left\">Mean number of HAPCs (range)</td><td align=\"left\">0.4 (0–1)</td><td align=\"left\">0.00 (0)</td><td align=\"left\">0.1 (0–1)</td><td align=\"left\">8.9 (1–16)</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>The mean area under the pressure line during fasting, following a meal and erythromycin lactobionate administration. The mean number of HAPCs recorded during each of these periods and following bisacodyl stimulation are also shown</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-230X-8-38-1\"/>" ]
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{ "acronym": [], "definition": [] }
28
CC BY
no
2022-01-12 14:47:26
BMC Gastroenterol. 2008 Aug 21; 8:38
oa_package/96/96/PMC2529327.tar.gz
PMC2529328
18673531
[ "<title>Background</title>", "<p>Gastric and colorectal cancers are a cause of morbidity and mortality in the world today. If a curative surgical resection is impossible, these cancers respond very poorly to chemotherapy and resulting in a poor prognosis. In gastric cancer patients, 5-fluorouracil (5-FU) based combination chemotherapy have been attempted in order to improve the treatment outcomes [##REF##15550582##1##]. With colorectal cancer, 5-FU has been the most widely used drug for more than 40 years. However, other agents such as irinotecan or oxaliplatin have been used to improve the antitumor efficacy in combination with 5-FU [##REF##17625587##2##]. 5-FU interferes with DNA synthesis by blocking the production of pyrimidine nucleotide dTMP from dUMP during <italic>de novo </italic>DNA synthesis through the inhibition of thymidylate synthase as well as through the incorporation of fluoro-nucleotides into the DNA and RNA [##REF##15205195##3##].</p>", "<p>P-glycoprotein (Pgp) encoded by the multidrug resistance 1 (<italic>MDR1</italic>) gene is a representative membrane efflux pump of ATP-binding cassette (ABC) transporters [##REF##500733##4##, ####REF##10025956##5##, ##REF##10493507##6####10493507##6##]. Pgp functions as energy-dependent efflux pumps of a variety of structurally diverse chemotherapeutic agents such as doxorubicin, vincristine, vinblastine, paclitaxel, colhicine, actinomycin D and mitomycin C [##REF##12127970##7##], which can decrease the intracellular level of drug accumulation. As a result, overexpression of these proteins confers MDR to cancer cells by evading the cytotoxic effects of drugs. In the human intestine, Pgp is strongly expressed on the apical surface of the superficial columnar epithelial cells of the ileum and colon, and its expression level decreases gradually proximally into the jejunum, duodenum and stomach [##REF##12692067##8##]. Regulation of the transcriptional activity of the <italic>MDR1 </italic>gene is dependent on several trans-acting proteins that bind the consensus cis-elements [##REF##12213590##9##]. The accessibility of the promoter elements to their binding factors is regulated at the level of chromatin assembly. The levels of both DNA methylation and histone deacetylation regulate <italic>MDR1 </italic>gene expression [##REF##11865062##10##, ####REF##9834236##11##, ##REF##10411657##12####10411657##12##]. So far, the transcriptional regulation of <italic>MDR1 </italic>gene expression through epigenetic mechanisms has been reported in expression in colon cancer cells [##REF##16091741##13##, ####REF##8103760##14##, ##REF##9632821##15##, ##REF##12427779##16####12427779##16##] but none in gastric cancers cells. Furthermore, the relationships between the transcriptional expression of <italic>MDR1 </italic>gene expression and epigenetic mechanisms in gastric and colon cancer cells have not been compared. Therefore, it is unclear why chemotherapy regimens have been differently used to treat gastric and colorectal cancers and why <italic>MDR1 </italic>mRNA is expressed differentially in gastric and colorectal cancer cells. Therefore, this study examined whether or not the degree of methylation at the promoter site of the <italic>MDR1 </italic>gene is closely associated with <italic>MDR1 </italic>gene expression in both cancer cells.</p>" ]
[ "<title>Methods</title>", "<title>Cell culture</title>", "<p>The 10 human gastric cancer cell lines (SNU-1, -5, -16, -216, -484, -601, -620, -638, -668 and -719) and 9 colon cancer cell lines (SNU-C1, -C4, -C5, Colo320HSR, LoVo, DLD-1, HT-29, HCT-8 and HCT-116) were obtained from the Cancer Research Center at Seoul National University (South Korea). All the cells were cultured at 37°C in a 5% CO<sub>2 </sub>atmosphere using RPMI 1640 medium (GibcoBRL, Gland Island, NY, USA) with 10% heat inactivated fetal bovine serum (Sigma, ST. Louis, MO, USA). The cells were maintained either as a suspension or a monolayer culture, and subcultured until they reached confluence.</p>", "<title>Reverse transcription-polymerase chain reaction (RT-PCR) assay</title>", "<p>The total RNA was extracted using MagExtractor<sup>® </sup>for the MFX-2100 (Toyobo, Osaka, Japan) auto-nucleic acid purification system, according to the manufacturer's instructions. The <italic>MDR1 </italic>and <italic>β-actin </italic>mRNA transcripts were detected using the RT-PCR assay. <italic>MDR1 </italic>expression was detected with the 5' and 3' primers corresponding to the nucleotides 907–930 (5'-CTGGTTTGATGTGCACGATGTTGG-3') and 1179–1201 (5'-TGCCAAG-ACCTCTTCAGCTACTG-3'), respectively, of the published cDNA sequence [##REF##2876781##17##], yielding a 296-bp PCR product. β<italic>-actin </italic>mRNA expression was used as a control for the amount of RNA, and was detected with the 5' and 3' primers corresponding to nucleotides 1912–1932 (5'-GACTATGACTTAGTTGCGTTA-3') and 2392–2412 (5'-GTTGAACTCTCTACATACTTCCG-3'), respectively, of the published cDNA sequence [##REF##2994062##18##], yielding a 501-bp PCR product. The RNA from each sample was reverse transcribed using 200 units of Moloney murine leukemia virus reverse transcriptase (Gibco-Bethesta Research Laboratory, Grand Island, NY, USA) and 0.18 μg/ml oligo (dT<sub>20</sub>) primer for 1 hr at 37°C. The resulting cDNA of the gastric cancer cells (2-fold diluted cDNA in the colon cancer cells) were amplified with 1.25 units of Taq polymerase (PE Applied Biosystems, Foster City, CA, USA), 1 mM MgCl<sub>2 </sub>and 10 pmole of each primer in a thermal cycler (GeneAmp 2400, PE Applied Biosystems, Boston, MA, USA) for 22 cycles with the colon cancer cells but 35 cycles with the gastric cancer cells for <italic>MDR1 </italic>and 17 cycles for β<italic>-actin </italic>of the sequential denaturation (94°C for 30 s), annealing (65°C for <italic>MDR1</italic>, 53°C for β<italic>-actin</italic>), and extension (72°C for 30 s). After the final cycle, all the PCR products were subjected to a final extension of 5 min at 72°C. For quantitation, 3 μCi of [α-<sup>32</sup>P] dCTP were added to each reaction mixture. After PCR, the PCR products were combined and then electrophoresed on a 7.5% nondenaturing polyacrylamide gel. The bands were scanned with a densitometer (Pdi, Huntington Station, NY, USA). The amount of each mRNA transcript was normalized with that of each β<italic>-actin </italic>mRNA.</p>", "<title>Protein extraction and Western blot analysis</title>", "<p>Total cell lysates were prepared by lysing harvested cells in extraction buffer (1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS in phosphate-buffered saline) supplemented with 2 mM phenylmethylsulfonyl fluoride (Sigma) and 10 μg/ml leupeptin (Sigma). DNA was sheared by sonication and Western blotting analysis was performed using a slight modification of the method first described by Towbin et al. [##REF##388439##19##]. Proteins were transferred onto a nitrocellulose membrane by electroblotting using a current of 60 V overnight. The membrane was incubated in blocking solution (5% skim milk) for 1 hr at room temperature, washed, and then incubated with primary goat polyclonal antibody (1:1000, Santa Cruz, Biotechnology, CA, USA) for Pgp. The membrane was washed and incubated with horseradish peroxidase-conjugated secondary antibody (diluted 1:1000) against each IgG for hosts of primary antibodies for 1 hr. The membrane was then stained using the detection reagent of the ECL detection kit (Amersham, Piscataway, NJ, USA).</p>", "<title>Real-time PCR</title>", "<p>Extraction of mRNA was performed according to the RNeasy proctocol (Qiagen, Hilden, Germany). One microgram of total RNA was reversely transcribed into cDNA in a volume of 20 μl with 200 units of Moloney murine leukemia virus reverse transcriptase (Gibco-Bethesta Research Laboratory, Grand Island, NY, USA) and 0.18 μg/ml oligo (dT<sub>20</sub>) primers (Promega, Madison, USA) according to the manufacture's manual. Real-time PCR was performed with the Light Cycler 2.0 Instrument (Roche, Mannheim, Germany) using the Fast Start DNA Master SYBR Green I Kit (Roche). For verification of the correct amplification product, PCRs were analyzed on a 2% agarose gel stained with ethidium bromide. The sequences of the primers are as follows: for <italic>β-actin</italic>, 5'-GACTATGACTTAGTTGCGTTA-3' and 5'-GTTGAACTCTCTACATACTTCCG-3'; for <italic>MDR1</italic>, 5'-CTGGTTTGATGTGCACGATGTTGG-3' and 5'-TGCCAAGACCTCTTCAGCTACTG-3'. Each reaction (20 μl) contained 4 μl cDNA (10-fold dilution), 4 mM MgCl<sub>2</sub>, 10 pmole of each primer and 2 μl of Fast Start DNA Master SYGR Green I Mix containing buffer, dNTPs, SYBR Green dye and Tag polymerase. The amplification procedure of target genes was as follows: pre-denaturing at 95°C for 10 min, 40 cycles of denaturing at 95°C for 15 sec, annealing for <italic>MDR1 </italic>at 67°C (<italic>β-actin </italic>at 55°C) for 5 sec, and extension at 72°C for 7 sec (<italic>β-actin </italic>for 21 sec). Melting curve analysis was performed to confirm production of a single product. Negative controls without template were produced for each run. Gene expression values (relative mRNA levels) are expressed as ratios (difference between the Ct values = The point on the curve in which the amount of fluorescence begins to increase rapidly, usually a few standard deviations above the baseline, is termed the threshold cycle (Ct value).) between the gene of interest (<italic>MDR1 </italic>mRNA) and an internal reference gene (<italic>β-actin </italic>mRNA) that provide a normalization factor for the amount of RNA isolated from a specimen. Analysis of data was performed using Light Cycler software version 4.0 (Roche).</p>", "<title>Cytotoxicity test using MTT assay</title>", "<p>The <italic>in vitro </italic>cytotoxicity of the drugs was measured using an MTT assay, as described elsewhere [##REF##3165705##20##]. The cells were seeded at a 2 × 10<sup>4</sup>cells/ml and incubated overnight to allow for attachment and stabilization. The cells were incubated at 37°C for 3 days, and MTT [3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyl tetrazolium bromide, Sigma] solution was then added to each well containing the cells. After shaking for 1 min, the plate was incubated for 5 hr. Formazan crystals of the suspension culture were dissolved in 150 μl of dimethylsulfoxide (DMSO) after removing the supernatant. The optical density of the wells was measured with a microplate reader (μQuant, Bio-tek Instruments Inc., Winooski, VT, USA) at 540 nm.</p>", "<title>Quantification PCR-based methylation analysis</title>", "<p>Five micrograms of the genomic DNA was digested with 50 U of <italic>Msp </italic>I or <italic>Hpa </italic>II (Fermentas MBI, Vilnius, Lithuania) at 37°C for 16 hours, added to a 1/15 volume of 0.6 M Tris (pH 7.5) and 1.5 M NaCl, and digested with 50 U of <italic>Pst </italic>I (New England Biolabs, MA, USA) at 37°C for 8 hours. The methylation status of the <italic>MDR1 </italic>5'CpG promoter region was examined by analyzing 100 ng restriction-digested DNA by PCR in 25 μL reactions containing 1.25 units of Taq DNA polymerase and 10 pmole of each primer. The quantification PCR-based methylation analysis was carried out according to the method reported previously [##REF##9834236##11##]. The PCR primers used were 5'-TCTAGAGAGGTGCAACGGAAG-3' and 5'-TCAGCCTCACCACAGATGAC-3' for the MS1 methylation-sensitive primers (121 bp), 5'-TGAAGTCCTCTGGCAAGTCC-3' and 5'-ATTCTCCCTCCCGGTTCC-3' for the MS2 methylation-sensitive primers (206 bp), 5'-ATTTCACGTCTTGGTGGCC-3' and 5'-TCCAGTGCCACTACGGTTT-G-3'for the MC positive control primers (240 bp), and 5'-GGCGAAGGAGGT-TGTCTATTC-3' and 5'-AACGTTCTAGGAGAGTCGGG-3' for MN negative control primers (240 bp) derived from the triosephosphate isomerase gene promoter region. Amplification was performed in a DNA thermal cycler for 35 cycles for the MN, MC, MS1 and MS2 primers involving in sequence denaturation (95°C for 30 s), annealing (60°C for 30 s), and extension (72°C for 30 s). After the final cycle, all the PCR products were subjected to a final extension for 5 min at 72°C. The PCR products were separated by electrophoresis on 7% PAGE gels. The gels were then stained with ethidium bromide, and photographed by using a Kodak Image Station 4000 MM (Eastman Kodak, Rochester, NY, USA).</p>", "<title>Bisulfite DNA sequencing analysis</title>", "<p>One μg of genomic DNA was chemically modified by sodium bisulfite using the EZ DNA Methylation kit (Zymo Research, Orange, CA, USA) to convert unmethylated cytosines to uracils while leaving methylated cytosines unaltered. The bisulfite-modified DNA was used for PCR amplification. Extended MS1 primer contains 10 CpG sites, and 2 SP-1 sites which are mandatory for the functional <italic>MDR1 </italic>promoter to be activated [##REF##8103518##21##]. The primer sequences for amplification of bisulfite-treated strands (223 bp) were as follows: S: 5'-GGAAGTTAGAATATTTTTTTTGGAAAT-3'; AS: 5'-ACCTCTACTTCTTTAAACTTAAAAAAACC-3'. Amplification was performed in the same PCR conditions except 48°C annealing temperature and 45 PCR cycles. After the final cycle, all the PCR products were subjected to a final extension at 72°C for 5 min. Sequence of PCR products was analyzed using an automated sequencer (Applied Biosystems, Foster City, CA, USA).</p>", "<title>Statistical Analysis</title>", "<p>The results are presented as a mean ± SE and the data was analyzed using the Student's <italic>t-test</italic>. P values &lt; 0.05 were considered significant.</p>" ]
[ "<title>Results</title>", "<title>Comparison of expression profiles of MDR1 mRNA in gastric and colon cancer cells</title>", "<p><italic>MDR1 </italic>mRNA expression was analyzed using the RT-PCR assay with the expression level being normalized with the <italic>β-actin </italic>mRNA levels obtained after 17 cycles of PCR. The <italic>MDR1 </italic>mRNA was not detected after 22 cycles of PCR in the 10 gastric cancer cell lines but could be detected at variable levels after 35 cycles of PCR with the exception of SNU-16, suggesting a significantly low level of <italic>MDR1 </italic>mRNA expression in the gastric cancer cells tested (Figure ##FIG##0##1##). As shown in Figure ##FIG##0##1##, the rank order according to the <italic>MDR1-β-actin </italic>ratio in the gastric cancer cell lines is as follows: SNU-668 (1.51) &gt; SNU-484 (1.37) &gt; SNU-5 (0.63) &gt; SNU-601 (0.33) &gt; SNU-719 (0.32) &gt; SNU-216 (0.29) &gt; SNU-638 (0.07) &gt; SNU-1 (0.04) &gt; SNU-16 (0). Of the 9 colon cancer cell lines, variable <italic>MDR1 </italic>mRNA levels could be detected in 7 colon cancer cell lines after 22 cycles of PCR but not in the SNU-C5 and HT-29 cells. The <italic>MDR1 </italic>mRNAs of the two latter cells could be not detected even after 35 cycles of PCR. These results suggest a relatively high level of <italic>MDR1 </italic>mRNA expression in spite of some exceptions in the colon cancer cells. As shown in Figure ##FIG##1##2##, the rank order according to the <italic>MDR1</italic>/<italic>β-actin </italic>ratio in the colon cancer cell lines is as follows: Colo320HSR (0.90) &gt; SNU-C4 (0.45) &gt; HCT-8 (0.26) &gt; SNU-C1 (0.12) &gt; HCT-116 (0.11) &gt; LoVo (0.10) &gt; DLD-1 (0.07) &gt; SNU-C5 (0) = HT-29 (0).</p>", "<p>We performed again the real-time RT-PCR assay for the quantitative validation of <italic>MDR1 </italic>mRNA levels obtained from RT-PCR assay. The <italic>MDR1 </italic>mRNA was not detected in the 10 gastric cancer cell lines. However, of the 9 colon cancer cell lines, variable <italic>MDR1 </italic>mRNA levels could be detected in 7 colon cancer cell lines except the SNU-C5 and HT-29 cells as the RT-PCR data (Figure ##FIG##2##3##).</p>", "<p>Taken together, the <italic>MDR1 </italic>mRNA levels in the gastric cancer cell lines were significantly lower than those in the colon cancer cell lines.</p>", "<title>Chemosensitizing effects of Pgp inhibitors in gastric and colon cancer cells</title>", "<p>Although the protein levels were not examined in this study, functional studies were performed using the Pgp inhibitors in the gastric and colon cancer cell lines expressing the highest level of <italic>MDR1 </italic>mRNA expression. As shown in Figure ##FIG##3##4A##, the Colo320HSR cells (colon, mutant p53, highest expression of <italic>MDR1 </italic>mRNA) were 14-fold and &gt; 200 times resistant to paclitaxel than the SNU-C5 and SNU-668 cells (gastric, mutant p53, highest expression of <italic>MDR1 </italic>mRNA) as compared on the basis of the IC<sub>50 </sub>values, respectively, representing a significant difference in the Pgp levels. In addition, the resistance of the Colo320HSR cells to paclitaxel was reversed by the Pgp inhibitors including cyclosporine A, verapamil, and PSC833 (Figure ##FIG##3##4B##). However, this reversal was not observed in the SNU-C5 (colon, no <italic>MDR1 </italic>mRNA) cells as well as SNU-668. This suggests that Pgp expressed in colon cancer cells but not gastric cancer cells works functionally and can be inhibited by the Pgp inhibitors.</p>", "<title>Comparison of methylation status of MDR1 in gastric and colon cancer cells</title>", "<p>The methylation status was determined by quantification PCR-based methylation analysis for a CpG-rich domain to be approximately 1 Kb containing exon 1 and intron 1 among the <italic>MDR1 </italic>promoter. To determine the degree of methylation of the <italic>MDR1 </italic>gene promoter region, two primers (MS1 and MS2) containing the <italic>Msp </italic>I/<italic>Hpa </italic>II sites were designed from exon 1 and intron 1, respectively. The primer pair MC was used as a positive control to determine the quality of the source genomic DNA. In contrast, the MN that crosses the <italic>Msp </italic>I/<italic>Hpa </italic>II site at the triosephosphate isomerase gene promoter region and is never methylated was used as the negative control. Figure ##FIG##4##5B## shows typical quantification PCR-based methylation analysis images of the SNU-5 (gastric) and HT-29 (colon) cells. The quantification PCR-based methylation analysis revealed that any PCR products for the MS1 and MS2 were not produced from <italic>Pst</italic>1-digested genomic DNA treated with <italic>Msp </italic>I (methylation-insensitive enzyme). On the other hand, PCR products for both MS1 and MS2 in the SNU-5 cells but a PCR product for the MS1 alone in the HT-29 cells were obtained after <italic>Hpa </italic>II (methylation-sensitive enzyme) treatment, indicating methylation of CpG at the MS1 and MS2 sites in the SNU-5 cells and only at the MS2 site in the HT-29 cells. As summarized in Table ##TAB##0##1##, methylation was detected at the MS1 and MS2 sites of the 9 gastric cancer cell lines with the exception of SNU-484 but 2 (SNU-C5 and HCT-116) of the 9 colon cancer cell lines. On the other hand, the HT-29 cells were methylated only at the MS2 site. The SNU-C5, HT-29 (colon) and SNU-16 (gastric) cells not expressing <italic>MDR1 </italic>mRNA were methylated. Bisulfite DNA sequencing analysis was performed to confirm the methylation. As show in Table ##TAB##0##1##, methylation degree (%) of 10 CpG sites on the expended MS1 site is completely matched with results obtained by quantification PCR-based methylation analysis.</p>", "<title>Effects of 5-aza-2'-deoxcytidine (5AC) and/or trichostatin A (TSA) on the expression of MDR1 mRNA in gastric and colon cancer cell lines</title>", "<p>The DNA methyltransferase inhibitor 5AC and the histone deacetylase (HDAC) inhibitor TSA have been well known to relieve epigenetic gene repression [##REF##15809275##22##]. This study examined the effect of 5AC and/or TSA on <italic>MDR1 </italic>mRNA expression in the gastric and colon cancer lines. In 10 gastric and 9 colon cancer cells, <italic>MDR1 </italic>mRNA expression was determined by RT-PCR after treating them with 2.5 μM 5AC for 96 hr and/or 100 ng/ml TSA for 48 hr. An increase was defined in cases showing more than a 1.5-fold increase. As shown in Table ##TAB##0##1##, the 5AC treatment increased the <italic>MDR1 </italic>mRNA levels in the SNU-1, -5, -601, -620, -638 and -719 gastric cancer cell lines, and that in the HCT-116 colon cancer cell line (Figures ##FIG##5##6## and ##FIG##6##7##). However, 5AC did not induce <italic>MDR1 </italic>mRNA expression even in the SNU-16 and SNU-C5 and HT-29 cells whose <italic>MDR1 </italic>gene was methylated. The TSA treatment increased the <italic>MDR1 </italic>mRNA levels in the SNU-1, -16, -216, -601, -638, -668 and -719 gastric cancer cell lines and the SNU-C1, Colo320HSR, DLD-1, H29 and HCT-116 colon cancer cell lines (Figure ##FIG##5##6## and ##FIG##6##7##). 5AC showed high cytotoxicity alone or in combination with TSA, particularly in the HT-29 cells. Also, TSA showed highly cytotoxic activity alone or in combination with 5AC, particularly in the SNU-620 cells. This study also examined the effects of the combined treatment of 5AC with TSA, which increased the <italic>MDR1 </italic>mRNA levels additively in the SNU-5 and -638 cells but synergistically in the SNU-16, -601, -668, -719 and SNU-C5 cells (Table ##TAB##0##1##).</p>", "<p>These results suggest that <italic>MDR1 </italic>mRNA expression is differentially regulated in gastric and colon cancer cells with respect to the silencing of <italic>MDR1 </italic>expression through epigenetic mechanisms such as DNA methylation and/or histone deacetylation.</p>" ]
[ "<title>Discussion</title>", "<p>In this study, we found that the <italic>MDR1 </italic>mRNA levels in the gastric cancer cell lines were significantly lower than those in the colon cancer cell lines, although there were some variations. These results are consistent with a report showing that Pgp is strongly expressed on the ileum and colon, at a level that gradually decreases proximally into the jejunum, duodenum and stomach [##REF##12692067##8##]. Since the stomach and colon play major roles in digestion and absorption, respectively, it is not surprising that transporters such as Pgp were differentially expressed in the two normal tissues. Our finding that the differential expression of <italic>MDR1 </italic>mRNA in cancer cell lines derived from the stomach and colon is also consistent with published reports [##REF##9045962##23##, ####REF##1967320##24##, ##REF##1977924##25##, ##REF##9677442##26####9677442##26##]. Immunopathological studies revealed that Pgp expression on human tumors was most commonly detected in colon, renal, and adrenal carcinomas but rarely in lung and gastric carcinomas and certain germ cell tumors [##REF##1974900##27##].</p>", "<p>The three-way connection between DNA methylation, chromatin structure and gene expression was recently reviewed [##REF##11498374##28##, ####REF##9724627##29##, ##REF##15881894##30####15881894##30##]. An important consequence of CpG methylation is the local silencing of gene expression, which can be mediated by the direct interference of methylation with the binding of various transcription factors. The major component of silencing of gene expression appears to be the binding of methyl-CpG-binding protein 2 (MeCp2), which has an affinity for methyl-CpG [##REF##1606614##31##,##REF##9601009##32##]. DNA demethylation by 5AC causes the release of the MeCp2 from the promoter, which activates transcriptional gene expression [##REF##11865062##10##]. It is known that MeCp2 is also enriched on the <italic>MDR1 </italic>promoter and is related to its silencing [##REF##14567978##33##]. 5AC alters the methylation pattern of the <italic>MDR</italic>1 promoter in Pgp-negative cells to resemble that of Pgp-positive cells and activates the promoter such that <italic>MDR1 </italic>mRNA is detectable [##REF##9815593##34##].</p>", "<p>In this study, the methylation status was also analyzed in order to determine if the <italic>MDR1 </italic>silencing is due to hypermethylation of the promoter region. Quantification PCR-based methylation analysis showed methylation in 9 (90%) out of 10 gastric cancer cell lines but only 3 (33%) out of 9 colon cancer cells, which were completely matched with the results obtained by bisulfite DNA sequencing assay. The latter frequency is relatively high compared with a different study showing <italic>MDR1 </italic>methylation in 24% of 275 colorectal cancers [##REF##12427779##16##]. As showed in Table ##TAB##0##1##, complete but not partial methylation in the extended MS1 site was responsible for increased <italic>MDR1 </italic>mRNA expression by the treatment with 5AC. In addition, MS1 site derived from exon 1 of <italic>MDR1 </italic>promoter has shown to be more important with respect to gene expression than MS2 site from intron 1 of <italic>MDR1 </italic>promoter.</p>", "<p>The histone-modifying enzymes such as histone acetyltransferase (HAT) and HDAC enzymes also modulate transcription of <italic>MDR1 </italic>[##REF##9632821##15##]. Therefore, we have investigated how epigenetic mechanisms, such as DNA methylation and histone deacetylation, are involved in the differential expression of <italic>MDR1 </italic>mRNA between gastric and colon cancer cells using 5AC and/or TSA. The following summarizes the results obtained after the 5AC and/or TSA treatment. Effects of 5AC and/or TSA are defined as positive when &gt; 1.5-fold is increased after treatment.</p>", "<p>1) In gastric cancer cells, 5AC and TSA induced <italic>MDR1 </italic>mRNA expression at a frequency of 6/10 (60%) and 7/10 (70%), respectively. On the other hand, in colon cancer cells, 5AC and TSA induced <italic>MDR1 </italic>mRNA expression at a frequency of 1/9 (11%) and 5/9 (55%), respectively. This suggest that DNA methylation is at least partly responsible for the low level of <italic>MDR1 </italic>mRNA expression in gastric cancer cells but is rarely involved in colon cancer cells whereas HDAC may play important roles in <italic>MDR1 </italic>mRNA expression in both cells.</p>", "<p>2) 5AC alone had no effect but combined with TSA synergistically increased the <italic>MDR1 </italic>mRNA expression level in 20% (SNU-16 and -668) of gastric cancer cells but only the SNU-C5 colon cancer cells. This suggests that the expression of a methylated <italic>MDR1 </italic>gene insensitive to 5AC alone increased with the assistance of TSA. This result is consistent with a previous report that silencing conferred by MeCp2 and methylated DNA can be also relieved by inhibition of HDAC, facilitating the remodelling of chromatin and transcriptional activation [##REF##9620779##35##]. Although TSA alone cannot activate hypermethylted <italic>MDR1 </italic>[##REF##11865062##10##], it can lead to upregulation of non-methylated or sparely methylated promoters [##REF##9916800##36##]. Thus, epigenetic modifications of DNA and histone have been shown to be responsible for <italic>MDR1 </italic>gene silencing. However, it is still unclear which one is first, DNA methylation or histone modifications [##REF##12559178##37##]. Moreover, combined effect of 5AC with TSA was less than that of 5AC or TSA alone in gastric (SNU-1) and colon (SNU-C1, Colo320HSR and DLD-1) cancer cells, indicating a more complex relation between the methylated DNA and HDAC.</p>", "<p>3) TSA but not 5AC induced <italic>MDR1 </italic>mRNA expression in 30% (SNU 16, -216 and – 668) of gastric cancer cells and 40% (SNU-C1, COLO32HSR, DLD-1 and HT-29) of colon cancer cells. These findings suggest that HDAC is dominant over DNA methylation in cancer cells whose <italic>MDR1 </italic>genes are not methylated. However, synergistic effects of TSA when combined with 5AC showing no effect in gastric cancer cells (SNU-16 and -668) with unmethylated <italic>MDR1</italic>gene are not fully understood. The possibility of involvement of histone methylation in silencing of <italic>MDR1 </italic>expression remains to be determined.</p>", "<p>4) The combined treatment of 5AC with TSA increased <italic>MDR1 </italic>mRNA expression either additively 20% (SNU-5 and -638) or synergistically 40% (SNU-16, -601, -668 and -719) in the gastric cancer cells but only synergistically in the SNU-C5 colon cancer cells. The synergistic effect of 5AC and TSA in gastric cancer cells can be explained on the basis of a report showing that the methylated gene binds MeCP2, which in turn recruits HDAC resulting in the suppression of transcription [##REF##9601009##32##,##REF##9620779##35##].</p>", "<p>5) Neither 5AC nor TSA induced <italic>MDR1 </italic>mRNA expression even in gastric (SNU-484) and colon (SNU-C4, -C5 and Lovo) cancer cells even though combination of 5AC and TSA increased <italic>MDR1 </italic>mRNA expression in the SNU-C5 cells. This suggests the involvement of other factors in <italic>MDR1 </italic>mRNA expression or inappropriate concentrations and incubation period of each inhibitor.</p>", "<p>One of the aims of this study was to explain why the different 5-FU-based anticancer therapies have been used in gastric and colorectal cancers using differential <italic>MDR1 </italic>expression. It has been well known that high levels of thymidylate synthase activity are responsible for the resistance to 5-FU [##REF##17172411##38##]. However, antimetabolites such as 5-FU are not substrates for the ATP-dependent efflux transporters such as Pgp expressed on the apical (brush-border) membrane of intestinal epithelial cells [##REF##11090958##39##, ####REF##16146333##40##, ##REF##15635178##41####15635178##41##]. Therefore, the <italic>in vitro </italic>and <italic>in vivo </italic>anticancer efficacy of 5-FU can be explained not only by the increase in its intracellular accumulation in cancer cells but also by the enhancement of its bioavailability when administered orally. In gastric cancer, even anthracyclines (doxorubicin or epirubicin) and mitomycin C, which are good Pgp substrates, have been used to treat gastric cancer cells, which are characterized by zero or low levels of Pgp, which would make them sensitive to these anticancer drugs. In colon cancer, a number of novel anticancer drugs including oxaliplatin and irinotecan have been used in various combinations [##REF##15205195##3##]. Platinum compounds such as oxaliplatin, which are not substrates for Pgp and breast cancer resistance protein (BCRP), have been used as effective agents against colorectal cancer. Even though irinotecan is a BCRP substrate, it has been shown to be effective in colon cancers with a significantly lower BCRP expression level than that of the normal colon [##REF##16554028##42##]. Nevertheless, it is essential that substrate drugs (topotecan, irinotecan, anthracyclines, mitomycin C and trimetrexate) for Pgp and/or BCRPS used clinically in colon cancer be administered in conjunction with chemosensitizers (VX-710 [##REF##15014037##43##], GF120918 [##REF##8402633##44##,##REF##10656616##45##], and XR-9576 [##REF##10510451##46##,##REF##11205902##47##]), which can reverse both Pgp and BCRP.</p>" ]
[ "<title>Conclusion</title>", "<p>The <italic>MDR1 </italic>mRNA levels in the gastric cancer cell lines were significantly lower than those in the colon cancer cell lines, which is at least in part due to differential epigenetic regulations such as DNA methylation and/or HDAC. Therefore, <italic>MDR1/</italic>Pgp plays more important roles in the transporting function in colon cancer cells than in gastric cancer cells. These results can provide a better understanding of the efficacy of combined chemotherapy as well as their oral bioavailability.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The membrane transporters such as P-glycoprotein (Pgp), the <italic>MDR1 </italic>gene product, are one of causes of treatment failure in cancer patients. In this study, the epigenetic mechanisms involved in differential <italic>MDR1 </italic>mRNA expression were compared between 10 gastric and 9 colon cancer cell lines.</p>", "<title>Methods</title>", "<p>The <italic>MDR1 </italic>mRNA levels were determined using PCR and real-time PCR assays after reverse transcription. Cytotoxicity was performed using the MTT assay. Methylation status was explored by quantification PCR-based methylation and bisulfite DNA sequencing analyses.</p>", "<title>Results</title>", "<p>The <italic>MDR1 </italic>mRNA levels obtained by 35 cycles of RT-PCR in gastric cancer cells were just comparable to those obtained by 22 cycles of RT-PCR in colon cancer cells. Real-time RT-PCR analysis revealed that <italic>MDR1 </italic>mRNA was not detected in the 10 gastric cancer cell lines but variable <italic>MDR1 </italic>mRNA levels in 7 of 9 colon cancer cell lines except the SNU-C5 and HT-29 cells. MTT assay showed that Pgp inhibitors such as cyclosporine A, verapamil and PSC833 sensitized Colo320HSR (colon, highest <italic>MDR1 </italic>expression) but not SNU-668 (gastric, highest) and SNU-C5 (gastric, no expression) to paclitaxel. Quantification PCR-based methylation analysis revealed that 90% of gastric cancer cells, and 33% of colon cancer cells were methylated, which were completely matched with the results obtained by bisulfite DNA sequencing analysis. 5-aza-2'-deoxcytidine (5AC, a DNA methyltransferase inhibitor) increased the <italic>MDR1 </italic>mRNA levels in 60% of gastric cells, and in 11% of colon cancer cells. Trichostatin A (TSA, histone deacetylase inhibitor) increased the <italic>MDR1 </italic>mRNA levels in 70% of gastric cancer cells and 55% of colon cancer cells. The combined treatment of 5AC with TSA increased the <italic>MDR1 </italic>mRNA levels additively in 20% of gastric cancer cells, but synergistically in 40% of gastric and 11% of colon cancer cells.</p>", "<title>Conclusion</title>", "<p>These results indicate that the <italic>MDR1 </italic>mRNA levels in gastric cancer cells are significantly lower than those in colon cancer cells, which is at least in part due to different epigenetic regulations such as DNA methylation and/or histone deacetylation. These results can provide a better understanding of the efficacy of combined chemotherapy as well as their oral bioavailability.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>T-BL: Study execution, data analysis and manuscript preparation. J-HP: Study execution, data analysis and manuscript preparation. Y-DM: Concept, study design and clinical considerations for gastric cancer. K-JK: Concept, study design and clinical considerations for colon cancer. C-HC: Idea, study design and manuscript preparation. All authors have read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-230X/8/33/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Dr. Hansik Na of the Nahansik Internal Medicine (Mokpo, Korea) for their help and critical reading of the manuscript. This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) through the Research Center for Resistant Cells (R13-2003-009).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold><italic>MDR1 </italic>mRNA expression in gastric cancer cell lines</bold>. The level of <italic>MDR1 </italic>mRNA expression was determined by RT-PCR, and normalized by that of mRNA <italic>β-actin</italic>, which was used as a control for RNA. The cDNA reverse-transcribed from the mRNA was amplified separately with each primer pair for <italic>MDR1 </italic>and <italic>β-actin </italic>genes. Aliquots of each PCR reaction mixture were separated on 7% polyacrylamide gel. The gel was dried and exposed on X-ray film overnight.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold><italic>MDR1 </italic>mRNA expression in the colon cancer cell lines</bold>. The same methodology reported in Figure 1 was used.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold><italic>MDR1 </italic>mRNA expression in gastric and colon cancer cell lines</bold>. The level of <italic>MDR1 </italic>mRNA expression was determined by real-time RT-PCR, and normalized by that of mRNA <italic>β-actin</italic>, which was used as a control for RNA. The cDNA reverse-transcribed from the mRNA was amplified separately with each primer pair for <italic>MDR1 </italic>and <italic>β-actin </italic>genes.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Comparison of Pgp expression and function in gastric and colon cancer cell lines</bold>. (A) Comparative sensitivity of Colo320HSR (colon, highest), SNU-668 (gastric, highest) and SNU-C5 (colon, no expression) to paclitaxel; (B) Effects of Pgp inhibitors on the sensitivity of Colo320HSR, SNU-668 and SNU-C5 to paclitaxel (IC10 concentration; 50 μM, 0.3 nM and 0.5 nM, respectively). Sensitivity to paclitaxel was determined using MTT assay in the presence or absence of the Pgp inhibitors (cyclosporin A, verapamil and PSC833 of 0.8 μM each). *, P &lt;0.05 versus the control.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>The quantification PCR-based methylation analysis of gastric and colon cancer cell lines</bold>. (A) CpG sites and <italic>Hpa II</italic>/<italic>Msp I </italic>sites in the human <italic>MDR1 </italic>promoter region. Top: The CpG sites are represented by the short vertical bars. The positions of exons 1 and 2 are indicated as closed boxes. The position corresponding to these fragments are indicated. Middle: <italic>Hpa II</italic>/<italic>Msp I </italic>recognition sites are represented by short vertical bars. Bottom, PCR primers used in methylation analysis. (B) Representative methylation status of the <italic>MDR1 </italic>promoter region by quantification PCR-based methylation analysis in SNU-5 (gastric) and HT-29 (colon). 1: MN, Never-methylated <italic>Hpa II</italic>/<italic>Msp I </italic>site at the triosephosphate isomerase gene promoter region (negative control) (240 bp); 2: MC, the positive control primer pair (240 bp); 3: MS1, <italic>Hpa II</italic>/<italic>Msp I </italic>site 1 (121 bp); 4: MS2, <italic>Hpa II</italic>/<italic>Msp I </italic>site 2 (206 bp).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Activation of <italic>MDR1 </italic>mRNA expression by 5AC and/or TSA in gastric cancer cells</bold>. The expression level is reported as the ratio of <italic>MDR1</italic>/β-actin. The total RNA was isolated after treatment with 2.5 μM 5AC for 96 hr and/or 100 ng/ml TSA for 48 hr. RT-PCR was performed using the same methodology reported in Figure 1.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Activation of <italic>MDR1 </italic>mRNA expression by 5AC and/or TSA in various colon cancer cell lines</bold>. RT-PCR assay after treating the cells with 5AC and/or TSA using the same method described in Figure 6. The <italic>MDR1/β-actin </italic>ratio obtained through 35-cycle PCR after TSA in combination with 5AC, and alone in SNU-C5 and HT-29 expressing no <italic>MDR1 </italic>mRNA, respectively, was omitted in the histogram.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Methylation status and the effects of 5AC and/or TSA on <italic>MDR1 </italic>mRNA expression and in various gastric and colon cancer cell lines</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td/><td/><td align=\"center\" colspan=\"3\">DNA methylation assay</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td colspan=\"3\"><hr/></td><td/><td/><td/><td/></tr><tr><td align=\"center\">Tissue</td><td align=\"center\">Cell line</td><td align=\"center\" colspan=\"2\">5AC</td><td align=\"center\" colspan=\"2\">Restriction Enzyme</td><td align=\"center\">Sodium Bisulfite</td><td align=\"center\" colspan=\"2\">TSA</td><td align=\"center\" colspan=\"2\">5AC +TSA</td></tr><tr><td/><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td/><td align=\"center\">Fold</td><td align=\"center\">Effect</td><td align=\"center\" colspan=\"2\">Site</td><td align=\"center\">Degree (%)<sup>1</sup></td><td align=\"center\">Fold</td><td align=\"center\">Effect</td><td align=\"center\">Fold</td><td align=\"center\">Effect</td></tr></thead><tbody><tr><td align=\"center\">Gastric</td><td align=\"center\">SNU-1</td><td align=\"center\">32.6</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">20.7</td><td align=\"center\">O</td><td align=\"center\">+23.6</td><td align=\"center\">D</td></tr><tr><td/><td align=\"center\">SNU-5</td><td align=\"center\">5.8</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">1.03</td><td align=\"center\">X</td><td align=\"center\">6.8</td><td align=\"center\">A</td></tr><tr><td/><td align=\"center\">SNU-16</td><td align=\"center\">n.d.</td><td align=\"center\">X</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">60</td><td align=\"center\">8.4</td><td align=\"center\">O</td><td align=\"center\">28.9</td><td align=\"center\">S</td></tr><tr><td/><td align=\"center\">SNU-216</td><td align=\"center\">-1.0</td><td align=\"center\">X</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">50</td><td align=\"center\">8.4</td><td align=\"center\">O</td><td align=\"center\">8.2</td><td align=\"center\">N</td></tr><tr><td/><td align=\"center\">SNU-484</td><td align=\"center\">-1.3</td><td align=\"center\">X</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">0</td><td align=\"center\">-5.1</td><td align=\"center\">X</td><td align=\"center\">-0.17</td><td align=\"center\">-</td></tr><tr><td/><td align=\"center\">SNU-601</td><td align=\"center\">3.2</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">3.3</td><td align=\"center\">O</td><td align=\"center\">13.7</td><td align=\"center\">S</td></tr><tr><td/><td align=\"center\">SNU-620</td><td align=\"center\">67.6</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">*</td><td align=\"center\">X</td><td align=\"center\">*</td><td align=\"center\">*</td></tr><tr><td/><td align=\"center\">SNU-638</td><td align=\"center\">68.9</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">5.7</td><td align=\"center\">O</td><td align=\"center\">72.9</td><td align=\"center\">A</td></tr><tr><td/><td align=\"center\">SNU-668</td><td align=\"center\">-1.0</td><td align=\"center\">X</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">80</td><td align=\"center\">1.8</td><td align=\"center\">O</td><td align=\"center\">4.4</td><td align=\"center\">S</td></tr><tr><td/><td align=\"center\">SNU-719</td><td align=\"center\">5.8</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">4.2</td><td align=\"center\">O</td><td align=\"center\">12.4</td><td align=\"center\">S</td></tr><tr><td colspan=\"11\"><hr/></td></tr><tr><td align=\"center\">Colon</td><td align=\"center\">SNU-C1</td><td align=\"center\">-1.96</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">1.7</td><td align=\"center\">O</td><td align=\"center\">+1.0</td><td align=\"center\">D</td></tr><tr><td/><td align=\"center\">SNU-C4</td><td align=\"center\">1.0</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">-1</td><td align=\"center\">X</td><td align=\"center\">-1.6</td><td align=\"center\">N</td></tr><tr><td/><td align=\"center\">SNU-C5</td><td align=\"center\">n.d.</td><td align=\"center\">X</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">n.d.</td><td align=\"center\">X</td><td align=\"center\">8</td><td align=\"center\">S</td></tr><tr><td/><td align=\"center\">Colo320HSR</td><td align=\"center\">1.4</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">1.6</td><td align=\"center\">O</td><td align=\"center\">+1.3</td><td align=\"center\">D</td></tr><tr><td/><td align=\"center\">LoVo</td><td align=\"center\">-1.9</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">1.1</td><td align=\"center\">X</td><td align=\"center\">-2</td><td align=\"center\">N</td></tr><tr><td/><td align=\"center\">DLD-1</td><td align=\"center\">1.1</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">3.5</td><td align=\"center\">O</td><td align=\"center\">+1.1</td><td align=\"center\">D</td></tr><tr><td/><td align=\"center\">HT-29</td><td align=\"center\">*</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">MS2</td><td align=\"center\">0</td><td align=\"center\">∞</td><td align=\"center\">O</td><td align=\"center\">*</td><td align=\"center\">*</td></tr><tr><td/><td align=\"center\">HCT-8</td><td align=\"center\">1.0</td><td align=\"center\">X</td><td align=\"center\">n.d.</td><td align=\"center\">n.d.</td><td align=\"center\">0</td><td align=\"center\">1.0</td><td align=\"center\">X</td><td align=\"center\">1.1</td><td align=\"center\">N</td></tr><tr><td/><td align=\"center\">HCT-116</td><td align=\"center\">1.7</td><td align=\"center\">O</td><td align=\"center\">MS1</td><td align=\"center\">MS2</td><td align=\"center\">100</td><td align=\"center\">1.6</td><td align=\"center\">O</td><td align=\"center\">1.6</td><td align=\"center\">N</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p><sup>1</sup>Sequence was analyzed using PCR products containing the extended MS1 site that has been shown to play an important role in <italic>MDR1 </italic>mRNA expression. n.d., not detected; *, highly cytotoxic; ∞, an increase as compared with no <italic>MDR1 mRNA </italic>expression; D, decreased; A, additive; S, synergistic; N, not changed</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-230X-8-33-1\"/>", "<graphic xlink:href=\"1471-230X-8-33-2\"/>", "<graphic xlink:href=\"1471-230X-8-33-3\"/>", "<graphic xlink:href=\"1471-230X-8-33-4\"/>", "<graphic xlink:href=\"1471-230X-8-33-5\"/>", "<graphic xlink:href=\"1471-230X-8-33-6\"/>", "<graphic xlink:href=\"1471-230X-8-33-7\"/>" ]
[]
[]
{ "acronym": [], "definition": [] }
47
CC BY
no
2022-01-12 14:47:26
BMC Gastroenterol. 2008 Aug 1; 8:33
oa_package/63/e8/PMC2529328.tar.gz
PMC2529329
18700986
[ "<title>Background</title>", "<p>HIV-2, the second AIDS-causing virus, is found predominantly in the Portuguese speaking countries of West Africa, with the highest rates of infection in Guinea-Bissau [##REF##17559692##1##]. The prevalence of HIV-2 in the United States is extremely low [##UREF##0##2##], and the current guidelines recommend testing for HIV-2 only in the case of an indeterminate western blot or in patients with known links to West Africa [##UREF##0##2##,##REF##18322061##3##]. While this screening practice may make sense for the majority of U.S. cities where the percentage of the population of West African descent is decidedly small, cities with a significant immigrant community from infected regions should consider increased surveillance. We present the case of a patient of Cape Verdean descent with likely PML in the setting of HIV-2, and discuss the difficulties of diagnosing HIV-2 in the United States.</p>" ]
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[ "<title>Discussion</title>", "<p>We present a case report of an unusual and challenging diagnosis of HIV-2 in the United States. Our patient emigrated from Cape Verde, an archipelago off the west coast of Africa with an estimated population of 460,000. Cape Verde is a place defined by migration, with approximately 500,000 people living abroad, 265,000 of which are estimated to be in the United States [##UREF##1##4##]. The migration of Cape Verdeans to the United States began in the 1800's with whaling ships that carried West Africans to New England's shores. New Bedford, Massachusetts and Providence, Rhode Island are America's oldest Cape Verdean communities, with 1,592 legal immigrants settling in the Providence Metro area between 1991 and 1998 alone [##UREF##2##5##].</p>", "<p>The prevalence of non-subtype B HIV in the United States is approximately 2% [##REF##14709241##6##]. The virus isolated in this patient, HIV-2, is common in parts of Western Africa, but is rare in the United States [##REF##10885765##7##]. HIV-2 is thought to have a milder disease course with a longer time to the development of AIDS than HIV-1 [##REF##7909009##8##,##REF##7915856##9##]. Clinical presentations of neurological syndromes with HIV-2 are extremely rare [##REF##16816570##10##,##REF##16652302##11##]. Since being discovered in 1985 [##REF##3006256##12##], only 79 cases of HIV-2 have been reported in the United States with 52 of those patients having originated in Western Africa [##UREF##0##2##]. Given the low prevalence of HIV-2 in industrialized countries, the clinical course and optimal treatment strategies are unknown [##REF##3006256##12##]. Non-nucleoside reverse transciptase inhibitors (NNRTI's) are not effective against HIV-2, whereas nucleoside reverse transciptase inhibitors (NRTI's) may be less effective [##REF##12386343##13##,##REF##15040537##14##]. Protease inhibitors have varying efficacy against HIV-2 [##REF##14565609##15##, ####REF##18227188##16##, ##REF##17116674##17##, ##REF##16464891##18####16464891##18##] and use should be guided by genotype/phenotype profiles, not commercially available in the United States. Ritonavir-boosted atazanavir was initially started in this patient before the diagnosis of HIV-2, but was later changed to ritonavir-boosted lopinavir which has better efficacy against HIV-2 [##REF##18227188##16##].</p>", "<p>The identification of HIV-2 represents a diagnostic dilemma in the United States. The standard diagnosis of HIV-1 infection relies on a positive EIA followed by a confirmatory western blot assay in which two of the three HIV antigens (p24, gp41, and gp120) must be present. Screening EIA assays, including the newer rapid tests, are not always sensitive for detecting HIV-2 or group O HIV-1 [##REF##9132574##19##, ####REF##7910884##20##, ##REF##7968029##21##, ##REF##16455942##22####16455942##22##], however the newer 4<sup>th </sup>generation assays are better [##UREF##2##5##,##REF##11427563##23##, ####REF##17078473##24##, ##REF##16908076##25####16908076##25##]. Routine western blots are specific mainly for HIV-1 antibodies and indeterminate western blots (i.e. detection of only one antigen, usually p24) may suggest infection with HIV-2. The only FDA-approved EIA assays that are able to detect HIV-2 are Abbott HIVAB HIV-1/2 (rDNA) EIA, Genetic Systems HIV-1/2 Peptide EIA, and Genetic Systems HIV-2 EIA.</p>", "<p>Current CDC guidelines [##REF##1324395##26##] state that HIV-2 serology should be checked in patients who: 1) Are from areas of high prevalence, mainly Western Africa; 2) Share needles or have sexual partners known to be infected with HIV-2 or are from endemic areas; 3) Received transfusions or other non-sterile medical care from endemic areas; 4) Are children of women with risk factors for HIV-2 infection. As sometimes clinical history is not available in patients with a high-suspicion for HIV infection and negative or indeterminate serology for HIV-1, additional testing should be performed for HIV-2.</p>", "<p>Regarding viral RNA quantification, there are no FDA approved assays for the determination of HIV-2 viral load in the United States (Table ##TAB##0##1##). This creates a dilemma in the treatment of HIV-2 infected patients as viral loads are an integral part of patient monitoring. The five methods for detecting viral loads all routinely detect HIV-1 viral RNA from most group M subtypes, although small differences may exist in quantification capabilities [##REF##17243923##27##, ####REF##12354861##28##, ##REF##11917236##29##, ##REF##16286047##30##, ##REF##12367656##31##, ##REF##16675034##32####16675034##32##]. Assays for HIV-2 are mainly developed for research purposes and none are commercially available [##REF##14565609##15##,##REF##12354861##28##,##REF##10195267##33##, ####REF##10479138##34##, ##REF##15019261##35####15019261##35##]. The NucliSens EasyQ assay (BioMerieux, Netherlands) is approved for HIV-1 viral load quantification and has been shown to detect subtype A of HIV-2 by nucleic acid amplification [##REF##17093034##36##]. Similarly, the Roche Amplicor assay was able to detect three of four HIV-2 samples [##UREF##3##37##]. Neither the branched DNA nor other RT-PCR assays have been shown to detect HIV-2, and none are approved by the FDA or regularly used to detect HIV-2. Differences between the assays are likely due to primers which are more likely to anneal and be specific to certain areas of both HIV-1 and HIV-2 depending on target sequences. Further studies are needed to define the sensitivity and specificity of these tests' ability to detect HIV-2.</p>", "<p>In our institution, patients are tested for HIV using the Bayer ADVIA Centaur HIV-1/O/2 EHIV EIA [##REF##16769045##38##]. This was positive in our patient, but a western blot for HIV-1 was negative. The Versant branched DNA assay is our standard measure for HIV viral loads, but this technique did not quantify any viral RNA in this particular patient. Follow-up testing with a western blot specific for HIV-2 was positive and subsequent viral quantification based the Roche Amplicor system showed a significant viral load.</p>", "<p>Our patient exemplifies the diagnostic difficulties of identifying HIV-2 in the United States. Fortunately, we were able to elucidate a history of Western African origin from our patient. All physicians involved in screening for HIV should be aware of the limitations between assays and know which test their institution uses. Clinicians need to have a high index of suspicion in patients with risk factors for HIV-2 to appropriately diagnose and treat the disease.</p>" ]
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Current diagnostic assays for HIV-1 do not always test for the presence of HIV-2 in the United States. We present the case of a patient from Cape Verde, who was admitted to our hospital with rapidly deteriorating neurological function and multiple white matter lesions on MRI likely secondary to progressive multifocal leukoencephalopathy (PML). Initially, the patient had a positive EIA for HIV, but a negative HIV-1 Western Blot and no viral load detected on a branched-DNA assay. A repeat viral load by reverse transcriptase methodology (RT-DNA) detected 121,000 copies and an HIV-2 Western Blot was positive. The case highlights an extremely rare presentation of HIV-2 with severe neurological disease. We discuss the different tests available for the diagnosis and monitoring of HIV-2 in the United States.</p>" ]
[ "<title>Case presentation</title>", "<p>A 48 year-old male with a past medical history significant only for cataracts was admitted to our hospital with weakness, difficulty walking, and confusion that began one day prior to admission. In addition to the neurological symptoms, the patient had experienced a flu-like illness three to four weeks earlier accompanied by a ten to fifteen pound weight loss. The patient was afebrile with mental status changes and abnormal cerebellar findings on neurological exam including a wide-based gait, ataxia, dysmetria on finger-to-nose, and difficulty with rapid alternating hand movements. On further history, the patient took no medications and was born in Cape Verde, immigrating to the United States six years earlier. While he admitted to having multiple recent female sexual partners, he denied any drug use or any male sexual partners. His wife and five children remained in Cape Verde. His history raised the possibility of acute HIV infection or an AIDS-related illness.</p>", "<p>An initial HIV enzyme immunoassay (EIA) was performed in the emergency room and returned positive (Bayer ADVIA Centaur HIV-1/O/2 EHIV EIA). Other laboratory tests were normal, aside from a slightly decreased white count of 3,800 cells/uL. A non-contrast CT of the brain on admission noted no acute abnormalities. An MRI with and without contrast of the brain was performed on the second day of admission, which showed multifocal supra and infratentorial T2 flare hyperintense lesions felt to be consistent with multiple sclerosis, an acute demyelinating process, or Lyme disease. Two lumbar punctures were subsequently performed showing four nucleated cells, an elevated protein of 103 mg/dL, and a normal glucose. Testing of the cerebrospinal fluid (CSF) was negative for cytomegalovirus (CMV, PCR), Epstein-Barr Virus (EBV, PCR), varicella zoster virus (VZV, PCR), Lyme (IgM and IgG antibodies, PCR), herpes simplex virus (HSV-1 and -2, IgM and IgG antibodies), Toxoplasmosis (IgM and IgG antibodies), India Ink, Cryptococcal antigen, Streptococcal antigen, rapid plasma reagin (RPR), JC Virus (PCR), acid-fast bacilli (AFB), and cytology. Other labs sent for the evaluation of mental status change were normal including electrolytes, B12, TSH, and a urine drug screen. Serum tests looking for an infectious etiology were also negative, including an RPR, fluorescent treponemal antibody (FTA-ABS), CMV antibodies, and Lyme antibodies. A hepatitis panel revealed past infections with hepatitis A and B, and a negative hepatitis C antibody. Blood, urine, and CSF cultures were negative, as was a rapid influenza. The patient's CD4 count returned at 202 (17%, ratio 0.3).</p>", "<p>The patient improved with supportive care over the hospital course and was discharged on day five with follow-up to an outpatient clinic. Six days later the patient was seen at HIV clinic, at which point his confirmatory western blot for HIV-1 was still pending. Based on the patient's clinical history, CD4 count, positive EIA, and recent immigration from Western Africa, it was felt that the patient was most likely suffering from an HIV-related neurological process. He was started on the anti-retroviral regimen of ritonavir-boosted atazanavir (ATV), tenofovir (TDF), and emtricitabine (FTC), and a plasma viral load was sent.</p>", "<p>Six days after the clinic visit, the patient was readmitted to the hospital for continued confusion and gait disturbance. The patient's initial confirmatory western blot for HIV-1 returned negative and the viral load was undetectable (branched DNA technology, Versant HIV-1 RNA 3.0, Bayer). Given the confusing picture and strong clinical suspicion for HIV, a second Western blot specific for HIV-2 was sent, as well as a repeat viral load using RT-PCR analysis (Roche Amplicor RT-PCR). The patient had a repeat MRI which showed interval worsening of the white matter lesions, but no new processes. During the course of this hospitalization, the patient's second viral load returned at 121,000 copies/mL and the western blot was positive for HIV-2. The differential diagnosis based on the patient's clinical history and imaging included PML, HIV encephalitis and/or lymphoma. Given the patient's negative CSF EBV and disseminated (non-solitary) MRI lesions, it was deemed highly unlikely that the patient had CNS Lymphoma. Although the patient had a negative JC virus PCR, review of the MRI found the lesions to be most consistent with PML and/or HIV encephalitis. A brain biopsy was considered, but the patient and his family refused.</p>", "<p>Looking back over the hospital's records, it was discovered that the patient had been seen two years prior to this admission for a unilateral facial droop. In addition, a steadily declining WBC count was noted through several emergency room visits. Based on these findings, as well as the clinical presentation, the patient was felt to have chronic, as opposed to acute, HIV-2. While the patient may have had sexual contact with other West African immigrants in this country, it seemed most likely that he had become infected while living in Cape Verde more than six years earlier.</p>", "<p>Over the first week of the second admission, the patient worsened neurologically, becoming incontinent and acutely agitated requiring medication with anti-psychotics. A repeat MRI of the brain on day eight of admission showed rapid progression of the white matter disease, as described above. Given the patient's poor prognosis, hospice was considered. The patient was changed to ritonavir-boosted lopinavir, and over the next couple weeks made significant clinical improvements, regaining continence, becoming increasingly lucid, and improving in gait and balance. By the fourth week of his admission, his CD4 count had increased to 331 (17.4%), although his MRI showed no regression of the lesions. The patient was discharged on HAART in stable condition, but with persistent neurological deficits.</p>", "<title>Competing interests</title>", "<p>SC reports receiving grant support for an unrelated study from Bristol-Myers Squibb. All other authors declare there are no competing interests in this work. The present study was unfunded.</p>", "<title>Authors' contributions</title>", "<p>PC and SW participated in the research, writing, and editing of the manuscript. TF, SC, EK, and RK participated in the writing and editing of the manuscript. All authors read and approved the final manuscript.</p>" ]
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[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>FDA approved assays for the quantification of HIV RNA</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Assay</bold></td><td align=\"center\"><bold>Manufacturer</bold></td><td align=\"center\"><bold>Technique</bold></td><td align=\"center\"><bold>Sensitivity (copies/ml)</bold></td><td align=\"center\"><bold>HIV-1 Subtypes</bold></td><td align=\"center\"><bold>HIV-2</bold></td></tr></thead><tbody><tr><td align=\"center\">AMPLICOR [##REF##8150937##39##,##REF##9508301##40##]</td><td align=\"center\">Roche</td><td align=\"center\">RT-PCR</td><td align=\"center\">50<sup>†</sup>-750,000</td><td align=\"center\">Group M (subtypes A-H)</td><td align=\"center\">Detected 3/4 HIV-2 [##UREF##3##37##]</td></tr><tr><td align=\"center\">Versant HIV-1 RNA 3.0 [##REF##7697440##41##]</td><td align=\"center\">Bayer</td><td align=\"center\">bDNA</td><td align=\"center\">75–500,000</td><td align=\"center\">Group M (subtypes A-G)</td><td align=\"center\">No [##UREF##4##42##]</td></tr><tr><td align=\"center\">NucliSens HIV RNA QT [##REF##12354861##28##,##REF##8161439##43##]</td><td align=\"center\">BioMereiux</td><td align=\"center\">NASBA</td><td align=\"center\">176–3.4 million</td><td align=\"center\">Group M (not subtype G)</td><td align=\"center\">YES [##REF##17093034##36##] (subtype A)</td></tr><tr><td align=\"center\">COBAS AmpliPrep, Taqman HIV-1 [##REF##17329164##44##]</td><td align=\"center\">Roche</td><td align=\"center\">RT-PCR</td><td align=\"center\">48–10 million</td><td align=\"center\">Group M (subtypes A-H)</td><td align=\"center\">No [##REF##18322061##3##,##REF##17259908##45##]</td></tr><tr><td align=\"center\">RealTime HIV-1 [##REF##17259908##45##]</td><td align=\"center\">Abbott</td><td align=\"center\">RT-PCR</td><td align=\"center\">40–10 million</td><td align=\"center\">Group M, N, O, recombinants</td><td align=\"center\">No [##REF##17259908##45##]</td></tr></tbody></table></table-wrap>" ]
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[]
[ "<table-wrap-foot><p>NASBA: Nucleic acid sequence based amplification assay</p><p>RT-PCR: Reverse transcription polymerase-chain reaction</p><p>bDNA: Branched DNA assay</p><p><sup>†</sup>The standard assay can detect 400–750,000 copies/ml and the ultra-sensitive assay can detect 50–100,000 copies/ml.</p></table-wrap-foot>" ]
[]
[]
[{"surname": ["Factsheet"], "given-names": ["C"], "year": ["1998"]}, {"surname": ["DeParle"], "given-names": ["J"], "source": ["In a World on the Move, a Tiny Land Strains to Cope"], "year": ["2007"], "publisher-name": ["The New York Times"]}, {"article-title": ["Federation for American Immigration Reform"]}, {"surname": ["Roche"], "article-title": ["Summary Insert: AMPLICOR RT-PCR Version 1.5"]}, {"surname": ["Bayer"], "article-title": ["Summary Insert: Versant HIV-1 RNA 3.0"]}]
{ "acronym": [], "definition": [] }
45
CC BY
no
2022-01-12 14:47:26
AIDS Res Ther. 2008 Aug 14; 5:18
oa_package/90/dc/PMC2529329.tar.gz
PMC2529333
18673563
[ "<title>Background</title>", "<p>Most epidemiological studies have identified tobacco smoking and alcohol drinking as the main risk factors for esophageal squamous-cell carcinoma or unspecified esophageal cancer [##UREF##0##1##, ####UREF##1##2##, ##REF##14657432##3####14657432##3##], usually with a monotonic and strong dose-response relationship [##REF##10417762##4##]. The evidence that high alcohol consumption increases the risk of cancer of the esophagus is quite convincing at present; most of it suggests that it is the amount of alcohol consumed, rather than the particular drink that determines the risk. However, a prospective cohort study in Denmark observed that low wine consumption, compared to beer and hard liquor, did not increase risk [##REF##9748175##5##]. Two population-based case-control studies also presented data supporting this observation [##REF##9293918##6##,##REF##10652424##7##]. In an ecological study in Spain, trends of per capita consumption of beer, spirits, and total alcohol consumption were positively correlated with oesophageal cancer mortality in men whereas wine consumption showed no relationship with oesophageal cancer mortality either in men or women[##REF##1795145##8##]. As the consumption of all types of alcoholic drinks is quite prevalent in Spain, it is relevant to assess the risk of esophageal cancer in relation to them.</p>", "<p>Tobacco smoking has also a clear role in the aetiology of esophageal cancer [##UREF##0##1##,##UREF##1##2##]. Despite the high prevalence of consumption, particularly of black tobacco, an association between the type of tobacco smoking and esophageal cancer has never been explored by epidemiological studies in Spain, only in South America or France[##REF##10417762##4##,##REF##10750601##9##].</p>", "<p>In addition to smoking and alcohol, other factors such as a low socioeconomic status, an infrequent consumption of fruits and vegetables and, in some areas of Asia, the betel nut chewing, have also been related to esophageal cancer [##REF##16800779##10##, ####REF##11159155##11##, ##REF##16620285##12##, ##REF##12740918##13####12740918##13##]; these factors account for a high proportion of cases, particularly for the esophageal squamous cell type, the most frequent histological type [##REF##11159155##11##,##REF##16620285##12##,##REF##13130116##14##].</p>", "<p>We conducted a case-control study in Valencia and Alicante, Spain, to estimate the independent effects of different alcoholic beverages (beer, wine and spirits) and type of tobacco smoking (black and blond) on the risk of esophageal cancer and its main histological cell type (squamous cell carcinoma).</p>" ]
[ "<title>Methods</title>", "<title>Design</title>", "<p>This research was part of the PANESOES project, a prospective hospital-based case-control study designed to explore the influence of major lifestyles and diet on the risk of three gastrointestinal cancers, pancreas, oesophagus and stomach. The PANESOES Study aimed to recruit approximately 200 cases for oesophagus, 200 for pancreas cancer, 400 cases for stomach cancer, and 400–450 controls. This sample size was planned to estimate as statistically significant (p &lt; 0.05) a RR over 1.5 for stomach cancer and high prevalent exposures such as tobacco smoking and alcohol drinking, and a RR over 1.8 for oesophagus and pancreas cancer.</p>", "<p>Eligible subjects were Spanish-speaking men and women 30–80 years old, and hospitalized between January 1995 and March 1999 in any of nine participant hospitals in the provinces of Alicante and Valencia. These nine hospitals are among the ten main hospitals from the Health Care Service in Valencia and Alicante that were invited to participate (only one hospital in Valencia declined participation). The access to the Health Care System in Spain is free and universal; thus, our case series may be considered representative as the participant hospitals accounted for approximately 90% of cases in both provinces.</p>", "<p>All subjects were informed of the study objectives and gave their informed consent before the interview. Research protocols were approved by the local ethics and/or research committees of the participating Hospitals and the University.</p>", "<title>Subjects</title>", "<p>Cases were patients newly diagnosed with a primary invasive cancer of the oesophagus. Pathology reports were obtained for all case patients initially diagnosed as esophageal cancer, and final inclusion in the case series was based on histological confirmation. A total of 211 cases were initially identified as eligible for participation; two cases refused interview (0.9%), and seven cases were not finally confirmed (3.3%). The final analysis was thus based on 202 cases. The pathological diagnosis was confirmed as squamous cell carcinoma in 160 cases (79.2%) and adenocarcinoma in 42 cases (20.8%).</p>", "<p>Control subjects were selected from the same hospitals from which cases were identified and during the same period. Controls were frequency matched to the expected distribution of case subjects of the whole PANESOES study (i.e. cases of oesophagus, stomach and pancreas, and controls) by three age groups (&lt;60 years, 60–69 and 70–80 years), sex, and province (Alicante and Valencia). A wide inclusion criterion was used to select controls from diseases not related a priori to the main exposures of interest (tobacco, alcohol and diet). The overall participation rate of the 457 eligible controls was 99.6%, leaving 455 controls subjects with completed interviews. The distribution of the main diagnoses for the control group was, in decreasing order: hernia (34.0%), degenerative osteoarthritis/arthritis (21.3%), fractures/injuries/orthopaedic processes (18.9%), appendicitis (6.4%) and other, less prevalent, conditions (19.3%).</p>", "<title>Exposure data</title>", "<p>Face-to-face interviews were conducted in-hospital for all participants by trained interviewers, using a structured questionnaire. Interviews were administered directly to the study subjects, rather than to the next of kin, for 89.2% of the target case subjects and for 96% of the target control subjects. The average time required to complete the questionnaire was 40 minutes. While interviewers could not be blinded to the case/control status, they were unaware of the main study hypothesis and were trained to administer strictly the structured questionnaires in an equal manner to case and controls.</p>", "<p>Information was collected on demographic characteristics, tobacco and alcohol use, medical history and other lifestyle factors. The interview elicited details on usual tobacco use, including the product/tobacco type (black or blond cigarettes, cigars, pipes) as well as intensity of use, the age at which the habit started and stopped, the total duration of use excluding the years stopped, the years since last use for each type of product, and the level of inhalation when smoking (partially or totally). A never smoker was defined as someone having smoked fewer than 100 cigarettes ever or less than one cigarette per day for one year. A former smoker was defined as someone having stopped smoking 1 or more years before the interview. We computed the average number of cigarettes smoked per day including type of tobacco smoked, lifetime number of cigarettes smoked and pack-years of smoking (number of 20-cigarette packs per day multiplied by number of years smoking). Only 2 cases and 8 controls simultaneously smoked both blond and black tobacco, and they were considered as mainly blond tobacco smokers.</p>", "<p>Alcohol consumption patterns were assessed through inquiries into the usual intake for each type of beverage separately, i.e. beer, wine, or liquor. Since the intake of only wine and/or beer intake was uncommon among cases, these two alcoholic beverages were combined in the analysis. Since only 8 participants consumed liquors but not beer or wine, we estimated a category for the combined consumption of liquors with any combination of beer and/or wine (All types of beverages). The average relative contributions of beer, wine and liquor consumption in this category were 30%, 50% and 20% respectively. The three types of alcohol were also combined to give an overall estimate of the alcohol consumption. A never drinker was defined as having consumed less than one drink per month. One drink was defined as 200 cc of beer, 125 cc of wine, or 50 cc of hard liquor. The content of pure alcohol was calculated according to the following concentrations specific for Spain: 5% for beer; 12 percent for wine; and 40 percent for hard liquors. The resulting values were converted to grams multiplying 1 ml of pure ethanol per 789 mg [##UREF##2##15##]. The average in grams of pure ethanol consumed per day, type of alcoholic beverage, and life time duration of the habit, the age at starting and stopping the habit, and then the years since quitting drinking were also estimated. A former drinker was defined as having stopped drinking 1 or more years before the interview.</p>", "<p>The fruit and vegetable intake was assessed by a food frequency questionnaire (FFQ). For this study, we adapted and validated a FFQ of 93 food items similar to the Harvard questionnaire in order to assess the diet five years before the interview in the hospital [##REF##4014201##16##, ####UREF##3##17##, ##UREF##4##18####4##18##]. Participants were asked to report their average consumption of 12 vegetables and 10 fruit items. The average daily intakes for each fruit and vegetable were summed to compute the total fruit and vegetable intake in grams, and they were adjusted for energy intake using the residual method [##UREF##5##19##]. We further computed tertiles of energy-adjusted intake of fruit and vegetable using the distribution of cases and controls in the whole PANESOES study (cases of oesophagus, stomach and pancreas, and controls).</p>", "<title>Statistical Analysis</title>", "<p>We used unconditional logistic regression to estimate odds ratios (OR), as an estimate of relative risk, and corresponding 95% confidence intervals (CIs) [##UREF##6##20##]. All regression models included as covariates the three frequency-matched factors, sex (men/women), age (&lt;60 years, 60–69 years, 70–80 years) and hospital origin (Valencia/Alicante) entered as indicator variables; the educational level (&lt;primary, primary completed, and ≥ secondary school entered as indicator variable); and the different variables of alcohol intake and tobacco smoking also using indicator variables. In addition, we adjusted for a potential confounding effect of fruit and vegetable intake (in tertiles and entered as indicator variables), and energy intake.</p>", "<p>Tests for trend in the ORs across exposure strata were calculated for ordinal variables by using logistic models that included categorical terms as continuous variables in a model with all the potential confounders and, where appropriate, omitting the never or former users/exposed. For trend-tests, we used the likelihood ratio test statistic with one degree of freedom. Although the study sample size did not allow us to estimate tests of interactions, we performed exploratory analyses for the effects of alcohol and tobacco by strata of never-ever smoking status and never-ever drinking status, respectively. All analyses were performed with STATA-8 [##UREF##7##21##]. Statistical significance was set at 0.05. All reported <italic>P </italic>values are from two-sided tests.</p>" ]
[ "<title>Results</title>", "<p>Table ##TAB##0##1## shows the distribution of cases and controls according to demographic characteristics and main exposure variables. The distribution of the frequency-matched variables age, sex and province were comparable between the control series and the overall case series of the PANESOES study (i.e., cancers of oesophagus, stomach and pancreas, data not shown). The educational level was comparable between cases and controls. Alcohol intake, tobacco smoking and a low intake of fruit and vegetable were more prevalent among cases than controls.</p>", "<p>Table ##TAB##1##2## shows risk estimates according to various patterns of alcohol intake. We identified moderate to strong effects for all measures of amount and duration (years of alcohol drinking); risk was particularly strong for all types of beverages. Former drinkers and current drinkers experienced, respectively, a 4.3 and 2.1-fold increase of risk over never drinkers. An increasing risk of esophageal cancer was observed according to the daily amount of pure ethanol consumption (p-trend &lt; 0.00001); risk was very high and significant among subjects consuming ≥ 75 g/d (OR = 7.65). Concerning the beverage type, drinkers of all types of beverages showed the highest risk (OR = 4.39) and ever drinkers of only beer also showed a significant effect (3.07); however, no significant effects were observed for drinkers of only wine or wine and/or beer. When the daily amount by type of beverage was considered, no significantly increased risks were observed for wine-beer combined consumption. For all types of alcoholic beverages combined, a non-significant risk was observed for a daily consumption of 1–24 g/day (OR = 1.53), whereas risk increased sharply to 3.9 and 10.6-fold for the upper categories (p-trend&lt;0.0001).</p>", "<p>Regarding duration of drinking, statistically significant increased risks were observed for up to 40 years of drinking, after which no significant increase was observed (Table ##TAB##1##2##). Age at start drinking was not significantly associated to a higher risk of esophageal cancer. Drinking cessation in the last 5 years was associated with a strong risk excess compared with persistent drinkers (OR = 3.60, 95% CI 1.34–9.69); the risk decreased thereafter although still remained higher than in current drinkers.</p>", "<p>Table ##TAB##1##2## also shows the estimated ORs according to various patterns of alcohol intake specifically for the esophageal squamous-cell carcinoma histological type (n = 160 cases). Most effects were approximately 3 times higher than risk estimates for the whole case series (n = 202 cases). The effects of all types of alcoholic beverages, were in general 2 to 4-fold higher than those observed for drinkers of only wine and/or beer. The highest risk of esophageal cancer was observed among cases with an average daily intake of pure ethanol ≥ 75 g/d (OR = 23.20, 95% CI: 7.19–74.90), and particularly for those consuming ≥ 75 g/d of all types of alcoholic beverages (OR = 35.03, 95% CI: 10.28–119.31). No significant effects were observed for only wine or beer drinkers 1–24 g/d.</p>", "<p>Table ##TAB##2##3## shows adjusted risk estimates according to various patterns of tobacco smoking. The risk of esophageal cancer was more than doubled among ever smokers. Current smokers presented a 2.6-fold increase of risk with respect to never smokers. The adjusted ORs increased with the number of cigarettes smoked per day up to 5 among cases smoking ≥ 30 cigarettes/day (p-trend = 0.002), and with increasing years of smoking (p-trend = 0.030). Pack-years also showed a significant dose-response (p-trend = 0.005). No association was found with age at which subjects started smoking. After smoking cessation there was a reduction of risk of 35% for ten or more years and 45% for less than ten years that overall was statistically significant (LRS, p-value = 0.042). A statistically significant increased risk was observed for black tobacco but not for blond tobacco. Smokers who totally inhaled the tobacco experienced a statistically significant higher risk than non- or partially inhaling smokers. The risks for esophageal squamous-cell carcinoma were very similar to those observed for the whole case series.</p>" ]
[ "<title>Discussion</title>", "<p>Our results confirm that alcohol drinking and tobacco smoking are both strong and independent risk factors for esophageal cancer in Spain. We found that heavy drinkers had higher increased risk than heavy smokers, particularly for the esophageal squamous-cell carcinoma. These results are consistent with most of the epidemiological studies carried out in Western countries and some areas of Asia [##REF##10417762##4##,##REF##11159155##11##,##REF##16620285##12##,##REF##8903469##22##, ####REF##10728609##23##, ##REF##10977104##24##, ##REF##15455377##25####15455377##25##]. Other studies have found however, chewing or tobacco smoking to be a similar or even a stronger risk factor than alcohol drinking [##REF##12740918##13##,##REF##9486469##26##, ####REF##16951537##27##, ##REF##16586535##28####16586535##28##] although some of these studies did not include subjects with a high consumption of alcohol [##REF##12740918##13##,##REF##9486469##26##].</p>", "<p>Regarding alcohol drinking, we found that the intensity (i.e. the average daily alcohol intake in grams of pure ethanol) was a more relevant predictor of risk than the duration of the habit. The age at which subjects started drinking was not associated with risk and the cessation of drinking was not associated with any beneficial effect even ≥ 5y after cessation. The consequences of drinking cessation has been studied less frequently than smoking cessation and results are more controversial, probably because the number of people who quit drinking is lower than those who quit smoking. A beneficial effect has been found in some studies particularly 10 years after giving up drinking [##REF##15455377##25##,##REF##9155065##29##] although some studies have shown a rapid decline in risk after cessation of drinking [##REF##10417762##4##,##REF##2714883##30##,##REF##7742674##31##]. On the contrary, other studies have shown either a non beneficial effect [##REF##10728609##23##] or a higher risk among former drinkers [##REF##7742674##31##,##REF##8547825##32##]. In our study we observed a statistically significant increased risk among former drinkers who stopped drinking &lt;5y. Unfortunately, we had an insufficient number of former drinkers to explore the drinking cessation after 10 years in more detail. Although we considered as former drinkers those who reported quitting at least one year before the interview, it is possible that some of the patients were in fact heavy drinkers quitting the habit because of their disease; alternatively, it may also be possible that some heavy drinker cases misreported their habit, declaring no consumption when in fact they were still drinking and consequently exhibited a higher risk than never drinkers.</p>", "<p>We were also interested in exploring the effect of the alcohol type since the consumption of wine, beer and spirits is quite common in Spain [##UREF##8##33##] and thus, it may have a great interest for public health. Most of the studies have supported the hypothesis that the amount of alcohol consumed is the determinant of the risk rather than the particular drink, however, a prospective cohort study in Denmark showed that a moderate intake of wine, compared to beer and hard liquor, did not increase the risk [##REF##9748175##5##]. Previously, two population-based case-control studies in the United States did not find an association between wine drinking and the risk of esophageal cancer [##REF##8080945##34##,##REF##7742727##35##], and later, two other studies showed data supporting a lack of effect for wine drinking as well [##REF##9293918##6##,##REF##10652424##7##]. Conversely, in other studies where wine was by far the most common alcoholic beverage, wine drinkers showed higher risk than those of other beverages [##REF##10728609##23##,##REF##10231282##36##]. In our study, the consumption of all types of beverages that include hard liquors was a much stronger risk factor than wine and/or beer. We observed a statistically significant increased risk of EC among only beer drinkers but not among only wine or wine/beer drinkers. When the daily amount by type of beverage was considered, no increased risks were observed for a low-moderate wine-beer consumption (1–24 d/day), and a 2-fold increase risk among drinkers of ≥ 25g/d although it was not statistically significant. However, we observed a 5.76-fold increase risk among drinkers of ≥ 25 g/d of all types of beverages (risk for the combined categories of 25–74 g/d and ≥ 75 g/d of all types of beverages, table ##TAB##1##2##). A low-moderate wine-beer consumption of 1–24 g/d did not increase significantly the risk of esophageal squamous-cell carcinoma either. If the lack of effect of wine and/or beer is real, there may be some protective ingredients in wine such as resveratrol [##REF##8985016##37##] and other antioxidants that may cause such a protective effect [##REF##10940346##38##].</p>", "<p>It is not clear the exact mechanisms by which alcoholic beverages induce esophageal cancer risk. An ingredient common to all beverages is ethanol although it is possible that other components or contaminants such as N-nitrosamines and urethane with carcinogenic properties may increase cancer risk. It has been shown in practically all the studies that the risks are greatest for drinkers of hard liquors which is consistent with evidence that the concentration of ethanol plays an important role in alcohol-related tumours of the upper aero-digestive tract [##REF##12740918##13##,##REF##1544150##39##,##REF##12746240##40##]. Although ethanol has not been shown as carcinogenic in laboratory animals, it may act through its major metabolite, acetaldehyde, a carcinogen in animal models [##UREF##9##41##,##REF##9390529##42##]. Thus, it has been suggested that in addition to a systemic effect, ethanol can be converted to acetaldehyde in saliva and exert a promoting effect by either solubilising tobacco-specific carcinogens or enhancing their penetration into the esophageal mucosa, by nutritional deficiencies associated with heavy drinking or by other mechanisms (e.g., direct toxic or oxidative effect on the epithelial mucosa) [##UREF##9##41##,##REF##14696101##43##]. In order to explain the differential effect of alcoholic beverages it should be considered that certain compounds such as N-nitrosamines are found in liquors and to lesser extent in beer, and urethane, a potential carcinogen in experimental studies, may be found in liquor but not in beer and wine. In addition, while most of the antioxidant compounds found in wine and to some extent in beer, are almost completely lacking in spirits [##REF##10940346##38##].</p>", "<p>It has been also suggested that part of the effect observed for alcohol and/or tobacco smoking could be due to other factors or different lifestyles, such as a low fruit and vegetable intake [##REF##16800779##10##]. However, our data were adjusted for the intake of fruit and vegetables showing that alcohol and tobacco were strong risk factors although the risk estimates were much higher when we did not adjust for fruits and vegetables intake (data not shown). We also observed that alcohol drinking was a much stronger risk factor for squamous-cell carcinoma than for the whole control series. This finding would indirectly support the hypothesis that the effect of alcohol for adenocarcinoma may be much weaker, if any. In fact, when we estimated the risk of adenocarcinoma for wine-beer drinkers and all types of alcohol beverages with respect to never drinkers, we did not find any increase of risk (OR = 0.83 and OR = 0.97, respectively); and for former and current drinker <italic>vs </italic>never drinkers, we did not find any significant risk either (OR = 1.83 and OR 0.74, respectively). Although some studies have found slight excesses of adenocarcinoma among drinkers [##REF##8080945##34##,##REF##7742727##35##,##REF##8481491##44##], most of the previous case-control studies between alcohol drinking and the risk of oesophageal adenocarcinoma have generally found no association [##REF##9293918##6##,##REF##10652424##7##,##REF##2335388##45##, ####REF##8026880##46##, ##REF##11562112##47####11562112##47##]. Unfortunately, the number of adenocarcinoma cases in our study was too small (n = 42) to allow us a separate analysis in great detail. The reasons for the differential effect of alcohol drinking on oesophageal squamous cell-carcinoma and adenocarcinoma should be further examined.</p>", "<p>Concerning tobacco smoking, the effect estimates were lower than the observed for alcohol intake although dose-response trends were still evident for the amount and the duration of smoking. Although the effect of tobacco smoking has been considered by practically all studies on esophageal cancer only a few have explored the effects by type of tobacco black/blond. The increased risk of black tobacco compared to blond tobacco is consistent with ecological studies showing a relatively high incidence of cancers of upper aero-digestive tract in southern Europe and Latin America, where this kind of tobacco is mainly consumed [##REF##1835845##48##]. Our results are consistent with other studies carried out in high-risk areas of South America [##REF##10417762##4##,##REF##8471336##49##]. We observed a statistically significant effect for black tobacco but not for blond tobacco although the lack of effect for low-to-moderate blond tobacco smoking was based on a small number of cases (n = 31). It has been suggested that the higher concentrations of some carcinogenic compounds such as N-nitrosamines found in smoke of black tobacco could be a mechanism involved [##REF##14696101##43##].</p>", "<p>Unlike our findings for alcohol cessation, we observed a beneficial effect of cessation of smoking. Similarly to others [##REF##10417762##4##,##REF##9293918##6##], we found that the risk of esophageal cancer declined within a decade of smoking cessation which may suggest that smoking could act during later stages as a promoter in the development of esophageal cancer, predominantly for the esophageal squamous-cell carcinoma.</p>", "<p>In order to explore the joint effects of alcohol and tobacco, unfortunately we could not perform interaction tests because of the small numbers. There was evidence that the effect of both exposures were nearly multiplicative. If we accept a multiplicative effect, we could assume that the risk of being simultaneously a heavy drinker (≥ 75 g/d) and heavy smoker (&gt;50 p/y) could be as high as 40 times that of abstainers, or even 100 times when referring to esophageal squamous-cell carcinoma.</p>", "<p>As in other case-control studies, this study may present limitations. The sample size was small, and we could not explore interaction between tobacco and alcohol in more detail but the effects of each exposure became evident even among those non-exposed to the other. Another limitation may relate to the use of hospital controls. It is known that controls in hospital-based studies may be heavier smokers and heavier drinkers compared with general population; but we used a wide criterion to select controls from diagnosis a priori not related to the main risk factors of our study. In fact, the prevalence of drinking or smoking among control subjects was similar to that observed in the adult general population of the same sex and age composition of Alicante and Valencia, and thus it could be considered as representative of that population[##UREF##8##33##]. The presence of other types of bias were minimized by selecting controls from the same hospital as the cases, using the same interviews and procedures in both cases and controls to avoid any differential misclassification. The strength of the associations, the existence of a dose-response, the reduction of effect after cessation, the control for other potential confounder such as education and the intake of fruits and vegetables, and the consistency with other studies would support that the study results are real, and that alcohol drinking and tobacco smoking are likely to be causally related to esophageal cancer.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, this case-control study shows that the risk of esophagus cancer, and particularly the squamous cell type, is strongly associated with alcohol drinking. The consumption of any combination of hard liquors seems to be harmful whereas a low consumption of only wine may not. Tobacco smoking is also a strong risk factor, black more than blond. Smoking cessation was shown a beneficial effect within ten years whereas drinking cessation was not. A possible differential effect of alcohol drinking on oesophageal squamous cell-carcinoma (harmful) and adenocarcinoma (no effect) should be further examined.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The effect of tobacco smoking and alcohol drinking on esophageal cancer (EC) has never been explored in Spain where black tobacco and wine consumptions are quite prevalent. We estimated the independent effect of different alcoholic beverages and type of tobacco smoking on the risk of EC and its main histological cell type (squamous cell carcinoma) in a hospital-based case-control study in a Mediterranean area of Spain.</p>", "<title>Methods</title>", "<p>We only included incident cases with histologically confirmed EC (n = 202). Controls were frequency-matched to cases by age, sex and province (n = 455). Information on risk factors was elicited by trained interviewers using structured questionnaires. Multiple logistic regression was used to estimate adjusted odds ratios and 95% confidence intervals (CI).</p>", "<title>Results</title>", "<p>Alcohol drinking and tobacco smoking were strong and independent risk factors for esophageal cancer. Alcohol was a potent risk factor with a clear dose-response relationship, particularly for esophageal squamous-cell cancer. Compared to never-drinkers, the risk for heaviest drinkers (≥ 75 g/day of pure ethanol) was 7.65 (95%CI, 3.16–18.49); and compared with never-smokers, the risk for heaviest smokers (≥ 30 cigarettes/day) was 5.07 (95%CI, 2.06–12.47). A low consumption of only wine and/or beer (1–24 g/d) did not increase the risk whereas a strong positive trend was observed for all types of alcoholic beverages that included any combination of hard liquors with beer and/or wine (p-trend&lt;0.00001). A significant increase in EC risk was only observed for black-tobacco smoking (2.5-fold increase), not for blond tobacco. The effects for alcohol drinking were much stronger when the analysis was limited to the esophageal squamous cell carcinoma (n = 160), whereas a lack of effect for adenocarcinoma was evidenced. Smoking cessation showed a beneficial effect within ten years whereas drinking cessation did not.</p>", "<title>Conclusion</title>", "<p>Our study shows that the risk of EC, and particularly the squamous cell type, is strongly associated with alcohol drinking. The consumption of any combination of hard liquors seems to be harmful whereas a low consumption of only wine may not. This may relates to the presence of certain antioxidant compounds found in wine but practically lacking in liquors. Tobacco smoking is also a clear risk factor, black more than blond.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests</p>", "<title>Authors' contributions</title>", "<p>JV, Principal Investigator, conceived, designed and coordinated the study, performed the statistical analysis and drafted the manuscript; XB, performed the statistical analysis; FB, MP, MS and MGdlH made substantial contributions to the interpretation of data and drafting the manuscript; EM-O, made substantial contributions to acquisition of data, verifying the diagnoses for case series and drafting the manuscript. All the authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/221/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>Funded by the Spanish Ministry of Health (Instituto Salud Carlos III FIS 91/0435, CIBERESP), and the Generalitat Valenciana (CTGCA/2002/06; G03/136). We would like to appreciate the writing assistance provided by Mr. Jonathan Whitehead.</p>", "<p>We would like to acknowledge all members of <bold>PANESOES Study Group </bold>for their contribution to the selection of the study participants and the provision of information for them: Jesus Vioque (principal investigator and coordinator of the study), Esperanza Ponce, María Guillén, Miguel Santibáñez, Xavier Barber <italic>Departamento de Salud Pública, Universidad Miguel Hernández, Elche-Alicante, Spain; </italic>Miguel Bixquert, Jorge Alonso, Vicente Cervera, Remedios Giner, Juan Ruiz, Carlos Sanchos-Aldás, Javier Arenas, <italic>Hospital Arnau Vilanova de Valencia</italic>; Joaquin Berenguer, Teresa Sala, Sonia Pascual, Liria Argüello, Marco Bustamante, Salvador Sancho, Constantino Herranz, Jorge Aparicio, Dr. Baixauli, Jorge Mir, Pedro Sendrá, <italic>Hospital La Fe de Valencia</italic>; Enrique Medina, Alicia Tomé, Luis Ferrer, Ramón Truyenque, Luis Olabarrieta, Ricardo Fabra, Carlos Camps, Jose Maria Vicent, <italic>Hospital General de Valencia</italic>; Eduardo Moreno-Osset, Ramón Añón, José Ballester, Vicente Alfonso, Dr. Martínez-Abad, Francisco Blanes, Carmen Molins, Daniel Almenar, Santiago Olmos, Dr. Fenollosa, <italic>Hospital Doctor Peset de Valencia</italic>; Adolfo Benages-Martinez, Andrés Peña-Aldea, Dra. I. Pascual, Dr. García-Conde, Andrés Cervantes, Pilar Azagra, Dr. Lledó, Blas Flor, Vicente Martí, <italic>Hospital Clínico de Valencia</italic>; Miguel Pérez-Mateos, Juan Antonio Casellas, Eva Girona, Jose Ramón Aparicio, Mar López, Antonio Arroyo, Fernando Camuñas, Jesus de Anta, <italic>Hospital General de Alicante</italic>; Juan Custardoy, Concepción Martínez, Enrique Gaspar, Eduardo Muñoz, <italic>Hospital Comarcal de la Vega Baja</italic>; Alfredo Carrato, Maria Luisa Gozálvez, Rafael Calpena, Dr. Gassent, Dr. Pérez, Carlos Sillero C, <italic>Hospital General de Elche</italic>; Justo Medrano, Francisco Mauri, Marta Corona, Jorge Minguel, <italic>Hospital Universitario Sant Joan de Alicante</italic>.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Distribution of socio-demographic characteristics and other exposure variables among case and control subjects</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Variables</bold></td><td align=\"center\"><bold>Cases<sup>a</sup></bold></td><td align=\"right\"><bold>%</bold></td><td align=\"center\"><bold>Controls</bold></td><td align=\"center\"><bold>%</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Sex</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Men</td><td align=\"center\">187</td><td align=\"right\">92.61</td><td align=\"center\">285</td><td align=\"center\">62.64</td></tr><tr><td align=\"left\"> Women</td><td align=\"center\">15</td><td align=\"right\">7.39</td><td align=\"center\">170</td><td align=\"center\">27.78</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Age</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt;60 years old</td><td align=\"center\">86</td><td align=\"right\">42.36</td><td align=\"center\">149</td><td align=\"center\">32.75</td></tr><tr><td align=\"left\"> 60–69 years old</td><td align=\"center\">79</td><td align=\"right\">39.41</td><td align=\"center\">167</td><td align=\"center\">36.70</td></tr><tr><td align=\"left\"> ≥ 70 years old</td><td align=\"center\">37</td><td align=\"right\">18.23</td><td align=\"center\">139</td><td align=\"center\">30.55</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Province</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Alicante</td><td align=\"center\">45</td><td align=\"right\">22.66</td><td align=\"center\">139</td><td align=\"center\">30.55</td></tr><tr><td align=\"left\"> Valencia</td><td align=\"center\">157</td><td align=\"right\">77.34</td><td align=\"center\">316</td><td align=\"center\">69.45</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Educational level</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt;Primary</td><td align=\"center\">114</td><td align=\"right\">56.65</td><td align=\"center\">246</td><td align=\"center\">54.07</td></tr><tr><td align=\"left\"> Primary</td><td align=\"center\">67</td><td align=\"right\">33.00</td><td align=\"center\">172</td><td align=\"center\">37.80</td></tr><tr><td align=\"left\"> ≥ Secondary</td><td align=\"center\">21</td><td align=\"right\">10.34</td><td align=\"center\">37</td><td align=\"center\">8.13</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Alcohol intake</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">16</td><td align=\"right\">7.88</td><td align=\"center\">171</td><td align=\"center\">37.36</td></tr><tr><td align=\"left\"> Ever</td><td align=\"center\">186</td><td align=\"right\">92.12</td><td align=\"center\">284</td><td align=\"center\">62.64</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Tobacco smoking</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never smoker</td><td align=\"center\">23</td><td align=\"right\">11.39</td><td align=\"center\">218</td><td align=\"center\">47.91</td></tr><tr><td align=\"left\"> Ever smoker</td><td align=\"center\">179</td><td align=\"right\">88.61</td><td align=\"center\">237</td><td align=\"center\">52.09</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Fruit and Vegetable daily intake (in tertile)</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt; 166 g/d</td><td align=\"center\">121</td><td align=\"right\">59.90</td><td align=\"center\">104</td><td align=\"center\">22.86</td></tr><tr><td align=\"left\"> 166–255 g/d</td><td align=\"center\">43</td><td align=\"right\">21.29</td><td align=\"center\">141</td><td align=\"center\">30.99</td></tr><tr><td align=\"left\"> &gt;255 g/d</td><td align=\"center\">38</td><td align=\"right\">18.81</td><td align=\"center\">210</td><td align=\"center\">46.15</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Adjusted<sup>a </sup>odds ratios (OR) for esophageal cancer, and esophageal squamous-cell carcinoma, according to alcohol consumption</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>No. of controls</bold></td><td align=\"center\" colspan=\"3\"><bold>All esophageal cancer cases</bold></td><td align=\"center\" colspan=\"3\"><bold>Esophageal squamous-cell carcinoma cases</bold></td></tr><tr><td/><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td/><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>OR<sup>a</sup></bold></td><td align=\"center\"><bold>95% CI</bold></td><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>OR<sup>a</sup></bold></td><td align=\"center\"><bold>95% CI</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Alcohol Drinking Status</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never&lt;</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Ever</td><td align=\"center\">284</td><td align=\"center\">186</td><td align=\"center\">2.41</td><td align=\"center\">(1.24 – 4.71)</td><td align=\"center\">154</td><td align=\"center\">5.34</td><td align=\"center\">(2.05 – 13.91)</td></tr><tr><td align=\"left\"> Never</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Former drinker</td><td align=\"center\">49</td><td align=\"center\">38</td><td align=\"center\">4.28</td><td align=\"center\">(1.92 – 9.56)</td><td align=\"center\">31</td><td align=\"center\">11.03</td><td align=\"center\">(3.73 – 32.62)</td></tr><tr><td align=\"left\"> Current drinker</td><td align=\"center\">235</td><td align=\"center\">148</td><td align=\"center\">2.06</td><td align=\"center\">(1.04 – 4.08)</td><td align=\"center\">123</td><td align=\"center\">4.48</td><td align=\"center\">(1.69 – 11.83)</td></tr><tr><td align=\"left\"><bold>Average of pure ethanol (g/day)</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Former drinker</td><td align=\"center\">49</td><td align=\"center\">38</td><td align=\"center\">5.40</td><td align=\"center\">(2.43 – 12.00)</td><td align=\"center\">31</td><td align=\"center\">16.03</td><td align=\"center\">(5.34 – 48.07)</td></tr><tr><td align=\"left\"> 1–24 g/d</td><td align=\"center\">147</td><td align=\"center\">27</td><td align=\"center\">1.16</td><td align=\"center\">(0.54 – 2.49)</td><td align=\"center\">12</td><td align=\"center\">1.71</td><td align=\"center\">(0.56 – 5.20)</td></tr><tr><td align=\"left\"> 25–74 g/d</td><td align=\"center\">62</td><td align=\"center\">45</td><td align=\"center\">2.89</td><td align=\"center\">(1.29 – 6.48)</td><td align=\"center\">38</td><td align=\"center\">8.02</td><td align=\"center\">(2.64 – 24.40)</td></tr><tr><td align=\"left\"> ≥ 75 g/d</td><td align=\"center\">26</td><td align=\"center\">75</td><td align=\"center\">7.65</td><td align=\"center\">(3.16 – 18.49)</td><td align=\"center\">72</td><td align=\"center\">23.20</td><td align=\"center\">(7.19 – 74.90)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>&lt;0.00001</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>&lt;0.00001</italic></bold></td></tr><tr><td align=\"left\"><bold>Type of drink (g/day)</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Only wine</td><td align=\"center\">65</td><td align=\"center\">12</td><td align=\"center\">1.20</td><td align=\"center\">(0.49 – 2.91)</td><td align=\"center\">7</td><td align=\"center\">1.92</td><td align=\"center\">(0.56 – 6.64)</td></tr><tr><td align=\"left\"> Only beer</td><td align=\"center\">26</td><td align=\"center\">8</td><td align=\"center\">3.07</td><td align=\"center\">(1.06 – 8.90)</td><td align=\"center\">6</td><td align=\"center\">7.98</td><td align=\"center\">(2.13 – 29.92)</td></tr><tr><td align=\"left\"> Only wine and/or beer</td><td align=\"center\">67</td><td align=\"center\">15</td><td align=\"center\">1.44</td><td align=\"center\">(0.59 – 3.47)</td><td align=\"center\">9</td><td align=\"center\">2.49</td><td align=\"center\">(0.73 – 88.46)</td></tr><tr><td align=\"left\"> All types of beverages</td><td align=\"center\">126</td><td align=\"center\">151</td><td align=\"center\">4.39</td><td align=\"center\">(2.08 – 9.22)</td><td align=\"center\">132</td><td align=\"center\">12.15</td><td align=\"center\">(4.17 – 34.56)</td></tr><tr><td align=\"left\"> Never</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Former drinker</td><td align=\"center\">49</td><td align=\"center\">38</td><td align=\"center\">5.50</td><td align=\"center\">(2.47 – 12.27)</td><td align=\"center\">31</td><td align=\"center\">16.74</td><td align=\"center\">(5.53 – 50.67)</td></tr><tr><td align=\"left\"> Wine and/or Beer 1–24 g/d</td><td align=\"center\">107</td><td align=\"center\">14</td><td align=\"center\">1.04</td><td align=\"center\">(0.45 – 2.41)</td><td align=\"center\">6</td><td align=\"center\">1.48</td><td align=\"center\">(0.43 – 5.05)</td></tr><tr><td align=\"left\"> Wine and/or Beer ≥ 25 g/d</td><td align=\"center\">30</td><td align=\"center\">13</td><td align=\"center\">2.04</td><td align=\"center\">(0.76 – 5.46)</td><td align=\"center\">11</td><td align=\"center\">5.48</td><td align=\"center\">(1.52 – 19.71)</td></tr><tr><td align=\"left\"> All types 1–24 g/d</td><td align=\"center\">40</td><td align=\"center\">13</td><td align=\"center\">1.53</td><td align=\"center\">(0.58 – 4.01)</td><td align=\"center\">6</td><td align=\"center\">2.47</td><td align=\"center\">(0.62 – 9.86)</td></tr><tr><td align=\"left\"> All types ≥ 25 g/d</td><td align=\"center\">58</td><td align=\"center\">108</td><td align=\"center\">5.76</td><td align=\"center\">(2.59 – 12.83)</td><td align=\"center\">100</td><td align=\"center\">17.22</td><td align=\"center\">(5.68 – 52.21)</td></tr><tr><td align=\"left\">   All types 25–74 g/d</td><td align=\"center\">40</td><td align=\"center\">39</td><td align=\"center\">3.88</td><td align=\"center\">(1.64 – 9.15)</td><td align=\"center\">32</td><td align=\"center\">10.47</td><td align=\"center\">(3.26 – 33.64)</td></tr><tr><td align=\"left\">   All types ≥ 75 g/d</td><td align=\"center\">18</td><td align=\"center\">69</td><td align=\"center\">10.62</td><td align=\"center\">(4.14 – 27.24)</td><td align=\"center\">68</td><td align=\"center\">35.03</td><td align=\"center\">(10.28 – 119.31)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>&lt;0.0001</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>&lt;0.0001</italic></bold></td></tr><tr><td align=\"left\"><bold>Years of alcohol drinking</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> 0</td><td align=\"center\">171</td><td align=\"center\">16</td><td align=\"center\">1.00</td><td/><td align=\"center\">6</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> 1–19</td><td align=\"center\">16</td><td align=\"center\">15</td><td align=\"center\">4.06</td><td align=\"center\">(1.37 – 12.00)</td><td align=\"center\">11</td><td align=\"center\">8.21</td><td align=\"center\">(2.08 – 32.32)</td></tr><tr><td align=\"left\"> 20–39</td><td align=\"center\">121</td><td align=\"center\">92</td><td align=\"center\">2.96</td><td align=\"center\">(1.42 – 6.14)</td><td align=\"center\">76</td><td align=\"center\">6.28</td><td align=\"center\">(2.27 – 17.35)</td></tr><tr><td align=\"left\"> ≥ 40</td><td align=\"center\">129</td><td align=\"center\">71</td><td align=\"center\">1.71</td><td align=\"center\">(0.80 – 3.61)</td><td align=\"center\">62</td><td align=\"center\">3.99</td><td align=\"center\">(1.41 – 11.24)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>0.316</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>0.036</italic></bold></td></tr><tr><td align=\"left\"><bold>Age at starting</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt;18 yr</td><td align=\"center\">51</td><td align=\"center\">43</td><td align=\"center\">1.00</td><td/><td align=\"center\">39</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> 18–20 yr</td><td align=\"center\">88</td><td align=\"center\">50</td><td align=\"center\">1.94</td><td align=\"center\">(0.88 – 4.27)</td><td align=\"center\">40</td><td align=\"center\">0.63</td><td align=\"center\">(0.29 – 1.37)</td></tr><tr><td align=\"left\"> 21–29 yr</td><td align=\"center\">59</td><td align=\"center\">34</td><td align=\"center\">1.04</td><td align=\"center\">(0.53 – 2.02)</td><td align=\"center\">31</td><td align=\"center\">0.83</td><td align=\"center\">(0.34 – 2.02)</td></tr><tr><td align=\"left\"> ≥ 30 yr</td><td align=\"center\">72</td><td align=\"center\">52</td><td align=\"center\">1.71</td><td align=\"center\">(0.86 – 3.40)</td><td align=\"center\">40</td><td align=\"center\">0.91</td><td align=\"center\">(0.36 – 2.29)</td></tr><tr><td align=\"left\"><bold>Years since quitting</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Current Drinker</td><td align=\"center\">235</td><td align=\"center\">148</td><td align=\"center\">1.00</td><td/><td align=\"center\">123</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> &lt;5 yr</td><td align=\"center\">12</td><td align=\"center\">16</td><td align=\"center\">3.60</td><td align=\"center\">(1.34 – 9.69)</td><td align=\"center\">14</td><td align=\"center\">5.89</td><td align=\"center\">(2.01 – 17.25)</td></tr><tr><td align=\"left\"> ≥ 5 yr</td><td align=\"center\">37</td><td align=\"center\">22</td><td align=\"center\">1.71</td><td align=\"center\">(0.86 – 3.41)</td><td align=\"center\">17</td><td align=\"center\">1.70</td><td align=\"center\">(0.79 – 3.66)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Adjusted<sup>a </sup>odds ratios (OR) for esophageal cancer, and esophageal squamous-cell carcinoma, according to tobacco smoking</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>No. of controls</bold></td><td align=\"center\" colspan=\"3\"><bold>All esophageal cancer cases</bold></td><td align=\"center\" colspan=\"3\"><bold>Esophageal squamous-cell carcinoma cases</bold></td></tr><tr><td/><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td/><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>OR<sup>a</sup></bold></td><td align=\"center\"><bold>95% CI</bold></td><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>OR<sup>a</sup></bold></td><td align=\"center\"><bold>95% CI</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Smoking Status</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Ever</td><td align=\"center\">237</td><td align=\"center\">179</td><td align=\"center\">2.12</td><td align=\"center\">(1.06 – 4.23)</td><td align=\"center\">145</td><td align=\"center\">1.70</td><td align=\"center\">(0.72 – 4.03)</td></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Former-Smoker</td><td align=\"center\">117</td><td align=\"center\">55</td><td align=\"center\">1.61</td><td align=\"center\">(0.76 – 3.40)</td><td align=\"center\">39</td><td align=\"center\">1.08</td><td align=\"center\">(0.43 – 2.73)</td></tr><tr><td align=\"left\"> Current Smoker</td><td align=\"center\">120</td><td align=\"center\">124</td><td align=\"center\">2.58</td><td align=\"center\">(1.26 – 5.28)</td><td align=\"center\">106</td><td align=\"center\">2.28</td><td align=\"center\">(0.95 – 5.51)</td></tr><tr><td align=\"left\"><bold>Average No. of cigarettes/day</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Former smoker</td><td align=\"center\">117</td><td align=\"center\">55</td><td align=\"center\">1.68</td><td align=\"center\">(0.79 – 3.57)</td><td align=\"center\">39</td><td align=\"center\">1.16</td><td align=\"center\">(0.45 – 2.99)</td></tr><tr><td align=\"left\"> &lt;15</td><td align=\"center\">27</td><td align=\"center\">11</td><td align=\"center\">1.70</td><td align=\"center\">(0.63 – 4.58)</td><td align=\"center\">8</td><td align=\"center\">1.35</td><td align=\"center\">(0.41 – 4.48)</td></tr><tr><td align=\"left\"> 15–29</td><td align=\"center\">58</td><td align=\"center\">58</td><td align=\"center\">2.45</td><td align=\"center\">(1.11 – 5.44)</td><td align=\"center\">47</td><td align=\"center\">2.05</td><td align=\"center\">(0.78 – 5.42)</td></tr><tr><td align=\"left\"> ≥ 30</td><td align=\"center\">23</td><td align=\"center\">51</td><td align=\"center\">5.07</td><td align=\"center\">(2.06 – 12.47)</td><td align=\"center\">48</td><td align=\"center\">5.82</td><td align=\"center\">(1.96 – 17.23)</td></tr><tr><td align=\"left\"> Pipe/Cigars</td><td align=\"center\">12</td><td align=\"center\">4</td><td align=\"center\">1.58</td><td align=\"center\">(0.41 – 6.21)</td><td align=\"center\">3</td><td align=\"center\">1.49</td><td align=\"center\">(0.30 – 7.46)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>0.002</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>0.006</italic></bold></td></tr><tr><td align=\"left\"><bold>Years of cigarette smoking</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> &lt;20</td><td align=\"center\">39</td><td align=\"center\">14</td><td align=\"center\">1.78</td><td align=\"center\">(0.69 – 4.59)</td><td align=\"center\">4</td><td align=\"center\">0.54</td><td align=\"center\">(0.12 – 2.11)</td></tr><tr><td align=\"left\"> 20–29</td><td align=\"center\">48</td><td align=\"center\">32</td><td align=\"center\">1.94</td><td align=\"center\">(0.81 – 4.63)</td><td align=\"center\">26</td><td align=\"center\">1.62</td><td align=\"center\">(0.57 – 4.65)</td></tr><tr><td align=\"left\"> ≥ 30</td><td align=\"center\">150</td><td align=\"center\">134</td><td align=\"center\">2.25</td><td align=\"center\">(1.10 – 4.58)</td><td align=\"center\">115</td><td align=\"center\">2.03</td><td align=\"center\">(0.84 – 4.92)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>0.030</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>0.029</italic></bold></td></tr><tr><td align=\"left\"><bold>Pack-years<sup>b</sup></bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never smoker (0 p-y)</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Past</td><td align=\"center\">117</td><td align=\"center\">55</td><td align=\"center\">1.67</td><td align=\"center\">(0.79 – 3.55)</td><td align=\"center\">39</td><td align=\"center\">1.15</td><td align=\"center\">(0.45 – 2.95)</td></tr><tr><td align=\"left\"> &lt;20</td><td align=\"center\">38</td><td align=\"center\">11</td><td align=\"center\">1.30</td><td align=\"center\">(0.49 – 3.47)</td><td align=\"center\">6</td><td align=\"center\">0.69</td><td align=\"center\">(0.19 – 2.51)</td></tr><tr><td align=\"left\"> 20–49</td><td align=\"center\">55</td><td align=\"center\">59</td><td align=\"center\">2.81</td><td align=\"center\">(1.27 – 6.19)</td><td align=\"center\">48</td><td align=\"center\">2.49</td><td align=\"center\">(0.96 – 6.51)</td></tr><tr><td align=\"left\"> ≥ 50</td><td align=\"center\">27</td><td align=\"center\">54</td><td align=\"center\">3.79</td><td align=\"center\">(1.60 – 8.95)</td><td align=\"center\">52</td><td align=\"center\">3.93</td><td align=\"center\">(1.40 – 10.99)</td></tr><tr><td align=\"left\">  <bold><italic>p-value for linear trend</italic></bold></td><td/><td/><td/><td align=\"center\"><bold><italic>0.005</italic></bold></td><td/><td/><td align=\"center\"><bold><italic>0.007</italic></bold></td></tr><tr><td align=\"left\"><bold>Age at starting</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> &lt;15</td><td align=\"center\">40</td><td align=\"center\">41</td><td align=\"center\">1.00</td><td/><td align=\"center\">35</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> 15–17</td><td align=\"center\">78</td><td align=\"center\">61</td><td align=\"center\">1.11</td><td align=\"center\">(0.56 – 2.21)</td><td align=\"center\">49</td><td align=\"center\">1.45</td><td align=\"center\">(0.65 – 3.23)</td></tr><tr><td align=\"left\"> ≥ 18</td><td align=\"center\">116</td><td align=\"center\">70</td><td align=\"center\">0.91</td><td align=\"center\">(0.47 – 1.74)</td><td align=\"center\">56</td><td align=\"center\">1.21</td><td align=\"center\">(0.57 – 2.58)</td></tr><tr><td align=\"left\"><bold>Years since quitting</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Current Smoker</td><td align=\"center\">120</td><td align=\"center\">124</td><td align=\"center\">1.00</td><td/><td align=\"center\">106</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> &lt;10</td><td align=\"center\">50</td><td align=\"center\">26</td><td align=\"center\">0.55</td><td align=\"center\">(0.29 – 1.06)</td><td align=\"center\">20</td><td align=\"center\">0.44</td><td align=\"center\">(0.20 – 0.96)</td></tr><tr><td align=\"left\"> ≥ 10</td><td align=\"center\">67</td><td align=\"center\">29</td><td align=\"center\">0.65</td><td align=\"center\">(0.35 – 1.21)</td><td align=\"center\">19</td><td align=\"center\">0.49</td><td align=\"center\">(0.23 – 1.06)</td></tr><tr><td align=\"left\"><bold>Type of tobacco</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">23</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Blond</td><td align=\"center\">71</td><td align=\"center\">31</td><td align=\"center\">1.19</td><td align=\"center\">(0.50 – 2.82)</td><td align=\"center\">24</td><td align=\"center\">0.80</td><td align=\"center\">(0.28 – 2.30)</td></tr><tr><td align=\"left\"> Black</td><td align=\"center\">150</td><td align=\"center\">144</td><td align=\"center\">2.50</td><td align=\"center\">(1.23 – 5.09)</td><td align=\"center\">118</td><td align=\"center\">2.05</td><td align=\"center\">(0.85 – 4.97)</td></tr><tr><td align=\"left\"><bold>Inhalation of smoke</bold></td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Never</td><td align=\"center\">218</td><td align=\"center\">24</td><td align=\"center\">1.00</td><td/><td align=\"center\">15</td><td align=\"center\">1.00</td><td/></tr><tr><td align=\"left\"> Partially</td><td align=\"center\">164</td><td align=\"center\">93</td><td align=\"center\">1.50</td><td align=\"center\">(0.74 – 3.07)</td><td align=\"center\">77</td><td align=\"center\">1.32</td><td align=\"center\">(0.54 – 3.25)</td></tr><tr><td align=\"left\"> Totally</td><td align=\"center\">72</td><td align=\"center\">85</td><td align=\"center\">2.88</td><td align=\"center\">(1.37 – 6.06)</td><td align=\"center\">67</td><td align=\"center\">2.53</td><td align=\"center\">(1.00 – 6.46)</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p><sup>a </sup>All cases were histologically confirmed.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>Adjusted for sex, age, educational level, province and tobacco smoking (never, past, &lt;20, 20–49 and ≥ 50 pack-years), the energy-adjusted intake of fruit and vegetable in tertiles, and energy intake.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>Adjusted for sex, age, educational level, province, and alcohol drinking (never, past, 1–24 g/d, 25–74 g/d, ≥ 75 g/d), the energy-adjusted intake of fruits and vegetables in tertiles, and energy intake</p><p><sup>b </sup>Pack-years = the number of packs of 20 cigarettes per day multiplied by the number of years smoking.</p></table-wrap-foot>" ]
[]
[]
[{"surname": ["Day", "Mu\u00f1oz", "Schottenfeld D, Fraumeni J"], "given-names": ["NE", "N"], "article-title": ["Esophagus"], "source": ["Cancer epidemiology and prevention"], "year": ["1996"], "publisher-name": ["New York: Oxford University"], "fpage": ["681"], "lpage": ["706"]}, {"surname": ["Nyr\u00e9n", "Adami", "Adami HO, Hunter D"], "given-names": ["O", "H"], "article-title": ["Esophageal cancer"], "source": ["Textbook of Cancer Epidemiology"], "year": ["2002"], "publisher-name": ["Oxford: Oxford University"], "fpage": ["137"], "lpage": ["161"]}, {"collab": ["IARC working group on the evaluation of carcinogenic risks to humans"], "source": ["IARC monographs on the evaluation of carcinogenic risks to humans Alcohol drinking"], "year": ["1988"], "volume": ["44"], "publisher-name": ["Lyon, france: International Agency for Research on Cancer"]}, {"surname": ["Vioque", "Gonzalez"], "given-names": ["J", "L"], "article-title": ["Validity of a food frequency questionnaire (preliminary results)"], "source": ["Eur J Cancer Prev"], "year": ["1991"], "volume": ["1"], "fpage": ["19"], "lpage": ["20"], "pub-id": ["10.1097/00008469-199110001-00029"]}, {"surname": ["Vioque", "Serra-Majem L Aranceta J"], "given-names": ["J"], "article-title": ["Validez de la evaluaci\u00f3n de la ingesta diet\u00e9tica"], "source": ["Nutrici\u00f3n y Salud P\u00fablica M\u00e9todos, bases cient\u00edficas y aplicaciones"], "year": ["2006"], "publisher-name": ["Barcelona: Masson-Elservier"], "fpage": ["199"], "lpage": ["210"]}, {"surname": ["Willett", "Willett W"], "given-names": ["W"], "article-title": ["Reproducibility and validity of food-frecuency questionnaires"], "source": ["Nutritional Epidemiology"], "year": ["1998"], "publisher-name": ["New York: Oxford University"], "fpage": ["101"], "lpage": ["147"]}, {"surname": ["Breslow", "Day"], "given-names": ["N", "N"], "article-title": ["Statistical methods in cancer research"], "source": ["The analysis of case-control studies IARC Sci Publ"], "year": ["1980"], "volume": ["I"], "fpage": ["5"], "lpage": ["338"]}, {"collab": ["Stata Statistical Sofware"], "source": ["STATA 8.2"], "year": ["2005"], "publisher-name": ["College Station, TX: Stata Corporation"]}, {"surname": ["Vioque", "Quiles"], "given-names": ["J", "J"], "source": ["Encuesta de Nutrici\u00f3n y salud de la Comunidad Valenciana"], "year": ["2003"], "publisher-name": ["Alicante; Universidad Miguel Hernandez"]}, {"surname": ["Blot", "McLaughlin", "Fraumeni", "Schottenfeld D, Fraumeni J"], "given-names": ["W", "J", "JF"], "article-title": ["Esophageal Cancer"], "source": ["Cancer Epidemiology and Prevention"], "year": ["2006"], "publisher-name": ["New York: Oxford University Press"], "fpage": ["697"], "lpage": ["706"]}]
{ "acronym": [], "definition": [] }
49
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Aug 1; 8:221
oa_package/c5/47/PMC2529333.tar.gz
PMC2529334
18687149
[ "<title>Background</title>", "<title>The context of prostate cancer</title>", "<p>Prostate cancer is the most common male cancer and second most common cause of cancer death in men in the Western world (excluding non melanoma skin cancer). In Australia, 1 in 11 men will be diagnosed with prostate cancer in their lifetime (0–74 years) and 1 in 82 will die from the disease [##UREF##0##1##]. In 2003 there were 13,526 Australian men diagnosed with prostate cancer with this number expected to increase to over 18,000 for 2006 [##UREF##1##2##]. Improved survival from prostate cancer has been demonstrated worldwide [##UREF##2##3##]. Around half of all newly diagnosed men are predicted to be alive 15 years after diagnosis [##UREF##2##3##] such that the large cohort of men living with the consequences of diagnosis and treatment is increasing.</p>", "<p>The most frequently received treatment for prostate cancer in Australia is radical prostatectomy and the predominance of radical prostatectomy as the primary treatment approach for this cancer is mirrored elsewhere such as in North America [##REF##11752011##4##,##UREF##3##5##]. While sexual dysfunction after all treatment approaches is common, the trajectory of this dysfunction and severity varies by treatment modality [##REF##14581420##6##]. Men treated with radiation therapy experience less erectile dysfunction (ED) initially following treatment however, in contrast to radical prostatectomy, function is more likely decline over time. In addition, many men now receive neo-adjuvant hormone therapy with radiotherapy, further complicating the course of their sexual adjustment. For radical prostatectomy early adjuvant hormone therapy is uncommon and ED will be immediate due to surgical damage to the neurovascular bundle that lies adjacent to the prostate, with some improvement over the two years after surgery [##UREF##4##7##]. However, even with nerve sparing surgical techniques that aim to reduce damage to erectile function as few as 18.5% of men report being able to achieve erections firm enough for sexual intercourse two years after surgery [##REF##14581420##6##,##UREF##4##7##]. Compared with their age mates, men with prostate cancer have a 10 to 15 fold increase in ED [##REF##12899583##8##]. Other distressing effects of treatment include: penile shortening (68% of men), loss of sexual desire (60–80%), less satisfying orgasms (64–87%), overall sexual dissatisfaction (61–91%) [##REF##16094058##9##,##REF##12365027##10##]. These effects can lead to: impaired sexual performance; changes in relationships with women and sexual partners; lost enjoyment of sexual imaginings; decrements in masculine self esteem [##REF##12365027##10##,##REF##11679031##11##]. Problematically, many men are reluctant to seek help for sexual difficulties, with only about half of men seeking medical treatment for ED up to five years after treatment [##REF##12365027##10##]. Reluctance to seek help is particularly problematic for men who receive radical prostatectomy as, for these men, an early return to sexual activity (by three months after surgery) may increase the recovery rate of spontaneous erections and improve responses to ED treatments [##REF##12771740##12##]. Thus, support services for men with prostate cancer that are targeted to sexuality concerns need to reach men who receive radical prostatectomy within weeks of their cancer treatment.</p>", "<p>Sexual dysfunction is a shared problem within couples, with regret and loss common among both members of the couple [##REF##12011916##13##]. However, existing medical and support services for men with prostate cancer are oriented towards the patient, do not pay sufficient attention to the couple relationship and virtually ignore the needs of female partners of these men. Partners are more likely to focus on building their husband's self-esteem and putting the sexual dysfunction into perspective within the relationship, and less likely to focus on their own sexual needs [##REF##11268141##14##]. Partners' quality of life is related to their reports of sexual function within the relationship and sexual dysfunction has implications for the longer-term psychosocial well-being of partners [##REF##12027035##15##]. Women often are less focused on finding 'mechanical' treatments to regain erectile function and more open to counseling that might assist the couple to experience intimacy and closeness even if intercourse is not possible [##REF##12833559##16##]. The attention to improving erectile rigidity in the man, for which 'mechanical' treatments are usually needed, may overshadow the partner's needs for sexual pleasure and stimulation [##REF##16294343##17##].</p>", "<p>The psychological distress of female partners is increased if they have limited knowledge of what to expect during the course of their husband's treatment and after care, and unmet supportive care needs are often reported. Female partners may be reluctant to share their distress with their husband in order to minimize the stress of the illness on the couple's experience; and may avoid discussing issues that create emotional tension, such as sexual concerns [##REF##11268141##14##]. This lack of communication means that partners often have to deal with their distress and anxiety alone with limited opportunities for psychosocial care [##REF##16521081##18##]. The distress experienced by partners is exacerbated by their husbands' reliance on them for emotional support, with partners having to manage not only their own anxiety, but also the distress of their husbands [##REF##11268141##14##].</p>", "<p>Protecting one's partner from emotional distress may have significant costs to one's own well being and diminish relationship quality over time [##REF##14729426##19##]. Patients' and partners' abilities to cope with prostate cancer and subsequent treatment side-effects are interrelated [##REF##15858824##20##] and can negatively impact on the marital relationship [##REF##12690945##21##]. The reactions of partners to sexual dysfunction and the support they provide appears to affect the level of acceptance of sexual changes experienced by men [##REF##11879321##22##]. As well, the female partner's ability to still enjoy sex without major dysfunction is a strong predictor of better sexual satisfaction in the male partner [##REF##12365027##10##]. The disparate needs of couples experiencing sexual dysfunction highlights the need to provide couples with targeted support that promotes communication and adjustment to sexual outcomes. In work with couples in which the woman had breast or gynecological cancer, enhancing couple communication and conjoint coping with cancer treatments significantly enhanced women's sexual satisfaction [##UREF##5##23##]. Moreover, this couple focused approach increased couple discussion of cancer related issues, and reduced the unhelpful tendency of some people to avoid discussion. In a similar manner, it is proposed that attending to the couple relationship, promoting a sense of conjoint coping and addressing sexual needs within the relationship, will enhance both partners' adjustment to prostate cancer and increase the chance of adherence and better sexual outcomes including erectile function.</p>", "<title>Approaches to Intervention Delivery</title>", "<p>By contrast to women, men are less likely to seek help for psychological distress; are under-represented as clients to cancer support services; are reluctant to utilise effective sexual aids after prostate cancer treatment <italic>despite </italic>high levels of dissatisfaction with the sexual outcomes of treatment. Effective support interventions need to utilise delivery methods and sources that are acceptable to this patient group. Men and their partners prefer individual consultations for sexuality support after prostate cancer [##REF##12833559##16##]. Tele-delivered interventions are highly acceptable to this group, and web/computer based programs are frequently accessed by men for medical and procedural information [##REF##11920549##24##,##REF##16361029##25##]. Remote access delivery methods overcome geographical barriers to access and so are applicable to geographically dispersed populations with high potential for population-based translation.</p>", "<p>A source of support that has high uptake amongst men with prostate cancer in Australia and internationally is peer support, with men reporting that peer discussions provide informational and emotional support and reduce feelings of social isolation [##REF##15663527##26##]. A feasibility study of a dyadic peer support program for men with prostate cancer reported reduced depression and improved self efficacy in the short term, with men most frequently discussing incontinence, erectile dysfunction and Prostate-Specific Antigen testing with their matched peers [##REF##14745745##27##]. As well, a randomised controlled trial of a group education program to assist men to adjust to prostate cancer treatments [##REF##14570527##28##] found that only by adding peer discussion to the provision of information by an expert was sexual bother alleviated significantly, relative to a control group. An advantage of peer support that is provided by veteran patients is that it is inexpensive by comparison to professionally delivered approaches, such as specialist nurses. While this approach is highly promising, to date randomised controlled trials to assess the effectiveness of peer support in improving men's adjustment have not been undertaken. However, based on research to date a peer delivered counselling intervention paired with education may have equal efficacy to health professional delivery. As well, the relative cost savings for a peer support approach as compared to professional approaches, although not yet quantified, make this a potentially cost effective source of support.</p>", "<title>Intervention Studies Targeting Sexuality</title>", "<p>To date intervention research targeting sexuality after prostate cancer is scant. Two trials noted improvements in sexual satisfaction, but not functioning, following general psycho-educational interventions [##REF##11920549##24##,##REF##14570527##28##]. These studies were limited by not including the man's partner [##REF##11920549##24##,##REF##14570527##28##]; not targeting men early in the cancer treatment continuum [##REF##14570527##28##]; and not controlling for type of cancer treatment [##REF##11920549##24##,##REF##14570527##28##]. One of the only intervention studies to focus specifically on improving sexual function was a randomized trial comparing four face-to-face couple counselling sessions to similar sessions for the man alone, with the female partner just reading educational material and collaborating with homework tasks [##REF##16294343##17##]. Men and their partners in both conditions reported improved sexual function and satisfaction at three month follow up and increased utilisation of medical treatments for ED at three and six months; gains in sexual function diminished at six months. Study limitations included low statistical power from a small sample size and that as men were an average of 27 to 30 months post-treatment at baseline, the critical opportunity for early intervention was missed. As well, face-to-face delivery method is relatively expensive, hard to access, and difficult to translate into a population-based cost-effective approach.</p>", "<p>We propose that greater attention to the couple relationship in the intervention would improve female sexual or couple relationship satisfaction. Moreover, given the strong association between sexual and relationship satisfaction, particularly for women [##UREF##6##29##], enhancing the couple relationship is likely to improve long-term maintenance of sexual satisfaction improvements.</p>" ]
[ "<title>Methods/Design</title>", "<title>Study Aims and Hypotheses</title>", "<p>The overall study aim is to compare the efficacy of peer-delivered telephone support with DVD educational resource, vs. oncology nurse-delivered telephone counselling with DVD educational resource, vs. usual care in improving both men's and women's sexual and psychosocial adjustment at 3, 6 and 12 months after treatment for localised prostate cancer. In doing so we will also compare the cost-effectiveness of support by trained peers vs. nurse counsellors; and identify demographic, medical and psychosocial variables that predict improvement in psychosexual adjustment in prostate cancer patients and their partners with each intervention approach.</p>", "<p>The intervention will utilise a cognitive behavioural approach that has been found to be effective in promoting positive adjustment after cancer [##REF##8539178##30##], along with couple relationship education focussed on relationship enhancement and helping the couple to conjointly manage the stresses of cancer diagnosis and treatment [##REF##14729426##19##]. The study will have three arms: (1) usual care (2) telephone support by a trained male peer support volunteer who is a prostate cancer survivor with DVD education and (3) oncology nurse-delivered telephone counselling with DVD education.</p>", "<p>It is hypothesised that 3, 6, and 12 months after surgery for localised prostate cancer:</p>", "<p>1. By contrast to couples in usual care, couples who receive either the peer or nurse delivered intervention will have a more positive sexual adjustment; lower unmet sexuality supportive care needs; more positive attitudes to sexual help seeking; higher uptake of erectile aids; improved psychological adjustment and quality of life.</p>", "<p>2. Couples who receive the peer and nurse delivered intervention will have similar sexual adjustment; sexuality supportive care needs; attitudes to sexual help seeking; uptake of erectile aids; psychological adjustment and quality of life.</p>", "<p>3. The peer delivered intervention will be more cost effective by comparison to the nurse delivered intervention.</p>", "<title>Intervention</title>", "<p>Usual care will consist of the man's standard medical management and existing written educational materials. For the two intervention arms, the eight sessions of phone support/counselling will include enhanced couple communication and conjoint coping content and material relevant to the early treatment phase. An audiovisual DVD resource with Tip Sheets will accompany the intervention to enhance the psycho-education and sexuality education components and to also provide actor role models for effective couple communication about sexuality and intimacy. The nurse counselling sessions will follow principles of cognitive-behavioural sex and marital therapy and will utilise an adult learning approach in which partners' self-select goals to focus on while working through the program. Content includes education about prostate cancer, menopause, and sexuality; assigned behavioural homework including increasing expression of affection and non-demanding sexual touch; challenging negative beliefs about prostate cancer, aging, and sexuality; and helping the couple choose a medical treatment for ED that is acceptable to both partners, and integrating this into their sexual relationship. Additional components that target the challenges of the early treatment phase (e.g., urinary incontinence, pain, sleep disturbance, psychological distress) will be additionally selected by the couple if relevant.</p>", "<p>Peer support is based on the support partner or 'veteran' patient having personal experience and knowledge about the cancer experience; a unique personal insight into effective ways to cope; and the ability to form a support relationship that is derived from the connection of shared experience. In this way peer support can reduce feelings of isolation and stigma (the sense of being the 'only one'); can convey emotional, social, informational and practical support; and through role modelling can communicate realistic hope and optimism about the future. Peer support volunteers will be prostate cancer survivors who are at least 12 months post treatment and who have support group experience. The intervention will follow the same couples-based approach as the nurse counselling intervention but will be oriented to empathic mutual support and education rather than in depth sex and marital therapy.</p>", "<p>The patient's partner will be invited to participate in all phone sessions, and actual participation will be recorded by the peer/therapist for each phone session, as well as minutes of counselling time, for inclusion in analyses. Support/counselling calls are timed to correspond with the challenges associated with preparing for and recovering from radical prostatectomy. The first two calls will occur prior to surgery; four fortnightly calls (Sessions 3 to 6) are timed to commence two weeks after surgery; a further two calls (Session 7 to 8) 16 and 22 weeks post-surgery.</p>", "<title>Participants</title>", "<p>With the strong endorsement and support of Queensland Urologists, patients will be referred to the project from private urology practices and public hospital outpatient clinics in Queensland, Australia. Informed written consent will be obtained by study trained research nurses who will contact potential participants after referral to the study. We will recruit 70 couples per condition over a 12 month period (allowing for 10% attrition from treatment; 210 couples in total to be recruited). Assuming a moderate effect size of d = 0.5, alpha at .05, the resulting power with 70 couples per condition is 0.8. Inclusion criteria are that the men must: (1) have been newly diagnosed with localised prostate cancer and have chosen radical prostatectomy as their treatment approach (2) be currently in a heterosexual cohabitating couple relationship (3) be able to read and speak English (4) have no previous history of head injury, dementia or psychiatric illness (5) have no other concurrent cancer. As the intervention has been developed based on previous data for heterosexual couples this intervention is unlikely to be helpful for homosexual couples.</p>", "<title>Study Integrity</title>", "<p>Ethical approval has been obtained from the Griffith University Human Research Ethics Committee. The study design will be guided by the CONSORT statement [##REF##11304107##31##]. Randomisation to study condition will occur following the completion of baseline assessment. Assessments will be by self-report pen and paper measures and project staff tracking assessments will be blinded to condition. Randomisation will occur in blocks of 12, with each condition randomly generated 4 times within each block to ensure an unpredictable allocation sequence with equal numbers of couples in each group at the completion of each block. This sequence will be undertaken by the project manager and concealed from investigators. Therapy will be manualised and all intervention calls audiotaped with 25% reviewed to ensure treatment adherence. All analyses will be conducted on the basis of intention to treat.</p>", "<title>Measures</title>", "<p>A series of previously validated and reliable self report measures will be administered by mail. Domain specific quality of life (QOL) will be included as a potential moderator of intervention effect and challenge appraisals and therapeutic alliance as mediators. Primary outcomes are: sexual adjustment; unmet sexual supportive care needs; masculine self-esteem; marital satisfaction; utilisation of erectile aids. Secondary outcomes are: psychological distress; overall QOL and benefit finding. Disease variables (e.g. cancer grade, stage) will be assessed through medical and cancer registry records review. Use of medical services and associated costs will be assessed through Medicare Australia records.</p>", "<title>Moderators/Mediators</title>", "<title>Domain specific QOL</title>", "<p>The International Prostate Symptom Score [##REF##1279218##32##] and the urinary and bowel symptom subscales of the UCLA Prostate Cancer Index [##REF##9674618##33##] will assess disease specific QOL. Women will complete a menopausal symptom scale derived from the Breast Cancer Prevention Trial (BCPT) Symptom Checklist [##REF##10561339##34##].</p>", "<title>Challenge appraisal</title>", "<p>A person's cognitive appraisal of an event will determine if that event is perceived as stressful and this will be assessed using a Stress Appraisal Measure based on the work of Roesch [##REF##16171419##35##].</p>", "<title>Therapeutic alliance</title>", "<p>The quality of the bond between the peer and nurse counsellors and the couple and extent of agreement about therapy goals will be assessed by the Working Alliance Inventory [##UREF##7##36##].</p>", "<title>Primary Outcome Variables</title>", "<title>Sexual function</title>", "<p>Men will complete the International Index of Erectile Functioning (IIEF) [##REF##9187685##37##], which allows sexual function to be assessed in five domains: erectile function, orgasmic function, sexual desire, intercourse satisfaction and overall sexual satisfaction. Women will complete the Female Sexual Function Index (FSFI) [##REF##10782451##38##]. This questionnaire parallels the IIEF and examines sexual function among women in six domains: sexual desire, arousal, lubrication, orgasm, satisfaction, and pain.</p>", "<title>Sexual Supportive Care Needs</title>", "<p>Couples needs related to sexual relationships will be assessed using the sexuality needs subscale of the Supportive Care Needs Survey [##UREF##8##39##,##REF##11180578##40##].</p>", "<title>Sexual Self-Confidence</title>", "<p>The Short Form Psychological and Interpersonal Relationship Scale (SF-PAIRS) [##REF##16163372##41##] will assess sexual confidence and spontaneity associated with ED.</p>", "<title>Masculine Self-Esteem</title>", "<p>The Masculine Self-Esteem scale will assess men's appraisal of their masculinity [##REF##12886172##42##].</p>", "<title>Utilisation of sexual aids</title>", "<p>A scale developed by Schover [##REF##12436448##43##] will assess whether men have obtained medical help for sexual dysfunction and the impact of each treatment on their sex life.</p>", "<title>Marital satisfaction</title>", "<p>An abbreviated version of the Dyadic Adjustment Scale (A-DAS) [##UREF##9##44##] will assess marital satisfaction among couples.</p>", "<title>Secondary Outcome Variables</title>", "<title>Psychological Distress</title>", "<p>The Hospital Anxiety and Depression Scale [##REF##6880820##45##] will provide a global measure of current psychological distress with subscale scores for anxiety and depression.</p>", "<title>Quality of Life</title>", "<p>Health related quality of life will be assessed with the SF-36, the most widely used QOL measure in the world with norms for the Australian general population available. The SF-36 [##UREF##10##46##] contains a mental health and physical health summary scale to measure the impact of the intervention on patients' and partners' wellbeing.</p>", "<title>Benefit Finding</title>", "<p>Benefit finding [##REF##11199062##47##] will be used to measure the perceived positive experiences and outcomes (eg appreciation of life, changes in life priorities) resulting from the diagnosis of cancer.</p>", "<title>Statistical Analyses</title>", "<p>The study hypotheses will be tested by multilevel modelling (MLM). This class of procedures is the appropriate way to analyse hierarchical data sets such as the longitudinal data of the proposed research in which observations are nested within persons who in turn are nested within couples. Study condition is modelled as a fixed effect at the couple level. The typical RCT effects that have been tested by ANOVAs are all available with MLM; however there are several fundamental differences between the statistical models. First, in MLM individual and couple trajectories of change in time can be modelled directly as random effects. This provides appropriate tests of Hypotheses 1 and 2 and allows precise examination of predictors of individual versus group change. Second, MLM minimises the loss of data through attrition in that unlike ANOVA all available data points from participants are included in analyses. The direct ML estimation normally used in MLM is currently the most favoured technique (along with multiple imputation) for minimising bias and enhancing precision in parameter estimates from incomplete data [##REF##12090408##48##]. Hypotheses 1 and 2 will be tested with appropriate contrasts on the fixed effect of study condition. Although power calculations from multilevel longitudinal analyses are not as well articulated as for older techniques, the study will have at least as much power as the equivalent ANOVA (i.e., 80% for a moderate effect size;) as the intervention effects are all based upon the level 3 (i.e., couple) sample size.</p>", "<p>A cost-utility analysis will also be undertaken to address Hypothesis 3 where intervention resources and health outcomes are combined in an analysis to produce information on the relative economic efficiency between the peer, nurse specialist and usual care options. The analysis will take the perspectives of the health provider and health system and involve the assessment of 1) cost data on resources used in each of the three arms by identifying, quantifying and valuing resources using standard methods and, 2) health outcomes, in terms of quality-adjusted life years (QALYs). Using Medicare Australia data, we also wish to capture health utilisation costs for GP visits and medication use to assess whether the interventions change typical health care use. Quality of life will be measured in participants using the preference-based utility instrument SF-6D [##REF##11939242##49##] which is based on the SF-36 quality of life tool. The key outcome for the cost-utility analysis will be the <italic>incremental cost-effectiveness ratio</italic>, expressed as the incremental cost per QALY. This ratio represents the difference in costs between the intervention and usual care options divided by the difference in QALYs gained across the two options. This means that it is the <italic>additional </italic>cost and health benefits of each of the two interventions over and above what occurs in usual care that is important. Secondary economic endpoints will include incremental cost-effectiveness ratios for cost per % gain in sexual function and psychological distress. A Bayesian statistical approach to the analysis will be followed so that probabilistic statements on the efficiency of the intervention will be produced [##REF##11910068##50##]. Data will be analysed using the computer program TreeAge Pro (Healthcare Module) 2005 [##UREF##11##51##]. The results will be scrutinised using probabilistic sensitivity analysis which is standard practice in economic evaluations to address data uncertainty and potentially strengthens the generalisability of the results. Specifically, Monte Carlo simulations will produce cost-effectiveness acceptability curves and probabilistic statements on cost-effectiveness.</p>" ]
[]
[ "<title>Discussion</title>", "<p>This study will address a critical but as yet unanswered research question: to identify a cost-effective and population based approach to promoting optimal psychosexual adjustment for men with prostate cancer and their partners. To date, for this patient group, no sexuality intervention studies have: targeted couples at diagnosis when distress is highest; been adequately powered to look differentially at intervention effects; trialled peer support; or included economic analyses. This research will overcome these limitations. The intervention will be able to be utilised by trained nurses in a range of settings including broad reach tele-health lines and also through peer support programs that are conducted internationally. This means that project outputs will be immediately translatable into practice to improve the sexual health and overall well-being of men with prostate cancer and their partners.</p>" ]
[]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Prostate cancer is the most common male cancer in the Western world. The most substantial long term morbidity from this cancer is sexual dysfunction with consequent adverse changes in couple and intimate relationships. Research to date has not identified an effective way to improve sexual and psychosocial adjustment for both men with prostate cancer and their partners. As well, the efficacy and cost effectiveness of peer counselling as opposed to professional models of service delivery has not yet been empirically tested. This paper presents the design of a three arm randomised controlled trial (peer vs. nurse counselling vs. usual care) that will evaluate the efficacy of two couples-based sexuality interventions (ProsCan for Couples: Peer support vs. nurse counselling) on men's and women's sexual and psychosocial adjustment after surgical treatment for localised prostate cancer; in addition to cost-effectiveness.</p>", "<title>Methods/design</title>", "<p>Seventy couples per condition (210 couples in total) will be recruited after diagnosis and before treatment through urology private practices and hospital outpatient clinics and randomised to (1) usual care; (2) eight sessions of peer-delivered telephone support with DVD education; and (3) eight sessions of oncology nurse-delivered telephone counselling with DVD education. Two intervention sessions will be delivered before surgery and six over the six months post-surgery. The intervention will utilise a cognitive behavioural approach along with couple relationship education focussed on relationship enhancement and helping the couple to conjointly manage the stresses of cancer diagnosis and treatment. Participants will be assessed at baseline (before surgery) and 3, 6 and 12 months post-surgery. Outcome measures include: sexual adjustment; unmet sexuality supportive care needs; attitudes to sexual help seeking; psychological adjustment; benefit finding and quality of life.</p>", "<title>Discussion</title>", "<p>The study will provide recommendations about the efficacy of peer support vs. nurse counselling to facilitate better sexual and couple adjustment after prostate cancer as well as recommendations on whether the interventions represent efficient health service delivery.</p>", "<title>Trial Registration</title>", "<p>ACTRN12608000358347</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>SKC and JD developed the study concept and aims and initiated the project. LS, KH, MF, LG, RAG and SO assisted in further development of the protocol. SKC was responsible for drafting the manuscript. SKC, SC, MF, SO and JD will implement the protocol and oversee collection of the data. All authors contributed to the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/226/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>This project was funded by The National Health and Medical Research Council and Andrology Australia. SKC and LG are supported by NHRMC Fellowships. We gratefully acknowledge the support of the Urological Society of Australia and New Zealand; of Mr Bill McHugh and Mr Spence Broughton as consumer advisors; and of Ms Sylvia Milner as prostate cancer nurse advisor in the undertaking of this research.</p>" ]
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[{"collab": ["Australian Institute of Health and Welfare", "Australasian Association of Cancer Registries"], "source": ["Cancer in Australia 2001. AIHW cat. no. CAN 23."], "year": ["2004"], "publisher-name": ["Canberra , Australian Institute of Health and Welfare and Australasian Association of Cancer Registries"]}, {"collab": ["Australian Institute of Health and Welfare", "Australasian Association of Cancer Registries"], "surname": ["(AIHW) AIHW"], "article-title": ["Cancer in Australia: an overview, 2006. AIHW cat. no. CAN 32"], "source": ["Cancer Series"], "year": ["2007"], "publisher-name": ["Canberra , Australian Institute of Health and Welfare"]}, {"surname": ["Baade", "Steginga", "Aitken"], "given-names": ["PD", "SK", "JF"], "source": ["Current status of prostate cancer in Queensland: 1982 to 2002"], "year": ["2005"], "publisher-name": ["Brisbane , Viertel Centre for Research in Cancer Control"]}, {"surname": ["Smith", "Picker", "Armstrong"], "given-names": ["DP", "J", "BK"], "article-title": ["Patterns of care for prostate cancer in NSW: Preliminary results from the Prostate Cancer Outcomes Study"], "source": ["Annual Conference of the Australian Prostate Cancer Collaboration"], "year": ["2006"], "publisher-name": ["Garvan Institute Sydney "]}, {"surname": ["Stanford", "Feng", "Hamilton", "Gilliland", "Stephenson", "Eley", "Albertsen", "Harlan", "Potosky"], "given-names": ["JL", "Z", "A", "F", "R", "JW", "P", "L", "A"], "article-title": ["Urinary and sexual function after radical prostatectomy for clinically localised prostate cancer: The Prostate Cancer Outcomes Study"], "source": ["Journal of the Amercian Medical Association"], "year": ["2000"], "volume": ["283"], "fpage": ["354"], "lpage": ["360"], "pub-id": ["10.1001/jama.283.3.354"]}, {"surname": ["Scott", "Halford", "Ward"], "given-names": ["J", "K", "B"], "article-title": ["United we stand? The effects of a couple-coping intervention on adjustment to early stage gynaecological or breast cancer "], "source": ["Journal of Clinical and Consulting Psychology"], "year": ["2004"], "volume": ["72"], "fpage": ["1122"], "lpage": ["1135"], "pub-id": ["10.1037/0022-006X.72.6.1122"]}, {"surname": ["Halford"], "given-names": ["K"], "source": ["Brief couple therapy"], "year": ["2001"], "publisher-name": ["New York , Guilford"]}, {"surname": ["Hatcher", "Gillaspy"], "given-names": ["RL", "JA"], "article-title": ["Development and validation of a revised short version of the Working Alliance Inventory"], "source": ["Psychotherapy Research"], "year": ["2006"], "volume": ["16"], "fpage": ["12"], "lpage": ["25"], "pub-id": ["10.1080/10503300500352500"]}, {"surname": ["McElduff", "Boyes", "Zucca", "Girgis"], "given-names": ["P", "A", "A", "A"], "source": ["The Supportive Care Needs Survey: A guide to administration, scoring and analysis."], "year": ["2004"], "publisher-name": ["Newcastle, Australia , Centre for Health Research and Psycho-Oncology"]}, {"surname": ["Sharpley", "Cross"], "given-names": ["C", "D"], "article-title": ["A psychometric evaluation of the Spanier Dyadic Adjustment Scale"], "source": ["Journal of Marriage and Family"], "year": ["1982"], "volume": ["44"], "fpage": ["739"], "lpage": ["741"], "pub-id": ["10.2307/351594"]}, {"collab": ["Medical Outcomes Trust and Quality Metric Incorporated"], "source": ["SF-36: SF-36v2TM Health Survey; (IQOLA SF36v2 Standard, English (Australia), 7/03)."], "year": ["2003"], "publisher-name": [" M.O.T.a.Q.M.I. by Health Assessment Lab"]}, {"collab": ["TreeAge Software Inc"], "source": ["TreeAge Pro 2005 - Healthcare Module Edition"], "year": ["2005"], "publisher-name": ["Williamstown, MA , TreeAge Software Inc."]}]
{ "acronym": [], "definition": [] }
51
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Aug 8; 8:226
oa_package/00/e4/PMC2529334.tar.gz
PMC2529335
18702823
[ "<title>Background</title>", "<p>Among American men and women, colon cancer is the third most frequently diagnosed malignancy and third leading cause of cancer death [##REF##17237035##1##]. In the past two decades, incidence and mortality rates for colon cancer have declined by more than 20% in women and men [##REF##17237035##1##,##REF##16204691##2##]. While some authors attribute these downward trends to early detection and more effective therapy [##REF##16204691##2##], the exact reasons are not yet fully understood. One factor that may have contributed to these declines is the widespread intake of aspirin, ibuprofen and other nonsteroidal anti-inflammatory drugs (NSAIDs)[##REF##17612044##3##]. Among 22 published epidemiologic studies that focused on the association between intake of NSAIDs and the risk of human colon cancer, 20 reported statistically significant risk reductions. Meta-analysis of these data suggests that regular NSAID intake (primarily aspirin) reduces the risk of colon cancer by about 60% [##REF##15756426##4##].</p>", "<p>Two selective COX-2 inhibitors, celecoxib (Celebrex) and rofecoxib (Vioxx), were approved for the treatment of arthritis by the United States Food and Drug Administration (FDA) in 1999 [##REF##17612044##3##]. Until the recall of Vioxx in September, 2004, these two compounds plus other selective COX-2 inhibitors valdecoxib (Bextra) and meloxicam (Mobic) were widely utilized in the United States for pain relief and treatment of osteoarthritis and rheumatoid arthritis [##REF##15486258##5##,##REF##15576585##6##]. The time period between approval of celecoxib to the recall of rofecoxib provides an approximate six-year window for evaluation of exposure to these compounds by a case control approach. The current case control study was designed to test and compare the chemopreventive value of selective and nonselective COX-2 inhibitors against human colon cancer.</p>" ]
[ "<title>Methods</title>", "<p>We studied 326 cases of invasive colon cancer with histological verification based upon review of the pathology records, and 652 group-matched controls with no personal history of cancer and no current gastrointestinal disease. Cases were sequentially ascertained for interview at the time of their diagnosis during 2003 through September, 2004 at The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (CHRI), Columbus, Ohio. There were no refusals to participate among cases. The controls were ascertained from the mammography unit and prostate screening services of the cancer hospital during the same time period and frequency matched to the cases at a rate of 2:1 by five-year age interval, race, and place (county) of residence. We interviewed randomly selected controls from these screening facilities throughout the time frame of the study to achieve a 2:1 ratio by gender, age, race and county of residence. Among men and women approached and eligible for participation, 95% completed the questionnaire. The protocol was approved by the Human Subjects Cancer Internal Review Board of The Ohio State University Medical Center and informed consent documentation was obtained for participants.</p>", "<p>Critical information on exposure to NSAIDs and other factors were obtained utilizing a standardized risk factor questionnaire. The questionnaires were administered in person by trained medical personnel (who were blinded as to the purpose of the study) prior to definitive surgery or treatment for the cases and at the time of screening mammography or screening for prostate cancer for controls. The data variables collected consisted of demographic characteristics, height, weight, menstrual and pregnancy history for women, family history of colon cancer, comprehensive information on cigarette smoking, alcohol intake, pre-existing medical conditions (arthritis, chronic headache, cardiovascular conditions including hypertension, angina, ischemic attacks, stroke, and myocardial infarction, lung disease, and diabetes mellitus), and medication history including over the counter (OTC) and prescription NSAIDs, and exogenous hormones. Regarding selective COX-2 inhibitors and other NSAIDs, the use pattern (frequency, dose, and duration), and the type, (celecoxib, valdecoxib, rofecoxib, meloxicam, aspirin, ibuprofen, naproxen, indomethacin) were recorded. Data on the related analgesic, acetaminophen were collected for comparison with selective COX-2 inhibitors and other NSAIDs.</p>", "<p>Case-control differences in means and frequencies were checked for statistical significance by t-tests and chi square tests, respectively. Effects of the selective COX-2 inhibitors as a group were quantified by estimating odds ratios and their 95% confidence intervals. Odds ratios were adjusted for age and colon cancer risk factors (family history, body mass, chronic smoking, and regular alcohol intake) by logistic regression analysis [##UREF##0##7##,##UREF##1##8##]. Adjusted estimates were obtained for specific types of compounds, e.g., aspirin, ibuprofen or naproxen, selective COX-2 inhibitors (rofecoxib and celecoxib), and acetaminophen. Estimates for selective COX-2 inhibitors were also adjusted for prior intake of other types of NSAIDs.</p>" ]
[ "<title>Results</title>", "<p>Pertinent characteristics of the cases and controls are given in Table ##TAB##0##1##. The cases exhibited higher frequencies of hypertension (OR = 2.87, 95% CI = 2.10–3.92) family history of colon cancer (OR = 1.58, 95% CI = 1.08–2.30) and chronic cigarette smoking (OR = 2.07, 95% CI = 1.49–2.87). Cases and controls had similar distributions of matching variables, age, gender, race and county of residence as well as education, body mass and alcohol consumption.</p>", "<p>Table ##TAB##1##2## shows the comparative frequencies of the medications under study with multivariate-adjusted odds ratios and 95% confidence intervals. A significant reduction in the risk of colon cancer was observed for daily intake of selective COX-2 inhibitors for one year or more (Adjusted OR = 0.31, 95% CI = 0.16–0.57). Joint use of COX-2 inhibitors with aspirin or other NSAIDs was reported by 9.4% of subjects; however, the odds ratio for COX-2 inhibitors was not appreciably changed with additional adjustment for the prior intake of such compounds (OR = 0.40, 95% CI = 0.25–0.82). Estimates for smokers and nonsmokers and subjects with and without hypertension were also similar. When the data were stratified by gender, the risk reduction for the selective COX-2 inhibitors was stronger for women (OR = 0.20, 95% CI = 0.08–0.46) than men (OR = 0.75, 95% CI = 0.29–2.20).</p>", "<p>Significant risk reductions were also observed for the intake of one or more pills per week of regular aspirin (OR = 0.33, 95% CI = 0.20–0.56), and ibuprofen or naproxen (0.28, 95% CI = 0.15–0.54). Low dose (81 mg) aspirin produced a risk reduction with marginal significance (OR = 0.58, 95% CI = 0.35–1.20) whereas acetaminophen had no effect on the relative risk of colon cancer. Aspirin was used for cardioprotection by 9% of subjects taking 325 mg tablets compared to 93% of subjects taking 81 mg tablets.</p>", "<p>Table ##TAB##2##3## presents risk estimates for individual selective COX-2 inhibitors (celecoxib and rofecoxib) plus dose-response data for aspirin and ibuprofen. Daily use of either 200 mg celecoxib or 25 mg rofecoxib for more than one year produced similar risk reductions (65% and 68%, respectively). The average duration of use was 3.6 years. The trend data for OTC compounds suggests that 325 mg aspirin or 200 mg ibuprofen produced significant risk reductions when taken daily for 5 or more years.</p>" ]
[ "<title>Discussion</title>", "<p>Our observation of a significant risk reduction in human colon cancer due to intake of selective COX-2 inhibitors is similar to that reported by Rahme et al. [##REF##12891542##9##]. Standard daily dosages of celecoxib (200 mg) or rofecoxib (25 mg) were associated with a 69% reduction in colon cancer risk. Notably, comparator NSAIDs with non-selective COX-2 activity (325 mg aspirin or 200 mg ibuprofen) also produced significant risk reductions of similar magnitude. In contrast, the effect of low dose aspirin (81 mg) was only marginally significant and acetaminophen, an analgesic with little COX-2 activity, had no effect on the risk of colon cancer.</p>", "<p>Selective COX-2 inhibitors (celecoxib and rofecoxib) were only recently approved for use in 1999, and rofecoxib (Vioxx) was withdrawn from the marketplace in 2004 [##REF##17612044##3##, ####REF##15756426##4##, ##REF##15486258##5####15486258##5##]. Nevertheless, even in the short window of exposure to these compounds, the selective COX-2 inhibitors produced significant reductions in the risk of colon cancer, underscoring their strong potential for colon cancer chemoprevention.</p>", "<p>In general, NSAIDs inhibit cyclooxygenase which is the key rate-limiting enzyme of prostaglandin biosynthesis [##UREF##2##10##, ####UREF##3##11##, ##REF##1380156##12####1380156##12##]. Molecular studies show that the inducible COX-2 gene is over-expressed in human colon cancer and that genetic expression of COX-2 in cancer cells is correlated with mutagenesis, mitogenesis, angiogenesis, and deregulation of apoptosis [##REF##7926468##13##, ####REF##7641194##14##, ##REF##10728691##15####10728691##15##]. Over the counter NSAIDs have consistently shown antitumor effects in animal models of carcinogenesis [##UREF##4##16##], and striking antitumor effects of the specific COX-2 inhibitor, celecoxib, have been observed against colon cancer [##REF##17612053##17##]. Epidemiologic studies and randomized clinical trials provide convincing evidence that regular intake of aspirin and other NSAIDs not only inhibit the development of colon cancer <italic>per se</italic>, but also interrupt the evolution of preneoplastic lesions of the colonic mucosa [##REF##17612047##18##]. Furthermore, recent randomized clinical trials of selective COX-2 inhibitors indicate that celecoxib suppresses the development of colon adenomas [##REF##16943400##19##,##REF##16943401##20##]. The current study coupled with existing clinical, preclinical and molecular evidence suggest that aberrant induction of COX-2 and up-regulation of the prostaglandin cascade play a significant role in colon carcinogenesis, and that blockade of this process has strong potential for intervention. It is noteworthy that NSAIDs also manifest anticancer effects by mechanisms other than COX-2 inhibition [##REF##15949789##21##]. For example, celecoxib has been found to have multiple COX-independent anticancer effects including induction of apoptosis and inhibition of cell cycle progression, angiogenesis, and metastasis [##REF##15949789##21##,##REF##16757698##22##]. Thus, it will be important to exploit not only COX-2 blockade but also COX-independent molecular targets of these compounds [##REF##16757698##22##].</p>", "<p>Enthusiasm for the use of selective COX-2 blocking agents in the chemoprevention of colon cancer and other malignancies has been tempered by reports of adverse effects on the cardiovascular system leading to the recall of the popular anti-arthritic compound, rofecoxib (Vioxx) [##UREF##5##23##, ####REF##15713943##24##, ##REF##15713944##25####15713944##25##], and subsequently, the cardiovascular safety of all selective COX-2 inhibitors has come under scrutiny [##REF##15486258##5##]. However, such studies involved supra-therapeutic dosages given over long periods of time without consideration of body size or individual differences in metabolism [##REF##15821073##26##]. Nevertheless, not all studies reflect changes in cardiovascular risk with exposure to COX-2 inhibitors [##REF##11835924##27##,##REF##12914871##28##] and recently a large meta-analysis of existing randomized clinical trials found no risk increase at any dose level of celecoxib [##REF##17196469##29##]. Further studies will be required to determine the appropriate dose, frequency of intake, duration, side effects and cost effectiveness of selective and nonselective COX-2 inhibitors in the chemoprevention of cancer.</p>" ]
[ "<title>Conclusion</title>", "<p>We observed a significant reduction in the risk of human colon cancer due to intake of both selective and nonselective COX-2 inhibitors. Chemopreventive effects against colon cancer were associated with recommended daily doses of celecoxib (median dose = 200 mg) or rofecoxib (median dose = 25 mg) for an average duration of 3.6 years. Notably, the regular intake of over the counter NSAIDs such as aspirin and ibuprofen produced risk reductions in colon cancer similar in magnitude to the selective COX-2 inhibitors.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Epidemiologic and laboratory investigations suggest that aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) have chemopreventive effects against colon cancer perhaps due at least in part to their activity against cyclooxygenase-2 (COX-2), the rate-limiting enzyme of the prostaglandin cascade.</p>", "<title>Methods</title>", "<p>We conducted a case control study of colon cancer designed to compare effects of selective and non-selective COX-2 inhibitors. A total of 326 incident colon cancer patients were ascertained from the James Cancer Hospital, Columbus, Ohio, during 2003–2004 and compared with 652 controls with no history of cancer and matched to the cases at a 2:1 ratio on age, race, and county of residence. Data on the past and current use of prescription and over the counter medications and colon cancer risk factors were ascertained using a standardized risk factor questionnaire. Effects of COX-2 inhibiting agents were quantified by calculating odds ratios (OR) and 95% confidence intervals.</p>", "<title>Results</title>", "<p>Results showed significant risk reductions for selective COX-2 inhibitors (OR = 0.31, 95% CI = 0.16–0.57), regular aspirin (OR = 0.33, 95% CI = 0.20–0.56), and ibuprofen or naproxen (0.28, 95% CI = 0.15–0.54). Acetaminophen, a compound with negligible COX-2 activity and low dose aspirin (81 mg) produced no significant change in the risk of colon cancer.</p>", "<title>Conclusion</title>", "<p>These results suggest that both non-selective and selective COX-2 inhibitors produce significant reductions in the risk of colon cancer, underscoring their strong potential for colon cancer chemoprevention.</p>" ]
[ "<title>Competing interests</title>", "<p>This research was supported in part by a grant from Pfizer, New York, NY, and grant P30 CA16058 from the National Cancer Institute, Bethesda, MD.</p>", "<title>Authors' contributions</title>", "<p>REH designed and directed the study. JBD coordinated data collection and quality control, and assisted in the interpretation of results. GAA assisted in the analysis and interpretation of results. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/237/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors thank Elvira M. Garofalo, Program Manager of the James Cancer Mammography Unit, and Julie M. Coursey, Assistant Director of the James Cancer Medical Records Registry, for their assistance in the conduct of this investigation. Funding organizations were not involved in study design, collection, analysis and interpretation of data or manuscript preparation and the decision to submit the manuscript for publication.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Characteristics of colon cancer cases and controls.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Characteristic <sup>a</sup></bold></td><td align=\"left\"><bold>Cases (N = 326)</bold></td><td align=\"left\"><bold>Controls (N = 652)</bold></td></tr></thead><tbody><tr><td align=\"left\"><underline>Gender</underline></td><td/><td/></tr><tr><td align=\"left\"> Female</td><td align=\"left\">45%</td><td align=\"left\">45%</td></tr><tr><td align=\"left\"><underline>Age (yrs)</underline></td><td/><td/></tr><tr><td align=\"left\"> &lt;50</td><td align=\"left\">15%</td><td align=\"left\">11%</td></tr><tr><td align=\"left\"> 50–59</td><td align=\"left\">22</td><td align=\"left\">25</td></tr><tr><td align=\"left\"> 60–69</td><td align=\"left\">32</td><td align=\"left\">34</td></tr><tr><td align=\"left\"> &gt;65</td><td align=\"left\">31</td><td align=\"left\">30</td></tr><tr><td align=\"left\"> Mean (SEM)</td><td align=\"left\">63.2 (0.7)</td><td align=\"left\">63.5 (0.6)</td></tr><tr><td align=\"left\"><underline>Race</underline></td><td/><td/></tr><tr><td align=\"left\"> Caucasian</td><td align=\"left\">90%</td><td align=\"left\">86%</td></tr><tr><td align=\"left\"><underline>Residence</underline></td><td/><td/></tr><tr><td align=\"left\"> Franklin County, Ohio</td><td align=\"left\">81%</td><td align=\"left\">83%</td></tr><tr><td align=\"left\"> Adjacent Counties</td><td align=\"left\">18</td><td align=\"left\">15</td></tr><tr><td align=\"left\"> Other</td><td align=\"left\">1</td><td align=\"left\">2</td></tr><tr><td align=\"left\"><underline>Education</underline></td><td/><td/></tr><tr><td align=\"left\"> &lt; 12 yrs</td><td align=\"left\">12%</td><td align=\"left\">12%</td></tr><tr><td align=\"left\"> 12 yrs</td><td align=\"left\">53</td><td align=\"left\">55</td></tr><tr><td align=\"left\"> &gt; 12 yrs</td><td align=\"left\">35</td><td align=\"left\">33</td></tr><tr><td align=\"left\"><underline>Family History</underline></td><td/><td/></tr><tr><td align=\"left\"> Positive</td><td align=\"left\">20%</td><td align=\"left\">13% (p &lt; 0.01)</td></tr><tr><td align=\"left\"><underline>Body Mass</underline></td><td/><td/></tr><tr><td align=\"left\"> BMI &lt; 22</td><td align=\"left\">14%</td><td align=\"left\">12%</td></tr><tr><td align=\"left\"> BMI 22–27</td><td align=\"left\">32</td><td align=\"left\">40</td></tr><tr><td align=\"left\"> BMI &gt; 27</td><td align=\"left\">55</td><td align=\"left\">48</td></tr><tr><td align=\"left\"> Mean (SEM)</td><td align=\"left\">29.1 (0.5)</td><td align=\"left\">28.1 (0.3)</td></tr><tr><td align=\"left\"><underline>Smoking</underline></td><td/><td/></tr><tr><td align=\"left\"> (&gt;10 Pack-years)</td><td align=\"left\">33%</td><td align=\"left\">22% (p &lt; 0.01)</td></tr><tr><td align=\"left\"><underline>Alcohol Intake</underline></td><td/><td/></tr><tr><td align=\"left\"> None</td><td align=\"left\">47%</td><td align=\"left\">45%</td></tr><tr><td align=\"left\"> 1–2 drinks per week</td><td align=\"left\">36</td><td align=\"left\">35</td></tr><tr><td align=\"left\"> &gt; 2 drinks per week</td><td align=\"left\">17</td><td align=\"left\">20</td></tr><tr><td align=\"left\"><underline>Hypertension</underline></td><td align=\"left\">47%</td><td align=\"left\">28% (p &lt; 0.01)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Odds ratios with 95% confidence intervals for colon cancer and selective cyclooxygenase-2 (COX-2) inhibitors, and over the counter nonsteroidal anti-inflammatory drugs (OTC NSAIDS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Compound</bold></td><td align=\"center\"><bold>Number of Cases (%)</bold></td><td align=\"center\"><bold>Number of Controls (%)</bold></td><td align=\"left\"><bold>Multivariate OR<sup><bold><italic>d </italic></bold></sup>(95% CI)</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>None/Infrequent Use</bold><sup><bold><italic>a</italic></bold></sup></td><td align=\"center\">236 (72)</td><td align=\"center\">352 (54)</td><td align=\"left\">1.00</td></tr><tr><td align=\"left\"><bold>COX-2 Inhibitors</bold><sup><bold><italic>b</italic></bold></sup></td><td align=\"center\">15 (5)</td><td align=\"center\">53 (8)</td><td align=\"left\">0.31 (0.16–0.57)</td></tr><tr><td align=\"left\"><bold><underline>OTC NSAIDs</underline></bold><sup><bold><italic>c</italic></bold></sup></td><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Aspirin</bold></td><td align=\"center\">22 (7)</td><td align=\"center\">88 (13)</td><td align=\"left\">0.33 (0.20–0.56)</td></tr><tr><td align=\"left\"> <bold>Ibuprofen/Naproxen</bold></td><td align=\"center\">13 (4)</td><td align=\"center\">68 (11)</td><td align=\"left\">0.28 (0.15–0.54)</td></tr><tr><td align=\"left\"> <bold>Acetaminophen</bold></td><td align=\"center\">12 (3)</td><td align=\"center\">22 (3)</td><td align=\"left\">0.81 (0.35–1.61)</td></tr><tr><td align=\"left\"> <bold>Baby Aspirin</bold></td><td align=\"center\">28 (9)</td><td align=\"center\">69 (11)</td><td align=\"left\">0.58 (0.35–1.02)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Odds ratios for colon cancer by dose, frequency, and duration of exposure to celecoxib, rofecoxib, aspirin, and ibuprofen.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Compound</bold><sup><bold><italic>a</italic></bold></sup></td><td align=\"center\"><bold>Dose</bold></td><td align=\"center\"><bold>Cases</bold></td><td align=\"center\"><bold>Controls</bold></td><td align=\"center\"><bold>Frequency of Use</bold></td><td align=\"center\"><bold>Multivariate OR<sup><bold><italic>b </italic></bold></sup>(95% CI)</bold></td></tr><tr><td/><td/><td align=\"center\" colspan=\"2\"><bold>N (%)</bold></td><td/><td/></tr></thead><tbody><tr><td align=\"center\"><bold>None</bold></td><td align=\"center\">0</td><td align=\"right\">236 (72)</td><td align=\"right\">352 (54)</td><td align=\"center\">None</td><td/></tr><tr><td align=\"center\"><bold>Celecoxib</bold></td><td align=\"center\">200 mg</td><td align=\"right\">8 (2)</td><td align=\"right\">27 (4)</td><td align=\"center\">Daily</td><td align=\"center\">0.35 (0.18–0.93)</td></tr><tr><td align=\"center\"><bold>Rofecoxib</bold></td><td align=\"center\">25 mg</td><td align=\"right\">7 (2) 26 (4)</td><td/><td align=\"center\">Daily</td><td align=\"center\">0.32 (0.12–0.83)</td></tr><tr><td align=\"center\"><bold>Aspirin</bold></td><td align=\"center\">325 mg</td><td align=\"right\">5 (2)</td><td align=\"right\">14 (2)</td><td align=\"center\">1–2 weekly</td><td align=\"center\">0.87 (0.24–3.17)</td></tr><tr><td/><td/><td align=\"right\">4 (1)</td><td align=\"right\">8 (1)</td><td align=\"center\">3–6 weekly</td><td align=\"center\">0.73 (0.20–2.67)</td></tr><tr><td/><td/><td align=\"right\">13 (4)</td><td align=\"right\">66 (10)</td><td align=\"center\">Daily</td><td align=\"center\">0.22 (0.12–0.41)</td></tr><tr><td/><td/><td/><td/><td/><td align=\"center\"><italic>trend (p &lt; 0.05)</italic></td></tr><tr><td align=\"center\"><bold>Ibuprofen</bold></td><td align=\"center\">200 mg</td><td align=\"right\">7 (2)</td><td align=\"right\">17 (3)</td><td align=\"center\">1–2 weekly</td><td align=\"center\">0.67 (0.26–1.69)</td></tr><tr><td/><td/><td align=\"right\">2 (1)</td><td align=\"right\">12 (2)</td><td align=\"center\">3–6 weekly</td><td align=\"center\">0.18 (0.02–1.50)</td></tr><tr><td/><td/><td align=\"right\">4 (1)</td><td align=\"right\">39 (6)</td><td align=\"center\">Daily</td><td align=\"center\">0.19 (0.07–0.49)</td></tr><tr><td/><td/><td/><td/><td/><td align=\"center\"><italic>trend (p &lt; 0.01)</italic></td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
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[ "<table-wrap-foot><p><sup>a </sup>Family History: colon cancer among first or second degree relatives; Body Mass Index = weight (kg)/ht <sup>2 </sup>(m).</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>No use of any NSAID or analgesic or infrequent use of no more than one pill per week for less than one year;</p><p><sup>b </sup>COX-2 inhibitors include celecoxib, rofecoxib, valdecoxib, and meloxicam used daily for one year or more.</p><p><sup>c </sup>Over the counter (OTC) NSAIDs/analgesics used at least once per week for more than one year.</p><p><sup>d </sup>Multivariate odds ratios are adjusted for continuous variables (body mass) and categorical variables (hypertension, family history, smoking, and alcohol intake).</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>Minimum duration of exposure: one year for celecoxib or rofecoxib, 5 years for aspirin or ibuprofen.</p><p><sup>b </sup>Multivariate odds ratios are adjusted for continuous variables (body mass) and categorical variables (hypertension, family history, smoking, and alcohol intake).</p></table-wrap-foot>" ]
[]
[]
[{"surname": ["Schlesselman"], "given-names": ["JJ"], "source": ["Case Control Studies"], "year": ["1982"], "publisher-name": ["Oxford University Press, New York"]}, {"surname": ["Harrell"], "given-names": ["F"], "source": ["Logistic Regression Procedure"], "year": ["2005"], "publisher-name": ["Statistical Analysis System (SAS)"]}, {"surname": ["Vane"], "given-names": ["JR"], "article-title": ["Inhibition of prostaglandin synthesis as a mechanism of action for aspirin-like drugs"], "source": ["Nature"], "year": ["1971"], "volume": ["231"], "fpage": ["323"], "lpage": ["235"]}, {"surname": ["Herschman"], "given-names": ["HR"], "article-title": ["Regulation of prostaglandin synthase-1 and prostaglandin synthase-2"], "source": ["Cancer and Metas Rev"], "year": ["1994"], "volume": ["13"], "fpage": ["241"], "lpage": ["256"]}, {"surname": ["Reddy", "Chinthalapally", "Harris RE"], "given-names": ["BS", "RV"], "article-title": ["Role of synthetic and naturally occurring cyclooxygenase inhibitors in colon cancer prevention"], "source": ["COX-2 Blockade in Cancer Prevention and Therapy"], "year": ["2002"], "publisher-name": ["Humana Press, Totowa, NJ"], "fpage": ["71"], "lpage": ["83"]}, {"surname": ["Mukherjee", "Nissen", "Topol"], "given-names": ["D", "SE", "EJ"], "article-title": ["Risk of cardiovascular events associated with selective COX-2 inhibitors"], "source": ["J Am Med Assoc"], "year": ["2001"], "volume": ["286"], "fpage": ["954"], "lpage": ["959"]}]
{ "acronym": [], "definition": [] }
29
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Aug 14; 8:237
oa_package/55/dd/PMC2529335.tar.gz
PMC2529336
18721486
[ "<title>Background</title>", "<p>In patients with cancer, the body mounts an immune response following the onset of malignant disease since the new cells are recognized as non-self. It is composed of both immune cells that mediate innate, non-specific immunity, and adaptive, antigen-specific immunity [##REF##14722672##1##, ####REF##12615893##2##, ##REF##16730261##3####16730261##3##]. Tumor cell proteins can elicit an immune response for various reasons; aberrant gene expression (e.g. cancer-testis antigens) [##REF##18157007##4##, ####REF##15118836##5##, ##REF##15065093##6##, ##REF##9928550##7##, ##REF##10950148##8##, ##REF##15240519##9##, ##REF##18214856##10####18214856##10##], overexpression (neu/Her2) [##REF##12865628##11##,##REF##11694789##12##], aberrant processing (mucin) [##REF##12733128##13##,##REF##17064405##14##] and mutation events (p53) [##REF##12865628##11##,##REF##17415711##15##]. Although it is evident that a natural humoral response to cancer exists, tumor-associated antigens (TAAs) are generally notoriously bad immunogens. This is likely due to systemic tolerance to the autoantigens and, as a result, the natural humoral immune response against tumor antigens fails to reach high antibody titers and is not effective [##REF##17251916##16##].</p>", "<p>During the last decade, the search for TAAs that can be targeted by the immune system, and as such are \"immunovisible\", has been the focus of much research in cancer immunology. In addition, the isolation and production of fully human monoclonal antibodies (fhMAb) to such antigens has also made significant advances over the past few years [##REF##17251916##16##, ####UREF##0##17##, ##REF##17143505##18####17143505##18##]. The potential utility of these antibodies to identify TAAs, to discriminate between neoplastic and normal tissues and potentially act as anti-cancer therapeutics has been the impetus for this work.</p>", "<p>To identify tumor-associated antigens, one of the more fruitful approaches has been to employ naturally occurring anti-cancer antibodies that arise in cancer patients. To this end, serological expression technology (SEREX) has facilitated the identification of novel TAAs by screening patients' whole sera on cDNA expression libraries that were prepared from autologous tumors or human cancer cell lines [##UREF##1##19##, ####UREF##2##20##, ##REF##10508479##21##, ##REF##10211784##22##, ##REF##16391802##23####16391802##23##]. This technology has led to the creation of a database of protein antigens that are associated with and specific to a variety of cancers. However, the native immune response to these antigens is not identified or captured by this methodology. Therefore, although proteins that are associated specifically with cancer can be pinpointed, the antibodies that can effectively target these antigens remain mostly unidentified.</p>", "<p>To overcome this limitation we designed and implemented an alternate strategy that relies on a unique trioma fusion partner cell line, MFP-2, which we developed [##REF##12454369##24##]. MFP-2 can efficiently fuse with both peripheral blood and lymph node lymphocytes. Following fusion, surviving hybridoma clones are stable for prolonged periods and many produce significant quantities of human monoclonal antibodies. We employed this unique fusion partner cell line to develop a panel of native autologous fully human monoclonal antibodies (fhMAb) that were culled from the natural repertoire arising in breast cancer patients [##REF##12573104##25##]. These fhMAbs reacted specifically with breast cancer cells and malignant tissues. They are useful not only for identification of the target antigens, but also for immunodiagnostic procedures [##REF##16904309##26##] and eventually for immunotherapy of breast cancer, since they can be produced on an industrial scale.</p>", "<p>We identified the protein targets of two of the anti-breast cancer autoantibodies that we isolated, and determined that they target the protein GIPC1. Using our fhMAbs that target GIPC1, we studied its expression in human breast tissue and in cultured cells. We determined that this protein is specifically up-regulated in malignant breast epithelial tissue/cells and in breast cancer cell lines and is not detected in normal breast epithelia or in live primary fibroblast cell lines. Therefore, GIPC1 is a novel breast cancer-associated antigen that may play a role in the initiation and/or progression of breast cancer.</p>" ]
[ "<title>Methods</title>", "<title>Cell culture</title>", "<p>All human cancer and normal cell lines were purchased from ATCC. Human breast cancer cell lines MCF-7 and SK-BR-3 and primary human fibroblasts are among those used in this study. SK-BR-3 were cultured in McCoy's 5a medium supplemented with L-glutamine and 10% FBS. MCF-7 was grown in MEM medium supplemented with L-glutamine, non-essential aminoacids, 10% FBS and 0.01 mg/ml bovine insulin. Fibroblasts were cultured in DMEM, supplemented with L-glutamine and 10% FBS. Other normal and neoplastic cell lines were cultured according to the conditions recommended by the ATCC. Hybridoma clones were produced and cultured according to previously described techniques [##REF##12573104##25##].</p>", "<title>Antibody characterization</title>", "<p>The isotype of human Abs was determined by ELISA using murine anti-human isotype-specific MAbs to μ-, γ-, κ- and λ-chains (Sigma, USA) and goat anti-mouse Ig (25 μg/mL) conjugated to peroxidase and absorbed with human Ig.</p>", "<title>Immunocytochemistry and immunohistochemistry</title>", "<p>Cells were plated on ethanol pre-treated cover slips (Fisher, USA) and placed in 6-well plates (Falcon, USA) in culture medium. After 24 hours the cover slips with attached cells were repeatedly washed in PBS and fixed in ethanol. Following fixation and repeated washes, cover slips were incubated with the primary and secondary antibodies according to standard protocols, stained with propidium iodide 1 μg/ml and analyzed by confocal fluorescent microscopy using a Zeiss Axiovert 100 TV microscope and Zeiss software. For immunohistochemistry randomly selected 5 μm thick sections of paraffin embedded breast cancer tissue were used. Endogenous peroxidase activity was blocked by incubation of slides in 3% H<sub>2</sub>O<sub>2 </sub>in methanol. Following washing, tissue slides were blocked with 5% normal goat serum in PBS. Monovalent Fab fragments of goat anti-human IgM+IgG (Jackson Immunoresearch Laboratories, Inc.), in blocking solution, was then applied for secondary blocking. After 3 washes in PBS, the human monoclonal antibody was applied at an approximate concentration of 5 ug/ml. The slides were then washed and incubated with a second FITC conjugated antibody to human κ-light chains (Sigma, USA) and propidium iodide at 1 μg/ml. Following a few washes, mounting medium (Biomeda, USA) and cover slips were applied and sections were analyzed by standard fluorescent microscopy.</p>", "<title>Western blotting</title>", "<p>Cells were lysed with freshly prepared ice cold lysis buffer [20 mM Tris-HCl, pH 7.6, 420 mM NaCl, 0.25% NP40, 2 mM phenylmethylsulfonyl fluoride, 1 ug/ml leupeptin, 250 U/ml Trasylol (aprotinin)] and stored at -80°C or used immediately. Protein concentration was determined with the BioRad Protein Detection Reagent (BioRad). Tissue samples were mechanically homogenized on ice, spun down at 3000 g for 30 min at 4°C and the lower (non lipid) phase was used for further analysis.</p>", "<p>Equal amounts of protein were separated on 10% SDS polyacrylamide gels and either Coomassie blue or silver stained according to established techniques [##REF##6172996##27##]. Following electrophoresis, the proteins in the gel were transferred to a nitrocellulose membrane using a variation of the methods of Towbin [##REF##388439##28##] and Burnette [##REF##6266278##29##], and following blocking, probed with relevant primary and HRP-conjugated secondary antibody. Membranes were processed using an enhanced chemiluminescence kit (ECL, Amersham), and visualized on Kodak BioMax MR-1 film. The immunoblotting with recombinant GIPC1 protein was carried out as previously described [##REF##16904309##26##].</p>", "<title>Binding of <sup>125</sup>I-labeled monoclonal antibody to SK-BR-3 cells and Scatchard analysis</title>", "<p>Antibody 27.F7 was labeled with Na-<sup>125</sup>I (specific activity 17.4 mCi/mg) (New England Nuclear, MA, USA) using Iodogen as previously described [##REF##2185939##30##]. The resulting specific activity of <sup>125</sup>I-27.F7 was (100 mCi/mmol). SK-BR-3 cells were grown in 24-well plates supplemented with DMEM media with 10% FCS and used in these experiments at subconfluent phase at a density of 2 × 10<sup>5 </sup>cells per well. Cell monolayers were suspended with trypsin, cooled to 4°C by placing them on ice and washed twice with PBS containing 1% BSA (Sigma, USA). The cells (50,000 cells per sample) were blocked with 1% BSA-PBS for 1 hour at 4°C followed by incubation with <sup>125</sup>I-27.F7 (approx. 10<sup>5 </sup>cpm per sample) in the presence of increasing concentrations of cold unlabeled 27.F7 (ranging 0.1 – 200 ng/ml) for 1 hour at 4°C. After incubation the samples were applied to Millipore filters using Millipore 96-well membrane plates (Millipore, USA). The wells were broken off and counted individually in a Cobra γ-counter (Hewlett Packard, USA). Maximum binding, B<sub>max</sub>, was determined by incubating varying numbers of cells (ranging from 1.25 × 10<sup>4 </sup>to 32 × 10<sup>5 </sup>cells) with radiolabeled 27.F7 antibody. Nonspecific binding of the tracer was determined in the presence of an excessive amount of unlabeled antibody (500 μg/ml) and was generally less than 5% of maximum binding. All experimental measurements of K<sub>a </sub>and the number of antigen targets per cell were done in triplicate. Analysis of the data was performed according to previously described methods [##REF##12204417##31##, ####REF##8394012##32##, ##UREF##3##33####3##33##].</p>", "<title>Antigen identification</title>", "<p>RNA was purified from SK-BR-3 cells according to standard protocols. Preparation of mRNA was performed by oligo dT chromatography with a kit from Stratagene (La Jolla, CA). A lambda phage expression library was prepared in lambda ZAP (Stratagene, La Jolla, CA) and plaque lifts were screened with human monoclonal 27.B1 and 27.F7 according to previously described methods [##REF##10508479##21##,##REF##9610721##34##]. Phage plaques that were positive on the first screened were picked and two rounds of plaque purification was performed to ensure that they were true positives. Positive lambda phage clones were autoexcised according to the protocol provided by Stratagene, grown as plasmids according to standard protocols and sequenced to identify the cDNA inserts.</p>", "<title>Northern blot analysis</title>", "<p>Total cellular RNA was isolated by the Guanidinium/Phenol extraction method and Northern blotting was performed as previously described [##REF##7510863##35##,##REF##9256446##36##]. Briefly, 15 μg of RNA is denatured and electrophoresed in a 1.2% Agarose gel along with 3.5% formaldehyde, transferred to a nylon membrane and hybridized sequentially with <sup>32</sup>P-labeled cDNA probes. The GIPC1 cDNA fragment that we isolated using SEREX technology was used for the gene specific probe and a cDNA fragment of the GAPDH gene was used as an internal control to normalize expression. Following hybridization, the filters were washed and exposed for autoradiography.</p>" ]
[ "<title>Results</title>", "<title>A native fully human autoantibody to breast cancer identifies a cancer-associated antigen that localizes to the cytoplasm and membrane</title>", "<p>We previously described the construction of a unique fusion Partner cell line, MFP-2, and its use for the immortalization of both human peripheral blood and lymph node B-lymphocytes [##REF##12454369##24##,##REF##12573104##25##]. MFP-2 was employed for the generation of hybridoma cells from lymphocytes of breast cancer patients that produce autologous anti-breast cancer specific antibodies. The results of that study are described elsewhere [##REF##12573104##25##]. One of these native human monoclonal antibodies, designated 27.B1 (IgM, k), was chosen for further study. It demonstrated an intensely positive reactivity with two human breast cancer cell lines, SK-BR-3 and MCF-7 and no reaction with normal diploid primary human fibroblasts as tested by cELISA (In cELISA whole cells are used in place of a purified antigen as in ELISA) [##REF##12573104##25##]. Confocal microscopy with 27.B1 demonstrated the presence of the target antigen throughout the cytosol and in addition staining of the membrane was especially strong (see Figure ##FIG##0##1##). Furthermore, 27.B1 stained both primary and metastatic breast cancer with a high specificity and sensitivity [##REF##12573104##25##]. These results along with a more detailed immunocytochemical and immunohistochemical analysis are described elsewhere [##REF##12573104##25##].</p>", "<title>The target antigen for monoclonal antibodies 27.B1 and 27.F7 is a 42 kDa protein</title>", "<p>To identify the size of the 27.B1 target protein, Western blot analysis was performed using whole cell lysates from different cell lines and tissues. Cell lysates prepared from human breast cancer, normal breast tissue, human prostate cancer and two human fibroblast cell lines were run on PAGE under reducing conditions and blotted with fhMAb 27.B1. The antibody reacted with a protein band of approximately 42 kDa molecular weight that is detectable primarily in breast cancer cells (see Figure ##FIG##1##2-A##). There was no detectable immunoreactivity with the human fibroblasts' lysates and prostate cancer cell line LnCaP whereas only traces of immunoreactivity were detected to prostate cancer cell lines PC3 and DU-145 (data not shown). The protein band revealed by 27.B1 appeared as doublet with a dominant band that migrates slower on a gel. The doublet pattern was not the same in all 27.B1 positive cells; MCF-7 cells displayed the higher molecular weight band in much greater abundance, whereas SK-BR-3 showed both bands in more equivalent intensity (data not shown). Western blot analysis of the same cell lysates under non-reducing conditions displayed no difference in staining pattern, indicating that accessibility of the epitope bound by 27.B1 is disulfide bond independent and likely conformation independent.</p>", "<p>Interestingly, we determined that another antibody, 27.F7, which was previously identified as binding breast cancer cells and tissue with high specificity and sensitivity [##REF##12573104##25##], identified the same 42 kDa molecular weight doublet as 27.B1. Furthermore, this antibody detected the bands in the doublet with the same variability in different cell lines as 27.B1. To test the epitope specificity of the two antibodies one of them, 27.F7 was radiolabeled with <sup>125</sup>I and competitive Western blotting was performed with both antibodies (data not shown). Pretreatment of a SK-BR-3 cell lysate blot with unlabeled 27.F7 inhibited binding of <sup>125</sup>I-labeled 27.F7, whereas unlabeled 27.B1 antibody did not inhibit binding. This suggests that if these two antibodies are binding the same protein they likely bind different epitopes.</p>", "<p>To identify the molecular target(s) for 27.B1 and 27.F7, SEREX technology was applied as previously described [##REF##10508479##21##,##REF##12573104##25##,##REF##9610721##34##] to a cDNA expression library prepared from SK-BR-3 mRNA. Expression clones staining positively with 27.F7 and 27.B1 were selected and the cDNA sequences were found to encode the protein known as GIPC1, following a BLAST algorithm homology search [##REF##2231712##37##]. This protein was previously identified as being involved in the regulation of G protein signaling [##UREF##4##38##]. The sequence of the cloned cDNA inserts are identical to the respective sequence reported in GenBank for GIPC1.</p>", "<p>To confirm that the 42 kDa band demonstrated in Figure ##FIG##1##2-A## is indeed GIPC-1, we performed immunoblotting with recombinant GIPC-1 protein (Figure ##FIG##1##2-B##). For this purpose, 27.B1 antibody was preincubated with a bacterial expressed and refolded recombinant GIPC1 protein prior to blotting (Figure ##FIG##1##2-B##, lane 1) and compared to non-preincubated control (Figure ##FIG##1##2-B##, lane 2). These results demonstrate that fhMAb 27.B1 binding to the same 42 kDa band was specifically inhibited by the recombinant protein and this confirmed the specificity of this antibody to the GIPC1 antigen.</p>", "<title>Scatchard analysis</title>", "<p>Scatchard analysis of <sup>125</sup>I-27.F7 binding to SK-BR-3 cells revealed a two-mode pattern of binding, which matches a model with binders of two different avidities (see Figure ##FIG##2##3##) [##REF##12204417##31##]. One type of bound ligand is represented by approximately 20% of all 27.F7 targets and binds the antibody with high avidity (K<sub>a </sub>= 4.2 × 10<sup>11 </sup>M<sup>-1</sup>). The second type of ligand binds with a lower avidity (K<sub>a </sub>= 3.3 × 10<sup>9 </sup>M<sup>-1</sup>) and constitutes about 80% of the total antigen molecules. An estimate for the total number of antigen molecules per cell is approximately 3 × 10<sup>5 </sup>target GIPC1 molecules per cell. The identification of two binding avidities may be related to the fact that human monoclonal antibody 27.F7 (and 27.B1) identifies a two band doublet on a Western blot (see Figure ##FIG##1##2##). This suggests that one of the bands may be a ligand of higher avidity while the second one is of lower avidity. The doublet itself has been previously reported although the reason for two bands has not been determined [##UREF##4##38##]. It might be explained by an alternative start codon or posttranslational modification.</p>", "<title>GIPC1 is up-regulated in breast cancer cell lines</title>", "<p>The strong staining by the human anti-GIPC1 monoclonal antibodies, 27.B1 and 27.F7, on breast cancer cell lines and absent staining of normal cells [##REF##12573104##25##] suggests that the protein might be up-regulated. To semi-quantitatively examine gene expression we performed Northern blot analysis to determine if a higher level of GIPC1 specific mRNA was indeed present in the breast cancer cell lines relative to normal cell lines. RNA from a variety of breast cancer cell lines along with non-neoplastic cell lines was blotted and GAPDH expression was monitored as an internal control to normalize expression. The results are depicted in Figure ##FIG##3##4##. They indicate that breast cancer cells indeed have increased expression of GIPC1 specific RNA relative to that of normal cell lines.</p>" ]
[ "<title>Discussion and conclusion</title>", "<p>Our studies have determined that GIPC1 is a novel breast cancer associated antigen that is up-regulated in breast cancer cell lines and in malignant tissue from all breast cancer patients tested in this study. In our previous studies [##REF##12573104##25##], we determined that the reactivity of the human monoclonal antibodies 27.B1 and 27.F7 are specific to breast cancer tissue and cells. Our present study demonstrated that these antibodies identify the unique antigen target GIPC1 (GAIP interacting protein, C domain), a PDZ domain containing protein [##UREF##4##38##,##UREF##5##39##].</p>", "<p>GIPC1 was first described as a PDZ domain protein that binds to the C terminus of RGS-GAIP (for regulators of G signaling – Gα<sub>i3 </sub>interacting protein) [##UREF##4##38##]. GAIP itself is considered to be a Gα<sub>i3 </sub>regulator, which acts as GTP-ase activating protein switching Gα<sub>i3 </sub>into an inactive mode [##REF##8986788##40##, ####REF##9571244##41##, ##UREF##6##42##, ##REF##11912251##43##, ##REF##12038977##44##, ##REF##9950778##45####9950778##45##]. Although the functional pathway of Gα<sub>i3 </sub>is apparently vesicular trafficking, with GAIP serving as its regulator, the physiological relevance of the interaction between GIPC1 and GAIP is been actively investigated [##REF##16962991##46##, ####REF##17959809##47##, ##REF##17079444##48##, ##REF##17015470##49##, ##REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53####16226429##53##].</p>", "<p>With respect to breast tissue, the recognition by 27.B1 and 27.F7 of GIPC1 is strictly limited to neoplastic cells. Furthermore, the subcellular localization of the bound antibodies in the cell membrane and cytoplasm is consistent with what has been previously described for GIPC1 localization [##REF##16962991##46##]. Taken together, our findings suggest that the up-regulation of GIPC1 is cancer cell specific. Although 27.B1 does not detect any protein by FACS or Western blot analysis in normal cells and tissues it is likely below the level of detection since GIPC1 is likely involved in many diverse cellular processes [##REF##17959809##47##,##REF##17015470##49##, ####REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53##, ##REF##11798178##54##, ##REF##12011974##55####12011974##55##].</p>", "<p>GIPC1 plays a role in mediating the assemblage of molecules involved in signaling transduction pathways [##REF##16226429##53##]. As such, these molecules are involved in protein-protein interactions and likely modulate the activity of their targets [##REF##16226429##53##]. Proteins containing PDZ domains, and the interactions that they mediate, may be involved in a wide variety of signal transduction cascades including interaction with receptors, adhesion molecules, ion channels, gap junctions, cytoskeleton proteins and other vital proteins, such as Fas [##REF##17079444##48##, ####REF##17015470##49##, ##REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53##, ##REF##11798178##54####11798178##54##,##REF##12724327##56##]. Moreover, GIPC1 appears to be a highly conserved protein. In rodents it regulates distribution of M-Sem-F, a neuronal membrane-associated protein and binds to a glucose transporter protein, GLUT1 [##REF##10198040##57##,##REF##10318831##58##]. It is tempting to speculate on the role of over-expressed GIPC1 in binding to a glucose transporter protein with the subsequent influx of glucose supporting growth of tumor cells. Of course, other proteins may be linked to GIPC1 function in cancer cells; this is currently under investigation in our laboratory.</p>", "<p>Research of cancer-associated antigens is an extremely important pursuit. Identification of these antigens can provide insight into the cause of a malignancy, identify targets for immunotherapy and immunodiagnostics [##REF##16904309##26##] as well as lead to the development of new cancer vaccines. Previous studies on natural monoclonal autoantibodies from cancer patients did not describe the target antigens [##REF##8439957##59##,##REF##9823972##60##]. Our studies demonstrate that by combining the \"immunoprospecting\" of cancer autoantibodies and SEREX technology discovery of target antigens for monoclonal cancer autoantibodies can be accomplished. In conclusion, our studies revealed that GIPC1 is a novel cancer-associated antigen; its role in carcinogenesis, however, needs further clarification. It also needs to be clarified whether GIPC1 is a specific breast cancer-associated antigen or it is overexpressed in other malignant diseases as well.</p>" ]
[ "<title>Discussion and conclusion</title>", "<p>Our studies have determined that GIPC1 is a novel breast cancer associated antigen that is up-regulated in breast cancer cell lines and in malignant tissue from all breast cancer patients tested in this study. In our previous studies [##REF##12573104##25##], we determined that the reactivity of the human monoclonal antibodies 27.B1 and 27.F7 are specific to breast cancer tissue and cells. Our present study demonstrated that these antibodies identify the unique antigen target GIPC1 (GAIP interacting protein, C domain), a PDZ domain containing protein [##UREF##4##38##,##UREF##5##39##].</p>", "<p>GIPC1 was first described as a PDZ domain protein that binds to the C terminus of RGS-GAIP (for regulators of G signaling – Gα<sub>i3 </sub>interacting protein) [##UREF##4##38##]. GAIP itself is considered to be a Gα<sub>i3 </sub>regulator, which acts as GTP-ase activating protein switching Gα<sub>i3 </sub>into an inactive mode [##REF##8986788##40##, ####REF##9571244##41##, ##UREF##6##42##, ##REF##11912251##43##, ##REF##12038977##44##, ##REF##9950778##45####9950778##45##]. Although the functional pathway of Gα<sub>i3 </sub>is apparently vesicular trafficking, with GAIP serving as its regulator, the physiological relevance of the interaction between GIPC1 and GAIP is been actively investigated [##REF##16962991##46##, ####REF##17959809##47##, ##REF##17079444##48##, ##REF##17015470##49##, ##REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53####16226429##53##].</p>", "<p>With respect to breast tissue, the recognition by 27.B1 and 27.F7 of GIPC1 is strictly limited to neoplastic cells. Furthermore, the subcellular localization of the bound antibodies in the cell membrane and cytoplasm is consistent with what has been previously described for GIPC1 localization [##REF##16962991##46##]. Taken together, our findings suggest that the up-regulation of GIPC1 is cancer cell specific. Although 27.B1 does not detect any protein by FACS or Western blot analysis in normal cells and tissues it is likely below the level of detection since GIPC1 is likely involved in many diverse cellular processes [##REF##17959809##47##,##REF##17015470##49##, ####REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53##, ##REF##11798178##54##, ##REF##12011974##55####12011974##55##].</p>", "<p>GIPC1 plays a role in mediating the assemblage of molecules involved in signaling transduction pathways [##REF##16226429##53##]. As such, these molecules are involved in protein-protein interactions and likely modulate the activity of their targets [##REF##16226429##53##]. Proteins containing PDZ domains, and the interactions that they mediate, may be involved in a wide variety of signal transduction cascades including interaction with receptors, adhesion molecules, ion channels, gap junctions, cytoskeleton proteins and other vital proteins, such as Fas [##REF##17079444##48##, ####REF##17015470##49##, ##REF##16908842##50##, ##REF##16765607##51##, ##REF##16754745##52##, ##REF##16226429##53##, ##REF##11798178##54####11798178##54##,##REF##12724327##56##]. Moreover, GIPC1 appears to be a highly conserved protein. In rodents it regulates distribution of M-Sem-F, a neuronal membrane-associated protein and binds to a glucose transporter protein, GLUT1 [##REF##10198040##57##,##REF##10318831##58##]. It is tempting to speculate on the role of over-expressed GIPC1 in binding to a glucose transporter protein with the subsequent influx of glucose supporting growth of tumor cells. Of course, other proteins may be linked to GIPC1 function in cancer cells; this is currently under investigation in our laboratory.</p>", "<p>Research of cancer-associated antigens is an extremely important pursuit. Identification of these antigens can provide insight into the cause of a malignancy, identify targets for immunotherapy and immunodiagnostics [##REF##16904309##26##] as well as lead to the development of new cancer vaccines. Previous studies on natural monoclonal autoantibodies from cancer patients did not describe the target antigens [##REF##8439957##59##,##REF##9823972##60##]. Our studies demonstrate that by combining the \"immunoprospecting\" of cancer autoantibodies and SEREX technology discovery of target antigens for monoclonal cancer autoantibodies can be accomplished. In conclusion, our studies revealed that GIPC1 is a novel cancer-associated antigen; its role in carcinogenesis, however, needs further clarification. It also needs to be clarified whether GIPC1 is a specific breast cancer-associated antigen or it is overexpressed in other malignant diseases as well.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>We have been studying the native autoimmune response to cancer through the isolation of human monoclonal antibodies that are cancer specific from cancer patients. To facilitate this work we previously developed a fusion partner cell line for human lymphocytes, MFP-2, that fuses efficiently with both human lymph node lymphocytes and peripheral blood lymphocytes. Using this unique trioma fusion partner cell line we isolated a panel of autologous human monoclonal antibodies, from both peripheral blood and lymph node lymphocytes, which are representative of the native repertoire of anti-cancer specific antibodies from breast cancer patients.</p>", "<title>Methods</title>", "<p>The current study employs immunocytochemistry, immunohistochemistry, Western blot analysis as well as Northern blots, Scatchard binding studies and finally SEREX analysis for target antigen identification.</p>", "<title>Results</title>", "<p>By application of an expression cloning technique known as SEREX, we determined that the target antigen for two monoclonal antibodies, 27.B1 and 27.F7, derived from lymph node B-cells of a breast cancer patient, is the PDZ domain-containing protein known as GIPC1. This protein is highly expressed not only in cultured human breast cancer cells, but also in primary and metastatic tumor tissues and its overexpression appears to be cancer cell specific. Confocal microscopy revealed cell membrane and cytoplasmic localization of the target protein, which is consistent with previous studies of this protein.</p>", "<title>Conclusion</title>", "<p>We have determined that GIPC1 is a novel breast cancer-associated immunogenic antigen that is overexpressed in breast cancer. Its role, however, in the initiation and/or progression of breast cancer remains unclear and needs further clarification.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>SR carried immunochemical and biochemical studies and organized all the data for the manuscript; MS carried the molecular biology experiments and performed SEREX for identification of the antigen; GK developed hybridoma clones producing specific monoclonal antibody; VY performed flow cytometry and Western blot studies for confirmation of the antigen identity; CL did cell culture work related to cloning and selection of antibody-producing clones; AE, LO and GLC provided an expert clinical information on breast cancer and contributed to the interpretation of data and consideration of potential applications; LL and IT are senior co-investigators who conceived the study, developed its design participating in its coordination and drafting the manuscript. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/248/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>We would like to thank Marina Tashker and Ekaterina Hahiashvili for excellent technical assistance.</p>", "<p>This project was supported by American Society Grant and the Department of Medicine at Columbia College of Physicians and Surgeons (SR, GK, AE, IT); the grant form Ludwig Cancer Institute (MS and LO); The Morningside Foundation (GC) and Research Development Grant from BGU of Beer Sheva, Israel (VY, CL and LL).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Human monoclonal 27.B1 stains the membrane and cytosol</bold>. Staining of SK-BR-3 cells and breast cancer tissue was performed with human monoclonal 27.B1. Staining of the SK-BR-3 breast cancer cell line was analyzed by confocal microscopy and indicates that the target antigen is present in the membrane and cytoplasm. Staining of human breast cancer tissue was analyzed by standard fluorescent microscopy.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>The target antigen for human monoclonal 27.B1 is a GIPC1 protein</bold>. A. The target antigen for monoclonal 27.B1 in SK-BR-3 cells, breast cancer tissue and normal breast tissue was identified by Western blot and is displayed. Human Ig H and L chains are present in the tissue and are recognized by the secondary anti-human antiserum. The target antigen is detected as a doublet in both the breast cancer tissue and SK-BR-3 cell line but is not detected in normal breast tissue. Both bands in the doublet are present in all breast cancer cell lines analyzed by Western blot but their intensity is variable. B. Immunoblotting with 27.B1 antibody of total cell lysates prepared from SK-BR-3 cells. 27.B1 antibody was preincubated with recombinant GIPC1 protein prior to blotting (lane 1), and compared to non-preincubated control (lane 2).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Scatchard analysis of human anti-GIPC1 monoclonal antibody on antibody on SK-BR-3 cells</bold>. Scatchard analysis of human monoclonal antibody 27.F7 performed on SK-BR-3 cells revealed the presence of an antigen target with two affinities. This suggests that two populations of GIPC1 molecules exist in these cells and may be related to the protein doublet identified by Western blot analysis in Figure 2.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>GIPC1 RNA expression analysis in normal and neoplastic cell lines</bold>. Panel A: Northern blot analysis of total RNA was performed with RNA samples from a human microvascular endothelial cell line (HMEC), normal breast epithelium cell line HBL100 and breast cancer cell lines MCF-7, T47D, SK-BR-3, MDA231, MDA157 and MDA453. A probe for the GAPDH gene was used to normalize expression. Panel B: Densitometry analysis of the Northern blot was performed to quantitate the mRNA expression. The data indicates that the GIPC1 gene is upregulated in breast cancer cell lines.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1471-2407-8-248-1\"/>", "<graphic xlink:href=\"1471-2407-8-248-2\"/>", "<graphic xlink:href=\"1471-2407-8-248-3\"/>", "<graphic xlink:href=\"1471-2407-8-248-4\"/>" ]
[]
[{"surname": ["Trakht"], "given-names": ["I"], "source": ["Development of human monoclonal antibodies and uses thereof."], "year": ["2001"], "volume": ["040833"], "publisher-name": [" The Trustees of Columbia University in the City of New York (New York,NY)"]}, {"surname": ["Chen"], "given-names": ["YT"], "article-title": ["Cancer vaccine: identification of human tumor antigens by SEREX"], "source": ["Cancer JSciAm"], "year": ["2000"], "volume": ["6 Suppl 3"], "fpage": ["S208"], "lpage": ["S217"]}, {"surname": ["Pfreundschuh"], "given-names": ["M"], "article-title": ["Exploitation of the B cell repertoire for the identification of human tumor antigens"], "source": ["Cancer ChemotherPharmacol"], "year": ["2000"], "volume": ["46 Suppl"], "fpage": ["S3"], "lpage": ["S7"]}, {"surname": ["Scatchard"], "given-names": ["G"], "article-title": ["The attraction of proteins for small molecule ions."], "source": ["AnnNYAcadSci"], "year": ["1949"], "volume": ["51"], "fpage": ["660"], "lpage": ["672"]}, {"surname": ["De Vries", "Lou", "Zhao", "Zheng", "Farquhar"], "given-names": ["L", "X", "G", "B", "MG"], "article-title": ["GIPC, a PDZ domain containing protein, interacts specifically with the C terminus of RGS-GAIP"], "source": ["ProcNatlAcadSciUSA"], "year": ["1998"], "volume": ["95"], "fpage": ["12340"], "lpage": ["12345"]}, {"surname": ["Lou", "Yano", "Lee", "Chao", "Farquhar"], "given-names": ["X", "H", "F", "MV", "MG"], "article-title": ["GIPC and GAIP form a complex with TrkA: a putative link between G protein and receptor tyrosine kinase pathways"], "source": ["MolBiolCell"], "year": ["2001"], "volume": ["12"], "fpage": ["615"], "lpage": ["627"]}, {"surname": ["Vries"], "given-names": ["D"], "article-title": ["GAIP is membrane-anchored by palmitoylation and interacts with the activated (GTP-bound) form of G alpha i subunits."], "source": ["Proc Natl Acad Sci U S A"], "year": ["1996"], "volume": ["93"], "fpage": ["pp. 15203"], "lpage": ["15208"]}]
{ "acronym": [], "definition": [] }
60
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Aug 24; 8:248
oa_package/cc/c0/PMC2529336.tar.gz
PMC2529337
18667090
[ "<title>Background</title>", "<p>Lung cancer is more often diagnosed and is by far the most common cause of death from cancer in both genders worldwide [##REF##15761078##1##,##REF##16764752##2##]. Almost 80% of lung cancers can be classified as Non Small Cell Lung Cancer (NSCLC), with 65% to 75% of cases presenting as locally advanced (Stage III) or metastatic disease (Stage IV) [##REF##16368866##3##,##REF##7528933##4##].</p>", "<p>Significant improvements in median survival in advanced NSCLC patients have been achieved with the use of platinum-based chemotherapy [##REF##17035458##5##], particularly in patients with good performance status, and with newer cytotoxic agents, such as gemcitabine, paclitaxel, docetaxel or vinorelbine [##REF##15813659##6##,##REF##11441939##7##]. It is actually believed that the next significant advance in the treatment of NSCLC might derive from the use of targeted agents, as monotherapy or in combination with standard chemotherapy regimens, without increasing toxicity [##REF##15813659##6##].</p>", "<p>Pemetrexed is a new multi-target antifolate agent approved for the treatment of malignant pleural mesothelioma and NSCLC. Pemetrexed exerts its cytotoxic effect through inhibition of Thymidylate Synthase, Dihydrofolate Reductase and Glycinamide Ribonucleotide Formyl Transferase [##REF##9067281##8##], which are involved in DNA synthesis and folate metabolism [##UREF##0##9##]. The multiple inhibitions of several key folate-requiring enzymes may account both for the antitumor activity and the potential cytotoxic effect of pemetrexed. It has been found that the hematological and non-hematological toxicities of pemetrexed can be reduced through routine vitamin supplementation (folic acid and vitamin B12), without loss of efficacy [##REF##16556051##10##].</p>", "<p>Several reports have documented the effects of pemetrexed given as a single agent and in combination in first- or second-line chemotherapy in advanced NSCLC [##REF##15655932##11##]. In phase II trials, pemetrexed has shown high efficacy and favorable toxicity when given in combination with platinum agents, gemcitabine and vinorelbine [##REF##15818537##12##,##REF##15339059##13##]. A recent phase III trial that compared pemetrexed with docetaxel in previously treated NSCLC patients showed equivalent efficacy in response rate and survival, and significantly less toxicity in the pemetrexed group when compared to docetaxel [##REF##15117980##14##].</p>", "<p>According to Italian legislation, which establishes a drug dispensing as 'therapeutic use' prior to approval for use in local market, this study was aimed at extending the clinical experience with pemetrexed in pretreated patients with locally advanced or metastatic NSCLC.</p>" ]
[ "<title>Methods</title>", "<title>Patients</title>", "<p>Adult patients of both genders with locally advanced or metastatic NSCLC (Stage IIIB or IV at entry), previously treated with no more than two chemotherapy regimens for advanced disease, were eligible for the study. Prior chemotherapy and/or radiotherapy (excluding pemetrexed) were to be completed at least 2 weeks prior to study enrollment and the patients should have recovered from any acute toxic effect of previous therapy. Prior radiation therapy allowed to &lt; 25% of the bone marrow. Moreover, eligible patients were required to have a ECOG Performance Status 0 to 2, an estimated life expectation of at least 8 weeks, and an adequate bone marrow reserve. Patients with evidence of hepatic or renal insufficiency, active infection, inability to take folic acid, vitamin B12 supplementation or corticosteroids, signs of malnourishment or &gt; 10% weight loss in the past 6 weeks, or others serious concomitant disorders (including oncologic emergencies) were excluded from the study. Pregnant or breastfeeding females were also not allowed to taking part in the study, as well as an adequate contraceptive method was to be used for the whole study duration. Patients were to be discontinued from the study in the case of evidence of progressive disease or unacceptable toxicity despite dose adjustment.</p>", "<p>The participant patients gave their written informed consent prior to enter in the study. The study protocol and the informed consent form were reviewed and approved by the Independent Ethics Committees of each participating center prior to any study-related procedure was started.</p>", "<title>Treatments</title>", "<p>Pemetrexed 500 mg/m<sup>2 </sup>(Alimta<sup>®</sup>, Eli Lilly and Company, Indianapolis, IN) was administered i.v. over approximately 10 minutes on Day 1 of a 21-day cycle. Dexamethasone 4 mg or equivalent corticosteroid was taken orally twice daily on the day before, the day of, and the day after each dose of pemetrexed. Folic acid supplementation 350 to 600 μg or equivalent was taken orally daily beginning approximately 1 to 2 weeks prior to the first dose of pemetrexed and continued until 3 weeks after study therapy discontinuation. Patients also received a 1000 μg vitamin B12 i.m. injection approximately 1 to 2 weeks prior to the first dose of pemetrexed, to be repeated approximately every 9 weeks until 3 weeks after study therapy discontinuation.</p>", "<p>Any patient who required a pemetrexed dose reduction due to hematological or non-hematological toxicities was treated further according to dose reductions. Any patient requiring &gt; 2 reductions due to toxicity was to be withdrawn from study therapy. Treatment could have been delayed for up to 42 days from Day 1 of any cycle to allow recovering from study drug-related toxicities.</p>", "<p>No other chemotherapy, immunotherapy, hormonal cancer therapy, radiation therapy, surgery for cancer, or any other experimental medications was permitted during the study. Disease progression requiring alternative antitumor treatment led to early discontinuation of study therapy. If patient required radiotherapy treatment (both palliative or not) during the study, pemetrexed was discontinued until 2 weeks after the completion of radiation treatment.</p>", "<p>The use of growth factors was not allowed by study protocol.</p>", "<title>Outcome measures</title>", "<p>The analysis of safety was the primary endpoint of the study. The safety measures used in the study included adverse events, physical examinations and clinical laboratory tests (hematology, blood chemistry and urinary creatinine clearance). All the adverse events were evaluated in terms of severity and relation to study treatment, while toxicities according to the National Cancer Institute Common Toxicity Criteria (NCI CTC) version 2.0 [##UREF##1##15##].</p>", "<p>The evaluation of the best tumor response rate was performed at the end of the treatment period and the Response Evaluation Criteria in Solid Tumors (RECIST) were recommended [##REF##10655437##16##]. The progression free survival (PFS) was the time from study entry to disease progression or death, while the overall survival time was defined as the time from study entry to death due to any cause. Investigators followed-up the survival status of patients who had discontinued study therapy.</p>", "<p>Adverse events were considered those emerging during treatment or present at baseline and worsening during the study.</p>", "<title>Statistics</title>", "<p>The statistical analysis was performed using the SAS version 8.2 (Cary, NC, US). The analyses were mainly descriptive: summary statistics were given for patient characteristics, treatment administration and all safety variables (laboratory tests and adverse events). Adverse events were coded using the MedDRA dictionary. Tumor Response Rate (complete response/partial response [CR/PR]) was calculated considering all patients who received at least one dose of study drug. PFS and overall survival time were analyzed by means of Kaplan-Meier method.</p>" ]
[ "<title>Results</title>", "<p>A total of 102 patients were enrolled in 35 Italian centers from December 2004 to May 2005. The demographic and baseline clinical condition of treated patients are summarized in Table ##TAB##0##1##. Most of the patients were in good Performance Status: 87 patients (93.6% of valuable patients) were ECOG PS 0 to 1. Adenocarcinoma/Neoplasia NOS was the most frequent histological type, representing approximately half of cases.</p>", "<p>Ninety-five of them (93.1% of enrolled) received at least one dose of study drug. Seven patients were included but did not receive study drug (2 because of physician decision, 2 patient decision, 2 deaths, 1 entry criteria violation).</p>", "<p>The median received cycles was 4.0 (range 1–15), while the median number of weeks of treatment was 12.1 (range 1.4–57.3). Fifty patients (52.6%) had dose modification at least in one cycle: pemetrexed dose was reduced due to adverse events in 12 patients and was delayed (mostly due to adverse events or conflict in scheduling) in 48 patients. The median relative dose intensity was 97.8% (range 63.1–104.0). Deviations from the scheduled dosing of dexamethasone, folic acid and vitamin B12 were reported in 3, 7 and 8 patients, respectively.</p>", "<p>The main reasons for treatment discontinuation were lack of efficacy (46 patients, 48.4%), physician decision (13, 13.7%), objective responses (13, 13.7%) and patient decision (8, 8.4%). Fifteen patients had protocol violation and the most common was the incorrect dose reduction due to toxicity (7 patients).</p>", "<title>Safety</title>", "<p>Seventy-five patients (78.9% of treated) reported at least one adverse event during the study, 34 patients (35.8%) and 5 patients (5.2%) experienced grade 3 and grade 4 adverse events, respectively. Fifty-five patients (57.9%) had adverse events considered by physicians as possibly related to study treatment.</p>", "<p>Table ##TAB##1##2## shows adverse events reported in ≥ 5% of patients by preferred term and study drug relationship. The most common adverse events were pyrexia (reported in 26.3% of treated patients and judged as drug-related in 11.6%), asthenia (overall 13.7% of patients, drug-related in 9.5%) and dyspnea (overall 11.6% of patients, drug-related in only one case). General disorders and administration site conditions (26.3%), gastrointestinal disorders (23.2%) and blood and lymphatic system disorders (22.1%) were the system organ classes with the highest incidence of adverse events related to pemetrexed.</p>", "<p>The highest incidences of CTC grade 3/4 adverse events were reported as blood and lymphatic system disorders (17.9%), gastrointestinal disorders (9.5%) and general disorders and administration site conditions (9.5%). Grade 3 adverse events reported in &gt; 1 patient included anemia (3 patients), leukopenia (6), neutropenia (6), thrombocytopenia (2), diarrhea (6), nausea (2), vomiting (2), fatigue (3), mucosal inflammation (2), thrombocytopenia (2), and dyspnea (3 patients). Grade 4 adverse events included neutropenia (2 patients), and acute myocardial infarction, myocardial ischemia and melaena (all occurred in the same patient).</p>", "<p>A total of 20 patients (21.1% of treated population) had at least one event fulfilling the criteria for a serious adverse event; 5 of them were considered drug-related (neutropenia in 2 patients, diarrhea, pyrexia, melaena, anemia and vomiting in 1). Overall, 19 patients (20.0%) died due to disease progression: 5 patients (5.3%) died while on treatment or within 30 days of treatment discontinuation, 14 died after 30 days from treatment discontinuation. One patient died due to cardiac failure.</p>", "<p>Hematological assessments were performed on 90 out the 95 treated patients. Table ##TAB##2##3## shows the out of range hematological values observed during treatment (NCIC-CTC grading). NCIC-CTC grade 3 hematological toxicities were the following: anemia 2.2% of patients, leucopenia 17.8%, neutropenia 18.9%, and thrombocytopenia 4.4%.</p>", "<p>No clinically relevant changes in vital signs were reported during the study.</p>", "<title>Efficacy</title>", "<p>Table ##TAB##3##4## shows the results of the overall tumor best response in the treated patients population with measurable disease at baseline (N = 87): 8 patients (9.2%; 95%: 4.1 to 17.3) were responders (1 CR and 7 PR), 23 patients (26.4%) were stable on their disease, 49 patients (56.3%) had disease progression as best response and 7 patients (8.0%) were not evaluable for response.</p>", "<p>The Kaplan-Meier survival analysis at 4.5 months (median follow-up) was 79% (95% CI: 71 to 88%). The median PFS was 3.1 months (95% C.I. 2.4 to 3.8).</p>" ]
[ "<title>Discussion</title>", "<p>Previous phase 2 studies have indicated that pemetrexed (Alimta<sup>®</sup>) has clinical activity in NSCLC. A comparative trial of Pemetrexed and docetaxel (Eli Lilly Protocol H3E-MC-JMEI), compared 571 patients with locally advanced or metastatic NSCLC who had previously been treated with chemotherapy.</p>", "<p>The primary objective of this study was to confirm the safety profile of pemetrexed (500 mg/m<sup>2 </sup>dose, day 1 of a 21-day cycle) as second line treatment in patients with locally advanced or metastatic (Stage IIIB or IV) NSCLC. Pemetrexed was supplemented with dexamethasone, folic acid and vitamin B12 was given every 21 days. This regimen is recommended based on previous experiences [##REF##12598353##17##,##REF##15117980##14##], which showed a significant improved tolerance when pemetrexed is given with corticosteroids and vitamins supplementation.</p>", "<p>The secondary objective of the study was to assess the response rate in patients with measurable disease according to the RECIST criteria.</p>", "<p>In this study 95 patients were examined. The majority of patients (&gt;90%) had good clinical conditions (ECOG PS 0 or 1). The median number of cycles received was 4 and the median number of weeks of treatment was 12.1. Pemetrexed was well tolerated. The safety profile of pemetrexed did not differ from what observed in previous phase I/II studies and in the large phase III study comparing pemetrexed and docetaxel as second-line treatment in locally advanced or metastatic NSCLC [##REF##15117980##14##]. In the latter trial, which led to the regulatory approval of pemetrexed as monotherapy for the second-line treatment of NSCLC, the incidence of hematological toxicities (e.g. grade 3/4 neutropenia, febrile neutropenia, and neutropenia with infections) and other drug-related adverse events was significantly lower with pemetrexed than with docetaxel. The results of the present study confirm the favorable toxicity profile of pemetrexed when given over 500 mg/m<sup>2 </sup>and supplemented by vitamin B12 and folic acid.</p>", "<p>Vitamin supplementation significantly reduces the incidence of grade 3–4 hematological toxicity, as shown in a previous trial comparing pemetrexed administered with or without vitamins [##REF##12697881##18##].</p>", "<p>The most frequent hematological toxicities were neutropenia and anemia (any grade) and the most frequent non-hematological toxicities were pyrexia, fatigue and dyspnea (any grade).</p>", "<p>In the population of patients with measurable disease at baseline the observed response rate was 9.2% and it was similar to the Response Rate reported in the randomized phase III study (8.8%) when pemetrexed was compared to docetaxel [##REF##15117980##14##].</p>", "<p>It is generally agreed that response rate cannot be taken as indicator of clinical benefit in pretreated patients with locally advanced or metastatic NSCLC and the relationship between response rate and improved survival is unclear, so that response rate cannot be considered as a surrogate endpoint. However, a prolonged survival in pretreated advanced NSCLC patients has been observed, in spite of a response rate lower than 10%. This therefore suggests a possible contribution from cytotoxic agents to disease stabilization and to the clinical benefit observed [##REF##16794244##19##].</p>", "<p>In our study, the survival at approximately 4 months (median follow-up time) was 79% and the median progression-free survival was 3.1 months, what is in line with the reported survival rate in the reported phase III trial by Hanna et al. in the comparative study vs Docetaxel [##REF##15117980##14##].</p>", "<p>It is well known that, especially in 2<sup>nd </sup>line, tolerability and toxicity profile of a cytotoxic combination might influence the choice of treatment, even when the efficacy parameters of possible therapies (e.g. survival, progression free survival and response rate) are similar. The duration of infusion, schedule administration and patients acceptance should be also taken into consideration for the choice of a regimen. When compared to the other agents currently approved for 2<sup>nd </sup>line treatment in NSCLC, the 10-minutes infusion time of pemetrexed over Day 1 of a 21-day cycle might increase the convenience of the treatment and patient compliance.</p>", "<p>Therefore, the study confirmed that pretreated patients with locally advanced or metastatic NSCLC will likely benefit from single-agent pemetrexed treatment (with vitamin supplementation), with an additional advantage in decreasing hematological (including febrile neutropenia) and non-hematological toxicities.</p>", "<p>Since, due to a minor flaw in the original study design, there are no available data on whether patients were treated with pemetrexed in 2<sup>nd </sup>or 3<sup>rd </sup>line, it is not possible to assess any correlation between the number of previous lines of treatment and response to pemetrexed.</p>", "<p>It has been recently pointed out that the administration of Pemetrexed in combination with other agents (eg. Cisplatin, Carboplatin or gemcitabine) in the treatment of advanced NSCLC may provide further clinical benefits caused by its particular mode of action when blocking intracellular three enzymes system. A deeper knowledge about those enzyme system (eg: TS) may be used in future to identify patients responders to pemetrexed [##REF##16807472##20##]. The use of targeted compounds to specific molecular pathways, given in addition to standard chemotherapy regimens, might represent the next step in the treatment of NSCLC and overall characteristics of pemetrexed makes it a candidate in a tailored therapies context.</p>", "<p>The present study contributes to provide even more information on clinical experience with pemetrexed and further prospective randomized clinical trials will confirm pemetrexed (single agent or in combination) as a valid option for pretreated locally advanced or metastatic NSCLC patients.</p>" ]
[]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The main objective of this study was to evaluate the safety of second-line pemetrexed in Stage IIIB or IV NSCLC.</p>", "<title>Methods</title>", "<p>Overall, 95 patients received pemetrexed 500 mg/m<sup>2 </sup>i.v. over Day 1 of a 21-day cycle. Patients also received oral dexamethasone, oral folic acid and i.m. vitamin B12 supplementation to reduce toxicity. NCI CTC 2.0 was used to rate toxicity. All the adverse events were graded in terms of severity and relation to study treatment. Dose was reduced in case of toxicity and treatment was delayed for up to 42 days from Day 1 of any cycle to allow recovering from study drug-related toxicities. Tumor response was measured using the RECIST criteria.</p>", "<title>Results</title>", "<p>Patients received a median number of 4 cycles and 97.8% of the planned dose. Overall, 75 patients (78.9% of treated) reported at least one adverse event: 34 (35.8%) had grade 3 as worst grade and only 5 (5.2%) had grade 4. Drug-related events occurred in 57.9% of patients. Neutropenia (8.4%) and leukopenia (6.3 %) were the most common grade 3/4 hematological toxicities. Grade 3 anemia and thrombocytopenia were reported in 3.2% and 2.1% of patients, respectively. Diarrhea (6.3%), fatigue (3.2%) and dyspnea (3.2%) were the most common grade 3/4 non-hematological toxicities. The most common drug-related toxicities (any grade) were pyrexia (11.6%), vomiting, nausea, diarrhea and asthenia (9.5%) and fatigue (8.4%). Tumor Response Rate (CR/PR) in treated patients was 9.2%. The survival at 4.5 months (median follow-up) was 79% and the median PFS was 3.1 months. Twenty patients (21.1%) died mainly because of disease progression.</p>", "<title>Conclusion</title>", "<p>Patients with locally advanced or metastatic NSCLC could benefit from second-line pemetrexed, with a low incidence of hematological and non-hematological toxicities.</p>" ]
[ "<title>Competing interests</title>", "<p>The study was fully sponsored by Eli Lilly Italia. Francesca Russo and Gianni Pampaloni are employed at Eli Lilly Italia.</p>", "<title>Authors' contributions</title>", "<p>All authors have given substantial contributions to conception and design the study. FR and GP have given substantial contributions to analysis and interpretation of data, and in the revision of the manuscript. AB has given relevant contributions in the recruitment of patients, in the critical revision of the manuscript, and in final approval prior to publication. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/216/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors thank Luca Cantini for his contribution in the medical writing of this manuscript and Sara Ballasio and Farma Resa for their technical-administrative support. A special thank to Marco Pacini (Eli Lilly Italia), Giulia Calamai (Eli Lilly Italia) and Andrea Rossi (Eli Lilly Italia – member of European Medical Writers Association), for their supervision of the project and Marta Zanus (CROS Italia) for her contribution in performing statistical analysis.</p>", "<p>The investigators of the Italian Pemetrexed monotheerapy of NSCLC group are:</p>", "<p>Umberto Tirelli: Dipartimento oncologia Medica, C.R.O. (Centro di Riferimento Oncologico) Istituto Nazionale Tumori Aviano (PN); Roberto Bordonaro: Divisione di Oncologia Medica – Day Hospital, Azienda Ospedaliera Vittorio Emanuele II Catania; Claudio Verusio: Oncologia Medica, Ospedale di Circolo di Busto Arsizio – P.O. di Saronno (VA); Alberto Rosa Bian: Unità Operativa di Oncologia Medica, Ospedale Boldrini Thiene (VI); Maurizio Marangolo: Divisione di Oncologia Medica, Ospedale \"S. Maria delle Croci\" Ravenna; Angelo Raffaele Bianco: Dipartimento di Endocrinologia ed Oncologia Molecolare e Clinica,\"Università Studi di Napoli Federico II – II Facoltà di Medicina e Chirurgia Policlinico Federico II\" Napoli; Francesco Grossi: Oncologia Medica A – 5° piano, Istituto Nazionale Tumori Genova; Angelo Gambi: Centro Oncologico, Ospedale degli Infermi Faenza (FO); Guido Francini: U.O. Oncologia Medica, Pol. Le Scotte, Università degli Studi di Siena; Dr. Giorgio Sogno: Osp. San. Paolo di Savona; Paolo Manente: U.O. di Oncologia Medica, Unità Socio Sanitaria 8 Stabilimento Ospedaliero Castelfranco Veneto (TV); Nicola Gebbia: Servizio di Chemioterapia Antiblastica, Policlinico Universitario Paolo Giaccone Palermo; Fabrizio Artioli: Unità Operativa di Medicina Oncologica, Ospedale Civile S. Giacomo Carpi (MO); Andrea Ardizzoni Oncologia Medica, Az. Ospedaliera di Parma; Alessandro Masotti: Pneumologia, Ospedale Maggiore B. Trento di Verona; Ernesto Pozzi: Clinica di Malattie Apparato respiratorio, IRCCS Policlinico San Matteo Pavia; Salvatore Tumolo: U.O. Oncologia Medica, Osp. Santa Maria degli Angeli Pordenone; Rodolfo Passalacqua: Oncologia Medica, Azienda Ospedaliera Istituti Ospetalieri di Cremona; Clelia Casartelli: Struttura Semplice di Oncoematologia U.O. di Medicina, Ospedale \"Valduce\" Como; Franco Montanari: Azienda Ospedaliera di Vimercate Presidio Ospedaliero di Desio (MI); Giuseppe Colucci: Divisione di Oncologia Medica e Sperimentale, IRCCS Ospedale Oncologico Bari; Santi Barbera: U.O. di Pneumologia Oncologica, Presidio Ospedaliero Mariano Santo – Azienda Ospedaliera di Cosenza Cosenza; Sergio Ricci: U.O. Oncologia Medica, Ospedale Santa Chiara Pisa; Lucio Trodella: Radioterapia, Università Cattolica Sacro Cuore, Policlinico \"A. Gemelli\" Roma; Vincenzo Valentini: Radioterapia, Università Cattolica Sacro Cuore, Policlinico \"A. Gemelli\" Roma; Giovanni Mantovani: Oncologia Medica 1, Policlinico Universitario Monserrato, Monserrato (CA); Alberto Ravaioli: Oncologia Medica, Ospedale Civile degli Infermi Rimini; Silvio Monfardini: Oncologia Medica, Azienda Ospedaliera di Padova; Sandro Barni: Oncologia Medica, Ospedali Riuniti di Treviglio e Caravaggio Treviglio (BG); Raffaella Felletti: Divisione di pneumologia – Dipartimento di Medicina Specialistica, Ospedale San Martino – Padiglione Maragliano Genova; Franco Testore: \"U.O.A. Oncologia\", Ospedale Civile di Asti; Massimo Aglietta – Divisione di Oncologia Medica ed Ematologia, IRCC- Isutituto per la Ricerca e la Cura del Cancro Candiolo (TO); Antonio Ardizzoia: Ospedale S.Gerardo Monza (MI); Roberto Bollina: U.O.Oncologia MedicaF.B.F, Ospedale San Giuseppe Milano; Giorgio Cruciani: Servizio di Oncologia, Ospedale Umbero I di Lugo (Az. USL di Ravenna) Lugo di Romagna (Ra); Enzo Pasquini: Reparto di Oncologia Medica, Ospedale Cervesi Cattolica (RN).</p>" ]
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[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Demographic and baseline clinical condition of the treated patients (n = 95)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Age, <italic>years</italic>: mean ± SD (range)</td><td align=\"center\">62.4 ± 10.6 (25–82)</td></tr><tr><td align=\"left\"> Age ranges, N (%):</td><td/></tr><tr><td align=\"left\"> ≤ 50</td><td align=\"center\">11 (11.6)</td></tr><tr><td align=\"left\"> 51–60</td><td align=\"center\">21 (22.1)</td></tr><tr><td align=\"left\"> 61–70</td><td align=\"center\">44 (46.3)</td></tr><tr><td align=\"left\"> &gt; 70</td><td align=\"center\">19 (20.0)</td></tr></thead><tbody><tr><td align=\"left\">Sex: N (%)</td><td/></tr><tr><td align=\"left\"> Males</td><td align=\"center\">72 (75.8)</td></tr><tr><td align=\"left\"> Females</td><td align=\"center\">23 (24.2)</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">Weight, <italic>kg</italic>: mean ± SD (range)</td><td align=\"center\">72.2 ± 13.6 (41–110)</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">NSCLC, <italic>histological type</italic>: N (%)</td><td/></tr><tr><td align=\"left\"> Neoplasia NOS, adenocarcinoma</td><td align=\"center\">46 (48.4)</td></tr><tr><td align=\"left\"> Squamous cells</td><td align=\"center\">26 (27.4)</td></tr><tr><td align=\"left\"> Large cells</td><td align=\"center\">2 (2.1)</td></tr><tr><td align=\"left\"> Other</td><td align=\"center\">21 (22.1)</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">ECOG, <italic>score</italic>: N (%)</td><td/></tr><tr><td align=\"left\"> 0</td><td align=\"center\">58 (62.4)</td></tr><tr><td align=\"left\"> 1</td><td align=\"center\">29 (31.2)</td></tr><tr><td align=\"left\"> 2</td><td align=\"center\">5 (5.4)</td></tr><tr><td align=\"left\"> 3</td><td align=\"center\">1 (1.1)</td></tr><tr><td align=\"left\"> Not available</td><td align=\"center\">2</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Treatment-emergent adverse events reported by ≥ 5% of treated patients by preferred term and study drug relationship: data are number of patients with rates in brackets (N = 95)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">All Causalities</td><td align=\"center\">Treatment Related</td></tr></thead><tbody><tr><td align=\"left\">Patients with ≥ 1 adverse event</td><td align=\"center\">75 (78.9)</td><td align=\"center\">55 (57.9)</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">Pyrexia</td><td align=\"center\">25 (26.3)</td><td align=\"center\">11 (11.6)</td></tr><tr><td align=\"left\">Asthenia</td><td align=\"center\">13 (13.7)</td><td align=\"center\">9 (9.5)</td></tr><tr><td align=\"left\">Dyspnea</td><td align=\"center\">11 (11.6)</td><td align=\"center\">1 (1.1)</td></tr><tr><td align=\"left\">Neutropenia</td><td align=\"center\">10 (10.5)</td><td align=\"center\">10 (10.5)</td></tr><tr><td align=\"left\">Vomiting</td><td align=\"center\">10 (10.5)</td><td align=\"center\">9 (9.5)</td></tr><tr><td align=\"left\">Diarrhea</td><td align=\"center\">10 (10.5)</td><td align=\"center\">9 (9.5)</td></tr><tr><td align=\"left\">Anemia</td><td align=\"center\">10 (10.5)</td><td align=\"center\">8 (8.4)</td></tr><tr><td align=\"left\">Nausea</td><td align=\"center\">9 (9.5)</td><td align=\"center\">9 (9.5)</td></tr><tr><td align=\"left\">Fatigue</td><td align=\"center\">9 (9.5)</td><td align=\"center\">8 (8.4)</td></tr><tr><td align=\"left\">Cough</td><td align=\"center\">8 (8.4)</td><td align=\"center\">3 (3.2)</td></tr><tr><td align=\"left\">Anorexia</td><td align=\"center\">6 (6.3)</td><td align=\"center\">1 (1.1)</td></tr><tr><td align=\"left\">Leucopenia</td><td align=\"center\">5 (5.3)</td><td align=\"center\">5 (5.3)</td></tr><tr><td align=\"left\">Mucosal inflammation</td><td align=\"center\">5 (5.3)</td><td align=\"center\">5 (5.3)</td></tr><tr><td align=\"left\">Thrombocytopenia</td><td align=\"center\">5 (5.3)</td><td align=\"center\">5 (5.3)</td></tr><tr><td align=\"left\">Rash</td><td align=\"center\">5 (5.3)</td><td align=\"center\">4 (4.2)</td></tr><tr><td align=\"left\">Chest pain</td><td align=\"center\">5 (5.3)</td><td align=\"center\">1 (1.1)</td></tr><tr><td align=\"left\">Peripheral Edema</td><td align=\"center\">5 (5.3)</td><td align=\"center\">1 (1.1)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Hematology abnormalities observed during treatment (worst NCIC-CTC grading): data are number of patients with rates in brackets (N = 90)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Laboratory Parameter</td><td align=\"center\">Any grade ≥ 1</td><td align=\"center\">Grades 3–4</td></tr></thead><tbody><tr><td align=\"left\">Hemoglobin</td><td align=\"center\">63 (70.0)</td><td align=\"center\">2 (2.2)</td></tr><tr><td align=\"left\">Neutrophils</td><td align=\"center\">50 (55.6)</td><td align=\"center\">17 (18.9)</td></tr><tr><td align=\"left\">Platelets</td><td align=\"center\">37 (41.1)</td><td align=\"center\">4 (4.4)</td></tr><tr><td align=\"left\">WBCs</td><td align=\"center\">57 (63.3)</td><td align=\"center\">16 (17.8)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Results of the overall tumor response in the treated population: data are number of patients with rates in brackets</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Best Overall Tumor Response</td><td align=\"center\">Treated Population <break/>(N = 87)*</td></tr></thead><tbody><tr><td align=\"left\">Complete Response (CR)</td><td align=\"center\">1 (1.1)</td></tr><tr><td align=\"left\">Partial Response PR)</td><td align=\"center\">7 (8.0)</td></tr><tr><td align=\"left\">Response Rate (CR + PR)</td><td align=\"center\">8 (9.2)</td></tr><tr><td align=\"left\">Stable Disease/No Response (SD)</td><td align=\"center\">23 (26.4)</td></tr><tr><td align=\"left\">Progressive Disease (PD)</td><td align=\"center\">49 (56.3)</td></tr><tr><td align=\"left\">Not Evaluable</td><td align=\"center\">7 (8.0)</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>N = number of patients, % refers to total of treated patients with available data</p></table-wrap-foot>", "<table-wrap-foot><p>*Numbers and rates refer to the amount of patients assessed for tumor response (response was not available in 8 patients in the treated population)</p></table-wrap-foot>" ]
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[{"surname": ["Schilsky", "Perry MC"], "given-names": ["RL"], "article-title": ["Antimetabolites"], "source": ["The Chemotherapy Source Book"], "year": ["1992"], "publisher-name": ["Baltimore (MD): Williams & Wilkins"], "fpage": ["301"], "lpage": ["315"]}, {"collab": ["Cancer Therapy Evaluation Program"], "source": ["Common Toxicity Criteria, Version 20 DCTD, NCI, NIH, DHHS"], "year": ["1998"]}]
{ "acronym": [], "definition": [] }
20
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Jul 31; 8:216
oa_package/19/52/PMC2529337.tar.gz
PMC2529338
18691423
[ "<title>Background</title>", "<p>While investigation of the molecular basis of tumor metastasis has in large part focused on proliferation and dissemination of tumor cells from the primary tumor, later events that occur at sites of metastasis are most often responsible for patient mortality and morbidity. From a clinical standpoint, an understanding of the disease at metastatic sites is paramount since the number of breast cancer patients with detectable or occult metastases at the time of diagnosis is substantial and most patients will develop metastatic lesions at some point during the course of the disease. Metastasis is generally treated as a systemic disease with chemotherapy and/or radiation even though factors involved in establishment and growth of metastatic lesions differ from one site to the next and may differ in response to therapeutics. While currently used therapeutic regimens are capable of slowing the progression of metastatic disease, rarely is it possible to stop or reverse the process. Treatments that address the nature of metastatic disease at the site of metastasis could provide more effective therapeutic results for patients afflicted with the later stages of the disease.</p>", "<p>A major impediment for the study of metastasis has been the availability of suitable models that faithfully represent the metastatic process as it occurs <italic>in vivo</italic>. Xenograft models in which human tumor cells are introduced into immunocompromised mice have been used extensively for the study of tumor growth and metastasis and to validate specific gene products as drug targets for cancer therapy. While some human xenograft models can approximate primary tumor growth in mice, replication of tumor metastasis is more problematic [##REF##15120041##1##, ####REF##7736534##2##, ##REF##10759402##3####10759402##3##]. Human tumor cells generally metastasize poorly in mice and when metastasis does occur, unexpected metastatic characteristics are often observed. In contrast, murine tumor cell models often metastasize more effectively and display metastatic characteristics more similar to those observed in cancer patients [##REF##17657606##4##]. Given the importance of microenvironment and tumor-host interactions in tumor cell behavior, this is not surprising. Syngeneic mouse models such as the 4T1 model described here also have the important advantage of allowing analyses to be carried out in animals with normal immune function. Because the immune system plays an important role in the development and progression of cancer, models that can be used in immunocompetent mice are essential for analysis of cancer progression and evaluation of therapeutics for cancer treatment.</p>", "<p>The 4T1 mammary carcinoma cell line was originally isolated by Fred Miller and coworkers at the Karmanos Cancer Institute [##REF##6677618##5##,##REF##6677628##6##]. Its use has increased in recent years because of its high propensity to metastasize to bone and other sites [##REF##10898341##7##,##REF##10411109##8##]. When introduced orthotopically, 4T1 is capable of metastasis to several organs affected in breast cancer including lungs, liver and brain, as well as bone [##REF##10898341##7##,##REF##1540948##9##, ####REF##9537252##10##, ##REF##15671244##11####15671244##11##]. 4T1 sibling cell lines with different metastatic properties have been isolated and characterized. These lines were isolated from the same spontaneous arising BALB/c mammary tumor [##REF##6677618##5##,##REF##6677628##6##] but appear to have followed divergent pathways for acquisition of their metastatic phenotypes [##REF##11713608##12##].</p>", "<p>We have modified the 4T1 cell line for optimal use as a model for the study of late stage breast cancer. A modified line (4T1-12B) expressing high levels of firefly luciferase to allow non-invasive longitudinal imaging of <italic>in vivo </italic>growth and metastasis was isolated. A similar line (4T1-1V) was further modified by insertion of a FLP recombinase target (FRT) site into the 4T1 genome. The FRT site facilitates rapid generation of genetically modified isogenic cell lines for investigation of effector gene function. The extent and kinetics of metastasis to organs affected in human breast cancer indicated extensive colonization of lungs and liver in most animals within a six week period with lower efficiency of metastasis to bone, brain and other sites. Innate and adaptive immune responses were shown to play important roles in growth and metastasis of the lines in BALB/c mice. Analysis of gene expression comparing 4T1 and two of its non-metastatic sibling cell lines suggested prominent roles for several signaling pathways and secreted factors in directing microenvironmental changes within the tumor leading to tumor cell dissemination and metastasis.</p>" ]
[ "<title>Methods</title>", "<title>Materials</title>", "<p>The luciferase-containing pGL3-Control vector was obtained from Promega. The pKO-puro vector was from Stratagene. The pSHAG-1 vector was provided by Dr. G. Hannon at Cold Spring Harbor Laboratory. Other vectors including pcDNA5/FRT, pOG44, pFRT/lacZeo, and the Gateway Vector Conversion System Reading Frame Cassette C.1 were obtained from Invitrogen. Dulbecco's modified Eagle's medium (DMEM), Dulbecco's phosphate-buffered saline without calcium and magnesium (PBS), fetal bovine serum (FBS), newborn calf serum (NCS), non-essential amino acids (NEAA), penicillin, streptomycin and lipofectamine PLUS reagent were from Invitrogen. Puromycin and hygromycin were from Sigma. Luciferin was obtained from Caliper Life Sciences.</p>", "<title>Cell culture</title>", "<p>The 67NR, 168FARN and 4T1 mouse mammary tumor cell lines were obtained from Dr. Fred Miller at Karmanos Cancer Institute. Cells were cultured in high glucose DMEM supplemented 5% FBS, 5% NCS, NEAA and antibiotics (100 units/ml penicillin and 100 μg/ml streptomycin) at 37°C in a humidified atmosphere containing 5% CO<sub>2</sub>. Except where indicated, analyses were performed on same passage cells within 2 weeks after thawing. All cell lines used in the study were tested and shown to be free of mycoplasma and viral contamination.</p>", "<title>Expression of luciferase and puromycin resistance in 4T1 cell lines</title>", "<p>4T1 cells were cotransfected with firefly luciferase-containing pGL-3-Control vector and the puromycin resistance vector, pKO-puro, at a ratio of 10:1 using Lipofectamine PLUS as described by the vendor [Invitrogen]. Transfected cells were selected with puromycin at a final concentration of 10 μg/ml and several colonies were picked and expanded for analysis. Colonies displaying the highest level of luciferase expression were injected into mammary fat pads of female BALB/c mice and imaged 6 weeks later before and after sacrifice and necropsy, as described below. One cell line, designated 4T1-12B, which retained high level expression of luciferase in the absence of puromycin and displayed metastatic properties similar to the parental line, was retained for further analysis and modification. Sublines were obtained from the 4T1-12B line by limiting dilution cloning.</p>", "<title>Incorporation of FRT site into 4T1 cell line</title>", "<p>To introduce the FLP recombinase target (FRT) site in the 4T1 genome, cells were transfected with pFRT/lacZeo using lipofectamine PLUS and selected with 100 μg/ml zeocin. Colonies were picked and expanded and six clonal lines with a single integration of the vector, as determined by Southern blotting, were identified. The expanded lines were analyzed for efficiency of transfection and targeting and for <italic>in vivo </italic>tumor growth and metastasis. One line, designated 4T1-1V, displayed growth and metastatic characteristics similar to the parental 4T1 line and efficient transfection and targeting to the FRT site with pcDNA5/FRT (Invitrogen) and was retained for further analysis.</p>", "<title>Construction of shRNA targeting vector</title>", "<p>The pcDNA5/FRT targeting vector was modified to allow transfer of the shRNA expression cassettes from pSHAG-type shRNA expression vectors [##REF##11959843##13##] to the pcDNA5/FRT vector by Gateway site-specific recombination. Resulting vectors can then be used to target shRNA expression cassettes to FRT sites in 4T1-1V and other FRT-containing cell lines for creation of isogenic cell lines. A Gateway cloning site was inserted into the vector by blunt end ligation of Reading Frame Cassette C.1 (Invitrogen) into the vector's Bgl II site.</p>", "<title>Knockdown of luciferase with shRNA targeting vector</title>", "<p>To test the efficacy of the construct, an empty expression cassette and a cassette encoding a previously tested luciferase shRNA [##REF##11959843##13##] were transferred to the modified pcDNA5/FRT from corresponding pSHAG-1 vectors. The 4T1-1V cells were then cotransfected with each construct and pOG44 vector at a ratio of 1:10 using Lipofectamine PLUS. Transfected cells were selected with hygromycin B at a final concentration of 200 μg/ml. The pOG44 vector encodes FLP recombinase which directs insertion of the modified pcDNA5/FRT targeting vector into the cell's FRT site; hygromycin B resistance is conferred upon insertion of the vector into the site.</p>", "<p>Expanded clones of hygromycin B resistant cells as well as resistant cell pools from each transfection were then assayed for luciferase activity using a Turner Designs Model TD-20/20 Luminometer.</p>", "<title>Biophotonic imaging of animals and organs</title>", "<p>Luciferase-expressing cell lines were plated at 40% confluency and cultured for 24 h. The cells were then trypsinized, washed, and resuspended in DMEM at 10<sup>7 </sup>cells/ml and kept on ice before injection. Aliquots (100 μl) of the cells were injected into the no. 4 or no. 9 fatpad of 4–6 week old female BALB/c, athymic BALB/c nude or BALB/c SCID mice using a 26-gauge needle. Only cell preparations with viability &gt; 97%, as determined by Trypan Blue exclusion, were used for injection.</p>", "<p>At various times up to 6 weeks, animals were injected intraperitoneally with 100 μl of D-luciferin (10 mg/ml) in PBS, and after 10 min, imaged under anesthesia with 2.5% isofluorane in a Xenogen IVIS 200 biophotonic imager. At experimental endpoints, luciferin-injected animals were sacrificed and organs and hind limbs were removed and imaged within 15 minutes after injection. Luminescence is expressed as photons/sec/ROI (region of interest) minus background luminescence for a similarly sized region. All experiments with animals were carried out according to guidelines for the care and use of experimental animals and were approved by the Tufts University Institutional Animal Care and Use Committee.</p>", "<title>Chip hybridizations and analysis of expression data</title>", "<p>Biotin-labeled cRNAs were prepared from 250 ng of total RNA using Ambion's TotalPrep RNA Amplification Kit. Chip hybridizations, washing, Cy3-streptavidin (Amersham Biosciences) labeling, and scanning were performed on an Illumina BeadStation 500 platform using reagents and protocols provided by the manufacturer. cRNA samples were hybridized to Illumina MouseRef-8 BeadChips which cover 24,048 RefSeq transcripts. The manufacturing principle of randomly distributing large populations of oligonucleotide-coated beads across the available positions on the chip enables 30 intensity measurements per feature on average, and produces quantitative results closely matching those obtained by Q-PCR [##REF##15520296##14##].</p>", "<p>Biotin-labeled cRNAs were prepared from 3 biological replicates of cultured 4T1, 67NR and 168FARN cells for hybridization to the chips. Cells were plated at 5 × 10<sup>5 </sup>cells in 10 cm culture dishes and after 3 days, when the cells had reached confluence, the medium was changed and the cells were cultured for an additional 24 hours. Total RNA was isolated using the Absolutely RNA Kit from Stratagene and checked for integrity using an Agilent Lab-on-a-Chip Bioanalyzer.</p>", "<p>Initial analysis of the data was carried out using Illumina's BeadStudio software. Raw data for each sample were background-subtracted and normalized using the \"cubic spline\" algorithm. Further statistical analysis of the data was carried out using programs associated with BRB Array Tools [##REF##14668230##15##]. Differentially expressed genes for 4T1 samples relative to 67NR and 168FARN samples were determined separately with the Class Comparison program using the random variance model and a p value of 0.0001. Signals less than 10 were set to 10 to eliminate the inaccuracy of analyzing genes expressed at near background levels from being scored as differentially expressed. Genes that differed significantly (p &lt; 0.0001) by &gt; 2-fold for both comparisons (4T1/67NR and 4T1/168FARN) were considered those associated with the metastatic phenotype of the 4T1 cell line. Because of the high level of reproducibility that was achieved, a relatively high level of stringency (p &lt; 0.0001) was chosen for selection of significant differences. As described in RESULTS, this permitted a very low level of false positives with minimal loss of true positives.</p>", "<title>Histochemistry and hematological analysis</title>", "<p>Standard H &amp; E staining of paraffin embedded tissue was used for histological examination of primary tumors and metastases. Stained sections were examined and photographed using an Olympus Vanox-T microscope and an Olympus U-PMTVC CCD camera. Blood from control and tumor bearing animals was collected by cardiac puncture and analyzed by the Pathology Department at Tufts University Cummings School of Veterinary Medicine using standard hematological procedures.</p>" ]
[ "<title>Results</title>", "<title>Characteristics of luciferase-expressing 4T1 cell lines</title>", "<p>The luciferase-expressing 4T1-12B cell line was cloned from 4T1 cells co-transfected with vectors encoding firefly luciferase and puromycin drug resistance. The level of luciferase expression is high enough to allow imaging of as few as 10 cells <italic>in vitro </italic>using a Xenogen IVIS 200 biophotonic imager; the cells are fully resistant to inclusion of 10 μg/ml of puromycin in the culture medium. The modified cell line displayed a doubling time of 12 hours in culture and a plating efficiency of 34%. Expression of luciferase persisted when the cells were cultured for extended periods (&gt; 2 months) in the absence of selective pressure, a characteristic critical for reliable quantitation of tumor growth and metastasis <italic>in vivo</italic>.</p>", "<p>Imaging of a representative female BALB/c mouse six weeks after mammary fat pad injection of 4T1-12B cells is shown in Figure ##FIG##0##1##. At six weeks, primary tumors generally reach a size of 1 cm<sup>3 </sup>or more and metastasis to the thoracic region is apparent in most animals. Imaging of visceral organs and hind limbs revealed metastases in several organs including lungs, liver, bone and brain, sites affected in human breast cancer.</p>", "<p>A compilation of results for several animals injected with the 4T1-12B line and several clones isolated from the modified line after extended time in culture is shown in Table ##TAB##0##1##. All animals injected with the 4T1-12B line displayed metastases in lungs at six weeks with substantial numbers displaying metastasis to liver (5/6), spleen (3/6) and bone (2/6). Metastases were occasionally found in lymph nodes, brain, intestine, kidneys and adrenals. Recloned sublines isolated from the 4T1-12B line displayed a similar spectrum of organ metastasis indicating that most if not all of the cells in the preparation are tumorigenic and metastatic and that the tumorigenic and metastatic properties of the cells are stable when cells are expanded for as many as 20 to 30 generations in culture.</p>", "<p>Generation of the 4T1-1V cell line involved transfection of the 4T1 line with luciferase and puromycin vectors and a vector containing a FLP recombinase targeting (FRT) site contained within a lacZ-zeo fusion protein expression cassette as described in MATERIALS AND METHODS. The 4T1-1V line was shown to contain a single site of integration of the FRT vector, to be readily susceptible to integration by FRT-containing targeting vectors, to stably express luciferase, and to have metastatic characteristics similar to the 4T1-12B line and its sublines. A plasmid containing a Gateway cloning site for insertion of small hairpin siRNA sequences was constructed from the FRT targeting vector, pcDNA5/FRT, and tested for its ability to be incorporated into the genome of the 4T1-1V line in an FRT-dependent manner (Figure ##FIG##1##2##). A vector carrying a previously tested sequence [##REF##11959843##13##] encoding a small hairpin RNA for firefly luciferase was shown to effectively inhibit expression of luciferase in the 4T1-1V line.</p>", "<title>Progression of tumor growth and metastasis <italic>in vivo</italic></title>", "<p>The results of a longitudinal study of primary tumor growth and metastasis of the 4T1-1V line are shown in Figure ##FIG##2##3## and Table ##TAB##1##2##. Biophotonic imaging of animals each week over a six week period after implantation of 4T1-1V cells in the abdominal no. 9 (or no. 4) mammary fat pad revealed several previously unidentified characteristics of the 4T1 model. Tumor growth at the site as measured by biophotonic imaging was found to occur in a biphasic fashion with rapid growth during the first two weeks, regression between weeks 2 and 4, and increased growth again in weeks 5 and 6 (Fig. ##FIG##2##3##, Top Panels). Metastasis became apparent in the thoracic region and lower limbs in weeks 5 and 6 of the second growth phase although metastasizing cells probably seeded these sites earlier [##REF##10411109##8##,##REF##1540948##9##]. Examination of light emission from organs removed from the animals at week 6, revealed a spectrum of organ metastasis similar to that observed for the 4T1-12B line.</p>", "<p>Further analysis revealed that biphasic growth at the primary site was related to immune system function. The regression that was observed in weeks 2 through 4 in normal BALB/c mice was associated with necrosis and infiltration of leukocytes (Fig. ##FIG##2##3##, Bottom Right Panel). Biphasic tumor growth did not occur in athymic nude or SCID BALB/c mice (Fig. ##FIG##3##4##) suggesting involvement of an acquired immune response in the effect. Antibodies directed against multiple 4T1 cell antigens were found in the sera of mice at week 6 (data not shown) further supporting involvement of an acquired immune system response in the regressive process.</p>", "<p>Imaging of animals and organs at various times after introduction of 4T1-1V cells in the fat pad revealed a clear progression of metastasis first to lungs (beginning around 3 weeks) and later to liver, bone and spleen (weeks 3–6) with occasional metastasis to brain, heart and intestines at the later times (Table ##TAB##1##2##). Tumor cells were detected in lymph nodes adjacent to primary tumors and elsewhere in the animal consistent with previous studies suggesting that 4T1 cells metastasize via the lymphatic system as well as hematogenously [##REF##1540948##9##,##REF##11713608##12##].</p>", "<p>The results of histological examination of metastases at selected times and sites are shown in Figure ##FIG##4##5##. In lungs and kidneys metastases were found within or in close proximity to afferent vessels and in most cases appeared infiltrative. In adrenals and liver metastases were more localized, often appearing spherical in nature. Metastasis to bone was prevalent throughout the skeletal system including skull, ribs, sternum, and limbs. Bone-associated osteoclasts were often observed in areas adjacent to bone metastases indicating increased osteoclastogenesis and elevated degradation of bone in these areas (Fig. ##FIG##5##6##).</p>", "<p>A progressive increase in hematopoiesis was observed throughout the 6 week time course as primary tumors progressed and metastases developed at distant sites. This was evidenced by increasing levels of circulating neutrophils and other leukocytes (Table ##TAB##2##3##) and by enlargement of the spleen and liver resulting from extramedullary hematopoiesis that developed in these organs (Figs. ##FIG##6##7## and ##FIG##7##8##). Extramedullary hematopoiesis was apparent by week 2 when primary tumors began to regress and continued to increase until death ensued between weeks 6 and 8. Immature myelocytic cells (Band N) were found in the circulation at week 4. The histology of spleen and liver and the composition and levels of circulating leukocytes are consistent with expansion of granulocyte lineages with circulating leukocytes reaching leukemia-like levels by the end of the observation period (6 weeks).</p>", "<title>Genes associated with the 4T1 metastatic phenotype</title>", "<p>Gene expression analysis was carried out on the 4T1 cell line and two of its sibling lines, 67NR and 168FARN to identify expression differences associated with the 4T1 metastatic phenotype. Both of the sibling lines are non-metastatic when introduced orthotopically into BALB/c mice [##REF##1540948##9##]. The 67NR line displays little if any dissemination from the primary site, whereas the 168FARN line displays dissemination to lymph nodes, but not to blood or distant organs [##REF##1540948##9##]. Using Illumina MouseRef-8 BeadChip arrays, multiple replicates, and carefully controlled culture conditions, highly significant (p &lt; 0.0001) expression data for differences as low as 1.2-fold were achieved. Of the 24,048 genes represented on the arrays, 1.8% or 430 genes (347 annotated) differed by 2-fold or more in the 4T1 line relative to the other two lines (Fig. ##FIG##8##9##). The median false discovery rate for these genes was less than 1 in 500. The majority of all 2-fold differences were found to be significant (p &gt; 0.0001) for both the 4T1/67NR (66.4%) and 4T1/168FARN (98.7%) comparisons. These results indicate a very high level of confidence in the genelists that were produced from the data.</p>", "<p>Ingenuity Pathway Analysis (IPA) of genes differentially expressed in 4T1 relative to the two non-metastatic lines revealed significant association with cancer and other diseases including hematological and inflammatory disease (Table ##TAB##3##4##), findings consistent with the high level of inflammation and hematopoiesis observed for the 4T1 lines <italic>in vivo</italic>. Also consistent with the 4T1 metastatic phenotype was association with cell movement, cell signaling, cell growth, proliferation and death, and cell to cell signaling and interaction (Table ##TAB##3##4##). Many of the expression differences that characterize the 4T1 phenotype including those known to be involved in metastasis and/or tumorigenesis are listed in Additional File ##SUPPL##0##1##.</p>", "<p>Genes differentially expressed in 4T1 were categorized with respect to cellular location and function (Fig. ##FIG##9##10##). Among the genes are substantial numbers involved in cell adhesion, migration, angiogenesis, and extracellular matrix modification; cytoskeleton function; cell proliferation, apoptosis and survival; cellular metabolism; and inflammation and immune response. Altered expression of several transcription factors and genes involved in chromatin modification that regulate these processes were also observed. Elevated expression of genes associated with tight junctions (Cldn3, Cldn4, Cldn7 and Tjp2), adherins junctions (Cdh1 and Vil1) focal adhesions (Itga3, Itga6 and Lama5), and intermediate filaments (Krt1-18 and Krt2-7) indicate that the 4T1 line has greater epithelial character than the non-metastatic lines. An increased propensity for extracellular matrix (ECM) remodeling is suggested by elevated expression of matrix metalloproteinases (Mmp3, Mmp9 and Mmp13), urokinase-type plasminogen activator (Plau) and secreted protease inhibitors (Serpina3g, Serpin2 and Lcn2).</p>", "<title>Signaling pathways associated with phenotype</title>", "<p>Several signaling pathways appear to be activated in 4T1 cells (Fig. ##FIG##10##11##). Most conspicuous is activation of the Jak/Stat pathway as indicated by elevated expression of Jak2 and Stat1, decreased expression of Socs1 and increased expression of several Stat target genes (Myc, Irf1, Igsf3g and Usp20) (Fig. ##FIG##10##11A##). Also conspicuous is activation of p38 MAPK (Mapk12) as indicated by increased expression of CCAAT/enhancer binding protein beta (Cebpb) and high levels of expression of Cebpb/NFκB target cytokines (Ccl5/RANTES, Csf2, Csf3 and Tslp) and acute phase proteins (Saa3, C3 and Lcn2). Increased expression of TIAM1 (Tiam1) and genes in the IL-1 and TNF-α pathways (Il1a, Tnfrsf19, Traf1, Card10) suggest that these pathways, which are known to activate p38 MAPK, may be involved in the expression of Cebpb/NFκB targets. Targets of p38 MAPK are known to activate the Jak/Stat pathway (Fig. ##FIG##10##11##) so it is therefore likely that p38 MAPK signaling is responsible for the activation of the Jak/STAT pathway in these cells. Elevated expression of Wnt (Wnt10a) and its receptors (Fzd6 and Fzd7) suggests activation of the Wnt pathway (Fig. ##FIG##10##11B##). While some Wnt pathway targets (Myc and Plau) displayed elevated expression, other known targets (c-jun, cycD and Fosl1) did not. It may be that the purpose of altered expression of Wnt pathway ligand and receptors is to increase β-catenin levels to support junctional complexes that are more prevalent in these cells. Both the canonical and the non-canonical Wnt pathway are known to play an important role in establishment and maintenance of cellular junctions [##REF##17084354##16##]. Finally, 4T1 displayed significantly reduced levels of CDK2-associated protein 1 (CDK2ap1), p53 and two p53 targets, cyclin-dependent kinase inhibitor p21 (Cdkn1a) and cyclin G (Ccng1) (Fig. ##FIG##10##11C##). These alterations would be expected to accelerate the early phase of the cell cycle (G<sub>1 </sub>→ S) and attenuate the DNA damage response. Expression of a third p53 target, Gadd45, was elevated. Gadd45 is regulated by hypoxia and glucose deprivation as well as by p53. Elevated expression of several genes known to be sensitive to hypoxia and/or glucose deprivation (Pfkfb3, Vegfc, Flt1 and Trib3), suggest that elevated expression of Gadd45 may be due to these factors and that 4T1 cells exist in a state of stress or pseudo-stress even under optimal culture conditions.</p>", "<title>Alterations related to tumor microenvironment</title>", "<p>A variety of factors produced at elevated levels by 4T1 cells are secreted cytokines, chemokines, acute phase proteins and proteases that interact locally and systemically with the host to produce, recruit and activate cells of hematopoietic origin capable of remodeling the tumor microenvironment and facilitating tumor cell dissemination. These factors and their expected effects on the tumor microenvironment are depicted in Figure ##FIG##10##11D##. Two important modulators of endothelial cell function produced by 4T1 cells are vascular endothelial growth factor C (Vegfc) and angiopoietin 2 (Agpt2). VEGF-C interacts with VEGF receptors on endothelial cells to stimulate angiogenesis and lymphangiogenesis when existing vessels are destabilized. Angiopoietin 2 destabilizes vessels by antagonizing the stabilizing effects of angiopoietin 1. Together, these factors would be expected to induce both angiogenesis and lymphangiogenesis, increase tumor vascularization and provide routes of escape of tumor cells. The inhibitory effect of semaphorin 3F (Sema3f) on angiogenesis would be expected to shift vessel development toward lymphangiogenesis. Angiopoietin 2 and VEGF-C also serves as chemotactic factors for recruitment of circulating monocytes and macrophages.</p>", "<p>Macrophages and other cells recruited to the tumor are produced in the bone marrow and other tissues by hematopoiesis. Colony stimulating factors GM-CSF (Csf2) and G-CSF (Csf3) produced and secreted by 4T1 cells stimulate hematopoiesis along the myeloid lineages and are likely to be responsible for the high levels of hematopoiesis and circulating leukocytes observed when tumors from 4T1 cells are established <italic>in vivo </italic>[##REF##16919266##17##]. Several factors produced by 4T1 cells are known to play a role in recruitment of hematopoietic cells to tissues. RANTES (Ccl5) is chemotactic for mast cells [##REF##11424873##18##] and fragments generated autocatalytically from complement C3 (C3) are capable of stimulating mast cells to release TNFα, histamine, cytokines and other factors that can act to recruit a wide range of cells including monocytes, dendritic cells, neutrophils, eosinophils and lymphocytes. RANTES (Ccl5) is also known to stimulate secretion of interleukin 8 (Il8) from macrophages [##REF##15596298##19##]. Interleukin 8 is also released by stromal fibroblasts in response to interleukin 1α produced by 4T1 cells. Interleukin 8 along with chemokines (Cxcl6, Cxcl1) released by 4T1 are chemotactic for neutrophils [##REF##10820279##20##,##REF##8399143##21##]. Finally, matrix metalloproteinases produced by macrophages, fibroblasts and neutrophils recruited to the tumor would add to the already high levels of matrix metalloproteinases released by the tumor cells themselves thereby creating a high potential for dissolution of matrix and cell-matrix interactions, a condition likely to facilitate tumor cell invasion and metastasis.</p>", "<p>Previous studies have indicated that populations of immature myeloid cells called myeloid derived suppressor cells (MDSC) are induced by tumors and that these cells facilitate tumor growth and metastasis by suppressing the immune response [##REF##17016559##22##]. Ectopic expression of interleukin 1β in 4T1 cells has been shown to increase MDSC levels and stimulate growth and metastasis of 4T1 tumors <italic>in vivo </italic>[##REF##16365420##23##]. Because interleukin 1α rather than interleukin 1β is the predominant form of interleukin 1 produced by 4T1 cells, and because the two cytokines have similar biological activity, it is likely that expression of interleukin 1α by 4T1 is involved in production of MDSC and their effects on growth and metastasis of 4T1 <italic>in vivo</italic>.</p>" ]
[ "<title>Discussion</title>", "<p>Here we report on the generation of two clonal 4T1 cell lines (4T1-12B and 4T1-1V), both of which stably express firefly luciferase at a high level in the absence of selective pressure, and one (4T1-1V) which was also modified by addition of an FRT site in its genome. These lines were shown to have metastatic characteristics similar to the parental 4T1 line displaying metastasis to bone, lungs, and liver and brain organs primarily affected in human breast cancer. The ability to image the cells <italic>ex vivo </italic>with high sensitivity allowed detection and quantitation of metastases in affected organs more effectively than has been possible previously. Luciferase-expressing 4T1 variant cell pools and lines with increased propensity for metastasis to brain, liver, and bone have recently been isolated (to be published elsewhere). These variants will further expand the repertoire of syngeneic models available for the study of late stage breast cancer.</p>", "<p>An acquired immune response was found to play an important role in regulating 4T1 tumor growth and metastasis. 4T1 tumors established in normal BALB/c mice displayed a substantial loss of tumor cells beginning 2–3 weeks after introduction. This effect was not apparent in BALB/c nude and BALB/c SCID mice, in which 4T1 cells in tumors proliferated rapidly and continuously. Antibodies directed against several 4T1 antigens were detected in sera from normal tumor-bearing BALB/c mice further supporting the involvement of an acquired immune response to the cells. Myeloid derived suppressor cells (MDSC) which are known to be induced in 4T1 tumor-bearing mice are likely to be involved in establishment and maintenance of 4T1 tumors by attenuating the immune response to allow survival of the tumor in weeks 3–4 and re-emergence of tumor growth in weeks 5–6. Further work will be required to determine the actual role that MDSC and other immune system components play in regulating the growth and survival of 4T1 tumors.</p>", "<p>Metastasis of 4T1 tumors is associated with extensive necrosis and inflammation within the primary tumor and hematopoiesis in several mouse organs including spleen and liver. Elevated hematopoiesis has recently been reported for the 4T1 model [##REF##16919266##17##,##REF##17877537##24##]. Whether or not a causal relationship exists between these processes and metastasis remains to be demonstrated although two observations suggest that there may be such a connection. First, the extent of necrosis is greater in 4T1 tumors than those derived from less metastatic sibling cell lines (67NR, 168FARN) as indicated by the occurrence of large areas of visible necrosis in the 4T1 tumors. Second, a causal relationship between inflammation and metastasis is supported by the inhibitory effect of the COX-2 inhibitor, SC-236, on metastasis of 4T1 after primary tumor excision [##REF##12107848##25##]. Inflammation is known to have a positive effect on metastasis in several systems [##REF##18066650##26##, ####REF##17705880##27##, ##REF##16524717##28####16524717##28##] and is likely to have a pro-metastatic effect in this system as well.</p>", "<p>Inflammation in metastatic tumors is generally thought to result from signals produced by dying cells and ECM fragments in areas of insufficient vascularization [##REF##18243041##29##]. A noteworthy finding of this study is that 4T1 tumor cells, when cultured under optimal growth conditions, produce a wide range of factors capable of inducing production, recruitment and activation of inflammatory cells. These factors include colony stimulating factors GM-CSF (Csf2) and G-CSF (Csf3); cytokines Ccl5, Cxcl1, Cxcl6 and Tslp; angiogenic factors Agpt2 and Vegfc; and acute phase proteins Saa3, C3, and Lcn2. While this does not preclude the involvement of cell death in initiating an inflammatory response in the tumor, it does suggest that the tumor cells themselves may play a more direct and active role in directing pro-metastatic inflammatory processes than previously envisioned.</p>", "<p>The methodology used in this study for analysis of gene expression yielded highly significant data characterizing the 4T1 metastatic phenotype. The majority of genes that differed by more than 2-fold in 4T1 relative to the two non-metastatic sibling lines examined displayed an exceptionally high level of significance (p &lt; 0.0001) and genelists obtained at this level of significance displayed very low false positive rates. The statistics argue that the results obtained provide a relatively complete and accurate picture of expression differences associated with the 4T1 phenotype. The high level of accuracy and reproducibility that was achieved is attributed to use of the Illumina BeadChip platform and analysis of cells cultured under carefully controlled growth conditions that minimize differences between biological replicates. The data obtained from this study provide detailed information regarding the genes and pathways involved in breast cancer progression for this model and will be particularly useful for further analysis of the pathological processes responsible for progression to a metastatic phenotype.</p>", "<p>Unlike many cell lines used as xenograft models, subclones of the 4T1-12B cell line that had undergone more than 20 doublings were found to be homogeneous with respect to metastatic properties. These cells also display a high plating efficiency and no visibly apparent differentiation in culture or <italic>in vivo</italic>. Thus, the cells resemble stem cells found in populations of cell lines such as MCF7 [##REF##17881900##30##] in that they are self renewing, but differ in that they do not appear to differentiate. While more work is need to determine the basis for this property, the characteristic has utility for studies aimed at determining gene function since clonal lines in which a specific genes have been over-expressed or knocked down can be expected to retain the properties of the parental line from which they were derived. In this regard, the FRT site in the 4T1-1V line will be useful for production of isogenic lines for analysis of gene function.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, this study provides basic information for those interested in using two imagable 4T1 breast cancer models developed in this laboratory. Several characteristics of these models make them particularly attractive for the study of late stage breast cancer. First and foremost, because of their syngeneic nature, they provide a highly physiologic system suitable for analysis of innate and acquired immune system roles in tumor growth and metastasis. The relatively complete gene expression data provided offer numerous avenues for further study of the molecular and pathologic basis for these and other processes related to late stage breast cancer. To our knowledge, the 4T1 model is the only system that has the capacity to metastasize to all organs affected in breast cancer in humans when introduced orthotopically. For this reason, and because of the ease of use and reproducibility that can be achieved, these imagable models provide ideal systems for determining anti-metastatic effects of cancer drugs and therapeutic regimens and is well suited for investigating the molecular, cellular and pathologic basis for metastasis to specific organs and tissues.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The 4T1 mouse mammary tumor cell line is one of only a few breast cancer models with the capacity to metastasize efficiently to sites affected in human breast cancer. Here we describe two 4T1 cell lines modified to facilitate analysis of tumor growth and metastasis and evaluation of gene function <italic>in vivo</italic>. New information regarding the involvement of innate and acquired immunity in metastasis and other characteristics of the model relevant to its use in the study of late stage breast cancer are reported.</p>", "<title>Methods</title>", "<p>The lines were engineered for stable expression of firefly luciferase to allow tracking and quantitation of the cells <italic>in vivo</italic>. Biophotonic imaging was used to characterize growth and metastasis of the lines <italic>in vivo </italic>and an improved gene expression approach was used to characterize the basis for the metastatic phenotype that was observed.</p>", "<title>Results</title>", "<p>Growth of cells at the primary site was biphasic with metastasis detected during the second growth phase 5–6 weeks after introduction of the cells. Regression of growth, which occurred in weeks 3–4, was associated with extensive necrosis and infiltration of leukocytes. Biphasic tumor growth did not occur in BALB/c SCID mice indicating involvement of an acquired immune response in the effect. Hematopoiesis in spleen and liver and elevated levels of circulating leukocytes were observed at week 2 and increased progressively until death at week 6–8. Gene expression analysis revealed an association of several secreted factors including colony stimulatory factors, cytokines and chemokines, acute phase proteins, angiogenesis factors and ECM modifying proteins with the 4T1 metastatic phenotype. Signaling pathways likely to be responsible for production of these factors were also identified.</p>", "<title>Conclusion</title>", "<p>The production of factors that stimulate angiogenesis and ECM modification and induce hematopoiesis, recruitment and activation of leukocytes suggest that 4T1 tumor cells play a more direct role than previously appreciated in orchestrating changes in the tumor environment conducive to tumor cell dissemination and metastasis. The new cell lines will greatly facilitate the study of late stage breast and preclinical assessment of cancer drugs and other therapeutics particularly those targeting immune system effects on tumor metastasis.</p>" ]
[ "<title>Abbreviations</title>", "<p>cRNA: complementary RNA; DMEM: Dulbecco's Minimum Essential Medium; ECM: extracellular matrix; FBS: fetal bovine serum; FLP: flippase; FRT site: FLP recombinase targeting site; GEM: genetically engineered mouse; IPA: Ingenuity Pathway Analysis; MDSC: myeloid derived suppressor cells; NCS: normal calf serum; NEAA: nonessential amino acids; PBS: phosphate buffered saline; Q-PCR: quantitative PCR; ROI: region of interest; siRNA: small inhibitory RNA; shRNA: short hairpin RNA.</p>", "<title>Competing interests</title>", "<p>Cell lines described in this study are licensed by Tufts University for commercial use. Royalties are split between Tufts University (including GGS), Wayne State University and the NIH.</p>", "<title>Authors' contributions</title>", "<p>KT acquired and analyzed imaging data. MF acquired and analyzed imaging and gene expression data. JA evaluated histology data. GGS conceived of the study, analyzed imaging data, and wrote the manuscript. KT and MF contributed equally to the study. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2407/8/228/prepub\"/></p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Dr. Richard Proia at the NIH for hosting Dr. Sahagian's sabbatical in 2003. Much of the technology used in this study was developed at that time in his laboratory. We also thank Lauren Richey, D.V.M. (Division of Laboratory Animal Medicine, Tufts-New England Medical Center) for analysis of histologic data. This work was supported by grant 5R01CA66575 from the NCI and grants BCTR0504552 and PDF0600954 from the Susan B. Komen Breast Cancer Foundation.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Imaging of animals and organs at six weeks</bold>. 4T1-12B cells (10<sup>6</sup>) were implanted into the mammary fat pad of a normal female BALB/c mouse. After six weeks the whole animal and organs were imaged as described in MATERIALS AND METHODS. The relationship between color and light intensity in arbitrary units (counts) for the whole animal images is given by the color bar at the right side of the figure.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Targeting shRNAs and cDNAs to FRT site in 4T1-1V</bold>. <bold>(Top) </bold>The diagram shows the results of FLP recombinase-dependent insertion of the FRT targeting vector carrying a cDNA and/or siRNA, into the genome. The promoter driving expression of the lacZ-zeo fusion protein before insertion drives expression of the hygromycin resistance gene after insertion allowing hygromycin selection of cells that had undergone targeted insertion of the vector. <bold>(Bottom) </bold>Cells were cotransfected with the indicated vector and an expression vector encoding FLP recombinase. Cell pools and clones were isolated from the transfected cells and assayed for luciferase expression as described in MATERIALS AND METHODS. Light emission in arbitrary units per milligram of cell protein is shown for pools and clones transfected with empty targeting vector or targeting vector encoding luciferase shRNA. Error bars represent standard deviations for empty vector (n = 6) and luciferase siRNA vector (n = 7) transfected clones.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Progression of tumor growth and metastasis</bold>. <bold>(Left) </bold>4T1-1V cells (10<sup>6</sup>) were introduced into mammary fat pads of normal female BALB/c mice and the animals were imaged on a weekly basis for six weeks. The animals were sacrificed at the end of the sixth week and organs and hind limbs were removed and imaged. Images for two representative animals are shown. (<bold>Top Right</bold>) Quantitation of light emission from primary tumor over the six week period. (<bold>Bottom Right</bold>) H&amp;E staining of a section from a primary tumor illustrating a central area of necrosis infiltrated by leukocytes and neoplastic cells at the periphery. The neoplastic cells are poorly differentiated and characterized by the presence of large hyperchromatic nuclei and relatively small amount of cytoplasm. Identifiable neutrophils and mast cells that appear to be located extravascularly were observed in non-necrotic areas of the tissue.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Involvement of immune system in primary tumor growth</bold>. 4T1-12B cells (10<sup>6</sup>) were implanted into the mammary fat pad of two normal (○), athymic nude (□) and SCID (△) BALB/c mice and imaged weekly as described in MATERIALS AND METHODS. Average luminescence +/- sd for each time point is plotted.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Metastasis to lungs, kidneys, adrenals and liver</bold>. (<bold>A</bold>) Lung at 3 weeks showing metastases adjacent to blood vessels. (<bold>B</bold>) Tumor cells in a major vessel of the kidney at week 6. Note infiltration of tumor cells into kidney parenchyma (arrowhead, left panel). (<bold>C</bold>) Tumor-laden adrenal gland at 6 weeks with multiple spherically-shaped metastases. (<bold>D</bold>) Large metastasis on the surface of the liver at week 6. Note abnormal appearance of liver parenchyma and high levels of leukocytes in parenchyma (arrowheads, right panel) and sinusoids (arrows, right panel). Specimens were obtained from the experiment described in Figure 3.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Metastasis to bone</bold>. <bold>(Top Left) </bold>Metastasis near joint between femur and tibia at week 6. Note extensive degradation of bone adjacent to the upper surface of the tumor. (<bold>Top Right</bold>) Interface between tumor and bone at higher magnification. Note osteoclasts (arrowheads) lining the lower surface of the bone. (<bold>Bottom Left and Right</bold>) Femoral metastasis within the joint itself at low and high resolution at 6 weeks. Specimens were obtained from the experiment described in Figure 3.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Hematopoiesis in spleen</bold>. Spleen at 1 (<bold>Top</bold>) and 6 (<bold>Bottom</bold>) weeks. Spleen appears normal at week 1. Extensive extramedullary hematopoiesis is apparent at week 6 as evidenced by the presence of megakaryocytes (arrow heads). Specimens were obtained from the experiment described in Figure 3.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p><bold>Hematopoiesis in liver</bold>. Liver at 1 (<bold>Top</bold>), 2 (<bold>Middle</bold>) and 6 (<bold>Bottom</bold>) weeks. Liver appears normal at week 1. Islands of extramedullary hematopoiesis are seen at week 2 and extensive hematopoiesis throughout the liver is apparent at week 6. Note increased proportion of nucleated cells in blood vessels at weeks 2 and 6. Specimens were obtained from the experiment described in Figure 3.</p></caption></fig>", "<fig position=\"float\" id=\"F9\"><label>Figure 9</label><caption><p><bold>Genes with altered expression in 4T1 vs. 67NR and 168FARN</bold>. Genes with 2-fold expression differences for 4T1 vs. 67NR and 4T1 vs. 168FARN were determined as described in MATERIALS AND METHODS. Genes in the intersection between the two comparisons are those considered to be associated the metastatic phenotype of the 4T1 cell line. Genes in the intersection represent 1.8% of the total genes analyzed, 52% of genes differentially expressed for 4T1 vs. 67NR and 45% of those differentially expressed for 4T1 vs. 168FARN.</p></caption></fig>", "<fig position=\"float\" id=\"F10\"><label>Figure 10</label><caption><p><bold>4T1 genes categorized by cellular location and function</bold>. Genes associated with the 4T1 metastatic phenotype that fall into the categories shown are listed in the figure. Genes shown in red are elevated in 4T1 and genes shown in blue reduced in 4T1. The blue line to the right of middle represents the plasma membrane with genes falling to the right of it representing secreted genes. The blue rectangle to the left of middle represents intracellular membranes and genes falling inside the rectangle are genes located within intracellular organelles.</p></caption></fig>", "<fig position=\"float\" id=\"F11\"><label>Figure 11</label><caption><p><bold>Pathways involved in metastatic phenotype of 4T1</bold>. Pathways shown are based on known Kegg pathways with modifications based on recent literature relating 4T1 phenotype genes to these pathways. Red and blue boxes represent genes that are up-regulated or down-regulated by &gt; 2-fold, respectively. Red arrows represent chemotaxis. Numbers outside the boxes give the higher of the two ratios for expression in 4T1 relative to the non-metastatic 67NR and 168FARN lines.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Summary of sites of metastasis for 4T1-12B<sup>1</sup></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Cell Line</bold></td><td align=\"center\"><bold>Primary</bold></td><td align=\"center\"><bold>Lungs</bold></td><td align=\"center\"><bold>Spleen</bold></td><td align=\"center\"><bold>Liver</bold></td><td align=\"center\"><bold>Bone</bold></td><td align=\"center\"><bold>Other</bold></td></tr></thead><tbody><tr><td align=\"left\">4T1-12B line</td><td align=\"center\">6(6)<sup>2</sup></td><td align=\"center\">6(6)</td><td align=\"center\">3(6)</td><td align=\"center\">5(6)</td><td align=\"center\">2(6)</td><td align=\"left\">Brain 1(6)<break/>Intestine 1(6)<break/>Kidney 1(6)</td></tr><tr><td align=\"left\">4T1-12B recloned lines (5)</td><td align=\"center\">10(10)</td><td align=\"center\">8(10)</td><td align=\"center\">2(10)</td><td align=\"center\">3(10)</td><td align=\"center\">6(10)</td><td align=\"left\">Intestine 3(10)<break/>Kidney 1(10)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Kinetics and extent of metastasis for 4T1-1V<sup>1</sup></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Time after injection (days)</bold></td><td align=\"center\"><bold>Primary</bold></td><td align=\"center\"><bold>Lungs</bold></td><td align=\"center\"><bold>Spleen</bold></td><td align=\"center\"><bold>Liver</bold></td><td align=\"center\"><bold>Bone</bold></td><td align=\"center\"><bold>Kidney/Adrenals</bold></td><td align=\"center\"><bold>Other</bold></td></tr></thead><tbody><tr><td align=\"center\">8–14</td><td align=\"center\">3(3)<sup>2</sup></td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">15–21</td><td align=\"center\">3(3)</td><td align=\"center\">1(3)</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"center\">22–28</td><td align=\"center\">5(5)</td><td align=\"center\">5(5)</td><td align=\"center\">1(5)</td><td align=\"center\">2(5)</td><td align=\"center\">0(5)</td><td align=\"center\">1(5)</td><td align=\"left\">Intestine 1(5)</td></tr><tr><td align=\"center\">29–35</td><td align=\"center\">7(7)</td><td align=\"center\">7(7)</td><td align=\"center\">2(7)</td><td align=\"center\">6(7)</td><td align=\"center\">3(7)</td><td align=\"center\">2(7)</td><td align=\"left\">Intestine 1(7)</td></tr><tr><td align=\"center\">36–42</td><td align=\"center\">7(7)</td><td align=\"center\">7(7)</td><td align=\"center\">5(7)</td><td align=\"center\">6(7)</td><td align=\"center\">6(7)</td><td align=\"center\">2(7)</td><td align=\"left\">Brain 1(7)<break/>Heart 1(7)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Circulating white cell analysis<sup>1</sup></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>WBC Population</bold></td><td align=\"center\"><bold>Control (n = 1)</bold></td><td align=\"center\"><bold>Week 1 (n = 3)</bold></td><td align=\"center\"><bold>Week 4 (n = 4)</bold></td></tr></thead><tbody><tr><td align=\"left\">WBC</td><td align=\"center\">5.300<sup>2</sup></td><td align=\"center\">4.200</td><td align=\"center\">56.210</td></tr><tr><td/><td/><td align=\"center\">4.200</td><td align=\"center\">66.000</td></tr><tr><td/><td/><td align=\"center\">2.800</td><td align=\"center\">15.700</td></tr><tr><td/><td/><td/><td align=\"center\">134.000</td></tr><tr><td align=\"left\">Seg N</td><td align=\"center\">0.424 (8%)<sup>3</sup></td><td align=\"center\">1.386 (33%)</td><td align=\"center\">34.850 (62%)</td></tr><tr><td/><td/><td align=\"center\">0.546 (13%)</td><td align=\"center\">48.840 (74%)</td></tr><tr><td/><td/><td align=\"center\">1.148 (41%)</td><td align=\"center\">8.164 (52%)</td></tr><tr><td/><td/><td/><td align=\"center\">99.160 (74%)</td></tr><tr><td align=\"left\">Band N</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">3.373 (6%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">3.962 (6%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">0.157 (1%)</td></tr><tr><td/><td/><td/><td align=\"center\">10.720 (8%)</td></tr><tr><td align=\"left\">Metamyelocytes</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">0.562 (1%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">0.660 (1%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td/><td/><td/><td align=\"center\">2.680 (2%)</td></tr><tr><td align=\"left\">Lymphocytes</td><td align=\"center\">4.823 (91%)</td><td align=\"center\">2.772 (66%)</td><td align=\"center\">16.863 (30%)</td></tr><tr><td/><td/><td align=\"center\">3.654 (87%)</td><td align=\"center\">10.560 (16%)</td></tr><tr><td/><td/><td align=\"center\">1.624 (58%)</td><td align=\"center\">6.594 (42%)</td></tr><tr><td/><td/><td/><td align=\"center\">20.100 (15%)</td></tr><tr><td align=\"left\">Monocytes</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">1.320 (2%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">0.314 (2%)</td></tr><tr><td/><td/><td/><td align=\"center\">-</td></tr><tr><td align=\"left\">Eosinophils</td><td align=\"center\">0.053 (1%)</td><td align=\"center\">0.042 (1%)</td><td align=\"center\">0.562 (1%)</td></tr><tr><td/><td/><td align=\"center\">-</td><td align=\"center\">0.660 (1%)</td></tr><tr><td/><td/><td align=\"center\">0.028 (1%)</td><td align=\"center\">0.471 (3%)</td></tr><tr><td/><td/><td/><td align=\"center\">1.340 (1%)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Ontological analysis<sup>1</sup></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>p-value</bold></td><td align=\"right\"><bold># molecules</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Diseases and Disorders</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">Cancer</td><td align=\"center\">5.07E-14-5.72E-04</td><td align=\"right\">100</td></tr><tr><td align=\"left\">Hematological Disease</td><td align=\"center\">1.80E-08-5.72E-04</td><td align=\"right\">51</td></tr><tr><td align=\"left\">Connective Tissue Disorders</td><td align=\"center\">3.41E-08-1.44E-04</td><td align=\"right\">35</td></tr><tr><td align=\"left\">Dermatological Diseases and Conditions</td><td align=\"center\">1.75E-07-5.37E-04</td><td align=\"right\">41</td></tr><tr><td align=\"left\">Inflammatory Disease</td><td align=\"center\">8.81E-07-5.51E-04</td><td align=\"right\">38</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\"><bold>Molecular and Cellular Functions</bold></td><td/><td/></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">Cell Movement</td><td align=\"center\">1.18E-15-5.72E-04</td><td align=\"right\">69</td></tr><tr><td align=\"left\">Cell Signaling</td><td align=\"center\">1.30E-13-2.04E-04</td><td align=\"right\">106</td></tr><tr><td align=\"left\">Cell Death</td><td align=\"center\">1.67E-13-5.32E-04</td><td align=\"right\">96</td></tr><tr><td align=\"left\">Cellular Growth and Proliferation</td><td align=\"center\">1.03E-12-5.79E-04</td><td align=\"right\">111</td></tr><tr><td align=\"left\">Cell to Cell Signaling and Interaction</td><td align=\"center\">1.94E-08-5.19E-04</td><td align=\"right\">63</td></tr></tbody></table></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional File 1</title><p>Gene table. A list of genes associated with the 4T1 metastatic phenotype.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><sup>1</sup>Cells were introduced into fatpads of normal female BALB/c mice and imaged after 5–6 weeks as described in MATERIALS AND METHODS.</p><p><sup>2</sup>Number of animals positive (total number of animals)</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>Cells were introduced into fatpads of normal female BALB/c mice and imaged at the indicated times as described in MATERIALS AND METHODS.</p><p><sup>2</sup>Number of animals positive (total number of animals)</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>Whole blood was collected by cardiac puncture at the indicated time after orthotopic introduction of tumor cells as described in MATERIALS AND METHODS.</p><p><sup>2</sup>Total number of WBC/ml blood in millions for each animal tested.</p><p><sup>3</sup>Number of cells/ml blood in millions for each animal tested (% of total WBC for animal)</p></table-wrap-foot>", "<table-wrap-foot><p><sup>1</sup>304 annotated gene were included in the analysis</p></table-wrap-foot>" ]
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{ "acronym": [], "definition": [] }
30
CC BY
no
2022-01-12 14:47:26
BMC Cancer. 2008 Aug 9; 8:228
oa_package/82/e8/PMC2529338.tar.gz
PMC2529339
18671841
[ "<title>Background</title>", "<p>Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) has become a method of choice for gene expression studies in clinical samples, especially for low copy targets of interest and for samples of limited size [##REF##15283208##1##, ####REF##15956331##2##, ##REF##15815687##3####15815687##3##]. In comparison to microarrays [##REF##17202296##4##], qRT-PCR benefits from broad dynamic range, sensitivity, and enables accurate quantification [##REF##16171460##5##,##REF##16060372##6##].</p>", "<p>However, to precisely quantify changes in expression level of target genes by qRT-PCR, one must apply normalisation for heterogeneity in clinical samples and also for variability introduced during RNA extraction and cDNA synthesis [##REF##15283208##1##,##REF##15331581##7##]. Besides normalisation to sample size and total RNA, normalisation using endogenous reference genes represents relevant approach [##REF##15815687##3##]. Reference genes should ideally be constitutively expressed by all cell types and should not be affected by disease and experimental procedure. To date, a universal reference gene has not been identified yet. Housekeeping genes (HKGs) are most commonly used reference genes [##REF##15283208##1##]. Although HKGs are expressed by any cell, their expression varies among different cell types/organs [##REF##11773596##8##,##REF##11015593##9##]. Use of HKGs as reference genes for a particular sample type should be, therefore, validated.</p>", "<p>So far, only few reference genes have been validated for cells from respiratory compartment; specifically GNB2L1 was validated for bronchoalveolar macrophages in patients with chronic obstructive pulmonary disease (COPD) [##REF##16452584##10##] and GAPDH (glyceraldehyde-3-phosphate dehydrogenase) for non-small cell lung cancer [##REF##16319328##11##]. The majority of studies published on qRT-PCR in lung setting uses a general approach of normalisation against GAPDH or ACTB (beta-actin) [##REF##11463599##12##, ####REF##15095321##13##, ##REF##16484684##14##, ##REF##15579727##15##, ##REF##16394278##16####16394278##16##]. However, these \"traditional\" reference genes have been already found unsuitable for normalising of mRNA levels in asthmatic airways [##REF##12200519##17##,##REF##12200516##18##] and also for expression studies employing bronchoalveolar macrophages [##REF##16452584##10##].</p>", "<p>In order to identify suitable reference genes for qRT-PCR normalisation in the setting of bronchoalveolar compartment, our aim, therefore, was to identify HKGs with the most stable mRNA expression in bronchoalveolar (BAL) cells. Our choice of candidate HKGs was based on 1) their common use in previous qRT-PCR experiments (ACTB, GAPDH, G6PD), 2) stable expression in different human tissues in microarray experiments (ARF1, CANX, GPS1, PSMB2, PSMD2) [##REF##11773596##8##,##REF##11015593##9##], and 3) stable expression in bronchoalveolar macrophages and peripheral neutrophils (GNB2L1, RPL32) [##REF##16452584##10##,##REF##15720708##19##]. To account for variations of BAL cellular profile in different respiratory diseases, we studied stability of HKGs mRNA expression in seventy-one subjects across a spectrum of lung pathologies. Besides BAL cellular profile and type of lung pathology, four variables were investigated for their possible influence on mRNA expression of studied HKGs; these were: smoking, gender, treatment, and age. Further, mRNA expression stability of all ten HKGs was validated in the second, independent BAL cohort consisting of seventeen control subjects and sixty-three sarcoidosis patients with special emphasis on patient subgroups. Finally, by investigation of mRNA expression of two cytokines known associated with sarcoidosis, INFG (interferon gamma) and CCL2/MCP-1, we provided practical evidence, that normalisation with validated reference genes in clinical samples is absolute prerequisite for obtaining clinically unbiased valid information from qRT-PCR.</p>" ]
[ "<title>Methods</title>", "<title>Subjects</title>", "<p>BAL was performed according a standard procedure [##REF##8343944##20##] in 71 Caucasian subjects (1st cohort) with lung diseases diagnosed between 2004 and 2006 in one referral centre in the Czech Republic (Faculty Hospital Olomouc). The diagnoses were in compliance with the criteria from the International Statements/Standards of these diseases: 26 patients with interstitial lung diseases (sarcoidosis, idiopathic interstitial pneumonia, secondary fibrosis, asbestosis, lipoproteinosis and silicosis), 19 cancer patients and 26 COPD patients. For clinical and laboratory characteristics of studied subjects see Table E1 in the Additional file ##SUPPL##1##2##.</p>", "<p>The subgroups based on gender (45 males/26 females), smoking status (28 smokers/40 non-smokers), treatment before BAL (24 untreated/47 treated), age (median age of 60 years as the division point; 36 patients &gt;60 years/35 patients ≤ 60 years), and groups with normal (N)/pathological (P) differential BAL cell counts were also analyzed. The reference values for BAL cell counts (≥ 85% macrophages, ≤ 11% lymphocytes, &lt;3% neutrophils, ≤ 1% eosinophils) were based on our own laboratory values and correspond to Meyer [##REF##15564013##21##]. The subgroups according BAL cell composition were as follows: 37 N/28 P macrophage-, 46 N/19 P lymphocyte-, 48 N/17 P neutrophil- and 45 N/20 P eosinophil-counts.</p>", "<p>The second cohort, used for validation of mRNA expression stability of studied HKGs, consisted of 80 subjects: 63 patients with pulmonary sarcoidosis and 17 control subjects. The control group consisted of subjects (11 males, 6 females; 11 non-smokers, 5 smokers, 1 subject with unknown smoking history; age 42.2 ± 15.7 yrs) undergoing BAL within medical examination for \"non-inflammatory condition\" e.g. psychogenic cough. All had normal BAL fluid cytology, immunology, and microbiology &amp; CD4+/CD8+ ratio. For clinical and laboratory characteristics of studied subjects from the second cohort see Table E1 in the Additional file ##SUPPL##1##2##. None of the patients in the second cohort received corticosteroid therapy before BAL. The subgroups in the second cohort were based on gender (37 males/43 females), smoking status (23 smokers/56 non-smokers), and presence of lung disease (63 sarcoidosis patients/17 control subjects). Further subgroups were formed within the sarcoidosis patient group: based on the presence/absence of Löfgren's syndrome (LS) (11 patients with LS/52 patients without LS), involvement of parenchyma (17 patients with chest X-ray stage I/46 patients with chest X-ray stages II and III), involvement of other organs than lung (40 patients with only involvement of lung/23 patients with multiorgan involvement) and groups with normal (N)/pathological (P) differential BAL cell counts. The subgroups according BAL cell composition were as follows: 15 N/48 P macrophage-, 14 N/49 P lymphocyte-, 56 N/7 P neutrophil- and 58 N/5 P eosinophil-counts.</p>", "<p>The study was approved by the Ethics Committee of the Medical Faculty Palacky University &amp; Faculty Hospital Olomouc. All subjects signed informed consent about usage of an aliquot of BAL sample, taken primarily for diagnostic purposes, also for the research purposes of this study.</p>", "<title>BAL sample processing</title>", "<p>BAL cells (0.5–1.5 × 10<sup>6</sup>) were separated from the BAL fluid and washed as previously described [##REF##12449175##22##]. Briefly, BAL samples were filtered through one gauze layer followed by separation of BAL cells by centrifugation (400 g, 4°C). The cells were washed twice with 10 ml ice-cold PBS-DEPC, counted and resolved in 50 μl PBS-DEPC. After immediate addition of RNAlater (300 μl; Ambion, Austin, TX, USA), the cells were stored at 4°C overnight and then at -20°C until use. The time between BAL procedure and processing of sample did not exceeded 2 hours.</p>", "<title>Total RNA isolation and quality assessment, reverse transcription</title>", "<p>The cells stored in RNAlater were recovered by centrifugation (4000 g, 4°C, 45 min) after 1:2 dilution with ice-cold PBS-DEPC as recommended by the manufacturer. Total RNA was isolated using mirVana miRNA kit (Ambion) and genomic DNA was eliminated by TurboDNAfree kit (Ambion) according to the manufacturer's recommendation. The quantity and quality of RNA samples were assessed by 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA) using RNA 6000 Nano assays.</p>", "<p>Reverse transcription (0.5 μg total RNA, total volume of 20 μl) was performed with Reverse-iT RTase Blend using anchored dT primers (0.4 μg; ABgene, Epsum, U.K.) at 47°C for 45 min in triplicates and then combined. All cDNA samples were diluted to 4 ng input total RNA/μl and stored in aliquots at -20°C until use.</p>", "<title>Gene expression measurements by qRT-PCR</title>", "<p>Fluorescently labelled Locked Nucleic Acid probes (LNA, Universal ProbeLibrary; Roche Applied Science, Indianapolis, USA) and the primers (Metabion, Munich, Germany) for investigated genes (Table ##TAB##0##1##) were selected using ProbeFinder assay design tool <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.universalprobelibrary.com\"/> (Table ##TAB##1##2##). PCR reaction mixes were prepared as follows: equal amount of cDNA (5 μl, corresponding to 20 ng calculated on input total RNA) for each individual gene was added to 20 μl PCR-Mix (ABgene). The final concentrations were 900 nM each sense and antisense primers, 100 nM LNA probe, 3.5 mM MgCl2, 200 μM each dNTPs, 1 U Thermo-Start TAQ polymerase, 1× Thermo-Start Buffer (ABgene). After initial denaturation (one cycle at 94°C for 15 min), 40 cycles amplification (94°C for 45 s, 60°C for 30 s) were performed on RotorGene 3000 system (Corbett Research, Sydney, Australia).</p>", "<p>Relative expression was calculated using second derivative method (Additional file ##SUPPL##2##3##) (RotorGene Software 6.1.71, Corbett Research) as follows: Expression = average amplification<sup>(<italic>CTtcalibrator</italic>-<italic>CTtsample</italic>)</sup>. cDNA from human universal reference RNA (Stratagene, La Jolla, CA, USA) was used as calibrator (in quadruplicates) at concentration of 1.25 ng/reaction calculated on input RNA. For definition of the second derivative method, Takeoff point (CTt) and average amplification see Additional file ##SUPPL##2##3##.</p>", "<title>Statistical analysis</title>", "<p>Descriptive statistics, F-test for CTt variance equality, Kolmogorov-Smirnov test for normality of log-transformed relative expression values were calculated by software SPSS 13.0 (SPSS Inc, Chicago, IL, USA). Log-transformed relative expression values for INFG and CCL2 were used for statistical calculations by Student's t-test, one-way ANOVA. <italic>P </italic>&lt; 0.05 was considered significant. Equivalence test [##UREF##0##23##,##REF##15519565##24##], statistical applets BestKeeper [##REF##15127793##25##], geNorm [##REF##12184808##26##] and NormFinder [##REF##15289330##27##] were used for the analysis of gene expression stability. Normalisation factors (NF) for genes and gene pairs were calculated according to Vandesompele et al [##REF##12184808##26##]. For more details on statistical approaches and calculation of normalisation factor see the Additional file ##SUPPL##0##1##.</p>" ]
[ "<title>Results</title>", "<title>Quality of RNA isolated from BAL samples</title>", "<p>All investigated RNA samples were of good quality, mean RIN (RNA Integrity Number) values (± S.D.) were 7.4 ± 1.0 (range from 5.5 to 8.6). Among all samples, ratios 28S:18S varied between 1.0–1.4 with no visible degradation products (Fig. E1 in Additional file ##SUPPL##3##4##).</p>", "<title>Amplification efficiency and reproducibility of qRT-PCR with fluorescently labelled LNA-probes</title>", "<p>In order to determine the amplification efficiency for all studied genes, 5-point standard curves with known concentrations of transcribed human universal reference RNA were constructed. The amplification efficiencies of LNA-based qRT-PCR for studied HKGs varied between 95 to 100%, except for ARF1 where the amplification efficiency of 85% was achieved. The linear regression coefficient (R<sup>2</sup>) for all ten genes ranged between 0.998–0.999. Based on 16 replicates, intra-assay variation of less than 0.7% and inter-assay variation of less than 1.6% were achieved. Negative controls using not transcribed RNA samples for all genes were negative.</p>", "<title>Gene expression levels of ten housekeeping genes within the whole 1st cohort sample set</title>", "<p>In order to evaluate gene expression levels of all studied HKGs within the whole patient sample set of the 1st cohort, mRNA expressions for every gene were measured in individual BAL samples. Gene expression levels in individual samples showed a broad range of variance between CTt 13.1 (for GAPDH) and CTt 29.20 (for PSMD2) (Fig. ##FIG##0##1##). Out of ten studied genes, ACTB (mean CTt 17.92) and RPL32 (mean CTt 18.65) were expressed at the highest levels; PSMD2 (mean CTt 25.55) and GPS1 (mean CTt 24.86) at the lowest levels in BAL cells. The lowest expression variability within all samples was observed for the gene PSMB2 (mean CTt ± SD, 23.66 ± 0.86) and RPL32 (18.65 ± 0.92). Genes PSMD2 (25.55 ± 1.67) and GNB2L1 (21.97 ± 1.54) showed the most variable expression within the sample set. F-test showed that PSMB2 and RPL32 had significantly lower variance of CTt values when compared to CANX, GNB2L1, ACTB, PSMD2, ARF1, GPS1, G6PD and GAPDH (<italic>p </italic>&lt; 0.02). Descriptive statistics of gene expression data and corresponding absolute x-fold change values for all studied genes calculated by the applet Bestkeeper are shown in Table ##TAB##2##3##.</p>", "<title>Analysis of expression stability of ten HKGs in BAL cells from the 1st patient cohort by equivalence test and statistical applets Bestkeeper, geNorm and NormFinder</title>", "<p>In order to find out the most suitable reference genes for normalisation of gene expression in BAL cells, four different statistical approaches (equivalence test, applets Bestkeeper, geNorm and NormFinder) were applied in parallel to assess the gene expression stability of ten HKGs within the whole sample set and also in patient subgroups based on gender, smoking status, treatment, disease type, age, and BAL differential cell counts.</p>", "<title>a) Equivalence test</title>", "<p>In order to identify the most stably expressed genes in patient subgroups by equivalence test, we applied two-fold expression change cut-off for group-wise comparisons. Genes GAPDH and PSMD2 were identified as the least stably expressed genes in BAL samples, equivalently expressed only in subgroups according gender and age (Fig. ##FIG##1##2##, data for age comparison not shown). Genes ARF1, ACTB, CANX, GAPDH, GNB2L1, G6PD, GPS1, PSMD2 were found not equivalently expressed in more than two of eight studied subgroups. Out of all studied genes, only PSMB2 and RPL32 were found equivalently expressed in all studied subgroups (Fig. ##FIG##1##2##). The comparison of results of equivalence tests for two most stable genes (PSMB2, RPL32) and two \"traditional\" reference genes (ACTB, GAPDH) in all subgroups is shown in Fig. ##FIG##2##3##.</p>", "<title>b) Analysis by BestKeeper</title>", "<p>Analysis by the applet BestKeeper showed that only two genes (PSMB2 and RPL32) are stably expressed within the whole data set (Table ##TAB##2##3##), as well as in all studied subgroups based on gender, smoking status, treatment, type of the disease, age and BAL cellular composition (data not shown). The expression of genes ACTB, ARF1, CANX, GAPDH, G6PD, GPS1, GNB2L1 and PSMD2 has to be considered as inconsistent (standard deviation of the CTt value &gt; 1), thus they were excluded from further analysis. When the regression analysis was performed with two stable genes, the PSMB2 was shown to be more suitable as a reference gene (coefficient of correlation, r = 0.914; <italic>p </italic>= 0.001) than RPL32 (r = 0.865; <italic>p </italic>= 0.001).</p>", "<title>c) Analysis by geNorm</title>", "<p>Average expression stability measure of ten HKGs in the whole sample group during stepwise exclusion of the least stable genes by the applet geNorm resulted in following gene order: the most stable-ACTB-GPS1-GNB2L1-ARF1-PSMB2-RPL32-CANX-G6PD-PSMD2-GAPDH-the least stable. When the applet was applied to particular subgroups, we obtained various ranking lists of suitable reference genes for various subgroups: e.g. ACTB and GNB2L1 were the most stable genes for smokers and ACTB and PSMB2 for non-smokers; PSMB2 and RPL32 were the most stable genes for males and ACTB and GNB2L1 for females.</p>", "<title>d) Analysis by NormFinder</title>", "<p>Analysis by the applet NormFinder ranked ten genes according their expression stability in the whole patient set in the following order: the most stable-ACTB-PSMB2-GNB2L1-ARF1-GPS1-RPL32-CANX-G6PD PSMD2-GAPDH-the least stable. However, we obtained various ranking lists when we calculated the expression stability of ten investigated HKGs in subgroups: e.g. genes PSMB2 and ARF1 were the most stable genes in subgroups based on smoking status (smokers vs. non-smokers), ACTB and GNB2L1 were the most stable genes in subgroups based on gender (males vs. females).</p>", "<title>Validation of expression stability of ten HKGs in BAL cells from the 2nd patient cohort by equivalence test</title>", "<p>In order to confirm that PSMB2 and RPL32 genes, identified as the most stable genes in the aforementioned analyses in the 1st cohort, has indeed the most stable mRNA expression unaffected by range of tested variables, we investigated gene expression of all ten genes in the second, independent BAL cohort (63 patients with pulmonary sarcoidosis and 17 control subjects) by equivalence test. The relative gene expression values for all genes were compared among the patient subgroups based on gender, smoking status, and clinical characteristics such as presence of disease, presence of Löfgren's syndrome, involvement of parenchyma, involvement of other organs than lung and BAL differential cell counts (Fig. ##FIG##3##4##, Fig. E2 in Additional file ##SUPPL##4##5##). Out of ten studied genes, only PSMB2 and RPL32 genes were equivalently expressed in all tested subgroups of the second cohort.</p>", "<title>Assessment on the minimal number of reference genes for normalisation of qRT-PCR in BAL cells</title>", "<p>In order to evaluate the minimal number of reference genes for normalisation of qRT-PCR in BAL cells, we calculated the normalisation factors for novel reference genes and their combination (PSMB2, RPL32, PSMB2-RPL32) and for the \"traditional\" reference genes in lung settings (ACTB, GAPDH) in all individual samples in both cohorts separately. Generally, the most suitable reference genes are the genes with mean NF value closest to 1 and with the lowest SD. Gene PSMB2 alone showed the lowest mean NF, SD and coefficient of variation (CV) in both cohorts (1. cohort: mean NF ± SD, CV: 1.27 ± 0.89, 70%; 2. cohort: 1.17 ± 0.63, 54%). Gene RPL32 alone showed in both cohorts the same mean NF as PSMB2 gene, but higher SD and CV (1. cohort: 1.27 ± 0.93, 73%; 2. cohort: 1.17 ± 0.71, 61%). Pairing of PSMB2 with RPL32 did not significantly improve the mean NF value and the variability (1. cohort: 1.29 ± 0.91, 72%; 2. cohort: 1.14 ± 0.59, 52%) compared to PSMB2 or RPL32 alone. Genes ACTB (1. cohort: 1.64 ± 1.52, 93%; 2. cohort: 1.20 ± 0.75, 63%) and GAPDH (1. cohort: 1.60 ± 2.98, 186%; 2. cohort: 1.22 ± 0.76, 62%) were found less suitable as reference genes for BAL cells. We, therefore, recommend single genes PSMB2 and RPL32 as denominators for gene expression studies in BAL cells.</p>", "<title>Effect of the used reference gene on relative target gene expression values: study of mRNA expression of INFG and CCL2 known to be associated with pulmonary sarcoidosis (2. cohort)</title>", "<p>In order to demonstrate the effect of used reference genes on the result of target gene expression data in BAL cells, we investigated relative mRNA expression of two cytokines known to be associated with sarcoidosis, INFG and CCL2, in sarcoidosis patients and control subjects (2nd cohort). The following genes were applied as denominators: 1) reference genes validated in our study (PSMB2, RPL32) and 2) \"traditional\" reference genes (ACTB, GAPDH). The data are presented as a mean fold change of relative expression compared to control subjects (normalized to 1).</p>", "<p>Relative mRNA expression levels of INFG were higher in sarcoidosis patients than in control subjects when the normalisation was done with gene PSMB2 (fold change ± SD: 2.56 ± 1.62; <italic>p </italic>= 0.004), with gene RPL32 (2.58 ± 1.46; <italic>p </italic>= 0.004), and with gene pair PSMB2-RPL32 (2.44 ± 1.20; <italic>p </italic>= 0.02) (Fig. ##FIG##4##5##). When the expression level of INFG was normalised to ACTB (2.42 ± 1.71; <italic>p </italic>= 0.053) or to GAPDH (1.95 ± 1.48; <italic>p </italic>= 0.09), the mRNA expression of INFG did not differ between control subjects and sarcoidosis patients (Fig. ##FIG##4##5##).</p>", "<p>Similar, when CCL2 mRNA levels were expressed as a ratio to ACTB (1.00 ± 0.85; <italic>p </italic>= 0.46) or to GAPDH (1.37 ± 0.98; <italic>p </italic>= 0.43), there were not significant differences in mRNA levels in BAL cells between sarcoidosis patients with chest radiographic stage 2 and stage 1 patients. Using genes PSMB2 (1.95 ± 1.66; <italic>p </italic>= 0.02) and gene RPL32 (1.77 ± 1.25; <italic>p </italic>= 0.03), and gene pair PSMB2-RPL32 (1.65 ± 1.17; <italic>p </italic>= 0.02) as denominators, CCL2 mRNA levels differed between chest radiographic stage 2 and stage 1 sarcoidosis patients (Fig. ##FIG##4##5##).</p>" ]
[ "<title>Discussion</title>", "<p>Aiming at finding suitable reference genes for quantitative gene expression profiling studies in bronchoalveolar cells, we have investigated the gene expression of ten housekeeping genes selected according their expression stability reported in literature or their common use in qRT-PCR. Out of these, two genes PSMB2 and RPL32 were found constantly expressed in unseparated BAL cells from seventy-one subjects irrespective of lung pathology, smoking status, gender, treatment, age and BAL cellular composition. The stability of mRNA expression of PSMB2 and RPL32 genes was further validated in the second, independent BAL cohort of sixty-three sarcoidosis patients and seventeen control subjects. By contrast to PSMB2 and RPL32, expression levels of genes ACTB, ARF1, CANX, GAPDH, G6PD, GPS1, GNB2L1 and PSMD2 considerably varied among studied patient subgroups in both investigated cohorts thus making these genes less suitable for the normalisation in qRT-PCR. We, therefore, recommend PSMB2 and RPL32 as suitable reference genes for the normalisation of the gene expression in unseparated BAL cells, namely in interstitial lung diseases. Moreover, based on our data, PSMB2 and RPL32 represent promising candidate reference genes for other lung pathologies such as COPD and cancer. Finally, we demonstrated on the example of INFG and CCL2 mRNA expression in sarcoidosis that the normalisation with validated reference genes in clinical samples is absolute prerequisite for obtaining clinically meaningful information from qRT-PCR.</p>", "<p>Although qRT-PCR is an established method for quantifying of mRNA expression in BAL samples, normalisation for differences among individual samples is the major difficulty of this methodology [##REF##15283208##1##,##REF##15331581##7##]. Several normalisation strategies can be applied: normalisation to sample volume, to total RNA and to internal reference genes or their combination. Normalisation to equal volumes on its own is not suitable for respiratory settings because BAL samples differ in cell counts and cellular composition. The other approach, the normalisation for quantity of total RNA, is disqualified because it does not correct for differences in RNA quality and in reverse transcriptase efficiencies among samples [##REF##9894600##28##]. Nowadays, the endogenous reference genes represent the most suitable and easiest way for normalisation of clinical samples in qRT-PCR [##REF##15283208##1##,##REF##7681631##29##, ####REF##11328886##30##, ##REF##10948434##31##, ##REF##9918038##32####9918038##32##]. Moreover, reference genes may correct also for differences in RNA integrity among the samples [##REF##16469371##33##,##REF##15951835##34##]. Similarly to Huggett et al [##REF##15815687##3##], we affirm that the combination of similar sample size, similar RNA concentration in reverse transcription and use of validated reference genes represents the proper normalisation strategy for BAL samples.</p>", "<p>Although it is known that the normalisation with unsuitable reference gene may lead to misinterpretation of target genes expression data [##REF##12200519##17##,##REF##17026756##35##], most investigators have generally used the genes GAPDH and ACTB as reference genes to normalize qRT-PCR in lung settings without previous validation [##REF##11463599##12##, ####REF##15095321##13##, ##REF##16484684##14##, ##REF##15579727##15##, ##REF##16394278##16####16394278##16##]. The reason may be the fact that the known approaches for validation of reference gene stability have been introduced mainly for cell cultures and tissues [##REF##15956331##2##,##REF##12184808##26##,##REF##15289330##27##,##REF##15543203##36##] and no general approach for validation of reference genes in clinical samples is recommended nowadays. In our study, we applied four different mathematical and statistical models to select stably expressed HKGs genes in BAL samples. Similarly to Robinson et al [##REF##17074403##37##], we observed that the output of the most suitable reference genes using pair-wise approach geNorm [##REF##12184808##26##] is influenced by chosen set of candidate genes, and the ranking of the genes occurs according the similarity in expression profiles [##REF##15289330##27##]. Another applet, the model-based approach NormFinder [##REF##15289330##27##], takes already into account the individual gene expression variability and calculates the gene expression stability in subgroups. By contrast to Andersen et al [##REF##15289330##27##], who compared gene expression in two types of cancer tissues, we aimed to investigate the influence of many variables (e.g. gender, smoking, age, BAL cellular composition, lung pathology and treatment) on the expression stability of studied genes. Doing so, we obtained various ranking lists of suitable reference genes for various subgroups, thus making NormFinder approach less suitable for our purpose. Using the third used approach, the BestKeeper applet, only PSMB2 and RPL32 were revealed as stably expressed genes in BAL samples, the eight remaining genes were excluded from further analyses as inconsistently expressed [##REF##15127793##25##]. Moreover, the limitation of this approach is the use of Pearson's correlation [##REF##15127793##25##], which makes it unsuitable for analyses of non-normally distributed data commonly observed in clinical sample sets. The heterogeneity of the results obtained by the statistical applet Bestkeeper, geNorm and NormFinder and also having in mind our aim to identify genes stably expressed irrespective of many variables (gender, smoking, BAL cellular composition, lung pathology and medication) contributed to our final decision to apply equivalence test into our analyses of HKGs expression stability in BAL cells. Doing so, to exclude genes with high expression variability within the sample set and studied subgroups, we set very strict criteria corresponding to two-fold change in gene expression [##REF##15519565##24##]. Similarly to previous reports [##REF##16452584##10##,##REF##12200519##17##] we observed that \"traditional\" reference genes like ACTB and GAPDH are indeed unsuitable for normalisation of gene expression in BAL cells. Even in the case of GNB2L1, gene recommended as a reference gene for BAL macrophages from COPD patients [##REF##16452584##10##], we observed that its expression in BAL cells is influenced by lung pathology, treatment and by eosinophil and neutrophil counts in BAL samples. Only two genes, PSMB2 and RPL32, were found constantly expressed in all studied subgroups irrespective of smoking, gender, treatment, age, lung pathology and BAL cellular composition. To enhance the evidence about invariable expression of PSMB2 and RPL32 genes, their expression was further validated in second, independent BAL cohort of patients with sarcoidosis and control subjects. We are aware that we dealt mostly with bronchoalveolar cells from interstitial lung diseases and sample sizes of other diseases have been limited. Addressing this limitation in the future is prerequisite for definite conclusion about general usage of PSMB2 and RPL32 as reference genes for expression studies in lung compartment as a whole.</p>", "<p>PSMB2 belongs to the group of genes encoding for constitutively expressed 20S proteasomal core subunits, RPL32 is a gene encoding for a component of the 60S ribosomal subunit. Various ribosomal proteins have been already validated for qRT-PCR: RPL13A for the pancreas and the prostate tissues [##REF##12138232##38##], LRP10 for adipose tissue [##REF##15897472##39##], RPL32 for human neutrophils [##REF##15720708##19##] and BAL macrophages from COPD patients, where it was stable irrespective of disease severity [##REF##16452584##10##]. PSMB2 showed only 29% variation in expression among 19 human tissues by microarray technique [##REF##11773596##8##] and here we show for the first time its suitability as a reference gene for qRT-PCR also in unsepared BAL cells.</p>", "<p>There has been ongoing discussion about the minimal number of reference genes required for qRT-PCR in clinical samples. Although the combination of more than one normalisation gene resulted in improved accuracy in several studies [##REF##12184808##26##,##REF##15289330##27##,##REF##15945375##40##, ####REF##18460208##41##, ##REF##18226276##42####18226276##42##], other investigators showed that normalisation with a single gene is sufficient for most research applications [##REF##15543203##36##,##REF##18211679##43##, ####REF##16399877##44##, ##REF##16600798##45####16600798##45##]. Also our analyses showed that the combination of two most stable genes (PSMB2 and RPL32) did not yield improved precision over normalisation with PSMB2 or RPL32 genes alone. We, therefore, suggest that the use of single reference genes PSMB2 or RPL32 is sufficient for normalisation of target gene expression in BAL cells, at least in interstitial lung diseases, where we validated their expression stability in the second, independent BAL cohort. PSMB2 gene is a moderate-copy gene, thus can better control for RNA isolation efficiency, RNA quality and RT-efficiency than RPL32 expressed at high abundance.</p>", "<p>In order to demonstrate that the normalisation with reference genes with variable expression may indeed lead to the misinterpretation of target gene expression and even to missing the identification of clinically relevant molecules, we applied the newly defined reference genes for investigation of mRNA levels of two cytokine genes reported to be associated with sarcoidosis. These were: Th1 cytokine INFG, which mRNA and protein was elevated in Th1 polarised sarcoidosis [##REF##3923038##46##,##REF##10957763##47##] and CC chemokine ligand (CCL)-2/MCP-1, implicated in the development of sarcoid alveolitis namely in chest X-ray stage 2 disease [##REF##12449175##22##]. In our patients, increase of INFG mRNA in sarcoid BAL cells was observed only when PSMB2/RPL32 were used as denominators in the normalization procedure. Controversially, normalization of INFG transcripts to ACTB/GAPDH did not resulted in INFG mRNA up-regulation. Similarly, CCL2 mRNA up-regulation in sarcoid chest X-ray stage 2 disease was observed when stably expressed reference genes PSMB2/RPL32 were used. Use of ACTB/GAPDH as denominators again yielded inconclusive, ambiguous expression data. By these reports we emphasize that our results provide an important and clear message for pulmonary science because only using validated (i.e. stably expressed) reference genes for normalization will one ensure that detected changes in target gene expressions in BAL samples are valid and therefore clinically meaningful. By contrast, usage of genes with variable expression such as ACTB or GAPDH for normalization leads to misinterpretation of target gene expression in lung samples.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, our study aimed at identifying stable genes, the expression of which is not influenced by variables such as smoking, gender, age, lung pathology, treatment and BAL cellular composition. Genes PSMB2 and RPL32 fulfilled the above criteria, and, therefore, they represent suitable normalisation genes for qRT-PCR in bronchoalveolar cells, namely for studies in sarcoidosis and other interstitial lung diseases.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>For accuracy of quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), normalisation with suitable reference genes is required. To date, no reference genes have been validated for expression studies of bronchoalveolar (BAL) cells. The aims of this study were to identify gene(s) with stable mRNA expression in BAL cells irrespective of gender, smoking, BAL cellular composition, lung pathology, treatment; and to assess the influence of reference genes on target gene expression data.</p>", "<title>Results</title>", "<p>The mRNA expression of ten housekeeping genes (ACTB, ARF1, CANX, G6PD, GAPDH, GPS1, GNB2L1, PSMB2, PSMD2, RPL32) was investigated by qRT-PCR in BAL cells from 71 subjects across a spectrum of lung diseases. The analyses were validated in an independent BAL cohort from 63 sarcoidosis patients and 17 control subjects. A second derivative method was used to calculate expression values (CTt); an equivalence test, applets BestKeeper, geNorm and NormFinder were applied to investigate gene expression stability. Of the investigated genes, PSMB2 (CTt ± SD, 23.66 ± 0.86) and RPL32 (18.65 ± 0.92) were the most stable; both were constantly expressed in BAL samples from parallel investigated cohorts irrespective of evaluated variables. Finally, to demonstrate effect of traditional (ACTB/GAPDH) and novel (PSMB2/RPL32) reference genes as denominators, expression of two cytokines known associated with sarcoidosis was investigated in sarcoid BAL cells. While normalization with PSMB2/RPL32 resulted in elevated IFNG mRNA expression (<italic>p </italic>= 0.004); no change was observed using GAPDH/ACTB (<italic>p </italic>&gt; 0.05). CCL2 mRNA up-regulation was observed only when PSMB2/RPL32 were used as denominators (<italic>p </italic>&lt; 0.03).</p>", "<title>Conclusion</title>", "<p>PSMB2 and RPL32 are, therefore, suitable reference genes to normalize qRT-PCR in BAL cells in sarcoidosis, and other interstitial lung disease.</p>" ]
[ "<title>Authors' contributions</title>", "<p>EK as the main author conceived, designed and interpreted the study and was the primary author of the drafts and of the final version of the paper. AA performed the statistical analysis and contributed to writing the paper. RF performed the gene expression analyses and collected the clinical and gene expression data. FM and ZN collected the clinical patient characteristics. VK and JZ performed the bronchoalveolar lavage, selected the patients and helped to collect the clinical patient characteristics. RdB helped to design the study and contributed to writing the paper. MP is the person responsible for the integrity of the study; he participated in study conception &amp; design, sample acquisition, interpretation &amp; writing the final version of the paper. All authors read and approved the final manuscript.</p>", "<title>Competing interests</title>", "<p>The authors declare that there are no competing interests.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The authors acknowledge the help of the staff of the Bronchoscopy Div., Dept. of Respiratory Medicine, Faculty Hospital Olomouc. Ms R. Langerova is thanked for technical assistance and Dr. J. Srovnal for measurements of RNA integrity. This study was supported by the Grant Agency of Czech Republic (No. 310/05/2614, E.K., M.P.), the Czech Ministry of Health (IGA MZ CR NR/9037, R.F., A.A.) and the Ministry of Schools, Youth and Sport of the Czech Republic (MSM6198959205).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Expression levels of ten housekeeping genes in bronchoalveolar cells from the 1st cohort</bold>. Expression levels of ten HKGs in CTt values over all BAL samples (n = 71). The data are expressed as whisker box plots; the box represents the 25th–75th percentiles, the median is indicated by a bar across the box, the whiskers on each box represent the minimum and maximum values.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Equivalence test for ten housekeeping genes in the 1st cohort subgroups based on the type of lung disease, treatment, smoking status, gender, and bronchoalveolar lavage cellular composition</bold>. Differences of the means (◆) and matching symmetrical confidence intervals (-) are shown for the log2-transformed relative expression of HKGs. Y-axis represents the fold change in expression among subgroups. The deviation area [-1; 1] for a fold change ≤ 2 lies within the dashed lines. If the symmetrical confidence interval is a part of the deviation area and contains zero in them, the gene is considered to be expressed equivalently. For more details on calculation see the Additional files and for statistical methodology the references [##UREF##0##23##,##REF##15519565##24##]. Mean differences were calculated as follows: Mean(interstitial diseases)-Mean(other lung diseases), Mean(treated)-Mean(untreated), Mean(males)-Mean(females), Mean(smokers)-Mean(non-smokers), and Mean(pathological BAL cell counts)-Mean(normal BAL cell counts) for macrophages, lymphocytes, neutrophils and eosinophils. Reference BAL cell counts were based on own laboratory values and correspond to Meyer [##REF##15564013##21##], for more details see Methods section.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Comparison of results of equivalence tests in all studied subgroups of the 1st cohort for two most stable genes PSMB2 (the left upper part) and RPL32 (the left lower part) and two most commonly used genes ACTB (the right upper part) and GAPDH (the right lower part) in bronchoalveolar (BAL) cells</bold>. 1 – Type of lung disease; 2 – Treatment; 3 – Smoking status; 4 – Gender; 5 – BAL Macrophage count; 6 – BAL Lymphocyte count; 7 – BAL Neutrophil count; 8 – BAL Eosinophil count. For more details see the legend to Fig. 2.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Equivalence test for ten housekeeping genes in the 2nd cohort subgroups based on the presence of disease, involvement of parenchyma, presence of Löfgren's syndrome, multi-organ involvement, smoking status, gender, and bronchoalveolar lavage cellular composition</bold>. Mean differences were calculated as follows: Mean(sarcoidosis patients)-Mean(control subjects), Mean(patients with involvement of parenchyma: CXR stages II/III)-Mean(patients without involvement of parenchyma: CXR stage I), Mean(Löfgren's syndrome patients)-Mean(non-Löfgren's syndrome patients), Mean(multi-organ involvement)-Mean(involvement of lung only), Mean(smokers)-Mean(non-smokers), Mean(males)-Mean(females), and Mean(pathological BAL cell count)-Mean(normal BAL cell count) for macrophages, lymphocytes, neutrophils and eosinophils. For more details see the legend to Fig. 2.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Comparison between the relative mRNA expression (ratio target gene/reference gene) of INFG gene (the upper part) and CCL2 gene (the lower part) in unseparated bronchoalveolar cells of sarcoidosis patients (S, n = 63) and control subjects (C, n = 17) from the 2nd cohort using newly validated (PSMB2/RPL32) and \"traditional\" reference genes (ACTB/GAPDH) as denominators</bold>. The data are presented as a mean fold change of relative expression compared to control subjects (normalized to 1); the whiskers on each box represent the SD values. For details see Methods section Gene expression measurements by qRT-PCR. *<italic>p </italic>&lt; 0.05.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Description of investigated genes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Gene abbreviation</bold></td><td align=\"left\"><bold>Gene name (synonyms)</bold></td><td align=\"left\"><bold>GenBank* Accession number</bold></td><td align=\"left\"><bold>Function</bold></td></tr></thead><tbody><tr><td align=\"left\">ACTB</td><td align=\"left\">actin, beta</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_001101\">NM_001101.2</ext-link></td><td align=\"left\">Cytoskeletal structural protein</td></tr><tr><td align=\"left\">ARF1</td><td align=\"left\">ADP-ribosylation factor 1</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_001658.3\">NM_001658.3</ext-link></td><td align=\"left\">Activator of phospholipase D</td></tr><tr><td align=\"left\">CANX</td><td align=\"left\">calnexin</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_001746.3\">NM_001746.3</ext-link></td><td align=\"left\">Molecular chaperone</td></tr><tr><td align=\"left\">G6PD</td><td align=\"left\">glucose-6-phosphate dehydrogenase</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"X03674.1\">X03674.1</ext-link></td><td align=\"left\">NADPH production</td></tr><tr><td align=\"left\">GAPDH</td><td align=\"left\">glyceraldehyde-3-phosphate dehydrogenase</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_002046.3\">NM_002046.3</ext-link></td><td align=\"left\">Glycolysis enzyme</td></tr><tr><td align=\"left\">GNB2L1</td><td align=\"left\">Homo sapiens guanine nucleotide binding protein (G protein), beta polypeptide 2-like 1</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_006098.4\">NM_006098.4</ext-link></td><td align=\"left\">Receptor for activated C-kinase</td></tr><tr><td align=\"left\">GPS1</td><td align=\"left\">G protein pathway suppressor 1</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"U20285.2\">U20285.2</ext-link></td><td align=\"left\">G protein suppressor</td></tr><tr><td align=\"left\">PSMB2</td><td align=\"left\">proteasome (prosome, macropain) subunit, beta type, 2</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_002794.3\">NM_002794.3</ext-link></td><td align=\"left\">Peptide cleavage</td></tr><tr><td align=\"left\">PSMD2</td><td align=\"left\">26S proteasome subunit p97</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"D78151.1\">D78151.1</ext-link></td><td align=\"left\">Peptide cleavage</td></tr><tr><td align=\"left\">RPL32</td><td align=\"left\">ribosomal protein L32, transcript variant 1</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_000994.3\">NM_000994.3</ext-link></td><td align=\"left\">Member of 80 different ribosome proteins</td></tr><tr><td align=\"left\">INFG</td><td align=\"left\">interferon gamma</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_000619.2\">NM_000619.2</ext-link></td><td align=\"left\">Cytokine</td></tr><tr><td align=\"left\">CCL2</td><td align=\"left\">CC chemokine ligand-2/MCP-1</td><td align=\"left\"><ext-link ext-link-type=\"gen\" xlink:href=\"NM_002982.3\">NM_002982.3</ext-link></td><td align=\"left\">Chemotactic cytokine</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Characteristics of used primers; LNA probes and amplicon sizes in reverse transcriptase-polymerase chain reaction reaction.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Gene abbreviation</bold></td><td align=\"center\"><bold>Amplicon size (basepairs)</bold></td><td align=\"left\"><bold>Sense, antisense primers</bold></td><td align=\"center\"><bold>LNA probe¥</bold></td></tr></thead><tbody><tr><td align=\"left\">ACTB</td><td align=\"center\">76</td><td align=\"left\">5'-attggcaatgagcggttc-3'<break/>5'-ggatgccacaggactccat-3'</td><td align=\"center\">#11</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">ARF1</td><td align=\"center\">70</td><td align=\"left\">5'-gccactacttccagaacacaca-3'<break/>5'-tcgttcacacgctctctgtc-3'</td><td align=\"center\">#56</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">CANX</td><td align=\"center\">108</td><td align=\"left\">5'-aacaccagaactcaacctgga-3'<break/>5'-tgtcggaagatgaagtgcag-3'</td><td align=\"center\">#55</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">G6PD</td><td align=\"center\">75</td><td align=\"left\">5'-ctggtggccatggagaag-3'<break/>5'-gcatttcaacaccttgacctt-3'</td><td align=\"center\">#22</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">GAPDH</td><td align=\"center\">78</td><td align=\"left\">5'-tccactggcgtcttcacc-3'<break/>5'-ggcagagatgatgaccctttt-3'</td><td align=\"center\">#45</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">GNB2L1</td><td align=\"center\">72</td><td align=\"left\">5'-gctactaccccgcagttcc-3'<break/>5'-cagtttccacatgatgatggtc-3'</td><td align=\"center\">#55</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">GPS1</td><td align=\"center\">66</td><td align=\"left\">5'-gcaaccagatccatgtcaagt-3'<break/>5'-tgttggctggagtcagctc-3'</td><td align=\"center\">#36</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">PSMB2</td><td align=\"center\">72</td><td align=\"left\">5'-agagggcagtggaactcctt-3'<break/>5'-aggttggcagattcaggatg-3'</td><td align=\"center\">#50</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">PSMD2</td><td align=\"center\">68</td><td align=\"left\">5'-gcctcacccagattgacaag-3'<break/>5'-ggcaagaagagctcctgactta-3'</td><td align=\"center\">#82</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">RPL32</td><td align=\"center\">75</td><td align=\"left\">5'-gaagttcctggtccacaacg-3'<break/>5'-gcgatctcggcacagtaag-3'</td><td align=\"center\">#17</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">INFG</td><td align=\"center\">112</td><td align=\"left\">5'-ggcattttgaagaattggaaag-3'<break/>5'-tttggatgctctggtcatctt-3'</td><td align=\"center\">#21</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">CCL2</td><td align=\"center\">93</td><td align=\"left\">5'-agtctctgccgcccttct-3'<break/>5'-gtgactggggcattgattg-3'</td><td align=\"center\">#40</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Descriptive and correlation analysis for ten housekeeping genes in the 1st cohort obtained by BestKeeper statistical applet.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"right\"><bold>RPL32*</bold></td><td align=\"right\"><bold>GAPDH</bold></td><td align=\"right\"><bold>ACTB</bold></td><td align=\"right\"><bold>GPS1</bold></td><td align=\"right\"><bold>ARF1</bold></td></tr></thead><tbody><tr><td align=\"left\">GM [CTt]</td><td align=\"right\">18.65</td><td align=\"right\">20.44</td><td align=\"right\">17.92</td><td align=\"right\">24.86</td><td align=\"right\">22.15</td></tr><tr><td align=\"left\">AM [CTt]</td><td align=\"right\">18.69</td><td align=\"right\">20.53</td><td align=\"right\">17.99</td><td align=\"right\">24.91</td><td align=\"right\">22.20</td></tr><tr><td align=\"left\">Min [CTt]</td><td align=\"right\">15.90</td><td align=\"right\">13.10</td><td align=\"right\">14.30</td><td align=\"right\">22.20</td><td align=\"right\">18.80</td></tr><tr><td align=\"left\">Max [CTt]</td><td align=\"right\">22.00</td><td align=\"right\">24.60</td><td align=\"right\">21.60</td><td align=\"right\">28.50</td><td align=\"right\">25.60</td></tr><tr><td align=\"left\">SD [± CTt]</td><td align=\"right\">0.92</td><td align=\"right\">1.34</td><td align=\"right\">1.21</td><td align=\"right\">1.24</td><td align=\"right\">1.14</td></tr><tr><td align=\"left\">CV [% CTt]</td><td align=\"right\">4.93</td><td align=\"right\">6.51</td><td align=\"right\">6.73</td><td align=\"right\">4.99</td><td align=\"right\">5.12</td></tr><tr><td align=\"left\">Min [x-fold]</td><td align=\"right\">-6.73</td><td align=\"right\">-161.54</td><td align=\"right\">-12.31</td><td align=\"right\">-6.34</td><td align=\"right\">-10.21</td></tr><tr><td align=\"left\">Max [x-fold]</td><td align=\"right\">10.19</td><td align=\"right\">17.93</td><td align=\"right\">12.80</td><td align=\"right\">12.43</td><td align=\"right\">10.92</td></tr><tr><td align=\"left\">SD [± x-fold]</td><td align=\"right\">1.89</td><td align=\"right\">2.53</td><td align=\"right\">2.31</td><td align=\"right\">2.37</td><td align=\"right\">2.20</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"right\"><bold>PSMD2</bold></td><td align=\"right\"><bold>G6PD</bold></td><td align=\"right\"><bold>GNB2L1</bold></td><td align=\"right\"><bold>CANX</bold></td><td align=\"right\"><bold>PSMB2*</bold></td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"left\">GM [CTt]</td><td align=\"right\">25.55</td><td align=\"right\">24.36</td><td align=\"right\">21.97</td><td align=\"right\">24.42</td><td align=\"right\">18.65</td></tr><tr><td align=\"left\">AM [CTt]</td><td align=\"right\">25.63</td><td align=\"right\">24.42</td><td align=\"right\">22.05</td><td align=\"right\">24.48</td><td align=\"right\">18.69</td></tr><tr><td align=\"left\">Min [CTt]</td><td align=\"right\">21.40</td><td align=\"right\">20.90</td><td align=\"right\">17.60</td><td align=\"right\">20.50</td><td align=\"right\">15.90</td></tr><tr><td align=\"left\">Max [CTt]</td><td align=\"right\">29.20</td><td align=\"right\">28.40</td><td align=\"right\">25.50</td><td align=\"right\">28.30</td><td align=\"right\">22.00</td></tr><tr><td align=\"left\">SD [± CTt]</td><td align=\"right\">1.67</td><td align=\"right\">1.34</td><td align=\"right\">1.54</td><td align=\"right\">1.25</td><td align=\"right\">0.92</td></tr><tr><td align=\"left\">CV [% CTt]</td><td align=\"right\">6.51</td><td align=\"right\">5.48</td><td align=\"right\">7.00</td><td align=\"right\">5.10</td><td align=\"right\">4.93</td></tr><tr><td align=\"left\">Min [x-fold]</td><td align=\"right\">-17.81</td><td align=\"right\">-11.03</td><td align=\"right\">-20.63</td><td align=\"right\">-15.17</td><td align=\"right\">-6.73</td></tr><tr><td align=\"left\">Max [x-fold]</td><td align=\"right\">12.51</td><td align=\"right\">16.42</td><td align=\"right\">11.58</td><td align=\"right\">14.69</td><td align=\"right\">10.19</td></tr><tr><td align=\"left\">SD [± x-fold]</td><td align=\"right\">3.18</td><td align=\"right\">2.53</td><td align=\"right\">2.91</td><td align=\"right\">2.38</td><td align=\"right\">1.89</td></tr></tbody></table></table-wrap>" ]
[]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Description of used statistical approaches.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional file 2</title><p><bold>Table E1</bold>. Clinical and laboratory characteristics of investigated subjects.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional file 3</title><p>Definition of terms.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional file 4</title><p><bold>Figure E1. RNA quality assessment (a representative example) by 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA)</bold>. This figure shows typical chromatogram of microcapillary electrophoresis of total RNA preparation of good quality extracted from bronchoalveolar lavage cells. Electropherogram shows 18S and 28S rRNA peaks. FU – Fluorescence units.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional file 5</title><p><bold>Figure E2. Expression levels of ten housekeeping genes in bronchoalveolar cells from sarcoidosis patients and normal subjects from the 2nd cohort</bold>. Expression levels of ten housekeeping genes in CTt values in bronchoalveolar cells from sarcoidosis patients (n = 63) a normal subjects (n = 17). The data are presented as means (columns) ± SD (errorbars). White columns represent the control group, dark columns sarcoidosis patients.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><italic>*</italic>Gene sequences available online at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/\"/></p></table-wrap-foot>", "<table-wrap-foot><p>¥Numbers of LNA probes according to the commercially available library <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.universalprobelibrary.com\"/></p></table-wrap-foot>", "<table-wrap-foot><p>Definition of abbreviations: GM [CTt], geometric mean of CTt; AM [CTt], arithmetic mean of CTt; Min [CTt] and Max [CTt], minimum and maximum values of CTt; SD [± CTt], standard deviation of the CTt; CV [%CTt], coefficient of variance expressed as a percentage on the CTt level. Min [x-fold] and Max [x-fold]: the extreme values of expression levels expressed as an absolute x-fold over- or under- regulation coefficient; SD [± x-fold]: standard deviation of the absolute regulation coefficients.</p><p>* Stably expressed genes were selected according to the criteria (SD [± CTt] &lt; 1) published in Pfaffl et al [##REF##15127793##25##].</p></table-wrap-foot>" ]
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[{"surname": ["Wellek"], "given-names": ["S"], "source": ["Testing Statistical Hypotheses of Equivalence"], "year": ["2003"], "publisher-name": ["London: Chapman and Hall/CRC Press"]}]
{ "acronym": [], "definition": [] }
47
CC BY
no
2022-01-12 14:47:26
BMC Mol Biol. 2008 Jul 31; 9:69
oa_package/06/0e/PMC2529339.tar.gz
PMC2529340
18655712
[ "<title>Background</title>", "<p>Investigators are actively testing interventions intended to increase lifespan [##REF##17578509##1##]. Caloric restriction (CR) is the intervention most well established as able to increase lifespan in experimental models [##REF##10630588##2##], and investigators are now seeking other interventions that may mimic the life-prolonging effects of CR without requiring a reduction in caloric intake [##REF##16626389##3##]. It is frequently said that CR not only increases average lifespan, but also 'maximum' lifespan [##REF##3346517##4##]. Many researchers in the field of aging therefore wish to test whether other interventions increase maximum lifespan.</p>", "<p>Recognizing this and the fact that one cannot be assured of observing population maximum lifespans in finite samples, Wang et al. [##REF##15491681##5##] constructed and validated several tests (hereafter, the <italic>'Wang-Allison tests'</italic>) of differences in the upper parts of lifespan distributions by building on the work of Redden et al. [##REF##15287086##6##] in the area of quantile regression. Wang et al. also showed that a commonly used test for differences in maximum lifespan that involved comparing the means of the top <italic>p</italic>% (e.g., top 10%) of each of two samples (e.g., a treatment and a control sample) was not valid in that it had an excessive type-1 error rate. Nevertheless, there is appeal to using the full continuity of information in the upper tails of the sample distribution, and colleagues have recently suggested to us that a limitation of the Wang-Allison tests is that they only treat individual lifespans as being above or below some threshold defining 'old' or being in the tail of the survival distribution. That is, the Wang-Allison tests do not consider <italic>how much </italic>above the threshold any particular observation is, only <italic>whether </italic>the observation is above the threshold. We acknowledge this limitation and in response, we herein develop new tests that utilize the continuity of information among observations that exceed the threshold of interest, are more powerful than competing tests, including the Wang-Allison tests, in most cases, and remain valid under the null hypothesis of no effect on 'maximum' lifespan.</p>" ]
[ "<title>Methods</title>", "<title>Development of the tests</title>", "<p>Consider an experiment with two groups, <italic>treatment </italic>and <italic>control</italic>. The extension to more than two groups is straightforward (see discussion section). Let <italic>X </italic>be an indicator variable taking the value 1 for observations in the treatment group and 0 for observations in the control group. Let <italic>Y </italic>denote survival time. Let <italic>τ </italic>denote some threshold chosen by the investigator to denote an extreme portion of the distribution. In survival studies, <italic>τ </italic>can be chosen in advance to correspond to an age considered 'old' (e.g., 30 months in mice) or set to some high sample percentile (e.g., the 90th). Critically important, <italic>τ </italic>must be set to the same value for the two groups. That is, if <italic>τ </italic>is to be defined by an upper sample quantile, it should be the upper sample quantile of both of the two groups combined, not of each group separately.</p>", "<p>Although not described in exactly these terms in the paper by Wang et al. [##REF##15491681##5##], the Wang-Allison tests essentially create a new variable, <italic>W</italic>, where for the i<sup>th </sup>subject, <italic>W</italic><sub><italic>i </italic></sub>≡ 0 if <italic>Y</italic><sub><italic>i </italic></sub>≤ <italic>τ</italic>, and <italic>W</italic><sub><italic>i </italic></sub>≡ 1 if <italic>Y</italic><sub><italic>i </italic></sub>&gt; <italic>τ</italic>, and subsequently tests whether <italic>W </italic>is associated with <italic>X </italic>using an appropriate test statistic.</p>", "<p>Thus, the Wang-Allison tests test the following null hypothesis:</p>", "<p></p>", "<p>A problem with the Wang-Allison tests is that, hypothetically, <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) may equal <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0) and yet the average magnitude by which lifespans exceed <italic>τ </italic>when X = 1 may be radically different than when X = 0. This is exemplified in the hypothetical frequency distributions depicted in Figure ##FIG##0##1##. Note that these hypothetical distributions are not intended to be realistic, but only to clarify the point.</p>", "<p>Let <italic>X</italic><sup>1 </sup>and <italic>X</italic><sup>0 </sup>denote the numbers of observations with <italic>Y</italic><sub><italic>i </italic></sub>&gt; <italic>τ </italic>in the treatment group and control group, respectively. The Wang-Allison tests use the test procedures for two independent binomial proportions [##REF##12926729##7##] and these procedures require that <italic>X</italic><sup>1 </sup>and <italic>X</italic><sup>0 </sup>are independent. In the Wang-Allison tests, if the threshold is set in advance according to prior knowledge, <italic>X</italic><sup>1 </sup>and <italic>X</italic><sup>0 </sup>can satisfy the requirement of independence. But if <italic>τ </italic>is set to be the 90-th percentile, <italic>X</italic><sup>1 </sup>and <italic>X</italic><sup>0 </sup>may not be independent, this creates a theoretical problem. However, on an empirical level, our simulations show that in the sample sizes we considered, this is not an apparent problem because the Wang-Allison tests have very high power and can control type I error quit well in the simulation studies and are practical for the lifespan studies). When <italic>X</italic><sup>1 </sup>and <italic>X</italic><sup>0 </sup>are not independent, simulation studies (including estimation of power and type I error) are an effective way to evaluate the methods (such as Wang-Allison tests) using the test procedures for two independent binomial proportions.</p>", "<p>An alternative to testing <italic>H</italic><sub>0,<italic>A </italic></sub>is to test the following conceptually related but mathematically distinct null hypothesis:</p>", "<p></p>", "<p>where <italic>μ </italic>(•) denotes the population mean (or expectation) of (•). Though appealing, a problem with testing <italic>H</italic><sub>0,<italic>B </italic></sub>is that when <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) &gt;&gt; <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0) or <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) &lt;&lt;<italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0), for any finite sample with equal initial assignment to the two groups, <italic>E </italic>[<italic>n</italic><sub>0</sub>] &lt;&lt;<italic>E </italic>[<italic>n</italic><sub>1</sub>] or <italic>E </italic>[<italic>n</italic><sub>0</sub>] &gt;&gt; <italic>E </italic>[<italic>n</italic><sub>1</sub>], where <italic>E </italic>[<italic>n</italic><sub>0</sub>] denotes the expected number of observations in the control group for which <italic>Y </italic>&gt; <italic>τ</italic>, and <italic>E </italic>[<italic>n</italic><sub>1</sub>] denotes the expected number of observations in the treatment group for which <italic>Y </italic>&gt; <italic>τ</italic>. This imbalance between <italic>E </italic>[<italic>n</italic><sub>0</sub>] and <italic>E </italic>[<italic>n</italic><sub>1</sub>] will greatly reduce the power to reject <italic>H</italic><sub>0,<italic>B</italic></sub>. In fact, in the extreme, when either <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) or <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0), there will be zero power to reject <italic>H</italic><sub>0,<italic>B </italic></sub>(actually, it is appropriate to say that <italic>H</italic><sub>0,<italic>B </italic></sub>is undefined in such cases). Such a situation is exemplified in the hypothetical frequency distributions depicted in Figure ##FIG##1##2##. Again, these hypothetical distributions are not intended to be realistic, but only to clarify the point.</p>", "<p>Thus, one can conceive situations in which the power to reject <italic>H</italic><sub>0,<italic>A </italic></sub>will be zero and yet the upper tails of the distribution are clearly different. Similarly, one can conceive situations in which the power to reject <italic>H</italic><sub>0,<italic>B </italic></sub>will be zero and yet again the upper tails of the distribution are clearly different. Hence, we propose a single-step union-intersection test [##UREF##0##8##] of the following compound null hypothesis:</p>", "<p></p>", "<p>We construct the test of <italic>H</italic><sub>0,<italic>C </italic></sub>with the following simple procedure. Define a new variable <italic>Z </italic>such that <italic>Z</italic><sub><italic>i </italic></sub>≡ <italic>I</italic>(<italic>Y</italic><sub><italic>i </italic></sub>&gt; <italic>τ</italic>)<italic>Y</italic><sub><italic>i</italic></sub>, where <italic>I</italic>(•) denotes the indicator function taking on values of one if (•) is true and zero otherwise. One can then simply conduct an appropriate test (several candidates will be considered below) of whether the population mean of Z is significantly different between the treatment and control groups. This approach (hereafter new <italic>tests</italic>), has several desirable properties. First and foremost, when an appropriate test statistic is used, the approach will be valid. That is, unlike the conditional t-tests (CTTs) commonly used and shown to be invalid by Wang et al. [##REF##15491681##5##], when <italic>H</italic><sub>0,<italic>C </italic></sub>is true, it will only be rejected 100*<italic>α</italic>% of the time at the nominal <italic>α </italic>level even if <italic>f</italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) ≠ <italic>f</italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0), where <italic>f</italic>(•) denotes the probability density function of (•).</p>", "<p>Note that expectation (or population mean) of <italic>Z</italic>, <italic>μ</italic>(<italic>Z</italic>) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>) <italic>μ</italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ</italic>). Therefore the new test for <italic>H</italic><sub>0,<italic>C </italic></sub>is really testing whether</p>", "<p></p>", "<p>while the method for <italic>H</italic><sub>0,<italic>B </italic></sub>is testing whether <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) = <italic>μ</italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0) and the method for <italic>H</italic><sub>0,<italic>A </italic></sub>is testing whether <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 1) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 0). The mean difference of <italic>μ</italic>(<italic>Z</italic>) between two groups consists of two components: the difference between probabilities <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 1) and <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 0) and the difference between expectations <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) and <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0). The test for <italic>H</italic><sub>0,<italic>A </italic></sub>focuses on the first component and the test for <italic>H</italic><sub>0,<italic>A </italic></sub>focuses on the second one, while the test for <italic>H</italic><sub>0,<italic>C </italic></sub>is related to both components.</p>", "<p>We also note that Dominici and Zeger [##REF##15872022##9##] studied similar mean difference components for two groups (cases and controls) by estimating the mean difference Δ(<bold><italic>v</italic></bold>) for the two groups conditional on a vector of covariates <bold><italic>v </italic></bold>for zero-inflated data through smooth quantile ratio estimation with regression,</p>", "<p></p>", "<p>where, <italic>Y </italic>is nonnegative random variable denoting the health expenditures. While Dominici and Zeger [##REF##15872022##9##] estimate the mean difference of nonnegative random variables (<italic>Y</italic>) for two groups, our methods test the mean difference of random variables (<italic>Y</italic>) which are greater than threshold <italic>τ</italic>.</p>", "<title>Evaluation of the tests</title>", "<p>We evaluate the tests via computer simulation. For each scenario simulated, we evaluate the tests at the 2-tailed .05 <italic>α </italic>level and at the 2-tailed .01 <italic>α </italic>level using 5,000 simulated datasets per scenario (except for permutation tests where we use 1,000 datasets per scenario and 1,000 random permutations by Monte Carlo sampling for each dataset). In simulation 1, we first evaluate performance in simulation under the null hypothesis <italic>H</italic><sub>0,<italic>C </italic></sub>(i.e., both <italic>H</italic><sub>0,<italic>A </italic></sub>and <italic>H</italic><sub>0,<italic>B </italic></sub>are true) and yet <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0). After showing that the tests remain valid even in these extreme circumstances, we compare their power in several scenarios (simulations 2–4) described below. For each scenario, we assumed that there were two groups with an equal number of subjects per group. We ran scenarios with 50, 80, or 100 subjects in each of the two groups, realistic sample sizes for animal model longevity research.</p>", "<p>We simulated data using a concatenation of Weibull distributions to flexibly emulate the data observed in a real study [##REF##15897478##10##] of obese animals (control; X = 0) versus animals that were obese and then lost weight via CR (treatment; X = 1). Specifically, For example, in simulations 1–4, we simulated Y from the following distribution:</p>", "<p></p>", "<p>where j = 0 to 1, lifespan (Y) is measured in weeks, a<sub>j,L </sub>and b<sub>i,L </sub>are the parameters of a Weibull distribution for the lower 90% of the distribution, and a<sub>j,U </sub>and b<sub>i,U </sub>are the parameters of a Weibull distribution for the upper 10% of the distribution. <italic>r</italic><sub><italic>j </italic></sub>is a proportion parameter, for example <italic>r</italic><sub><italic>j </italic></sub>= 0.9. The specific values of the parameters used are provided in Figure ##FIG##2##3##.</p>", "<title>Delineation of tests to be evaluated</title>", "<p>Each of the tests listed below was implemented in two manners, first with <italic>τ </italic>set in advance to a fixed lifespan value (130 weeks), and second with <italic>τ </italic>set at the sample 90<sup>th </sup>percentile of the two groups combined. In real-life situations, one usually <italic>does </italic>know the threshold of interest <italic>a priori</italic>. We do recognize that we will not have such knowledge in all cases. It is for this reason that when analyzing the simulated data, we also consider a threshold of the 90<sup>th </sup>percentile of the data allowing for an <italic>ad hoc </italic>data-based determination of a threshold.</p>", "<title>Tests of H<sub>0,A </sub>(Wang-Allison tests)</title>", "<p>For comparative purposes, the first category of tests we evaluated were the tests denoted QT3 and QT4 in Wang et al [##REF##15491681##5##] which are, respectively, Boschloo's test and an exact unconditional test based on the observed difference divided by its estimated standard error under the null hypothesis (score statistic) and are described in more detail by Mehrotra et al. [##REF##12926729##7##]. These were the two tests that Wang et al. [##REF##15491681##5##] had found performed best as tests of <italic>H</italic><sub>0,<italic>A</italic></sub>.</p>", "<title>Tests of H<sub>0,B</sub></title>", "<p>In testing <italic>H</italic><sub>0,<italic>B</italic></sub>, subjects were only included in the analysis when their lifespans exceeded <italic>τ</italic>. Distributions of survival times (lifespans) are rarely Gaussian and, even if they were nearly Gaussian after, for example, log transformation, the distribution of just the tail portion (i.e., <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>&gt; <italic>τ</italic>) would not be. Hence, in constructing tests we relied on nonparametric statistical methods. Specifically, we used the Wilcoxon-Mann-Whitney (exact) test [##UREF##1##11##,##UREF##2##12##] and a permutation test (with t-statistic) as described by Good [##UREF##3##13##] to test for differences in lifespan among those subjects whose lifespans exceeded <italic>τ</italic>.</p>", "<title>Tests of H<sub>0,C </sub>(new tests)</title>", "<p>In testing <italic>H</italic><sub>0,<italic>C</italic></sub>, all subjects were analyzed, but the variable analyzed was Z as defined above. Because the distribution of Z cannot be normally distributed, we again used the Wilcoxon-Mann-Whitney test and a permutation test to test for differences in Z.</p>", "<p>For a dataset with <italic>n</italic><sub>1 </sub>(<italic>n</italic><sub>2</sub>) subjects in treatment (control) group, the permutation test can be performed in the following way: First put all the (<italic>n</italic><sub>1 </sub>+<italic>n</italic><sub>2</sub>) subjects together, and then generate 1000 replicated datasets. For each replicated dataset, we randomly sample <italic>n</italic><sub>1 </sub>subjects from the (<italic>n</italic><sub>1 </sub>+<italic>n</italic><sub>2</sub>) subjects and assign them to treatment group, and assign the left <italic>n</italic><sub>2 </sub>subjects to control group. We run T-test on the observed dataset and the 1000 replicated datasets. Let <italic>T</italic><sub>0 </sub>be the T value for the observed dataset, then p-value for the permutation test is calculated as the proportion of replicated datasets with absolute T values greater than or equal to the absolute valued of <italic>T</italic><sub>0</sub>.</p>" ]
[ "<title>Results</title>", "<p>Results are displayed in Tables ##TAB##0##1## to ##TAB##4##5##. As can be seen, the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>controls type I error rates quite well. The power of the new methods are always higher than or very close to that of the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>(Wang-Allison tests) and are higher than that of the methods for tests of <italic>H</italic><sub>0,<italic>B </italic></sub>(Wilcoxon-Mann-Whitney tests and permutation tests for observations above the threshold <italic>τ</italic>) in some of the simulations.</p>", "<p>Table ##TAB##0##1## shows the type I error rate of the tests (in simulation 1) when the null hypothesis <italic>H</italic><sub>0,<italic>C </italic></sub>is true (i.e., both <italic>H</italic><sub>0,<italic>A </italic></sub>and <italic>H</italic><sub>0,<italic>B </italic></sub>are true) and yet <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically differentfrom <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0). The type I error rates of the new methods are comparable to those of the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>and those of the methods for tests of <italic>H</italic><sub>0,<italic>B </italic></sub>. It is note worthy that there is a slight but fairly consistent excess of type I errors when the sample 90<sup>th </sup>percentile is used rather than a fixed cutoff point. This is because the sample 90<sup>th </sup>percentile is a random variable and when it falls below its population level, the null hypothesis is no longer strictly true in our simulations. That is, the tests remain valid tests of differences in distributions above the actual value used but should not be strictly interpreted as tests of differences in distributions above the 90<sup>th </sup>(or any other percentile). In practice, this distinction is probably trivial.</p>", "<p>In simulation 2 (see Table ##TAB##1##2##), where <italic>H</italic><sub>0,<italic>A </italic></sub>is true, <italic>H</italic><sub>0,<italic>B </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0), the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>and the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>have lower power than that of the corresponding methods for tests of <italic>H</italic><sub>0,<italic>B</italic></sub>, however, the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>can slightly improve the power compared to the methods for tests of <italic>H</italic><sub>0,<italic>A</italic></sub>.</p>", "<p>Table ##TAB##2##3## shows the power of the tests in Simulation 3, where <italic>H</italic><sub>0,<italic>B </italic></sub>is true, <italic>H</italic><sub>0,<italic>A </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0). The new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>and the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>have very similar power which is much higher than that of the corresponding methods for tests of <italic>H</italic><sub>0,<italic>B</italic></sub>.</p>", "<p>From simulation 4 (see Table ##TAB##3##4##), where <italic>H</italic><sub>0,<italic>B </italic></sub>is false, <italic>H</italic><sub>0,<italic>A </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0) are identical, we can find that the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>always have higher power than the corresponding methods for tests of <italic>H</italic><sub>0,<italic>A</italic></sub>. When <italic>τ </italic>being set to the 90th percentile of the sample, the new methods also have higher power than the corresponding methods for tests of <italic>H</italic><sub>0,<italic>B</italic></sub>.</p>", "<p>Finally, we conducted a set of simulations under what we perceived to be the most realistic situations. Here both <italic>H</italic><sub>0,<italic>A </italic></sub>and <italic>H</italic><sub>0,<italic>B </italic></sub>are false, <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is quite different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0), and the distributions have no discontinuities. In other words, there is just a simple reduction in the hazard rate when X = 1. Table ##TAB##4##5## presents the power of the tests in Simulation 5, where <italic>f </italic>(<italic>Y</italic>|<italic>X </italic>= 1) = 1.2<italic>f </italic>(<italic>Y</italic>|<italic>X </italic>= 0). In this simulation, the methods for tests of <italic>H</italic><sub>0,<italic>B </italic></sub>almost have no power because the control group always has no or few observations above the threshold <italic>τ </italic>. The new methods for tests of <italic>H</italic><sub>0,<italic>C</italic></sub>, when using a permutation test, have power higher than or equal to that of the methods for tests of <italic>H</italic><sub>0,<italic>A</italic></sub>.</p>", "<title>Illustration with real data</title>", "<p>To illustrate the methods, we applied them to two real datasets. In both of these datasets, prior research had shown differences in overall survival rate and we tested for differences in 'maximum lifespan' herein. The first was a subset of data reported by Vasselli et al [##REF##15897478##10##]. The subset of the data consists of two groups of Sprague-Dawley rats, those kept on a high-fat diet ad libitum throughout life and becoming obese (EO-HF) and those kept on a high-fat diet ad libitum until early-middle adulthood, becoming obese, and subsequently reduced to normal weight via caloric restriction, but on the same high-fat diet (WL-HF). Each group had 49 rats (see Figure ##FIG##3##4## for the histograms for the data). The second dataset was from a study comparing the lifespan of Agouti-related protein-deficient (AgRP(-/-)) mice to wildtype mice (+/+) as reported by Redmann &amp; Argyropoulos [##REF##17097059##14##]. This dataset consists of 16 mice with genotype '+/+' and 21 mice with genotype '-/-' (see Figure ##FIG##4##5## for the histograms for this dataset). From Figure ##FIG##3##4##, we can see the upper tails of the histograms of the two groups are different. Similar results can be found in Figure ##FIG##4##5##.</p>", "<p>Results (p values of tests) are shown in Table ##TAB##5##6##. As can be seen, when setting <italic>τ </italic>equal to 110 (100) for the first (second) datasets, both the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>and the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>can detect the differences in 'maximum lifespan' between groups at nominal alpha levels of 0.01 (0.05) for the first (second) datasets. But the methods for tests of <italic>H</italic><sub>0,<italic>B </italic></sub>cannot detect the difference for all different values of <italic>τ </italic>. The following description may provide some explanation to these results. For the first dataset, when set <italic>τ </italic>= 110, the proportions of the observations greater than <italic>τ </italic>in the EO-HF group and WL-HF group (i.e., estimations of <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 0) and <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 1)) are 0.061 and 0.306, respectively. These two proportions are significantly different and not surprisingly, the methods for tests of <italic>H</italic><sub>0,<italic>A </italic></sub>can detect the difference in 'maximum lifespan' between the two groups. Second, the sample means of the observations greater than <italic>τ </italic>in the two groups (i.e., estimations of <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) and <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0)) are 117.8 and 122.9, respectively, and there is no much difference between these sample means. However the sample means of the Z-values in the two group (i.e., the estimations of <italic>P</italic>(<italic>Z </italic>| <italic>X </italic>= 0) and <italic>P</italic>(<italic>Z </italic>| <italic>X </italic>= 1)) are 7.210 and 37.633, respectively, and are <italic>greatly </italic>different, where, <italic>Z</italic><sub><italic>i </italic></sub>≡ <italic>I</italic>(<italic>Y</italic><sub><italic>i </italic></sub>&gt; <italic>τ</italic>)<italic>Y</italic><sub><italic>i</italic></sub>. These may explain that the methods for tests of <italic>H</italic><sub>0,<italic>B </italic></sub>cannot reject the null but the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>can detect the difference in 'maximum lifespan' between the two groups. Similarly, for the second dataset, when set <italic>τ </italic>= 100, the proportions of the observations greater than <italic>τ </italic>in the group with genotype '+/+' and group with genotype '-/-' are 0.188 and 0.571, respectively. The sample means of the observations greater than <italic>τ </italic>in the two groups are 109.3 and 110.9, respectively. The sample means of the Z-values in the two groups are 20.5 and 63.4 respectively.</p>", "<p>From Table ##TAB##5##6## we can also see that in almost all situations the p-values of the new methods for tests of <italic>H</italic><sub>0,<italic>C </italic></sub>are somewhat smaller than those of the methods for tests of <italic>H</italic><sub>0,<italic>A</italic></sub>. This is consistent with the simulations showing greater power of the new methods.</p>" ]
[ "<title>Discussion</title>", "<p>Herein, we proposed new methods for testing the difference of 'maximum' lifespan between groups (e.g., treatment and control). By defining a new variable <italic>Z </italic>such that <italic>Z</italic><sub><italic>i </italic></sub>≡ <italic>I </italic>(<italic>Y</italic><sub><italic>i </italic></sub>&gt; <italic>τ</italic>)<italic>Y</italic><sub><italic>i </italic></sub>for each observation and then applying Wilcoxon-Mann-Whitney test or better still a permutation test to <italic>Z</italic>, the new methods achieve far better performance when considered across a broad range of circumstances in terms of both Type-1 error rates and power. In the new methods, we use the Wilcoxon-Mann-Whitney test or permutation test. One could also choose to use a bootstrap test in place of these two tests. However, additional simulations would likely be warranted to evaluate its performance relative to the permutation test we have evaluated herein.</p>", "<p>It is straightforward to extend the new methods to more than two groups. For example, one could use the Kruskal-Wallis Test to replace the Wilcoxon-Mann-Whitney test, or use permutation testing for multiple groups to replace that for two groups.</p>", "<p>We have shown that the new methods are effective by simulation studies when the sample size (N) of each group is 50, 100, or 200. We expect that these methods will be also be relatively more powerful than existing competitors for much larger sample sizes, such as N = 500 or even N = 5000. There are some mouse data sets (like those of the National Institute of Aging's Intervention Testing Program) where N &gt; 500, and worm and fly data sets in which N may sometimes even exceed 5000. We expect that the new methods are equally applicable to the analysis of such data.</p>", "<p>Finally, we note that the tests proposed here are described for the context of testing for differences in lifespan. However, there is nothing intrinsic to them that limits their use to survival data. They could be applied to any situation in which one wanted to test for group differences in the tails of distributions.</p>" ]
[]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Investigators are actively testing interventions intended to increase lifespan and wish to test whether the interventions increase maximum lifespan. Based on the fact that one cannot be assured of observing population maximum lifespans in finite samples, in previous work, we constructed and validated several tests of difference in the upper parts of lifespan distributions between a treatment group and a control group by testing whether the probabilities that observations are above some threshold defining 'old' or being in the tail of the survival distribution are equal in the two groups. However, a limitation of these tests is that they do not consider <italic>how much </italic>above the threshold any particular observation is.</p>", "<title>Methods</title>", "<p>In this article we propose new methods which improve upon our previous tests by considering not only whether an observation is above some threshold, but also the magnitudes by which observations exceed the threshold.</p>", "<title>Results</title>", "<p>Simulations show that the new methods control type I error rates quite well and that the power of the new methods is usually higher than that of the tests we previously proposed. In illustrative analyses of two real datasets involving rodents, when setting the threshold equal to 110 (100) weeks for the first (second) datasets, the new methods detected differences in 'maximum lifespan' between groups at nominal alpha levels of 0.01 (0.05) for the first (second) datasets and provided more significant results than competitor tests.</p>", "<title>Conclusion</title>", "<p>The new methods not only have good performance in controlling the type I error rates but also improve the power compared with the tests we previously proposed.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>DBA participated in all parts of the work of the study (including the study design, methodology development, simulations, data acquisition, and manuscript drafting). He wrote major sections of the original manuscript. He revised final version of the manuscript. DTR provided consulting on the statistical issues in the study and manuscript editing. SZ provided assistance in programming for simulation studies. WW provided consulting on simulation and prepared the figures. GG did all simulation studies and real data analyses and drafted the sections of Results, Illustration with real data, and Discussion of the manuscript and participated in revision of the manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2288/8/49/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Richard Miller, David Harrison, and Simon Klebanov for thought provoking dialogue that inspired this paper and George Argyropoulos for graciously providing data. This research was supported in part by NIH grants P30DK056336, R01DK067487, and P01AG11915 and by grant GM073766 from the National Institute of General Medical Sciences.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>The left graph is the density for control group (X = 0), 0.9*Weibull(5.73, 106.6)*I(X ≤ 130) + 0.1*Weibull(5.40, 100.06)*I(X &gt; 130), and the right graph is the density for treatment group (X = 1), 0.9*Weibull(5.73, 106.6)*I(X ≤ 130) + 0.1*Weibull(5.45, 130.06)*I(X &gt; 130), where <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0) and yet the average magnitude by which lifespans exceed <italic>τ </italic>when X = 1 is different than when X = 0. <italic>τ </italic>is 90<sup>th </sup>percentile of the all observations in treatment and control groups.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>The left graph is the density for control group (X = 0), 0.9*Weibull(5.07, 93.52)*I(X ≤ 130) + 0.1*Weibull(5.40, 100.06)*I(X &gt; 130), and the right graph for treatment group (X = 1), 0.6*Weibull(5.07, 93.52)*I(X ≤ 130) + 0.4*Weibull(5.40, 100.06)*I(X &gt; 130), where <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) ≠ <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0), <italic>μ </italic>(<italic>Y </italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) = <italic>μ </italic>(<italic>Y </italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0), and <italic>μ </italic>(•) denotes the population mean of (•). <italic>τ </italic>is 90<sup>th </sup>percentile of the all observations in treatment and control groups.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Parameter values and distributions for component Weibull distributions used in each simulation.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>The left (right) graph is the histogram of lifespan for WL-HF (EO-HF) group in the data from Vasselli et al. [##REF##15897478##10##].</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>The left (right) graph is the histogram of lifespan for group with genotype '+/+' ('-/-') in the data from Redmann &amp; Argyropoulos [##REF##17097059##14##].</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Performance (type 1 error rates) of the tests in simulation 1 under <italic>H</italic><sub>0,<italic>C </italic></sub>(i.e., both <italic>H</italic><sub>0,<italic>A </italic></sub>and <italic>H</italic><sub>0,<italic>B </italic></sub>are true) and yet <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0) (see Figure ##FIG##2##3## for details of simulation).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Test</bold></td><td align=\"center\" colspan=\"6\"><bold>Sample Size (N) Per Group</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">50</td><td align=\"center\" colspan=\"2\">80</td><td align=\"center\" colspan=\"2\">100</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"center\"><italic>α </italic>= .05</td><td align=\"center\"><italic>α </italic>= .01</td><td align=\"center\"><italic>α </italic>= .05</td><td align=\"center\"><italic>α </italic>= .01</td><td align=\"center\"><italic>α </italic>= .05</td><td align=\"center\"><italic>α </italic>= .01</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H </italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 130.</td><td align=\"center\">0.032 (.027, .036)<sup>#</sup></td><td align=\"center\">0.008 (.005, .011)</td><td align=\"center\">0.041 (.036, .046)</td><td align=\"center\">0.006 (.003, .009)</td><td align=\"center\">0.040 (.035, .045)</td><td align=\"center\">0.006 (.003, .009)</td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile</td><td align=\"center\">0.026 (.022, .030)</td><td align=\"center\"><bold>0.026* </bold>(.020, .032)</td><td align=\"center\"><bold>0.080 </bold>(.072, .088)</td><td align=\"center\">0.007 (.004, .010)</td><td align=\"center\">0.040 (.035, .045)</td><td align=\"center\">0.010 (.006, .014)</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 130.</td><td align=\"center\">0.038 (.033, .043)</td><td align=\"center\">0.008 (.005, .011)</td><td align=\"center\">0.051 (.045, .057)</td><td align=\"center\">0.009 (.006, .012)</td><td align=\"center\">0.047 (.041, .053)</td><td align=\"center\">0.007 (.004, .010)</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"center\">0.026 (.022, .030)</td><td align=\"center\"><bold>0.026 </bold>(.020, .032)</td><td align=\"center\"><bold>0.083 </bold>(.075, .091)</td><td align=\"center\"><bold>0.026 </bold>(.020, .032)</td><td align=\"center\">0.040 (.035, .045)</td><td align=\"center\">0.010 (.006, .014)</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>B</italic></bold></sub></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney** with <italic>τ </italic>set to 130.</td><td align=\"center\">0.041 (.036, .046)</td><td align=\"center\"><bold>0.017 </bold>(.012, .022)</td><td align=\"center\">0.044 (.038, .050)</td><td align=\"center\">0.008 (.005, .011)</td><td align=\"center\">0.046 (.040, .052)</td><td align=\"center\">0.008 (.005, .011)</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"center\">0.049 (.043, .055)</td><td align=\"center\">0.014 (.010, .018)</td><td align=\"center\"><bold>0.065 </bold>(.058, .072)</td><td align=\"center\"><bold>0.015 </bold>(.011, .019)</td><td align=\"center\"><bold>0.080 </bold>(.072, .088)</td><td align=\"center\"><bold>0.018 </bold>(.013, .023)</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"center\">0.050 (.036, .064)</td><td align=\"center\">0.009 (.001, .017)</td><td align=\"center\">0.050 (.036, .064)</td><td align=\"center\">0.011 (.002, .020)</td><td align=\"center\">0.064 (.049, .079)</td><td align=\"center\">0.015 (.005, .025)</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"center\"><bold>0.077 </bold>(.060, .094)</td><td align=\"center\">0.016 (.006, .026)</td><td align=\"center\"><bold>0.078 </bold>(.061, .095)</td><td align=\"center\">0.022 (.010, .034)</td><td align=\"center\"><bold>0.083 </bold>(.066, .100)</td><td align=\"center\">0.019 (.008, .030)</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"center\">0.042 (.036, .048)</td><td align=\"center\">0.007 (.004, .010)</td><td align=\"center\">0.049 (.043, .055)</td><td align=\"center\">0.010 (.006, .014)</td><td align=\"center\">0.051 (.045, .057)</td><td align=\"center\">0.008 (.005, .011)</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"center\">0.055 (.049, .061)</td><td align=\"center\"><bold>0.015 </bold>(.011, .019)</td><td align=\"center\"><bold>0.060 </bold>(.053, .067)</td><td align=\"center\"><bold>0.015 </bold>(.011, .019)</td><td align=\"center\"><bold>0.061 </bold>(.054, .068)</td><td align=\"center\"><bold>0.015 </bold>(.011, .019)</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"center\">0.052 (.038, .066)</td><td align=\"center\">0.015 (.005, .025)</td><td align=\"center\">0.047 (.034, .060)</td><td align=\"center\">0.009 (.001, .017)</td><td align=\"center\">0.057 (.043, .071)</td><td align=\"center\">0.007 (.000, .014)</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"center\">0.045 (.032, .058)</td><td align=\"center\">0.017 (.006, .028)</td><td align=\"center\">0.062 (.047, .077)</td><td align=\"center\">0.011 (.002, .020)</td><td align=\"center\"><bold>0.068 </bold>(.053, .084)</td><td align=\"center\">0.018 (.007, .029)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Performance of the tests in simulation 2, <italic>H</italic><sub>0,<italic>A </italic></sub>is true, <italic>H</italic><sub>0,<italic>B </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0) (see Figure ##FIG##2##3## for details of simulation).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Test</bold></td><td align=\"center\" colspan=\"6\"><bold>Sample Size (N) Per Group</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">50</td><td align=\"center\" colspan=\"2\">80</td><td align=\"center\" colspan=\"2\">100</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.032</td><td align=\"left\">0.008</td><td align=\"left\">0.041</td><td align=\"left\">0.006</td><td align=\"left\">0.040</td><td align=\"left\">0.006</td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.034</td><td align=\"left\">0.034</td><td align=\"left\">0.104</td><td align=\"left\">0.009</td><td align=\"left\">0.062</td><td align=\"left\">0.018</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.038</td><td align=\"left\">0.008</td><td align=\"left\">0.051</td><td align=\"left\">0.009</td><td align=\"left\">0.047</td><td align=\"left\">0.007</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.034</td><td align=\"left\">0.034</td><td align=\"left\">0.104</td><td align=\"left\">0.033</td><td align=\"left\">0.062</td><td align=\"left\">0.018</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>B</italic></bold></sub></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.264</td><td align=\"left\">0.090</td><td align=\"left\">0.504</td><td align=\"left\">0.261</td><td align=\"left\">0.631</td><td align=\"left\">0.368</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.16</td><td align=\"left\">0.051</td><td align=\"left\">0.314</td><td align=\"left\">0.143</td><td align=\"left\">0.406</td><td align=\"left\">0.220</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130</td><td align=\"left\">0.337</td><td align=\"left\">0.111</td><td align=\"left\">0.608</td><td align=\"left\">0.332</td><td align=\"left\">0.737</td><td align=\"left\">0.456</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.197</td><td align=\"left\">0.047</td><td align=\"left\">0.423</td><td align=\"left\">0.204</td><td align=\"left\">0.525</td><td align=\"left\">0.284</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.051</td><td align=\"left\">0.008</td><td align=\"left\">0.062</td><td align=\"left\">0.012</td><td align=\"left\">0.056</td><td align=\"left\">0.010</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.107</td><td align=\"left\">0.029</td><td align=\"left\">0.090</td><td align=\"left\">0.028</td><td align=\"left\">0.124</td><td align=\"left\">0.035</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.061</td><td align=\"left\">0.013</td><td align=\"left\">0.055</td><td align=\"left\">0.012</td><td align=\"left\">0.065</td><td align=\"left\">0.014</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.109</td><td align=\"left\">0.032</td><td align=\"left\">0.097</td><td align=\"left\">0.03</td><td align=\"left\">0.129</td><td align=\"left\">0.046</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Performance of the tests in simulation 3, <italic>H</italic><sub>0,<italic>B </italic></sub>is true, <italic>H</italic><sub>0,<italic>A </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) is radically different from <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0) (see Figure ##FIG##2##3## for details of simulation).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Test</bold></td><td align=\"center\" colspan=\"6\"><bold>Sample Size (N) Per Group</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">50</td><td align=\"center\" colspan=\"2\">80</td><td align=\"center\" colspan=\"2\">100</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.244</td><td align=\"left\">0.101</td><td align=\"left\">0.412</td><td align=\"left\">0.181</td><td align=\"left\">0.490</td><td align=\"left\">0.258</td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.102</td><td align=\"left\">0.102</td><td align=\"left\">0.332</td><td align=\"left\">0.051</td><td align=\"left\">0.297</td><td align=\"left\">0.143</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.266</td><td align=\"left\">0.102</td><td align=\"left\">0.418</td><td align=\"left\">0.187</td><td align=\"left\">0.514</td><td align=\"left\">0.274</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.102</td><td align=\"left\">0.102</td><td align=\"left\">0.332</td><td align=\"left\">0.151</td><td align=\"left\">0.297</td><td align=\"left\">0.143</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>B</italic></bold></sub></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.046</td><td align=\"left\">0.013</td><td align=\"left\">0.049</td><td align=\"left\">0.011</td><td align=\"left\">0.045</td><td align=\"left\">0.008</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.048</td><td align=\"left\">0.019</td><td align=\"left\">0.044</td><td align=\"left\">0.01</td><td align=\"left\">0.041</td><td align=\"left\">0.009</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.042</td><td align=\"left\">0.007</td><td align=\"left\">0.046</td><td align=\"left\">0.012</td><td align=\"left\">0.064</td><td align=\"left\">0.013</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.046</td><td align=\"left\">0.009</td><td align=\"left\">0.046</td><td align=\"left\">0.012</td><td align=\"left\">0.044</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.276</td><td align=\"left\">0.111</td><td align=\"left\">0.420</td><td align=\"left\">0.201</td><td align=\"left\">0.517</td><td align=\"left\">0.271</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.182</td><td align=\"left\">0.07</td><td align=\"left\">0.278</td><td align=\"left\">0.104</td><td align=\"left\">0.35</td><td align=\"left\">0.154</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.291</td><td align=\"left\">0.101</td><td align=\"left\">0.427</td><td align=\"left\">0.203</td><td align=\"left\">0.515</td><td align=\"left\">0.28</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.169</td><td align=\"left\">0.067</td><td align=\"left\">0.264</td><td align=\"left\">0.107</td><td align=\"left\">0.363</td><td align=\"left\">0.173</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Performance of the tests in simulation 4, <italic>H</italic><sub>0,<italic>B </italic></sub>is false, <italic>H</italic><sub>0,<italic>A </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 1) and <italic>f </italic>(<italic>Y</italic>|<italic>Y </italic>≤ <italic>τ </italic>∩ <italic>X </italic>= 0) are identical (see Figure ##FIG##2##3## for details of simulation).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Test</bold></td><td align=\"center\" colspan=\"6\"><bold>Sample Size (N) Per Group</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">50</td><td align=\"center\" colspan=\"2\">80</td><td align=\"center\" colspan=\"2\">100</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.244</td><td align=\"left\">0.101</td><td align=\"left\">0.412</td><td align=\"left\">0.181</td><td align=\"left\">0.490</td><td align=\"left\">0.258</td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.363</td><td align=\"left\">0.363</td><td align=\"left\">0.735</td><td align=\"left\">0.337</td><td align=\"left\">0.753</td><td align=\"left\">0.600</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.266</td><td align=\"left\">0.102</td><td align=\"left\">0.418</td><td align=\"left\">0.187</td><td align=\"left\">0.514</td><td align=\"left\">0.274</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.363</td><td align=\"left\">0.363</td><td align=\"left\">0.735</td><td align=\"left\">0.555</td><td align=\"left\">0.753</td><td align=\"left\">0.600</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H<sub><bold>0,<italic>B</italic></bold></sub></italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.409</td><td align=\"left\">0.172</td><td align=\"left\">0.684</td><td align=\"left\">0.411</td><td align=\"left\">0.804</td><td align=\"left\">0.56</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.245</td><td align=\"left\">0.142</td><td align=\"left\">0.33</td><td align=\"left\">0.144</td><td align=\"left\">0.434</td><td align=\"left\">0.176</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.517</td><td align=\"left\">0.244</td><td align=\"left\">0.81</td><td align=\"left\">0.568</td><td align=\"left\">0.913</td><td align=\"left\">0.728</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.169</td><td align=\"left\">0.039</td><td align=\"left\">0.428</td><td align=\"left\">0.190</td><td align=\"left\">0.569</td><td align=\"left\">0.249</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.374</td><td align=\"left\">0.171</td><td align=\"left\">0.528</td><td align=\"left\">0.280</td><td align=\"left\">0.629</td><td align=\"left\">0.373</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.602</td><td align=\"left\">0.353</td><td align=\"left\">0.734</td><td align=\"left\">0.552</td><td align=\"left\">0.865</td><td align=\"left\">0.724</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.393</td><td align=\"left\">0.177</td><td align=\"left\">0.524</td><td align=\"left\">0.288</td><td align=\"left\">0.626</td><td align=\"left\">0.377</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.619</td><td align=\"left\">0.365</td><td align=\"left\">0.726</td><td align=\"left\">0.553</td><td align=\"left\">0.852</td><td align=\"left\">0.704</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Performance of the tests in simulation 5, <italic>H</italic><sub>0,<italic>B </italic></sub>is false, <italic>H</italic><sub>0,<italic>A </italic></sub>is false and <italic>f </italic>(<italic>Y</italic>|<italic>X </italic>= 1) = 1.2<italic>f </italic>(<italic>Y</italic>|<italic>X </italic>= 0) (see Figure ##FIG##2##3## for details of simulation).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Test</bold></td><td align=\"center\" colspan=\"6\"><bold>Sample Size (N) Per Group</bold></td></tr><tr><td/><td colspan=\"6\"><hr/></td></tr><tr><td/><td align=\"center\" colspan=\"2\">50</td><td align=\"center\" colspan=\"2\">80</td><td align=\"center\" colspan=\"2\">100</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td/><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td><td align=\"left\"><italic>α </italic>= .05</td><td align=\"left\"><italic>α </italic>= .01</td></tr></thead><tbody><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.663</td><td align=\"left\">0.349</td><td align=\"left\">0.925</td><td align=\"left\">0.754</td><td align=\"left\">0.965</td><td align=\"left\">0.883</td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.815</td><td align=\"left\">0.815</td><td align=\"left\">0.996</td><td align=\"left\">0.885</td><td align=\"left\">0.997</td><td align=\"left\">0.986</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 130.</td><td align=\"left\">0.765</td><td align=\"left\">0.349</td><td align=\"left\">0.941</td><td align=\"left\">0.797</td><td align=\"left\">0.981</td><td align=\"left\">0.906</td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.815</td><td align=\"left\">0.815</td><td align=\"left\">0.996</td><td align=\"left\">0.969</td><td align=\"left\">0.997</td><td align=\"left\">0.986</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>B</italic></bold></sub></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.001</td><td align=\"left\">0.000</td><td align=\"left\">0.006</td><td align=\"left\">0.000</td><td align=\"left\">0.010</td><td align=\"left\">0.000</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.016</td><td align=\"left\">0.000</td><td align=\"left\">0.035</td><td align=\"left\">0.002</td><td align=\"left\">0.058</td><td align=\"left\">0.009</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130</td><td align=\"left\">0.001</td><td align=\"left\">0.000</td><td align=\"left\">0.036</td><td align=\"left\">0.003</td><td align=\"left\">0.061</td><td align=\"left\">0.005</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.032</td><td align=\"left\">0.002</td><td align=\"left\">0.082</td><td align=\"left\">0.017</td><td align=\"left\">0.124</td><td align=\"left\">0.041</td></tr><tr><td align=\"left\" colspan=\"7\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 130.</td><td align=\"left\">0.556</td><td align=\"left\">0.239</td><td align=\"left\">0.920</td><td align=\"left\">0.742</td><td align=\"left\">0.979</td><td align=\"left\">0.897</td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.932</td><td align=\"left\">0.767</td><td align=\"left\">0.995</td><td align=\"left\">0.964</td><td align=\"left\">0.999</td><td align=\"left\">0.992</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 130.</td><td align=\"left\">0.852</td><td align=\"left\">0.646</td><td align=\"left\">0.960</td><td align=\"left\">0.850</td><td align=\"left\">0.993</td><td align=\"left\">0.940</td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.942</td><td align=\"left\">0.786</td><td align=\"left\">0.995</td><td align=\"left\">0.958</td><td align=\"left\">0.997</td><td align=\"left\">0.986</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T6\"><label>Table 6</label><caption><p>Results (p values of tests) of application to two real datasets.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Test</bold></td><td align=\"left\"><bold>Data from Vasselli et al. </bold>[##REF##15897478##10##]<sup><bold>1</bold></sup></td><td align=\"left\"><bold>Data from Redmann &amp; Argyropoulos </bold>[##REF##17097059##14##]<sup><bold>2</bold></sup></td></tr></thead><tbody><tr><td align=\"left\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>A </italic></bold></sub><italic>(Wang-Allison tests)</italic></bold></td><td/><td/></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to 110/100<sup>#</sup>.</td><td align=\"left\">0.002</td><td align=\"left\"><bold>0.027</bold></td></tr><tr><td align=\"left\">QT3 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.038</td><td align=\"left\"><bold>0.186</bold></td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to 110/100.</td><td align=\"left\">0.002</td><td align=\"left\"><bold>0.022</bold></td></tr><tr><td align=\"left\">QT4 with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.033</td><td align=\"left\"><bold>0.146</bold></td></tr><tr><td align=\"left\"><bold><italic>Tests of H<sub><bold>0,<italic>B</italic></bold></sub></italic></bold></td><td/><td/></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 110/100.</td><td align=\"left\">0.289</td><td align=\"left\"><bold>0.868</bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.750</td><td align=\"left\"><bold>N/A*</bold></td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 110/100.</td><td align=\"left\">0.281</td><td align=\"left\"><bold>0.738</bold></td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.634</td><td align=\"left\"><bold>N/A*</bold></td></tr><tr><td align=\"left\"><bold><italic>Tests of H</italic><sub><bold>0,<italic>C </italic></bold></sub><italic>(new tests)</italic></bold></td><td/><td/></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to 110/100.</td><td align=\"left\">0.001</td><td align=\"left\"><bold>0.022</bold></td></tr><tr><td align=\"left\">Wilcoxon-Mann-Whitney with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\">0.026</td><td align=\"left\"><bold>0.243</bold></td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to 110/100.</td><td align=\"left\">0.001</td><td align=\"left\"><bold>0.014</bold></td></tr><tr><td align=\"left\">Permutation test with <italic>τ </italic>set to sample 90<sup>th </sup>percentile.</td><td align=\"left\"><bold>0.024</bold></td><td align=\"left\"><bold>0.072</bold></td></tr></tbody></table></table-wrap>" ]
[ "<disp-formula><italic>H</italic><sub>0,<italic>A </italic></sub>: <italic>P </italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0).</disp-formula>", "<disp-formula><italic>H</italic><sub>0,<italic>B </italic></sub>: <italic>μ </italic>(<italic>Y</italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) = <italic>μ </italic>(<italic>Y</italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0),</disp-formula>", "<disp-formula><italic>H</italic><sub>0,<italic>C </italic></sub>: [<italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 1) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ</italic>|<italic>X </italic>= 0)] ∩ [<italic>μ</italic>(<italic>Y</italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) = <italic>μ </italic>(<italic>Y</italic>|<italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0)].</disp-formula>", "<disp-formula><italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 1) <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 1) = <italic>P</italic>(<italic>Y </italic>&gt; <italic>τ </italic>| <italic>X </italic>= 0) <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; <italic>τ </italic>∩ <italic>X </italic>= 0),</disp-formula>", "<disp-formula>Δ(<bold><italic>v</italic></bold>) = <italic>P</italic>(<italic>Y </italic>&gt; 0 | <italic>X </italic>= 1, <bold><italic>v</italic></bold>) <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; 0, <italic>X </italic>= 1, <bold><italic>v</italic></bold>) - <italic>P</italic>(<italic>Y </italic>&gt; 0| <italic>X </italic>= 0, <bold><italic>v</italic></bold>) <italic>μ </italic>(<italic>Y </italic>| <italic>Y </italic>&gt; 0, <italic>X </italic>= 0, <bold><italic>v</italic></bold>),</disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"1471-2288-8-49-i1\" overflow=\"scroll\"><mml:semantics><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>|</mml:mo><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>r</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:msup></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>≤</mml:mo><mml:mn>130</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mi>r</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>Y</mml:mi><mml:mrow><mml:msub><mml:mi>a</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mi>b</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:msup></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>Y</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>130</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:semantics></mml:math></disp-formula>" ]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p><sup>#</sup>2-tailed 95% confidence interval.</p><p><bold>*</bold>The bolded values are those simulated type I error rates which are significantly higher than the nominal <italic>α </italic>at the 2-tailed 95% confidence level (i.e., the lower bound of the interval is higher than <italic>α</italic>). Note that for the permutation tests we used 1000 replicated datasets and for other tests we used 5000 replicated datasets.</p><p>**In all the simulation studies (Tables 1-5), we used Wilcoxon-Mann-Whitney exact test.</p></table-wrap-foot>", "<table-wrap-foot><p>Notes: In each dataset, males and females have been combined. <sup>1</sup>For the data from Vasselli et al. [##REF##15897478##10##] two groups of rats (EO-HF and WL-HF) are compared; each group has 49 observations.</p><p><sup>2</sup>The data from Redmann &amp; Argyropoulos [##REF##17097059##14##] consists of 16 mice with genotype '+/+' and 21 mice with genotype '-/-'.</p><p><sup># </sup>For Data from Vasselli et al. [##REF##15897478##10##]<italic>τ </italic>is set to 110; for data from Redmann &amp; Argyropoulos [##REF##17097059##14##]<italic>τ </italic>is set to 100.</p><p>*Only one group has observations above the threshold <italic>τ</italic>.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2288-8-49-1\"/>", "<graphic xlink:href=\"1471-2288-8-49-2\"/>", "<graphic xlink:href=\"1471-2288-8-49-3\"/>", "<graphic xlink:href=\"1471-2288-8-49-4\"/>", "<graphic xlink:href=\"1471-2288-8-49-5\"/>" ]
[]
[{"surname": ["Little", "Folks"], "given-names": ["RC", "JL"], "article-title": ["On the comparison of two methods of combining independent tests"], "source": ["Journal of the American Statistical Association"], "year": ["1972"], "volume": ["67"], "fpage": ["223"], "pub-id": ["10.2307/2284731"]}, {"surname": ["Wilcoxon"], "given-names": ["F"], "article-title": ["Individual comparisons by ranking methods"], "source": ["Biometrics"], "year": ["1945"], "volume": ["1"], "fpage": ["80"], "lpage": ["83"], "pub-id": ["10.2307/3001968"]}, {"surname": ["Mann", "Whitney"], "given-names": ["HB", "DR"], "article-title": ["On a test of whether one of two random variables is stochastically larger than the other"], "source": ["Annals of Mathematical Statistics"], "year": ["1947"], "volume": ["18"], "fpage": ["50"], "lpage": ["60"], "pub-id": ["10.1214/aoms/1177730491"]}, {"surname": ["Good"], "given-names": ["P"], "source": ["Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses"], "year": ["1994"], "publisher-name": ["New York: Springer-Verlag"]}]
{ "acronym": [], "definition": [] }
14
CC BY
no
2022-01-12 14:47:26
BMC Med Res Methodol. 2008 Jul 25; 8:49
oa_package/5a/be/PMC2529340.tar.gz
PMC2529341
18673555
[ "<title>Background</title>", "<title>The importance of the trial process</title>", "<p>Preventive medications require long-term trials to show their effectiveness and harms. Carrying out a trial of long duration is demanding for the researchers. Very few reports have described the long-term trial process in practice [##UREF##0##1##,##UREF##1##2##]. When trial processes are not described publicly, useful knowledge fails to accumulate. Especially for trials that are unable to meet their targets, information about the process is important to aid other researchers in anticipating and avoiding similar problems. Also, trials that have successfully coped with unanticipated difficulties should report their success stories.</p>", "<p>Previous studies of the process of preventive trials have mainly concentrated on the recruitment process [##UREF##0##1##,##REF##2317466##3##, ####REF##12893583##4##, ##REF##9129858##5####9129858##5##], failures in recruitment [##REF##15369597##6##], the non-medical intervention effect on compliance [##UREF##2##7##,##REF##12480850##8##], and randomization [##REF##4006485##9##]. Oakley et al. [##UREF##0##1##,##UREF##1##2##] have reported a trial process on social support in motherhood and on peer-led sex education. They found that an evaluation of the process was integral to understand the outcomes. In a trial on the delivery of very low birth weight infants, Lumley et al. [##REF##4006485##9##] failed to achieve randomization because of a critical shift in obstetric practice. In Finland the pilot for a non-blind, patient-managed trial on hormone therapy (HT) revealed several obstacles to the main trial, including the difficulty to discontinue HT and a negative attitude among Finnish physicians towards the trial [##REF##9129858##5##].</p>", "<p>We have found no process description of a successful trial involving preventive drug therapy. To run a trial over many years involves a significant risk that obstacles will emerge. The purpose of this article is to describe the research process in a long-term randomized controlled trial, the Estonian Postmenopausal Hormone Therapy trial (EPHT), and to discuss the impact and consequences of changes in the research environment. We hope that this report of the EPHT trial process would provide researchers with valuable information for planning and carrying out long-term trials.</p>" ]
[ "<title>Methods</title>", "<title>The EPHT trial</title>", "<p>The EPHT trial aimed to study the impact of postmenopausal hormone therapy (HT) on diseases, subjective well-being, social effects, health service utilization and health care costs. Furthermore, we aimed to study the impact of blinding on trial process and outcomes. In regard to the long-term health effects, the aim was to study whether HT increases the risk of cancers, and decreases the risk of heart and cardiovascular diseases, fractures and metabolic diseases. Because the EPHT alone did not have power enough to detect HT effects on diseases, these outcomes were planned to be pooled with the Women's International Study of Long-Duration Oestrogen after Menopause (WISDOM) to increase its power. The independent aims of the EPHT trial were to study whether HT increases women's well-being and decreases the prevalence of their symptoms, how HT affects experience of the climacteric and ageing, and partner relationship. The EPHT was specifically designed to investigate whether HT increases health services utilization and therefore the non-blind sub-trial was needed, which also made possible to study methodological questions like the effect of blinding on recruitment and trial results.</p>", "<p>A pilot showed that doing a trial on HT in Finland was unlikely to succeed due to women's and physicians' strong preferences to HT [##REF##9129858##5##], and it was decided to carry out the trial (EPHT trial) in Estonia. Originally we had planned to do our trial in close co-operation with WISDOM. In the mid-1990s, the UK-based WISDOM unsuccessfully sought financing from the European Union (EU) for a large European HT study [##REF##12626209##10##]. The UK Medical Research Council (MRC) decided to finance the trial covering the UK, Australia, and New Zealand. In view of this decision, we planned our trial as an independent study, though still working closely with WISDOM.</p>", "<p>The planning of the trial was started in 1995. It was a four-arm randomized controlled preventive trial on HT, originally planned for five years exposure, consisting of a blind and a non-blind sub-study (Figure ##FIG##0##1##). The blind sub-study represents a traditional randomized, double-blind, placebo-controlled trial except for the early randomization prior to informed consent, whereas, the non-blind sub-study was a randomized controlled trial with an open-label HT arm and a non-treatment arm. For details of the trial design and methods, see [##REF##16504428##11##].</p>", "<p>Participants were recruited using a postal questionnaire to establish eligibility and to ask whether they wished to participate in the trial. Those positive and potentially eligible women were randomized to the four trial arms. Women were mailed a letter briefly describing either the blind or non-blind sub-study to which they had been randomized and inviting them to the recruitment examination in one of the study clinics. [##REF##15617949##12##] The EPHT trial had three study centers, two women's clinics in Tallinn, the capital of Estonia, and a university women's clinic in Tartu, southern Estonia. Trial staff in each study centre consisted of 2–3 gynaecologists and two midwives in each clinic, 13 altogether.</p>", "<p>Local coordination in Estonia was at the Institute of Experimental and Clinical Medicine (EKMI, later the National Institute for Health Development, TAI). In 1998 the trial received a positive statement from the Tallinn Committee of Medical Ethics, and the trial was registered at the State Agency of Medicines in the same year. By agreement drugs were to be donated by Wyeth, a US-based pharmaceutical company, to the EPHT trial via the WISDOM trial in the UK.</p>", "<p>The trial drug was licensed in the USA and was a combined regimen of conjugated equine oestrogens 0.625 mg (CEE) and medroxyprogesterone acetate 2.5 mg (MPA). A similar drug with a higher MPA dose (10 mg) had already been licensed in Estonia. Women were to visit the study clinics semi-annually to collect their trial medication, and annually for an examination by the study physician; this did not apply to women in the no-treatment-arm in the non-blind sub-study, who were to visit only if needed.</p>", "<p>Financing for the trial came from public sources such as the Academy of Finland, STAKES, the Finnish Ministry of Education, and the Estonian Ministry of Education and Science, while the Universities of Tartu (Estonia) and Tampere (Finland) and the Estonian National Institute for Health Development (TAI, previously EKMI) offered institutional support.</p>" ]
[ "<title>Results</title>", "<title>Impact of other studies on EPHT</title>", "<p>Recruiting occurred from 1999–2001 (Figure ##FIG##1##2##) and 1823 women joined up. Half a year after recruiting was completed (July 2002), the Women's Health Initiative (WHI) prematurely published its results in the USA. This had a big influence on our trial, both directly and through its impact on WISDOM. Originally we had calculated that our trial exposure would have been completed before the WHI is ready. The WHI was initiated in 1992 with a planned completion date of 2007. It included two large trials to investigate the effects of HT on the morbidity and mortality of postmenopausal women aged 50 to 79. Between 1993 and 1998, the WHI randomized 16 608 women with a uterus to the estrogen plus progestin trial [##REF##12117397##13##] and 10 739 women without uterus to the estrogen only trial [##REF##15082697##14##] to be treated for an average follow-up of 8.5 years [##REF##12117397##13##].</p>", "<p>Already in 2000 and 2001, the WHI Data and Safety Monitoring Board recommended that the participating women should be informed that the original hypothesis of cardiovascular protection was no longer likely, but that the trial would continue because the balance of risks and benefits remained uncertain. In July 2002, the WHI estrogen-progestin trial was stopped because the breast cancer risk comparison exceeded the pre-defined limits and the overall risks were seen to exceed the benefits as measured by the global disease index [##REF##12117397##13##]. By summer 2003, results in several articles emerging from the WHI trial showed that HT is not safe for disease prevention [##REF##12117397##13##,##REF##12771112##15##, ####REF##12904517##16##, ##REF##12771113##17####12771113##17##]. The trial with estrogen alone continued until early 2004 when the intervention was withdrawn because the original hypothesis of estrogen preventing the risk of cardiovascular diseases was unlikely [##REF##15082697##14##].</p>", "<p>The first time we became aware of the WHI warnings to its trial participants was in 2001. The Trial Steering Committee kept itself updated via the WHI web site where we learnt that a warning had been given also in 2000. We tried to get further information with direct contacts, but were not successful. We had no reason to send further information to the EPHT trial women in 2001, because the original written information at recruitment had stated that \"HT probably decreases cardiovascular diseases (CVD), and estrogen is thought to have specific effects on blood coagulation and plasma lipid concentration, but their effect on CVD is still not clear. It is assumed that HT decreases the risk of myocardial infarction, but it may increase the risk of thrombosis for some women.\"</p>", "<p>The EPHT Trial Steering Committee had its meeting in September 2002 and unanimously decided to continue the trial unless WISDOM discontinued for ethical reasons. During the discussion it was suggested to open the blindness in the blind sub-study and continue EPHT as a totally non-blind trial, but that was not supported. We were satisfied with the way the recruitment letter described the uncertainties of HT effects. Thus, we did not change the protocol in 2002.</p>", "<p>By autumn 2002, WISDOM had recruited 5 700 women. After WHI prematurely stopped its first trial in July 2002, WISDOM's Data Monitoring and Ethics Committee recommended WISDOM to continue as long as women were informed of the current state of knowledge. Likewise, the Trial Steering Committee recommended continuation as they found no strong scientific or ethical reasons to stop. However, the Medical Research Council (MRC), who were the main funding agency, decided to convene an Independent International Committee to review the WHI findings, the progress of WISDOM and other evidence. The Committee concluded that \"WISDOM was unlikely to provide substantial evidence to influence clinical practice in the next 10 years\" [##REF##12626209##10##]. MRC decided in October 2002 to stop the funding of WISDOM on the basis of the lack of importance [##UREF##3##18##].</p>", "<p>The halting of WISDOM in October 2002 did not have an immediate effect on the continuation of our trial. In November 2002 the EPHT Data Monitoring Committee (DMC) made the annual interim analysis of the data and found no results that would have demanded cessation of the trial. A strong argument for us to continue the exposure was the WISDOM Steering Committee's recommendation to continue WISDOM. An important reason behind the MRC decision to stop WISDOM was financial rather than safety concerns. We wanted to obtain data to answer our research questions other than effects on diseases. However, as the WHI results suggested that breast cancer risk increased by the length of exposure, the EPHT Trial Steering Committee decided to shorten the trial treatment to four years from the original five for those women who had not yet been in the study for 4 years (December 2002).</p>", "<p>We kept the trial physicians and midwives informed about all new results in other trials via information letters and personal discussions. Also, the Tallinn Medical Research Ethics Committee was regularly informed with updated information from other HT trials. The participating women were kept up to date on the results of other trials and their influence on EPHT as well as with the process of the EPHT trial with an annual newsletter in Estonian. In September 2002 women were told why WHI estrogen-progestin trial was stopped, and were given the disease outcomes per 10 000 women for those using or not using HT. Women were told that they had been in the trial for a shorter period than women in the WHI, and women with less than four years of exposure were encouraged to continue the trial treatment. We encouraged the women to contact the researchers if they wanted more information.</p>", "<p>In spite of all the given information no decrease in adherence was detected, and only a few women had contacted the trial staff because of the new warnings. The media coverage of the 2002 results of the WHI estrogen-progestin trial was very low and did not raise any public discussion in Estonia. Instead, in Finland the WHI results were widely discussed both in the professional and lay press [##REF##15276308##19##]. EPHT investigators in Finland were interviewed and they explained that the WHI results should result in decreasing long-term HT use. Many leading Finnish gynaecologists belittled the significance of the WHI results with various arguments [##REF##15276308##19##].</p>", "<p>In August 2003, following the release of the WHI results of HT effects on dementia, cognitive functions, and quality of life [##REF##12771112##15##,##REF##12771113##17##,##REF##12642637##20##], and the results from the Million Women Study on HT effects on breast cancer [##REF##12927427##21##], the Trial Steering Committee shortened the exposure in our trial for the second time. The exposure was shortened to three years for women who had by that time received it for less than three years.</p>", "<p>In December 2003, our trial DMC had its annual meeting where all cumulative information about other HT studies were presented, as well as the results of the interim analysis of the EPHT data. EPHT data showed an unfavourable effect of HT on CVD, but not statistically significant. The DMC had no pre-defined rules on how to interpret data from other trials, but decided to recommend ceasing the trial treatment, as results from other trials were against the preventive use of HT. Based on the DMC recommendations, the Trial Steering Committee stopped the treatment over a period up until May 31<sup>st </sup>2004 to enable a final medical examination to all women. As a result, 597 women received trial treatment at least for four full years, 808 for three years, and the rest 418 at least for two years (Table ##TAB##0##1##).</p>", "<p>Our trial received its drugs from Wyeth via WISDOM. After WISDOM was discontinued, we sent Wyeth an application for registration in their trial registry to receive more drugs. However, the registration was never finalized: during the lengthy negotiations, we had twice shortened the trial, and in the end no more drugs were needed.</p>", "<p>Besides abbreviating the trial duration, the new information coming out from other trials led to a lot of additional work: we had to thoroughly analyze the data and consider its impact on our study protocol, to inform both women and the clinical staff, as well as to monitor news reports.</p>", "<title>Changes in research environment</title>", "<p>Between the initial planning year (1995) and the halting of the intervention (2004), Estonian society changed very rapidly. Estonia had been a part of the Soviet Union with a planned economy up until 1991, when the independent Estonia had adopted a liberal market economy. By 1994–95 pharmacies had been privatized and the availability of drugs was no longer a problem and medicine choices were determined mostly by prescribers [##REF##9626915##22##]. In 2004 Estonia became a member of the European Union and its economic and scientific contacts with Western Europe increased and income had increased, but less for poor people [##UREF##4##23##]. A better financial situation and availability of HT offered women in the blind sub-study and non-blind control arm a possibility to buy HT.</p>", "<p>In the early 1990s, Estonia was still a maiden country for conducting a trial with HT. HT use did increase in the 1990s, but by 2000 it was still notably lower than in Finland [##REF##14572923##24##]. In an Estonian survey in 1998, only 4% of women aged 45–64 reported current use of HT [##REF##16039416##25##] compared to 34% in Finland in 2000 [##UREF##5##26##].</p>", "<p>By the mid-1990s, prices and salaries in Estonia were lower than in most Western European countries, but the infrastructure (including health services) was good and western-style legislation and regulations had been developed [##UREF##6##27##]. The Estonian Health Insurance Fund Database, the Estonian Cancer Registry and the Estonian Mortality Database made it possible to collect information about women's health and health services use. The Estonian Health Insurance Fund Database is unique and includes information on all health care visits, diagnosis and prescriptions [##REF##17355268##28##].</p>", "<p>Various changes in legislation, relevant institutions, and financing occurred during our trial. The local co-ordinating centre, the Institute of Experimental and Clinical Medicine (EKMI), was also reorganized and it became the National Institute for Health Development (TAI) as of May 2003. Our study clinics were financed by the Estonian Sickness Insurance Fund (later Health Insurance Fund) [##UREF##7##29##]. Women's health outcomes were abstracted for the EPHT trial from the Health Insurance Fund register. Changes in the administration of the Health Insurance Fund required us to conduct several negotiations to ensure continuation of the trial. Women's recruitment visits were classified as health check-up visits and they were recorded in the Health Insurance Register.</p>", "<p>During the trial some unexpected expenses occurred (increase in salaries, payment for physicians, reimbursement of mammograms for participants, etc) and prices increased much faster than could have been expected at the time of planning. During the trial period salaries rose about 30%, which tightened the trial budget, which had been planned in 1997. Originally only midwives were to be paid and physicians were to be compensated by commodities like international medical journal subscription or participation in international congresses. This turned out to be impractical and physicians received compensation per recruited woman.</p>", "<p>While Estonia has good health registries, at the planning phase of the EPHT, Finland had better developed practices in data protection than Estonia and so these were adapted to our trial. During our trial, various changes were made to the Estonian data protection laws that led to reduced access to various registries. The 1996 Data Protection Law, or its updated 2003 version, were not clear in regard to the use of registries for research [##REF##18304955##30##], with the ambiguity in interpretations causing additional work and delays in obtaining outcome data. A rapid turnover of personnel in the ministries reduced experience in data protection practices. The trial participants had signed informed consent permitting their survey and health examination data to be linked with health registries, but the completeness of registries became a problem because the data protection authority challenged the updating of these registers. Furthermore, maintaining up-to-date addresses for the participating women who had moved residence became difficult, because we were not allowed to check addresses from the population registry.</p>", "<p>Data collection for disease outcomes in our trial was mainly based on registries, and new and changing regulations meant extra negotiations and time delays. Nevertheless, after difficult negotiations within the ministries all necessary data other than deaths had been obtained up to the end of 2004 as planned.</p>", "<p>At the start of the recruitment in 1999, screening for breast cancer with mammography was not in use in Estonia, not even among HT users. If a woman had breast problems she was referred to a mammologist, a specialized physician. To compensate for the missing screening program we advised our study women to regularly palpate their breasts through the Mama breast self-examination program [##UREF##8##31##]. However, beginning in 2000, mammogram screening in Estonia was gradually introduced. This development – in addition to the advice given by local mammologists – led us to add mammogram screening for all trial women who had already been in the trial for two years (Figure ##FIG##1##2##). Those women who were eligible for local free of charge mammogram mass-screening programs were encouraged to use the service. For others the costs were covered by the trial, and they were unexpectedly high.</p>", "<p>Many foreign drug companies were interested in Estonia and the number of approved clinical trials increased from five in 1992 to over 80 in 2004 [##UREF##9##32##]. With the increasing number of trials, we had to ensure that the women participating in our study were not recruited to other trials. This could effect EPHT results or reveal treatment medication to women in the blind sub-trial.</p>", "<title>Using a licensed regimen</title>", "<p>The estrogen used in our study (CEE) had been available since the 1940s [##UREF##10##33##] and combined estrogen-progestin since the 1970s [##REF##4457410##34##]. A wide variety of preparations have been available for climacteric women [##REF##2215268##35##]. When we started our trial, HT was already available and in use in Estonia, including the specific trial regimen.</p>", "<p>Studying an established therapy had its advantages and disadvantages. An advantage is that the ethical burden is lessened because women outside the study can be freely prescribed the drug. A disadvantage is that compliance in the non-treatment arm can be easily compromised through purchase of the drug outside the study. This was not a major issue in our trial: only some women receiving the placebo and less than 10% in the non-treatment arm had been subsequently prescribed HT by the exposure end [##REF##16055284##36##].</p>", "<p>The WHI researchers had chosen conjugated equine estrogens (CEE), which is the most widely prescribed preparation in the USA, but rarely used in Europe. WISDOM approached major HT manufacturers, but Wyeth was the only company prepared to supply drugs and matched placebos [##REF##12626209##10##]. So, both the WHI and WISDOM ended up studying the same type of HT out of the dozens of preparations available.</p>", "<p>As a consequence, most health data now available in relation to long-term HT use are based on one type of drug and, for example, transdermal preparations remain untested over the long-term. Before the start of the WHI and WISDOM, the applicability of specific HT preparations or formulations was not questioned [##REF##12626209##10##], but later proponents of preventive HT have used the specific features of CEE as one argument to support continued use [##REF##15276308##19##].</p>", "<title>Views of women and physicians</title>", "<p>According to the EPHT pilot survey (n = 2 000, response rate 69%) 53% of the women were of the opinion that the climacteric is a normal phase in a woman's life which does not need medical treatment and only 17% disagreed [##REF##16039416##25##]. Few women were familiar with HT, 11% had ever used HT and only 6% of the women supported HT for all postmenopausal women [##REF##16039416##25##]. Women's inexperience and hesitation in regard to HT may have contributed to the low adherence in the placebo and HT-arms in the trial [##REF##16055284##36##].</p>", "<p>On the contrary, according to our survey in 2000, Estonian gynaecologists favoured HT and 37% recommended HT for postmenopausal women in climacteric [##REF##15474754##37##]. GPs referred almost all of their patients with menopausal symptoms to a gynaecologist. Physicians thought that the increase in the use of HT in Estonia was more based on changes in physicians' opinions than that of women. Gynaecologists had frequently participated in education on HT, and education was often supported by industry [##REF##14572923##24##]. Trials can be considered as a means to increase drug use, and this possibly contributed to physicians supporting our trial.</p>", "<p>In preparing the information leaflets for women, it was revealed that still in the 1990s many physicians had paternalistic behaviours and that it was not considered crucial to inform patients. Discussions about cancer risks still seemed to be a taboo [##REF##14572923##24##].</p>", "<p>When we started our trial in Estonia, the culture of doing clinical trials was still new. Compared to the pharmaceutical companies, our resources were small, but due to the strong local academic participation, we had been successful in recruiting capable gynaecologists and midwives and had only a small turnover of research personnel. As the trial personnel had no prior epidemiological knowledge, the trial co-ordinators organized free of charge semi-annual seminars for the clinical staff on research methodology and controlled trials. In addition, when the local research assistant visited the clinics to follow the recruitment process and collect weekly summary sheets, she also discussed the trial progress with the midwives, and offered help in the case of problems. The trial staff and participating women could contact the trial co-ordinators any time by phone and by mail, and the midwives at trial clinics had special calling hours for trial participants. Trial participants were mailed a personal birthday greeting throughout the trial.</p>", "<title>Practical issues</title>", "<p>An application to the Estonian drug control authority for permission was unproblematic. However, the first shipment of drugs was in bulk and the Estonian law required that the tablets have to be packed into vials and labelled by a pharmaceutical company. Because of the small number of tablets packing would have to be done by hand, and many negotiations were needed before we reached an agreement with a local pharmaceutical company. The next shipment of drugs was actually pre-packed, with 215 tablets in each vial i.e. for seven months use, but we still needed a pharmaceutical company to put the labels on the vials. The company who had done the packing was unwilling to do it and we had to find a new company. More importantly, this changed the time of the second and later visits: the six-month period between the visits was changed to seven months.</p>", "<p>In the EPHT trial, all the trial clinics were located in Estonia, but the main scientific co-ordination was in Finland. Health care was different between the two countries. Personal contacts and open discussions were most valuable in bringing to light the different practices and in finding solutions. Many external changes placed additional demands especially on the local coordinator, who had to seek new solutions and contacts.</p>" ]
[ "<title>Discussion and Conclusion</title>", "<title>Keys for success</title>", "<p>Treatment in the EPHT trial was stopped earlier than planned, but the time was sufficient to provide answers to our short-term research questions. Taking into account all the outside changes occurring during the trial, we are satisfied with the trial process. However, the low adherence and the relatively short exposure time reduced the power of the study. A very good collaboration between the Trial Steering Committee, the Data Monitoring Committee, the trial coordinators, and the trial clinics made it possible to find a balance between the needs of achieving responses to the trial aims with a limited budget and simultaneously maintaining the safety of trial participants. Flexibility in finding the best solutions in every situation was the main key for success.</p>", "<p>The relative success of the trial was due to keeping the trial staff and participants continuously motivated as well as tireless negotiations with authorities. Repeated changes in the health care system and in legislation were keenly followed up by appropriate actions (meetings with ministers, data protection authorities, and other stakeholders, articles in newspapers, discussions on radio). Preventive drug trials usually have a long duration and the pressures for changes to the protocol are strong. Our trial was a small-scale trial which meant that it was easier to manage in the face of such constantly changing circumstances: the organization was flexible, while participants both in the decision-making board and the clinics were fully committed to the trial. Rapid changes are currently possible in every society and co-ordinator must be constantly aware of the trial environment and identify possible threats which can affect the trial process and be prepared to act in case these changes occur.</p>", "<p>Financing is a major challenge in a long-term preventive trial. In the Finnish financing system, funding decisions usually cover only a couple of years at a time, budgets are often made on current prices and resources are bound to budget years. More flexibility and longer commitment in financing would help in administering a long-term trial.</p>", "<p>In preventive drug trials the costs of drugs are usually high. Even publicly funded researchers usually ask for drugs to be donated from drug companies. Drug companies may not be so enthusiastic about sponsoring trials of an established therapy, because it is a financial risk. Beneficial results may increase sales, but not necessarily of the specific product of the sponsoring company. If the results are negative, pharmaceutical competitors may attempt to deflect the impact by insisting the negative results apply only to the drug used in the study. In the case of the WHI and WISDOM, Wyeth Ayerst was the only pharmaceutical company willing to take the risk. In 2001 Wyeth covered 70% of the global market [##UREF##11##38##]. When the non-beneficial results from the WHI were released in June 2002 sales of HT in the USA declined, with the decline in Wyeth products being especially dramatic [##UREF##12##39##, ####REF##15516400##40##, ##REF##14709575##41####14709575##41##]. Those companies that did not take the risk of donating drugs to the trials could now argue that their regimens are different from Wyeth's and the trial results do not apply to their products.</p>", "<p>An important question is who should prove the effectiveness of an (old) drug for a new preventive indication. Preventive drug trials are often long-term, and usually need large numbers of participants, thus increasing the costs involved [##REF##9492970##42##,##REF##8205268##43##]. Public funding is crucial in maintaining the independence of the trial from commercial biases. In the future, some procedure to share the costs of large trials should be negotiated where benefits and harms of licensed drugs need to be (re)evaluated. Public support for funding is needed for making evidence-based decisions in health care. Also, amendments to legislation for regulating the obligations of drug manufacturers to participate in post-marketing studies are needed.</p>", "<p>A big threat to long-term trials is new information from other trials challenging the hypotheses and initial reasoning of the trial. In the case of WISDOM, a large-scale, well-prepared trial was terminated in an early phase. The wisdom of that decision can be questioned, especially in the light of the slow changes in the practice of HT use and the current criticism of a lack of information. Many questions about HT effects are still unanswered. However, there is very little to be made to this extraneous threat to long-term trials, besides reconsiderations of the norms used in terminating trials. The role of ethical committees and of data monitoring committees needs to be further specified on how to interpret information from other studies and make decisions about the pre-term stopping of trials.</p>" ]
[ "<title>Discussion and Conclusion</title>", "<title>Keys for success</title>", "<p>Treatment in the EPHT trial was stopped earlier than planned, but the time was sufficient to provide answers to our short-term research questions. Taking into account all the outside changes occurring during the trial, we are satisfied with the trial process. However, the low adherence and the relatively short exposure time reduced the power of the study. A very good collaboration between the Trial Steering Committee, the Data Monitoring Committee, the trial coordinators, and the trial clinics made it possible to find a balance between the needs of achieving responses to the trial aims with a limited budget and simultaneously maintaining the safety of trial participants. Flexibility in finding the best solutions in every situation was the main key for success.</p>", "<p>The relative success of the trial was due to keeping the trial staff and participants continuously motivated as well as tireless negotiations with authorities. Repeated changes in the health care system and in legislation were keenly followed up by appropriate actions (meetings with ministers, data protection authorities, and other stakeholders, articles in newspapers, discussions on radio). Preventive drug trials usually have a long duration and the pressures for changes to the protocol are strong. Our trial was a small-scale trial which meant that it was easier to manage in the face of such constantly changing circumstances: the organization was flexible, while participants both in the decision-making board and the clinics were fully committed to the trial. Rapid changes are currently possible in every society and co-ordinator must be constantly aware of the trial environment and identify possible threats which can affect the trial process and be prepared to act in case these changes occur.</p>", "<p>Financing is a major challenge in a long-term preventive trial. In the Finnish financing system, funding decisions usually cover only a couple of years at a time, budgets are often made on current prices and resources are bound to budget years. More flexibility and longer commitment in financing would help in administering a long-term trial.</p>", "<p>In preventive drug trials the costs of drugs are usually high. Even publicly funded researchers usually ask for drugs to be donated from drug companies. Drug companies may not be so enthusiastic about sponsoring trials of an established therapy, because it is a financial risk. Beneficial results may increase sales, but not necessarily of the specific product of the sponsoring company. If the results are negative, pharmaceutical competitors may attempt to deflect the impact by insisting the negative results apply only to the drug used in the study. In the case of the WHI and WISDOM, Wyeth Ayerst was the only pharmaceutical company willing to take the risk. In 2001 Wyeth covered 70% of the global market [##UREF##11##38##]. When the non-beneficial results from the WHI were released in June 2002 sales of HT in the USA declined, with the decline in Wyeth products being especially dramatic [##UREF##12##39##, ####REF##15516400##40##, ##REF##14709575##41####14709575##41##]. Those companies that did not take the risk of donating drugs to the trials could now argue that their regimens are different from Wyeth's and the trial results do not apply to their products.</p>", "<p>An important question is who should prove the effectiveness of an (old) drug for a new preventive indication. Preventive drug trials are often long-term, and usually need large numbers of participants, thus increasing the costs involved [##REF##9492970##42##,##REF##8205268##43##]. Public funding is crucial in maintaining the independence of the trial from commercial biases. In the future, some procedure to share the costs of large trials should be negotiated where benefits and harms of licensed drugs need to be (re)evaluated. Public support for funding is needed for making evidence-based decisions in health care. Also, amendments to legislation for regulating the obligations of drug manufacturers to participate in post-marketing studies are needed.</p>", "<p>A big threat to long-term trials is new information from other trials challenging the hypotheses and initial reasoning of the trial. In the case of WISDOM, a large-scale, well-prepared trial was terminated in an early phase. The wisdom of that decision can be questioned, especially in the light of the slow changes in the practice of HT use and the current criticism of a lack of information. Many questions about HT effects are still unanswered. However, there is very little to be made to this extraneous threat to long-term trials, besides reconsiderations of the norms used in terminating trials. The role of ethical committees and of data monitoring committees needs to be further specified on how to interpret information from other studies and make decisions about the pre-term stopping of trials.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Preventive drugs require long-term trials to show their effectiveness or harms and often a lot of changes occur during post-marketing studies. The purpose of this article is to describe the research process in a long-term randomized controlled trial and discuss the impact and consequences of changes in the research environment.</p>", "<title>Methods</title>", "<p>The Estonian Postmenopausal Hormone Therapy trial (EPHT), originally planned to continue for five years, was planned in co-operation with the Women's International Study of Long-Duration Oestrogen after Menopause (WISDOM) in the UK. In addition to health outcomes, EPHT was specifically designed to study the impact of postmenopausal hormone therapy (HT) on health services utilization.</p>", "<title>Results</title>", "<p>After EPHT recruited in 1999–2001 the Women's Health Initiative (WHI) in the USA decided to stop the estrogen-progestin trial after a mean of 5.2 years in July 2002 because of increased risk of breast cancer and later in 2004 the estrogen-only trial because HT increased the risk of stroke, decreased the risk of hip fracture, and did not affect coronary heart disease incidence. WISDOM was halted in autumn 2002. These decisions had a major influence on EPHT.</p>", "<title>Conclusion</title>", "<p>Changes in Estonian society challenged EPHT to find a balance between the needs of achieving responses to the trial aims with a limited budget and simultaneously maintaining the safety of trial participants. Flexibility was the main key for success. Rapid changes are not limited only to transiting societies but are true also in developed countries and the risk must be included in planning all long-term trials.</p>", "<p>The role of ethical and data monitoring committees in situations with emerging new data from other studies needs specification. Longer funding for preventive trials and more flexibility in budgeting are mandatory. Who should prove the effectiveness of an (old) drug for a new preventive indication? In preventive drug trials companies may donate drugs but they take a financial risk, especially with licensed drugs. Public funding is crucial to avoid commercial biases. Legislation to share the costs of large post-marketing trials as well as regulation of manufacturer's participation is needed. [ISRCTN35338757]</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>All authors have participated in designing the study. SLH is the coordinating investigator; PV is the coordinator in Estonia and EH is the director of the trial and MR is the director of the trial in Estonia. SLH drafted the manuscript and other authors commented and revised it. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2288/8/51/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>The trial was financially supported by The Academy of Finland (Grant number 69838 and 201490), STAKES, the Finnish Ministry of Education (Doctoral Programs in Public Health), and the Ministry of Education and Science in Estonia (target funding 01921112s02 and SF0940026s07), and the National Institute for Health Development in Estonia. The drugs were donated by Wyeth-Ayerst via the WISDOM trial (Women's International Study of Long Duration Estrogen after Menopause), coordinated by Dr. Madge Vickers, London, U.K. We express our sincere gratitude to the trial physicians, trial midwives and women who participated in the trial. We acknowledge the efforts of Helle Karro, Mare Tekkel, Lea Laaniste and Sigrid Vorobjov in conducting the trial.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Recruitment flow.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Time flow of the trial.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Numbers of women by the length of exposure (years) at the time of stopping exposure in the EPHT trial (May 2004).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Sub-study</td><td align=\"left\">Arm</td><td align=\"center\" colspan=\"3\">Length of exposure (years)</td><td/></tr><tr><td colspan=\"1\"><hr/></td><td colspan=\"1\"><hr/></td><td colspan=\"3\"><hr/></td><td/></tr><tr><td/><td/><td align=\"center\">2–2.9</td><td align=\"center\">3–3.9</td><td align=\"center\">4+</td><td align=\"center\">Total</td></tr></thead><tbody><tr><td align=\"center\"><bold>Blind</bold></td><td align=\"left\">Hormone therapy</td><td align=\"center\">89</td><td align=\"center\">198</td><td align=\"center\">128</td><td align=\"center\">415</td></tr><tr><td/><td align=\"left\">Placebo</td><td align=\"center\">85</td><td align=\"center\">182</td><td align=\"center\">114</td><td align=\"center\">381</td></tr><tr><td align=\"center\"><bold>Non-blind</bold></td><td align=\"left\">Open-label hormone therapy</td><td align=\"center\">127</td><td align=\"center\">212</td><td align=\"center\">164</td><td align=\"center\">503</td></tr><tr><td/><td align=\"left\">Control</td><td align=\"center\">117</td><td align=\"center\">216</td><td align=\"center\">191</td><td align=\"center\">524</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"center\">Total</td><td/><td align=\"center\">418</td><td align=\"center\">808</td><td align=\"center\">597</td><td align=\"center\">1823</td></tr></tbody></table></table-wrap>" ]
[]
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[ "<graphic xlink:href=\"1471-2288-8-51-1\"/>", "<graphic xlink:href=\"1471-2288-8-51-2\"/>" ]
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[{"surname": ["Oakley"], "given-names": ["A"], "source": ["Social support and motherhood. The natural history of a research project."], "year": ["1992"], "publisher-name": ["Oxford, Blackwell"]}, {"surname": ["Oakley", "Strange", "Stephenson", "Forrest", "Monteiro"], "given-names": ["A", "V", "J", "S", "H"], "article-title": ["Evaluating Processes: A Case Study of a Randomized Controlled Trial of Sex Education"], "source": ["Evaluation"], "year": ["2004"], "volume": ["10"], "fpage": ["440"], "lpage": ["462"], "pub-id": ["10.1177/1356389004050220"]}, {"surname": ["Campbell", "Evans", "Tucker", "Quilty", "Dieppe", "Donovan"], "given-names": ["R", "M", "M", "B", "P", "JL"], "article-title": ["Why don't patients do their exercises? Understanding non-compliance with physiotherapy in patients with osteoarthritis of the knee"], "source": ["J Epidemiol Commun H"], "year": ["2001"], "volume": ["55"], "fpage": ["132"], "lpage": ["138"], "pub-id": ["10.1136/jech.55.2.132"]}, {"article-title": ["MRC stops study of long term use of HRT"], "source": ["MRC press release: October 23, 2002"], "year": ["2002"]}, {"surname": ["R\u00f5\u00f5m", "Kallaste"], "given-names": ["T", "E"], "article-title": ["Naised-mehed Eesti t\u00f6\u00f6turul: palgaerinevuste hinnang."], "source": ["Poliitikaanal\u00fc\u00fcs"], "year": ["2004"]}, {"surname": ["Topo"], "given-names": ["P"], "article-title": ["Hormonik\u00e4yt\u00f6n v\u00e4est\u00f6ryhmitt\u00e4iset erot"], "source": ["Vaihdevuosien hormonihoito - Miksi aihe puhuttaa?"], "year": ["2004"], "publisher-name": ["Vammala, Duodecim, Suomen Akatemia"], "fpage": ["31"], "lpage": ["37"]}, {"surname": ["R\u00e4go"], "given-names": ["L"], "source": ["An update of regulatory affairs in Estonia: June 15-16; Vilnius, Lithuania."], "year": ["1998"], "volume": ["NLN Publication No 49"], "publisher-name": ["Nordiska l\u00e4kemedelsn\u00e4mnden"], "fpage": ["19"], "lpage": ["24"]}, {"surname": ["Jesse", "Habicht", "Aaviksoo", "Koppel", "Irs", "Thomson"], "given-names": ["M", "J", "A", "A", "A", "S"], "article-title": ["Health care systems in transition: Estonia"], "source": ["Copenhagen, WHO Regional Office for Europe on behalf of the European Observatory on Health Systems and Policies; 2004"]}, {"surname": ["G\u00e4strin"], "given-names": ["G"], "article-title": ["The Mama programme for breast cancer control"], "source": ["Department of Public Health"], "year": ["1994"], "volume": ["ser A 394"], "publisher-name": ["Tampere, University of Tampere"]}, {"surname": ["State Agency of Medicines"], "article-title": ["Approved clinical trials in Estonia in 1992-2004."], "year": [" 2005"]}, {"surname": ["Coney"], "given-names": ["S"], "source": ["The menopause industry: how the medical establishment exploits women"], "year": ["1994"], "publisher-name": ["Alameda CA, Hunter House"]}, {"surname": ["Clark"], "given-names": ["J"], "article-title": ["A hot flush for Big Pharma"], "source": ["BMJ"], "year": ["2003"], "volume": ["327"], "fpage": ["400"], "pub-id": ["10.1136/bmj.327.7411.400"]}, {"surname": ["Hillman", "Zuckerman", "Lee"], "given-names": ["JJ", "IH", "E"], "article-title": ["The Impact of the Women's Health Initiative on Hormone Replacement Therapy in a Medicaid Program"], "source": ["J Womens Health"], "year": ["2004"], "volume": ["13"], "fpage": ["986"], "lpage": ["992"], "pub-id": ["10.1089/jwh.2004.13.986"]}]
{ "acronym": [], "definition": [] }
43
CC BY
no
2022-01-12 14:47:26
BMC Med Res Methodol. 2008 Aug 1; 8:51
oa_package/f6/2d/PMC2529341.tar.gz
PMC2529342
18673529
[ "<title>Background</title>", "<p>Neuropathic pain is the consequence of damage to the central nervous system (e.g. cerebrovascular accident, multiple sclerosis or spinal cord injury) or peripheral nervous system (e.g. painful diabetic neuropathy (PDN), postherpetic neuralgia (PHN), surgery). It has a significant negative impact on quality of life [##REF##17420400##1##]. Some patients with neuropathic pain respond well to treatment and others show no obvious response [##REF##1350803##2##, ####REF##7580659##3##, ##REF##9121808##4####9121808##4##]. No pharmacological intervention produces meaningful relief for more than half the patients with neuropathic pain [##REF##17038030##5##].</p>", "<p>The incidence of PHN and trigeminal neuralgia and PDN together is almost 0.1% per year in the UK [##REF##16545908##6##]. The incidence of neuropathic pain is growing, presumably because of increased numbers of older persons and diabetics, amongst whom about one in five develop painful neuropathy at some stage. Neuropathic pain is quite common in general medical practice with about 1% point prevalence in UK if fibromyalgia, PDN, PHN, and trigeminal neuralgia are included [##UREF##0##7##].</p>", "<p>The most common pharmacological approaches to the management of neuropathic pain include antidepressants (tricyclic antidepressants, serotonin and norepinephrine reuptake inhibitors), antiepileptics (valproate, carbamazepine, gabapentin, pregabalin), opioids, other analgesics, topical lidocaine patch, and topical capsaicin. The evidence for these has been reviewed extensively [##REF##9121808##4##,##REF##15972567##8##, ####REF##16213659##9##, ##REF##16013891##10##, ##REF##17443559##11##, ##REF##15033881##12##, ##REF##17943857##13##, ##REF##16034857##14##, ##REF##16034978##15##, ##REF##16034977##16####16034977##16##].</p>", "<p>5-hydroxytryptamine (5HT) and norepinephrine (NE) are involved in the modulation of endogenous analgesic mechanisms via descending inhibitory pain pathways in the brain and spinal cord [##REF##15530638##17##]. Disinhibition and imbalance of 5HT and NE in endogenous pain inhibitory pathways could contribute to persistent pain. An increase in 5HT and NE may increase inhibition of painful signals, improving pain relief.</p>", "<p>Duloxetine hydrochloride is a serotonin-norepinephrine reuptake inhibitor used to treat depression, generalized anxiety disorder, neuropathic pain, and stress incontinence in women. We investigated the efficacy of duloxetine in the management of PDN and fibromyalgia as duloxetine had not been included in the most recent systematic reviews, including one of antidepressants [##REF##17943857##13##]. Duloxetine in PDN alone has been the subject of a recent post hoc analysis [##REF##18164920##18##].</p>" ]
[ "<title>Methods</title>", "<p>We searched PubMed, EMBASE (via Ovid), and Cochrane CENTRAL up to June 2008 for randomised controlled trials using duloxetine to treat neuropathic pain. The detailed search strategy included use of the drug name \"duloxetine\" anywhere in an article, together with \"randomized controlled trial\" as subject heading, publication type or text word; this was modified appropriately for different databases. Reference lists of retrieved articles and reviews were also searched for relevant trials. We contacted Boehringer Ingelheim Limited as a UK distributor for duloxetine in neuropathic pain to enquire about relevant published or unpublished studies, and examined an on-line register [##UREF##1##19##].</p>", "<p>Included trials had to be randomised, double blind, placebo controlled, and use duloxetine to treat adult patients with painful neuropathies of any cause. Trials had to have a minimum of 10 patients per treatment arm, and a planned duration of at least four weeks.</p>", "<p>The abstracts were read, and potentially useful reports retrieved in full. No information was taken from posters or abstracts. Decisions on inclusion or exclusion of trials, assessment of trial quality and validity and all data extraction were made independently by three reviewers, with discrepancies resolved by consensus.</p>", "<p>Methodological quality of included studies was assessed using a validated 5-point scale [##REF##8721797##20##] utilising reporting of randomisation, blinding, and withdrawals. The maximum score possible was 5 points, and no study could be included with fewer than 2 points (one for randomisation and one for blinding). Study validity was assessed using a validated 16-point scale [##REF##10779669##21##].</p>", "<p>Data were abstracted into a standard form. Information extracted from the trials included details of the patients (number, age, sex, pain syndrome), duloxetine dose, and permitted rescue analgesia. The primary outcome sought was 50% pain relief. Other measures of pain relief were abstracted where reported. Secondary outcomes were withdrawals (all cause, lack of efficacy and adverse events) and adverse events (patients with at least one adverse event, serious adverse events, and specific adverse events).</p>", "<p>Guidelines for quality of reporting of meta-analyses were followed where appropriate [##REF##10584742##22##]. The prior intention was to pool data where there was clinical and methodological homogeneity, with similar patients, dose, duration, outcomes, and comparators, but not where numbers of events were small, and random chance might well dominate effects of treatment [##REF##9870574##23##]. Homogeneity tests and funnel plots, though commonly used in meta-analysis, were not used because they have been found to be unreliable [##REF##10781914##24##,##REF##11822262##25##]. Instead, clinical homogeneity was examined graphically [##REF##3300460##26##]. Relative benefit (or risk) and number needed to treat or harm (NNT or NNH) were calculated with 95% confidence intervals. Relative benefit or risk was calculated using a fixed effects model [##UREF##2##27##] with no statistically significant difference between treatments assumed when the 95% confidence intervals included unity. We added 0.5 to treatment and comparator arms of trials in which at least one arm had no events. NNT or NNH was calculated [##REF##7873954##28##] using the pooled number of observations only when there was a statistically significant difference of relative benefit or risk (where the confidence interval did not include 1). We used the following definitions:</p>", "<p>• When significantly more beneficial outcomes occurred with duloxetine than placebo, we used the term number needed to treat (NNT).</p>", "<p>• When significantly fewer adverse events occurred with duloxetine than placebo we used the term the number-needed-to-treat to prevent one adverse event (NNTp).</p>", "<p>• When significantly more adverse events occurred with duloxetine than placebo we used the term the number-needed-to-harm to cause one adverse event (NNH).</p>", "<p>Statistical significance of any difference between NNT for different doses was assumed if there was no overlap of the confidence intervals, and additionally tested using the z statistic [##REF##9310564##29##]. RevMan 5.0.12 was used to analyse continuous data. There was a prior intention to carry out sensitivity analyses for high versus low trial quality (&lt;3 vs ≥ 3) and validity (&lt;9 vs ≥ 9), duloxetine dose, and pain syndrome. A minimum of two trials and 250 patients was required in any sensitivity analysis [##REF##9870574##23##].</p>" ]
[ "<title>Results</title>", "<p>We identified six trials satisfying the inclusion criteria [##REF##15457467##30##, ####REF##16298061##31##, ##REF##15927394##32##, ##REF##16266355##33##, ##REF##17060567##34##, ##REF##18395345##35####18395345##35##]. Details of the included studies are in Additional File ##SUPPL##0##1##. A total of 2,216 patients were included, 1,510 treated with duloxetine and 706 with placebo. Three trials [##REF##15927394##32##, ####REF##16266355##33##, ##REF##17060567##34####17060567##34##] enrolled patients with PDN and three [##REF##15457467##30##,##REF##16298061##31##,##REF##18395345##35##] enrolled patients with fibromyalgia, in which 23% to 38% had a diagnosis of major depressive disorder. The trials in PDN excluded patients with any diagnosed psychological disorder. We did not include any trials in which the primary problem was a major psychiatric disorder but with a secondary painful condition [##REF##15504423##36##, ####REF##15119915##37##, ##REF##14709757##38##, ##REF##16700869##39##, ##REF##17541049##40##, ##REF##17587217##41####17587217##41##]. All patients had established baseline pain of at least moderate severity, measured using established scales. The mean age in the trials ranged between 49 and 61 years, and the majority of patients were Caucasian. One trial [##REF##16298061##31##] enrolled only women, and the others between 5% and 61% men.</p>", "<p>Trial duration was 12 to 13 weeks. One trial [##REF##16266355##33##] had a 13-week continuation phase, but results for the first 13 weeks (acute phase) only are analysed here, to make it comparable with the other trials. Duloxetine was used at doses of 20, 60, or 120 mg daily, with titration up to the 120 mg dose, which was given as a divided dose of 60 mg twice daily. Up to 2 g acetaminophen daily was permitted as rescue medication in the fibromyalgia trials, and up to 4 g daily in the PDN trials.</p>", "<p>Trials were of good methodological quality, with three scoring 5/5, two scoring 4/5, and one scoring 3/5 on the Oxford Quality Score [##REF##8721797##20##]. Two scored 16/16 and four scored 13/16 on the Oxford Pain Validity Score [##REF##10779669##21##]. No sensitivity analyses were therefore carried out for these criteria.</p>", "<title>Efficacy</title>", "<title>50% Pain Relief</title>", "<p>All six trials reported the outcome of at least 50% pain relief over baseline in the 24-hour average pain score by the end of the trial, and results are summarised in Figure ##FIG##0##1## and Table ##TAB##0##1##. Trials were consistent, and overall 41% of patients achieved 50% pain relief with any dose of duloxetine compared with 24% with placebo. Combining all doses in both conditions (2,216 patients), the NNT for one patient to achieve at least 50% pain relief with duloxetine compared with placebo was 5.9 (95% CI 4.8 to 7.7).</p>", "<p>Five of the trials used 60 mg, and all six used 120 mg; only 66 patients (in two treatment arms) received the 20 mg dose. The dose of duloxetine made little difference to the result (Figure ##FIG##0##1##, Table ##TAB##0##1##). There was no difference in the proportion of patients achieving at least 50% pain relief with 60 mg and 120 mg (z = 0.13; p = 0.89).</p>", "<p>There was no significant difference in the proportion of patients achieving at least 50% pain relief with PDN or fibromyalgia (z = 0.95; p = 0.34). The proportion of patients with this outcome was slightly lower for both placebo and duloxetine groups in the fibromyalgia trials (Table ##TAB##0##1##), with similar NNTs for both conditions.</p>", "<title>Average pain score (APS)</title>", "<p>Five of the trials [##REF##16298061##31##, ####REF##15927394##32##, ##REF##16266355##33##, ##REF##17060567##34##, ##REF##18395345##35####18395345##35##] recorded daily 24-hour average pain scores (APS) on a 0–10 scale, and reported this as a weekly mean, as well as the change from baseline to final weekly mean. The change in weekly mean APS on treatment was compared with placebo over the 12 or 13 weeks. Figure ##FIG##1##2## shows the calculations for different doses of duloxetine in different pain syndromes. The weighted mean difference for duloxetine 60 mg compared with placebo was 1.0 (0.71 to 1.4), and for duloxetine 120 mg compared with placebo was 0.9 (0.49 to 1.3). There was no difference in response between patients with PDN and fibromyalgia.</p>", "<title>Withdrawals</title>", "<p>Withdrawals for any cause occurred in slightly more patients with duloxetine (30%) than placebo (28%); the NNTp for all cause withdrawal with duloxetine rather than placebo was 26 (13 to 426) (Table ##TAB##0##1##). Withdrawals due to lack of efficacy occurred in significantly fewer patients (4%) taking duloxetine than placebo (9%); the NNTp for lack of efficacy withdrawal with duloxetine rather than placebo was 17 (12 to 35) (Table ##TAB##0##1##).</p>", "<p>Withdrawals for any cause or for lack of efficacy did not differ significantly between the 60 mg and 120 mg doses, although for any cause they were consistently 4% to 5% lower for 60 mg than 120 mg, except for Russell et al [##REF##18395345##35##] where the rates were almost identical.</p>", "<title>Adverse events</title>", "<title>Withdrawals</title>", "<p>Withdrawals due to adverse events occurred significantly more often with duloxetine (15%) than placebo (8%). The NNH was 15 (11 to 25) (Table ##TAB##0##1##). They were 2% to 8% lower with 60 mg than 120 mg, giving an NNH of 19 (11 to 86) for 120 mg compared to 60 mg.</p>", "<title>Any adverse event</title>", "<p>The \"at least one adverse event\" criterion was met in significantly more patients taking duloxetine (82%) than placebo (67%) in the four trials that reported this outcome. The NNH was 6.7 (5.0 to 10) (Table ##TAB##0##1##).</p>", "<title>Serious adverse events</title>", "<p>Serious adverse events were reported in only three trials; one trial did not report this outcome [##REF##15457467##30##], one did not report it for the 13-week phase [##REF##18395345##35##], and one did not separate rates between groups [##REF##15927394##32##]. In the three trials reporting serious adverse events they were uncommon and not significantly different between duloxetine or placebo, at about 2–3% over the 12 weeks of the trials (Table ##TAB##0##1##). Russell et al [##REF##18395345##35##] reported that serious adverse events were infrequent over the full 6 months of the trial.</p>", "<title>Specific adverse events</title>", "<p>Only three trials [##REF##16298061##31##,##REF##15927394##32##,##REF##17060567##34##] provided numbers of patients experiencing specific treatment emergent adverse events over 12 to 13 weeks. There were statistically significant increases in nausea (29% vs 10%), somnolence (14% vs 4%), constipation (13% vs 3%) and decreased appetite (7% vs 1%) with all doses of duloxetine compared with placebo (Table ##TAB##0##1##). There were small mean increases in laboratory tests and vital signs, but these were transient and not considered clinically relevant by the trialists.</p>" ]
[ "<title>Discussion</title>", "<p>This systematic review differs from the only other that considers duloxetine [##REF##18164920##18##]. That company-sponsored review was able to pool data from the three PDN trials. It calculated NNTs for at least 50% pain relief (with identical results to those calculated here), and also gave NNTs for at least 30% pain relief. It demonstrated the stability of NNTs over two to 12 weeks, an important observation, and no difference in estimate depending on treatment of dropouts. This review differs in demonstrating that the efficacy of duloxetine is similar in PDN and fibromyalgia, and also makes an informed comparison with other evidence on antidepressant treatments for neuropathic pain.</p>", "<p>For evidence to be credible, it has to fulfil criteria of quality, validity, and size [##UREF##3##42##]. The evidence here on duloxetine does that. Trials were randomised, and double blind, and quality and validity scores indicated low chance of bias. The trials were of sufficient length (12 or 13 weeks) to make them clinically relevant, and the outcome reported of at least 50% pain relief was a high hurdle. Most older neuropathic pain studies used less stringent measures, including undefined \"improvement\" as an outcome, and only trials of pregabalin have also consistently used at least 50% pain relief. Finally, with information on over 2,200 patients, including over 1,000 patients with PDN, the data set for duloxetine fulfils the requirements of size [##REF##9870574##23##] and is much larger than any previous data set for antidepressants in neuropathic pain [##REF##17943857##13##].</p>", "<p>Significantly more patients achieved the outcome of at least 50% pain relief with duloxetine (41%) than with placebo (24%) over 12 weeks. The outcome of 50% pain represents substantial clinical pain relief, and an NNT of 6 suggests that this is likely to be a useful drug in these difficult-to-treat conditions, where typically only a minority of patients respond. There was no dose response between 60 mg and 120 mg, nor was there any significant difference in the duloxetine response between PDN or fibromyalgia. There was a similar lack of dose response in this range for use of duloxetine in major depressive disorder [##REF##16960699##43##].</p>", "<p>Duloxetine was well tolerated in the trials, with fewer withdrawals due to adverse events with 60 mg than with 120 mg. Most adverse events were reported to be mild or moderate, with nausea, somnolence, constipation, decreased appetite and dry mouth frequently mentioned. In stress incontinence duloxetine affects the resting tone and contraction of the urethral striated sphincter muscle. It might be expected to cause symptoms of urinary hesitancy in patients without incontinence, but urinary problems were not reported in any of these trials, or in trials of duloxetine in depression [for example [##REF##16700869##44##,##REF##17590215##45##]].</p>", "<p>This review has some limitations. Firstly, pain intensity measurements used to calculate our primary outcome of at least 50% pain relief were derived from average pain intensity scores during the previous 24 hours. Secondly, the trials were 12 to 13 weeks in duration, and although they demonstrated a sustained response and good tolerability over this period, they provided no information for longer-term efficacy or safety. Russell et al [##REF##18395345##35##] included a 13-week continuation phase, and reported continuing efficacy and tolerability, as have open-label extension studies in neuropathic pain lasting 26 and 52 weeks [##REF##17014595##46##,##REF##17716324##47##].</p>", "<p>Duloxetine has been widely trialled in other conditions, in particular depression and stress-induced incontinence in women, and the many trials have been subject to systematic review in those therapeutic areas. An evaluation of cardiovascular safety in 42 placebo controlled trials involving 8,500 patients concluded that duloxetine did not appear to be associated with significant cardiovascular risks [##REF##17472422##48##].</p>", "<p>Finally, the studies in fibromyalgia included some patients with depression. Although only a minority were depressed (Additional file ##SUPPL##0##1##), it could be argued that duloxetine reduced pain intensity by improving depression. We identified a small number of other trials in patients with psychiatric disorders with painful physical symptoms (not neuropathic pain) [##REF##15504423##36##, ####REF##15119915##37##, ##REF##14709757##38##, ##REF##16700869##39##, ##REF##17541049##40##, ##REF##17587217##41####17587217##41##]. Although none reported our primary outcome of 50% pain relief, they did use the same scales to record pain intensity, and all reported improvements in pain with duloxetine 60 mg that did not entirely correlate with improvements in depression. Fava et al. estimated that 50% of the total effect of duloxetine on overall pain was independent of changes in depression [##REF##15119915##37##]. A counter view was that duloxetine was ineffective in treating pain in depression [##REF##18087203##49##]. In the trials included in this review, about one third of the patients with fibromyalgia also had major depressive disorder. It would be difficult to attribute all of the analgesic effect of duloxetine in these trials to improvements in depression in a minority, although one could not rule out a contributory effect. One of these trials estimated that around 20% of the overall treatment effect with 120 mg and 30% with 60 mg was due to improvements in depressive symptoms, the remainder being due to duloxetine's direct effect on pain reduction [##REF##18395345##35##].</p>", "<p>The quantity and quality of randomised trial data in neuropathic pain is limited. Table ##TAB##1##2## shows the results for duloxetine 60 mg and 120 mg over 12 weeks compared with other results for antidepressants calculated from a recent Cochrane review [##REF##17943857##13##]. Only patients with PDN are reported in Table ##TAB##1##2## for duloxetine, in order to keep to similar inclusion criteria. Even so, the amount of evidence on duloxetine dominates the evidence available, almost doubling the number of patients studied previously with antidepressants. Table ##TAB##1##2## shows the curious tendency for smaller amounts of information to be associated with greater benefit, either as higher values for relative risk or lower values for NNT. Size and quality may be linked: only one trial in the Cochrane meta-analysis had over 100 participants, while all the duloxetine trials had over 200, and many older studies used poorly defined outcomes of improvement, probably less stringent than that of at least 50% pain relief. Some small older studies also had a crossover rather than parallel design. This presents problems in determining relative efficacy among antidepressants for treatment of neuropathic pain.</p>", "<p>Several evidence-based recommendations for the treatment of neuropathic pain place use of antidepressants early in any care pathway [##REF##17038030##5##,##REF##16213659##9##,##REF##17920770##50##]. Comparing the evidence for different therapies within a class, and between classes, is key to determining the most effective, and most cost-effective pathway. In that circumstance, it is not enough just to calculate an NNT. The quality and credibility of the evidence behind those calculations needs to be evaluated, a function of the utility and validity of outcomes, and to have sufficient numbers of patients or events to avoid random chance. The example of duloxetine provides a firm evidential base for within and between class comparisons.</p>" ]
[ "<title>Conclusion</title>", "<p>Duloxetine is equally effective for the treatment of PDN and fibromyalgia, judged by the outcome of at least 50% pain relief over 12 to 13 weeks, and is well tolerated. The NNT of 6 for this outcome suggests that this is likely to be a useful drug in these difficult-to-treat conditions, where typically only a minority of patients respond. Doses higher than 60 mg do not provide additional pain relief, but do cause slightly more withdrawals due to adverse events. Comparing duloxetine with other antidepressants for pain relief in PDN shows inadequacies in the evidence for efficacy of antidepressants, which are currently recommended in PDN care pathways.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Duloxetine hydrochloride is a reuptake inhibitor of 5-hydroxytryptamine and norepinephrine used to treat depression, generalized anxiety disorder, neuropathic pain, and stress incontinence in women. We investigated the efficacy of duloxetine in painful diabetic neuropathy and fibromyalgia to allow comparison with other antidepressants.</p>", "<title>Methods</title>", "<p>We searched PubMed, EMBASE (via Ovid), and Cochrane CENTRAL up to June 2008 for randomised controlled trials using duloxetine to treat neuropathic pain.</p>", "<title>Results</title>", "<p>We identified six trials with 1,696 patients: 1,510 were treated with duloxetine and 706 with placebo. All patients had established baseline pain of at least moderate severity. Trial duration was 12 to 13 weeks. Three trials enrolled patients with painful diabetic neuropathy (PDN) and three enrolled patients with fibromyalgia. The number needed to treat (NNT) for at least 50% pain relief at 12 to 13 weeks with duloxetine 60 mg versus placebo (1,211 patients in the total comparison) was 5.8 (95% CI 4.5 to 8.4), and for duloxetine 120 mg (1,410 patients) was 5.7 (4.5 to 5.7). There was no difference in NNTs between PDN and fibromyalgia. With all doses of duloxetine combined (20/60/120 mg) there were fewer withdrawals for lack of efficacy than with placebo (number needed to treat to prevent one withdrawal 20 (13 to 42)), but more withdrawals due to adverse events (number needed to harm (NNH) 15 (11 to 25)). Nausea, somnolence, constipation, and reduced appetite were all more common with duloxetine than placebo (NNH values 6.3, 11, 11, and 18 respectively). The results for duloxetine are compared with published data for other antidepressants in neuropathic pain.</p>", "<title>Conclusion</title>", "<p>Duloxetine is equally effective for the treatment of PDN and fibromyalgia, judged by the outcome of at least 50% pain relief over 12 weeks, and is well tolerated. The NNT of 6 for 50% pain relief suggests that this is likely to be a useful drug in these difficult-to-treat conditions, where typically only a minority of patients respond. Comparing duloxetine with antidepressants for pain relief in DPN shows inadequacies in the evidence for efficacy of antidepressants, which are currently recommended in PDN care pathways.</p>" ]
[ "<title>Competing interests</title>", "<p>RAM, and SD have received research support from charities, government and industry sources at various times, and HG from government, but no such support was received for this work. No author has any direct stock holding in any pharmaceutical company.</p>", "<title>Authors' contributions</title>", "<p>AS, SD and HG carried out searches, selected studies and carried out data extraction. AS, SD and AM were involved with analysis. All authors were involved with writing, and all authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2377/8/29/prepub\"/></p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>No specific funding was obtained for this work. Pain Research is supported in part by the Oxford Pain Research Trust.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Proportion of patients with at least 50% pain relief with duloxetine 60 mg or 120 mg and placebo in individual trials. Pink circles are fibromyalgia trials. Inset scale shows number in comparison.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Mean change from baseline to endpoint on the 24-hour average pain score (APS) for treatment compared to placebo over 12 to 13 weeks, by duloxetine dose (60 mg and 120 mg) and condition (diabetic neuropathy and fibromyalgia).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Summary of efficacy and adverse event outcomes in duloxetine trials</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\" colspan=\"2\"><bold>Number of</bold></td><td align=\"center\" colspan=\"2\"><bold>Percent with</bold></td><td/><td/></tr><tr><td/><td/><td colspan=\"4\"><hr/></td><td/><td/></tr><tr><td align=\"center\"><bold>Outcome</bold></td><td align=\"center\"><bold>Dose (daily maximum)</bold></td><td align=\"center\"><bold>Trials</bold></td><td align=\"center\"><bold>Patients</bold></td><td align=\"center\"><bold>Duloxetine</bold></td><td align=\"center\"><bold>Placebo</bold></td><td align=\"center\"><bold>Relative benefit or risk (95% CI)</bold></td><td align=\"center\"><bold>NNT/NNTp/</bold><bold><italic>NNH </italic></bold><bold>(95% CI)</bold></td></tr></thead><tbody><tr><td align=\"left\" colspan=\"8\"><bold>Efficacy</bold></td></tr><tr><td align=\"left\">50% PR All</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">6</td><td align=\"center\">2,216</td><td align=\"center\">41</td><td align=\"center\">24</td><td align=\"center\">1.7 (1.4 to 1.9)</td><td align=\"center\"><bold>5.9 (4.8 to 7.7)</bold></td></tr><tr><td align=\"left\">50% PR PDN</td><td align=\"center\">60/120 mg</td><td align=\"center\">3</td><td align=\"center\">1,024</td><td align=\"center\">47</td><td align=\"center\">27</td><td align=\"center\">1.7 (1.4 to 2.1)</td><td align=\"center\"><bold>5.1 (3.9 to 7.3)</bold></td></tr><tr><td align=\"left\">50% PR fibromyalgia</td><td align=\"center\">60/120 mg</td><td align=\"center\">3</td><td align=\"center\">996</td><td align=\"center\">37</td><td align=\"center\">21</td><td align=\"center\">1.7 (1.4 to 2.1)</td><td align=\"center\"><bold>6.4 (4.7 to 9.9)</bold></td></tr><tr><td align=\"left\">50% PR</td><td align=\"center\">60 mg</td><td align=\"center\">5</td><td align=\"center\">1,211</td><td align=\"center\">43</td><td align=\"center\">26</td><td align=\"center\">1.7 (1.4 to 2.0)</td><td align=\"center\"><bold>5.8 (4.4 to 8.4)</bold></td></tr><tr><td align=\"left\">50% PR</td><td align=\"center\">120 mg</td><td align=\"center\">6</td><td align=\"center\">1,410</td><td align=\"center\">42</td><td align=\"center\">24</td><td align=\"center\">1.7 (1.5 to 2.0)</td><td align=\"center\"><bold>5.7 (4.5 to 7.8)</bold></td></tr><tr><td align=\"left\" colspan=\"8\"><bold>Adverse events general</bold></td></tr><tr><td align=\"left\">Withdrawal – all cause</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">6</td><td align=\"center\">2,418</td><td align=\"center\">30</td><td align=\"center\">26</td><td align=\"center\">1.2 (1.1 to 1.4)</td><td align=\"center\"><italic>26 (13 to 426)</italic></td></tr><tr><td align=\"left\">Withdrawal – LoE</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">5</td><td align=\"center\">1,872</td><td align=\"center\">4</td><td align=\"center\">9</td><td align=\"center\">0.5 (0.4 to 0.7)</td><td align=\"center\">20 (13 to 42)</td></tr><tr><td align=\"left\">Withdrawal – AE</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">6</td><td align=\"center\">2,220</td><td align=\"center\">15</td><td align=\"center\">8</td><td align=\"center\">1.8 (1.4 to 2.4)</td><td align=\"center\"><italic>15 (11 to 25)</italic></td></tr><tr><td align=\"left\">Any AE</td><td align=\"center\">60/120 mg</td><td align=\"center\">4</td><td align=\"center\">1,243</td><td align=\"center\">82</td><td align=\"center\">67</td><td align=\"center\">1.2 (1.2 to 1.3)</td><td align=\"center\"><italic>6.7 (5.0 to 10)</italic></td></tr><tr><td align=\"left\">Serious AE</td><td align=\"center\">60/120 mg</td><td align=\"center\">3</td><td align=\"center\">1,034</td><td align=\"center\">2</td><td align=\"center\">3</td><td align=\"center\">0.8 (0.4 to 1.7)</td><td align=\"center\">not calculated</td></tr><tr><td align=\"left\" colspan=\"8\"><bold>Specific adverse events</bold></td></tr><tr><td align=\"left\">Nausea</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">3</td><td align=\"center\">1,145</td><td align=\"center\">29</td><td align=\"center\">10</td><td align=\"center\">3.0 (2.2 to 4.3)</td><td align=\"center\"><italic>5.3 (4.3 to 6.9)</italic></td></tr><tr><td align=\"left\">Somnolence</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">3</td><td align=\"center\">1,145</td><td align=\"center\">14</td><td align=\"center\">4</td><td align=\"center\">2.9 (1.7 to 4.9)</td><td align=\"center\"><italic>11 (8.0 to 16)</italic></td></tr><tr><td align=\"left\">Constipation</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">3</td><td align=\"center\">1,145</td><td align=\"center\">13</td><td align=\"center\">3</td><td align=\"center\">3.6 (2.0 to 6.5)</td><td align=\"center\"><italic>11 (8.3 to 16)</italic></td></tr><tr><td align=\"left\">Decreased appetite</td><td align=\"center\">20/60/120 mg</td><td align=\"center\">2</td><td align=\"center\">811</td><td align=\"center\">7</td><td align=\"center\">1</td><td align=\"center\">4.9 (1.7 to 14)</td><td align=\"center\"><italic>18 (12 to 34)</italic></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Summary of efficacy in antidepressant meta-analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\" colspan=\"2\"><bold>Number of</bold></td><td align=\"center\" colspan=\"2\"><bold>Percent with</bold></td><td/><td/></tr><tr><td/><td/><td colspan=\"4\"><hr/></td><td/><td/></tr><tr><td align=\"left\"><bold>Antidepressant</bold></td><td align=\"center\"><bold>Outcome</bold></td><td align=\"center\"><bold>Trials</bold></td><td align=\"center\"><bold>Patients</bold></td><td align=\"center\"><bold>Placebo</bold></td><td align=\"center\"><bold>Active</bold></td><td align=\"center\"><bold>Relative benefit (95% CI)</bold></td><td align=\"center\"><bold>NNT (95% CI)</bold></td></tr></thead><tbody><tr><td align=\"left\">Duloxetine 60/120 mg</td><td align=\"left\">at least 50% pain relief</td><td align=\"center\">3</td><td align=\"center\">1,024</td><td align=\"center\">27</td><td align=\"center\">47</td><td align=\"center\">1.7 (1.4 to 2.1)</td><td align=\"center\">5.1 (3.9 to 7.3)</td></tr><tr><td align=\"left\">Amitriptyline all doses</td><td align=\"left\">global improvement</td><td align=\"center\">10</td><td align=\"center\">588</td><td align=\"center\">32</td><td align=\"center\">64</td><td align=\"center\">2.0 (1.6 to 2.4)</td><td align=\"center\">3.2 (2.6 to 4.2)</td></tr><tr><td align=\"left\">Other antidepressants</td><td align=\"left\">global improvement</td><td align=\"center\">3</td><td align=\"center\">216</td><td align=\"center\">12</td><td align=\"center\">50</td><td align=\"center\">4.2 (2.5 to 7.0)</td><td align=\"center\">2.6 (2.0 to 3.7)</td></tr><tr><td align=\"left\">Venlafaxine all doses</td><td align=\"left\">global improvement</td><td align=\"center\">3</td><td align=\"center\">200</td><td align=\"center\">25</td><td align=\"center\">57</td><td align=\"center\">2.3 (1.6 to 3.4)</td><td align=\"center\">3.1 (2.2 to 5.1)</td></tr><tr><td align=\"left\">Desipramine all doses</td><td align=\"left\">global improvement</td><td align=\"center\">2</td><td align=\"center\">78</td><td align=\"center\">10</td><td align=\"center\">59</td><td align=\"center\">5.8 (2.2 to 15)</td><td align=\"center\">2.1 (1.5 to 3.3)</td></tr><tr><td align=\"left\">Imipramine all doses</td><td align=\"left\">global improvement</td><td align=\"center\">2</td><td align=\"center\">58</td><td align=\"center\">5</td><td align=\"center\">97</td><td align=\"center\">19 (3.9 to 89)</td><td align=\"center\">1.1 (1.0 to 1.2)</td></tr></tbody></table></table-wrap>" ]
[]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional file 1</title><p>Included studies. Details of the design and outcomes of the included studies.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>PR – pain relief; PDN – painful diabetic neuropathy; LoE – lack of efficacy; AE – adverse event</p><p>In the right hand column, bold font is used for <bold>NNT – number needed to treat</bold>; normal font for NNTp – number need to treat to prevent; italic font for <italic>NNH – number needed to harm</italic></p></table-wrap-foot>", "<table-wrap-foot><p>NNT – Number need to treat</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2377-8-29-1\"/>", "<graphic xlink:href=\"1471-2377-8-29-2\"/>" ]
[ "<media xlink:href=\"1471-2377-8-29-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["McQuay", "Smith", "Moore", "Stevens A, Raftery J, Mant J, Simpson S"], "given-names": ["HJ", "LA", "RA"], "article-title": ["Chronic pain"], "source": ["Health Care Needs Assessment, 3rd series"], "year": ["2007"], "publisher-name": ["Oxford: Radcliffe Publishing"], "fpage": ["519"], "lpage": ["600"]}, {"article-title": ["Eli Lilly and Company Clinical Trial Registry"]}, {"surname": ["Morris", "Gardner"], "given-names": ["JA", "MJ"], "article-title": ["Calculating confidence intervals for relative risk, odds ratios and standardised ratios and rates"], "source": ["Br Med J (Clin Res Ed)"], "year": ["1995"], "volume": ["296 "], "publisher-name": ["London: British Medical Journal"], "fpage": ["1313"], "lpage": ["1316"]}, {"surname": ["Moore", "McQuay"], "given-names": ["A", "H"], "source": ["Bandolier's Little Book of Making Sense of the Medical Evidence"], "year": ["2006"], "publisher-name": ["Oxford: Oxford University Press"]}]
{ "acronym": [], "definition": [] }
50
CC BY
no
2022-01-12 14:47:26
BMC Neurol. 2008 Aug 1; 8:29
oa_package/f5/e6/PMC2529342.tar.gz
PMC2529374
18791646
[ "<title>Introduction</title>", "<p>In the effort to create a vaccine for human immunodeficiency virus type 1 (HIV-1), poor immune response to the HIV proteins is a fundamental problem. For a DNA vaccine, the immune response is correlated with protein expression levels, so an increase in expression of these proteins could alleviate a significant road block to the construction of a viable DNA vaccine.##REF##17032152##[1]##, ##REF##16818367##[2]## Transcription of the HIV DNA copy is an inefficient process that is aided by the addition of an HIV protein (TAT). In addition the large mRNAs from HIV have inherent RNA processing problems and are not efficiently exported from the nucleus in the absence of a helper protein (REV). These m-RNA synthesis and transport problems are presumably due to a set of RNA sequences or structures encoded in HIV DNA and RNAs.##REF##12932730##[3]##, ##REF##9188551##[4]## We hypothesize that identifying and removing these signals, which cause the poor synthesis and nuclear confinement of HIV RNA, should significantly increase expression levels of these proteins and improve the immune response.</p>", "<p>The genome of HIV-1 contains nine open reading frames (ORFs), all of which are expressed from a single promoter through alternative splicing. The splice forms for the six ORFs Gag, Pol, Env, Vpu, Vif, and Vpr, along with the full length mRNA, contain Rev response elements (RREs) encoded in their RNA. In the absence of the Rev protein, these six ORFs are poorly expressed. The remaining three ORFs, Tat, Rev, and Nef, are expressed efficiently independently of the Rev protein.##REF##12932730##[3]##\n</p>", "<p>The mRNAs which contain RREs likely also contain an as yet unidentified signal or set of signals which prevents normal expression.##REF##9188551##[4]## A primary cause of the poor expression of these ORFs is nuclear confinement.##REF##12932730##[3]## The genome of HIV-1 has an anomalous nucleotide distribution as compared with the set of known coding genes in the human genome. Only 314 of the approximately 25000 genes in the human genome have a higher percentage of adenine (A) than the average clinical isolate of HIV-1. Similar A content can be found in other retrotranscribing viruses (e.g. LINE elements, lentiviruses, spumaviruses); this suggests that retroviruses undergo different selection pressures than ones directing the evolution of the human genome. In the early 90's, the Pavlakis lab showed experimentally that synonymous changes to the Gag ORF, which decrease the A content, significantly increase expression of Gag in human cells.##REF##9188551##[4]## Codon-optimized strains, which are widely used in present experiments and vaccine trials, can increase the protein expression level of Gag transfected into human cells between 500 and 1000 fold.##REF##17032152##[1]##, ##REF##16818367##[2]## However, the substantial increase in expression due to codon optimization can, at best, indirectly address the problem of poor synthesis and nuclear isolation. We identify multiple nucleotide motifs from a systematic comparison of the HIV-1 genome and the human genome, which we conjecture to play a causative role in poor synthesis and nuclear confinement.</p>", "<p>In this study, the short motif, AGG, is found to have the maximal differential representation between the coding genes in the human genome and the HIV-1 genome. This identification was made through the use of an information theoretic motif-finding method called the Robins-Krasnitz algorithm, described previously##REF##16321941##[5]##.The algorithm identifies dozens of motifs that exhibit substantial differences in representation between the HIV-1 genome and the human coding genes. The study presented here focuses on a single motif in order to isolate its contribution to expression level in a controlled experiment. A codon-optimized version of HIV-1 consensus <italic>gag</italic> is modified, making synonymous changes to reduce the number of occurrences of AGG. Two plasmids are constructed, one with the original codon-optimized (CO) sequence of <italic>gag</italic> (AD-gag) and the other with the motif-optimized (MO) sequence with AGG significantly reduced (RK-gag). The (DNA) constructs are transfected into a human epithelial cell line (293 cells) and expression of Gag is shown to be 70% higher for the MO sequence. The two sequences of <italic>gag</italic> are also tested as DNA vaccines in a murine model for differential immune responses between the two constructs. The mice with the MO version of the vaccine have a 4.5-fold greater anti-Gag antibody response after 4 weeks. With a DNA boost at four weeks and a second readout of anti-Gag antibody titers measured at six weeks, the gap continues to widen between the MO and CO vaccines to 6-fold.</p>" ]
[ "<title>Methods</title>", "<title>Robins-Krasnitz algorithm</title>", "<p>The first step in the algorithm is the creation of a background sequence to compare with the human genome. This background is a completely randomized version of the coding sequences from the human genome subject to the constraints of amino acid order and codon usage in each gene. We design a Monte Carlo program that randomly permutes the codons for each amino acid within each gene. ##FIG##2##Figure 3## is an illustrative example.</p>", "<p>The shuffling procedure described above yields a set of randomized sequences. From these sequences, we need to extract a probability distribution. As long as the number of occurrences of each motif found in the total set of sequences is reasonably large, we can form a probability distribution, estimating the probability of a given motif by its fraction in the set of all motifs.</p>", "<p>After the shuffling procedure we can define two distributions, the real distribution found from the actual sequence and the Maximal Entropy Distribution (MED) which we use as the surrogate for the background. We now need a method for choosing under and over-represented motifs. The standard we used is from information theory. The motif that contributes the most bits of information to the difference between the real distribution and the MED is the first motif we chose. Using information theory has the nice feature of putting all results in the same units, number of bits. This allows us to compare motifs of different lengths and motifs that are either over or under-represented. The formula we employ to compute the motif contributing the most bits of information between the two distributions is the Kullback-Leibler distance or the Relative Entropy. We compute the Relative Entropy contribution for each motif and pick out the one with the largest value.</p>", "<p>Once we have found the most under- or over-represented motif in the sequence, we have to pick out the motif which is the next most under- or over-represented. However, we cannot simply take the motif which has the next largest Relative Entropy. This is because the motifs are overlapping, so under or over representation of a given motif affects the distribution of all the other motifs. The example of CpG illustrates this point. In the human genome, the dinucleotide motif CG will have the largest Relative Entropy. However, all eight trimers which contain CG fall within the top 50 highest Relative Entropy motifs. This is simply an artifact of the selection against CG. We are required to first remove the contribution of CG from the MED before recalculating the Relative Entropy to find the next motif. If we call the motif <italic>w</italic>, we rescale all motifs that contain <italic>w</italic> by the same amount so that the rescaled MED had the same distribution for <italic>w</italic> as the real distribution. This forces the Relative Entropy of <italic>w</italic> to zero and, at the same time, removes the contribution of <italic>w</italic> from all other motifs. We can readily show that this choice of rescaling monotonically decreases the overall Relative Entropy between the distributions.</p>", "<p>The procedure is iterated, so that we remove the contribution of one motif at a time from the Relative Entropy through rescaling of the MED. Then, we choose the next motif. We continue to iterate the procedure, and find additional motifs, until the motif with the largest remaining Relative Entropy is not statistically significant, as determined by comparing shuffled genomes.</p>", "<title>Experimental protocols</title>", "<p>HIV-1 subtype B <italic>gag</italic> consensus sequence was obtained from the Los Alamos HIV database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.hiv.lanl.gov\">www.hiv.lanl.gov</ext-link>). The complete sequence of parental consensus <italic>gag</italic> was codon optimized to reflect the codon characteristics of eukaryotic expression systems (AD-gag) and assembled in house using overlapping PCR.##REF##9188551##[4]##, ##REF##10775623##[6]## RK-gag was synthesized by BlueHeron Biotechnology (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.blueheronbio.com\">www.blueheronbio.com</ext-link>). Both constructs have an identical “Kozak signal” located immediately upstream of the initial ATG and were cloned into NotI and XbaI cloning sites of pVAX1 (Invitrogen).</p>", "<p>Plasmid DNAs were prepared by GenElute Endotoxin-free plasmid purification system (Sigma). For valuation of <italic>gag</italic> expression <italic>in vitro</italic>, multiple batches of plasmid DNA were prepared to ensure the reproducibility of each of the independent transfection experiments. Briefly, 0.5 and 1 µg of DNA were transfected into 293 cells using the Lipofectamine reagent in a 24-well plate format according to the manufacturer's specification (Invitrogen). Cell culture supernatants were collected at 48 or 72 hours post transfection. Gag expression was measured by a commercial ELISA kit that detects and quantifies P24 in supernatant (PerkinElmer).</p>", "<p>For assessing immunogenicity in mouse model, DNA was eluted into saline at the concentration of 0.5 µg/µl. Independent batches of DNA were prepared for immunizations. Six to eight week old female BALB/c mice (Charles River Laboratories) were housed and treated at the Laboratory Animal Research Center of The Rockefeller University in accordance with Institutional Animal Care and Use Committee guidelines. Groups of mice (4 to 5 per group) were immunized with a 25 µg of plasmid DNA vaccine in 50 µl of saline at week 0 and week 4. Serum samples were collected from individual mice at week 4 (4 weeks post first vaccination) and week 6 (2 weeks post second vaccination). Direct ELISA was used to measure serum anti-Gag antibody titers from immunized mice. Briefly, 96-well plates coated with 0.25 µg recombinant P24 protein overnight were blocked for 2 hours with PBS-T containing 5% dry milk and 0.5% BSA. Individual mouse serum samples were added in serial dilutions and incubated for 2 hours. The plates were washed five times with PBS-T and incubated for one hour with AKP-conjugated rat anti-mouse secondary antibodies. The plates were then washed six times with AMPAK washing solution, developed with AMPAK kit (DAKO Corporation). The plates were read on an ELISA reader at 490 nm. The end-point antibody titers were calculated as the reciprocal dilution of the last dilution that was at least 2-fold higher than normal mice sera controls and yields an absorbance of &gt;0.1.</p>" ]
[ "<title>Results</title>", "<title>Finding the signal and recoding Gag</title>", "<p>The Robins-Krasnitz algorithm finds short nucleotide motifs in coding regions of the human genome that are independent of amino acid order and codon usage.##REF##16321941##[5]## Codon usage is defined as the distribution of synonymous codons present in a given gene. The result of the Robins-Krasnitz algorithm is a set of exact nucleotide motifs of length 2–7 bases which are under and over represented in the coding regions of the human genome. The frequency of these motifs in the HIV genome can then be assessed. See <xref ref-type=\"sec\" rid=\"s4\">Methods</xref> for details.</p>", "<p>Beginning with the set of the 100 most under- and over-represented motifs in the human genome, our study attempts to identify the motif with the largest density difference between the HIV genome and the human genome, after accounting for A content. The motifs are restricted to the set of human genes with A content within 1% of the average HIV A content. The ratios of the densities in the HIV genome are then divided by densities in the human coding regions. If the human density is greater than that of HIV, the quantity is replaced by its reciprocal. We predict that the motif with the largest ratio of densities is responsible for nuclear isolation of HIV mRNAs.</p>", "<p>The triplet AGG, which is significantly under represented in the coding region of the human genome, is found with a high frequency in HIV when the nucleotide bias of HIV is taken into consideration. We hypothesize that recoding the ORFs of HIV by reducing the frequency of the motif AGG will increase protein expression.</p>", "<p>For this initial study, our experimental tests focused on the Gag gene. The codon-optimized sequence of <italic>gag</italic>, referred to as AD-gag, is recoded by systematically removing all AGGs such that the amino acid sequence is not modified and very rare codons are not introduced. The result is the motif optimized RK-gag. Both the AD-gag and RK-gag sequences are found in the supplementary materials.</p>", "<title>Testing expression</title>", "<p>First, we determine whether our RK-gag has increased expression as compared to the codon optimized version, AD-gag. Since our version of Gag is undoing part of the codon optimization present in the AD-gag sequence, the protein expression levels should be expected to decrease unless the motif AGG significantly inhibits mRNA synthesis or processing or transport. To compare expression levels, human 293 cells were transfected <italic>in vitro</italic> with one of the two different versions of Gag (see <xref ref-type=\"sec\" rid=\"s4\">methods</xref> for details). Gag protein expression was measured in the extracts of transfected cells by a quantitative P24 ELISA. RK-gag was 70% higher than the codon optimized AD-gag, with a p-value &lt;0.00001 calculated by a permutation test (see ##FIG##0##Figure 1##).</p>", "<title>Humoral immune response</title>", "<p>To test the effect of the almost two-fold gain in expression on the immune response, we created DNA vaccines from each of the sequences. These DNA vaccines were injected into the hind leg muscle of Balb/C mice. The mice were given a booster shot after four weeks. Anti-Gag antibody titers were measured by anti-P24 ELISA at the four week and six week time points (see <xref ref-type=\"sec\" rid=\"s4\">Methods</xref> for details). The results are found in ##FIG##1##Figure 2##. The 70% increase in the expression of the GAG protein <italic>in vitro</italic> translated into more than a five-fold difference in humoral immune response in a mouse model.</p>", "<p>Two weeks post-boost, mice were sacrificed and splenocytes were prepared for measuring Gag-specific cell-mediated immune (CMI) responses by an IFN-γ ELISpot assay. Although the difference between these two groups was not statistically significant, there was a trend for RK-gag immunized mice to have stronger CMI responses to both Gag-specific CD4 and CD8 peptides tested (data not shown).</p>" ]
[ "<title>Discussion</title>", "<p>Recoding the Gag gene in order to reduce the occurrences of a single triplet, AGG, substantially improves immune response to an HIV DNA vaccine in a mouse model. This short sequence motif occurs less frequently in the human coding sequence than in the mouse coding sequence by about twenty percent, so it is possible that these results would be even more dramatic in humans. A set of additional steps would be required to move in the direction of a clinically viable vaccine. These include a recoding of the ENV ORF and a test of its ability to induce an improved immune response. Testing these concepts in primates would be a useful step. The goal of this study was to provide convincing evidence that recoding the HIV ORFs can improve HIV protein expression and the immune response compared to the present codon optimization schemes. It likely that including the other motifs identified by the Robins-Krasnitz algorithm in a systematic way has the potential to improve upon the large gains displayed in this study.</p>", "<p>Finally this study brings up the question of why the HIV DNA sequence has been selected to express poorly in primate cells, only increasing its levels with the aid of additional proteins that recognize nucleic acid sequences in the genome. Several other retroviruses and retrotransposons have similar sequence complexities. This may reflect an optimal way to regulate these viruses and enhance the viral titers over an extended length of infection. In any event it is becoming clear that nucleotide sequence motifs, in addition to the choice of codons, can have dramatic impacts upon gene expression, RNA processing and transport in a cell.</p>" ]
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[ "<p>Conceived and designed the experiments: YH MK AL DDH HR. Performed the experiments: YH YS DDH. Analyzed the data: RR DMW AL HR. Contributed reagents/materials/analysis tools: RR DMW DDH HR. Wrote the paper: HR.</p>", "<p>This manuscript describes a novel strategy to improve HIV DNA vaccine design. Employing a new information theory based bioinformatic algorithm, we identify a set of nucleotide motifs which are common in the coding region of HIV, but are under-represented in genes that are highly expressed in the human genome. We hypothesize that these motifs contribute to the poor protein expression of <italic>gag, pol,</italic> and <italic>en</italic>v genes from the c-DNAs of HIV clinical isolates. Using this approach and beginning with a codon optimized consensus <italic>gag</italic> gene, we recode the nucleotide sequence so as to remove these motifs without modifying the amino acid sequence. Transfecting the recoded DNA sequence into a human kidney cell line results in doubling the <italic>gag</italic> protein expression level compared to the codon optimized version. We then turn both sequences into DNA vaccines and compare induced antibody response in a murine model. Our sequence, which has the motifs removed, induces a five-fold increase in gag antibody response compared to the codon optimized vaccine.</p>" ]
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[ "<fig id=\"pone-0003214-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003214.g001</object-id><label>Figure 1</label><caption><title>Gag expression in transiently transfected 293 cells.</title><p>\n##FIG##0##Figure 1## presents the results from four independent transfection experiments. The results are expressed as the mean P24 value (ng/ml, ±SD) of triplicates. The two different <italic>gag</italic> sequences are the codon optimized version (AD) and the motif optimized version (RK) that we created. Our (RK) version of the Gag gene has approximately two-fold higher expression than the codon optimized version.</p></caption></fig>", "<fig id=\"pone-0003214-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003214.g002</object-id><label>Figure 2</label><caption><title>Immunogenicity of Gag DNA vaccines in mouse.</title><p>The two different versions of Gag were made into DNA vaccines and injected into Balb/C mice with 25 µg/dose, then boosted at four weeks. Anti-Gag antibody levels were measured by ELISA at the four week and six week time points. The results are expressed as the geometric mean antibody titers (±SD) of each group. RK-Gag, induced an immune response that was five times larger than the codon optimized version at four weeks, which increased to a factor of 6 difference after six weeks.</p></caption></fig>", "<fig id=\"pone-0003214-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003214.g003</object-id><label>Figure 3</label><caption><title>Example of shuffling procedure.</title><p>The procedure to get the maximal entropy distribution (MED) involves a set of randomized iterations. The triplets of nucleotides coding for each amino acid are permuted randomly among themselves. This is an illustrative example of a mock short protein with eight amino acids. The shuffling procedure randomly permutes L<sub>1</sub>, L<sub>2</sub>, L<sub>3</sub>, and L<sub>4</sub> and separately permutes H<sub>1</sub>, H<sub>2</sub>, and H<sub>3</sub>. Each iteration produces a new sequence. For this example, there are 12 different combinations for the leucines and three combinations for the histidines resulting in 36 unique sequences. They are weighted in the shuffling procedure so that the MED is attained in the limit of a large number of iterations.</p></caption></fig>" ]
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[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This work was supported in part by the Simons Foundation, the Ambrose Monell Foundation, and the Leon Levy Foundation. YH, YS, and DH are supported in part by the Irene Diamond Fund.</p></fn></fn-group>" ]
[ "<graphic xlink:href=\"pone.0003214.g001\"/>", "<graphic xlink:href=\"pone.0003214.g002\"/>", "<graphic xlink:href=\"pone.0003214.g003\"/>" ]
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{ "acronym": [], "definition": [] }
6
CC BY
no
2022-01-13 07:14:34
PLoS One. 2008 Sep 15; 3(9):e3214
oa_package/12/1f/PMC2529374.tar.gz
PMC2529376
18355805
[ "<title>Introduction</title>", "<p>The ease of access to the embryo and subsequent manipulability has made the chick a reliable and powerful system for developmental biology. The power of this system has been enhanced recently by the availability of genomic data from both whole genome sequencing ##REF##15592404##(2004)## and large-scale EST projects (<xref rid=\"bib6 bib10 bib19 bib27\" ref-type=\"bibr\">Boardman et al., 2002; Carre et al., 2006; Hubbard et al., 2005; Kim et al., 2006</xref>). This opens up new opportunities for identifying all the genes that mediate the development of the embryo and its constituent parts and then using high throughput methods to test their function (##REF##12560806##Brown et al., 2003##). One of the first steps in this process is to document where and when specific genes are expressed in the embryo and a large amount of gene expression data is being generated. A repository of chick embryo gene expression data, GEISHA, has already been pioneered by Antin and colleagues (##REF##14991723##Bell et al., 2004##). This consists of a collection of photographs of embryos in which gene expression has been assayed mainly by using whole mount <italic>in-situ</italic> hybridisation, in some cases accompanied by sectioned material. The database that we describe here is different in that gene expression is visualised in 3D using OPT and expression patterns are mapped onto 3D digitised images. Here we describe how we have started to establish such a database of 3D gene expression patterns for the developing chick wing and investigated some of the practicalities involved.</p>", "<p>OPT was developed at the MRC Human Genetics Unit in Edinburgh and is one of a number of new microscopy techniques that have been developed in the last few years that allow capture of 3D image data. OPT has already been used to study the development of human, mouse, fly and plant embryos (<xref rid=\"bib13 bib26 bib28 bib34 bib40\" ref-type=\"bibr\">DeLaurier et al., 2006; Kerwin et al., 2004; Lee et al., 2006; McGurk et al., 2007; Sharpe et al., 2002</xref>) and one of its advantages is that it captures the three dimensional distribution of gene expression in an intact embryo.</p>", "<p>In order to compare large numbers of gene expression patterns, a number of recent atlas projects have taken the approach of mapping gene expression data to digital reference models. For example, the Edinburgh Mouse Atlas of Gene Expression (EMAGE) deals with 2D data in this way (##REF##15043218##Baldock et al., 2003##), section <italic>in-situ</italic> derived gene expression data in the mouse brain have been mapped to 3D models generated by Micromagnetic resonance imaging (##REF##17151600##Lein et al., 2007##) and a Zebrafish 3D anatomical Atlas has been produced based on sectioned material for the projection of gene expression data (##UREF##3##Verbeek et al., 1999##). Projects such as EMAGE (##REF##15043218##Baldock et al., 2003##) and GENEPAINT (##REF##14681479##Visel et al., 2004##) have begun to build large queryable databases containing both whole mount and section <italic>in-situ</italic> data for mouse embryos. Since the system has already been set up for mouse embryos, the establishment of a parallel database for the chick should allow direct comparisons between gene expression patterns in the two organisms. We have adopted the Bookstein thin plate spine algorithm for mapping our 3D data, which has previously been used extensively in morphometric analysis (<xref rid=\"bib1 bib9 bib17 bib45\" ref-type=\"bibr\">Albertson and Kocher, 2001; Bruner et al., 2004; Harmon, 2007; Yeh, 2002</xref>). Once gene expression data are assembled in a digital atlas, powerful modern data mining techniques can be used to examine relationships potentially leading to unexpected discoveries.</p>", "<p>We have focussed on the developing chick wing bud. The wing bud is a good test system for investigating the power of a 3D database because it is a structure with no significant morphological features at early stages. Many insights into the mechanisms that pattern the vertebrate limb have come from studies on chick embryos (##REF##15296968##Tickle, 2004##). In the long term, the ability to compare multiple patterns of gene expression should enable us to identify synexpression domains, complementary patterns and possibly also discrete boundaries of gene expression. We have optimised protocols to maximise consistency of initial whole mount <italic>in-situ</italic> hybridisation, OPT capture and mapping data to a reference model. In this paper, we have studied a number of previously described genes in terms of their expression patterns. This has allowed us to perform a pairwise analysis of overlap of expression for an initial set of genes and to identify some previously unappreciated features of expression with respect to dorso-ventral distribution. We have also used computational techniques based on microarray analysis to look for specific regions of the limb where genes are co-expressed.</p>" ]
[ "<title>Materials and methods</title>", "<title>Embryo preparation</title>", "<p>White Leghorn chick eggs were incubated in a humidified incubator at 38 °C for the appropriate time for the desired developmental stage as determined by the Hamburger and Hamilton stages (##REF##24539719##Hamburger and Hamilton, 1951##). Eggs were then windowed, embryos removed to ice-cold Phosphate Buffered Saline (PBS) (0.02 M phosphate, 0.15 M NaCl) and cleaned of extra-embryonic membranes. Eyes and forebrain were punctured with a tungsten needle to reduce trapping, and embryos were transferred to 4% (w/v) ice-cold paraformaldehyde (PFA) overnight. The embryos were then put through a graded methanol series at 4 °C; ending in 2, 100% methanol washes and stored at − 20 °C until use.</p>", "<title>Plasmid preparation and probe synthesis</title>", "<p>The plasmids used for the different genes were <italic>Shh</italic> (##REF##7916661##Echelard et al., 1993##), <italic>Fgf8</italic> (##REF##8548816##Crossley et al., 1996##), <italic>Msx1</italic> (##REF##2565278##Hill et al., 1989##), and <italic>Tbx3</italic> (##REF##9550719##Isaac et al., 1998##). All plasmids were linearised and transcribed according to their sources. EST clones acquired from ARK genomics were used as probes for <italic>Wnt3a</italic> (EST clone 603102629F1), <italic>Wnt5a</italic> (EST clone 603799237F1), <italic>Lmx1</italic> (EST clone 603127966F1) and <italic>HoxD13</italic> (EST clone 603499362F1). All EST clones were in pBluescript II KS+, which was linearised with Not1 (NEB) and transcribed with T3 RNA polymerase (Roche) to produce antisense probes. Plasmids were grown up using standard protocols and purified using Qiagen plasmid mini kits and individual clones were sequenced to check their identity.</p>", "<p>The RNA probes were synthesised accordance with standard protocols (<xref rid=\"bib33 bib37\" ref-type=\"bibr\">Maniatis et al., 1982; Nieto et al., 1996</xref>) and purified using the ProbeQuant G-50 spin column system (Amersham Biosciences). In some cases probe purification was performed using phenol chloroform extraction and Lithium Chloride precipitation as detailed in ##REF##8722478##Nieto et al. (1996)##.</p>", "<title><italic>In-situ</italic> hybridisation</title>", "<p>The <italic>in-situ</italic> protocol used was a modified version of that of ##REF##8722478##Nieto et al. (1996)##, full details of the modified protocol are in <xref rid=\"app1\" ref-type=\"sec\">supplementary materials</xref>.</p>", "<p>Before scanning under UV, embryos require some further washes to remove excess NBT–BCIP. Embryos were washed twice for 10 min in PBS at RT. Embryos were then moved to 10× TBST and allowed to equilibrate at RT, this should take between 10 and 20 min depending on the size of the embryo. Embryos were then washed 3 times for 20 min in 1× TBST and left to wash overnight in fresh 1× TBST at 4 °C. Embryos were washed 3 times for 5 min in PBT at RT and then fixed overnight in 4%PFA–DEPC–PBS at 4 °C. Embryos were washed briefly a further 2 times in PBS and then refixed in formal saline.</p>", "<title>Section <italic>in-situ</italic> hybridisation</title>", "<p>Section <italic>in-situs</italic> were performed on HH22 embryonic limbs according to the method of ##REF##11118473##Moorman et al. (2001)##. The <italic>Wnt5a</italic> probe was the same as above.</p>", "<title>Tyramide Signal Amplification (TSA)</title>", "<p>To modify the <italic>in-situ</italic> protocol for the fluorescent TSA colour reaction, glutaraldehyde was removed in the fixation step to minimise autofluorescence. Secondary detection was performed using a peroxidase (POD) linked anti-DIG antibody. The colour reaction was performed according to the Alexa Fluor 568 kit manufacturer’s instructions (Invitrogen — T-20914) with volumes increased to accommodate whole embryos.</p>", "<title>OPT sample preparation and scanning</title>", "<p>Standard reference embryos were fixed in 4% PFA/0.2% glutaraldehyde mix, which produces a stronger autofluorescent signal than PFA alone. The addition of 0.2% glutaraldehyde to the fix was not necessary for embryos that had been <italic>in-situ</italic> hydbridised, due to the presence of glutaraldehyde in the fixative steps of that protocol. Reference embryos were stored in 100% methanol until scanning, at which point they were taken back through a methanol series to PBS and briefly to water. Embryos having undergone <italic>in-situ</italic> hybridisation were washed 3 times for 20 min in PBS to remove storage fixative. In order to remove excess salts, embryos were washed twice for 10 min in distilled water and subsequently left overnight in distilled water followed by 1 wash of 10 min in fresh distilled water. OPT scanning was carried out following the protocol set out in ##REF##11964482##Sharpe et al. (2002)##, for more detailed protocols see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials</xref>. The resulting output was in the Wlz file format which the Edinburgh Mouse atlas Project (EMAP) and EMAGE projects have utilised and which can store 3D grey scale information. The MRC HGU has produced a set of software tools for manipulating data in this format and these tools were used to convert the data into a format that could be imported in to the AMIRA software package.</p>", "<title>3D mapping</title>", "<p>The mapping of the 3D gene expression data to the reference models was performed using the AMIRA 4.1 software from Mercury Computer Systems. The data to be mapped were first roughly aligned with the reference model. Two corresponding sets of landmarks were then set up between an isosurface for the reference embryo and an isosurface for the fluorescent/anatomical data from the scan to be mapped. The landmarks were based on prominent morphological landmarks such as the AER, the region where the limb attaches to the flank and to proportional distances along the main axes of the limb. The fluorescent/anatomical data was warped, using a Bookstein thin plate spline method (##UREF##0##Bookstein, 1989##) provided by the AMIRA software and based on the previously defined landmark sets. Provided the resulting warped fluorescent/anatomical data seemed consistent with the reference limb’s morphology the same warp was then applied to the brightfield channel data. For full details see methods in <xref rid=\"app1\" ref-type=\"sec\">supplementary materials</xref>.</p>", "<title>Real-time PCR</title>", "<p>Chick embryos (incubated for 4 days at 38 °C) <italic>i.e.</italic>, approx. stage 22–23, were harvested in ice-cold PBS, the limb buds removed and the distal third cut off with tungsten needles. These pieces were immediately transferred to RNALater (Qiagen). A similar procedure was carried out on the proximal and median thirds. RNA preps were made of pooled limb sections with 20 sections for each region using the Qiagen RNA Easy micro kit, and checked using an Agilent bioanalyzer, using their RNA 6000 nanochip. The integrity values of all RNA samples were between 9.9 and 10 and the 28S and 18S ratio between 1.9 and 2.1. These values indicate little degradation or contamination.</p>", "<p>Real-time PCR was carried out using an Applied BioSystems HT-7900 machine in a manner similar to ##REF##17148754##Jesmin et al. (2007)## using the FastStart Taqman Probe master (Rox) standard reaction mix (Roche). Primers were selected from the Roche Universal Library using their online software. For the <italic>Wnt5a</italic> reaction, probe 52 was used, and for <italic>β-actin</italic>, probe 43 was used. Here chick probes are not automatically checked against other possible hybridisation targets, so this was carried out manually by Blasting the candidate sequences against the chick genome in Ensembl. The primers used for the <italic>Wnt5a</italic> reaction were: forward 5′catgatgaacctacacaatga 3′; reverse 5′ ccacgtcagccaggttgta 3′. And for the <italic>β-actin</italic> reaction were: F 5′ cacacaagtgcccatttacga 3′; R 5′ caagtccagacgcaggatg 3′. For full protocol see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials</xref>.</p>", "<title>Computational analysis</title>", "<p>Simple arithmetical analyses were produced using either the AMIRA software package or Wlz based software tools. AMIRA’s arithmetic module was used to produce averages of multiple datasets and to produce masked datasets for domains of coexpression. Wlz based software developed by the MRC HGU was used to derive medians from multiple datasets and also to derive mean grey level intensity values for both discrete domains and serial sections through the limb. For fuller details of these image manipulations see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials and methods</xref>.</p>", "<p>For more complex computational analyses each of the experimental gene-expression spatial distributions has been mapped into the standard coordinate framework defined by the model limb. To analyse the gene-expression patterns we first divided the limb into 560 non-overlapping sub-regions each of 10 × 10 × 10 voxels. Each of these was used to sample the experimental gene-expression patterns. For each experimental pattern, the mean gene-expression strength (integrated optical density divided by the volume) within each box was calculated. If the box was partially external to the limb then only the intersecting volume was considered. By this means a 2D matrix of mean expression strengths across the limb was calculated. Each row of the matrix for a given gene is a low-resolution representation of the pattern and each column for a given sample-region is the genetic “signature” for that spatial location.</p>", "<p>The resulting matrix of gene expression values was analysed using the TMEV4 package from TIGR. The data were analysed using a hierarchical clustering method (##REF##9843981##Eisen et al., 1998##) to produce a nested tree of gene expression pattern similarity based on a Euclidean distance metric. A nested tree was also produced of the similarity of the individual sample regions of the limb making up the 3D data model based on gene expression. The resulting tree was then used to identify clusters of similar expression made of small groups of regions at the terminus of long branches. These regions were used to produce larger 3D domains corresponding to the whole volume occupied by the regions comprising each cluster, which were subsequently visualised using the AMIRA software package.</p>" ]
[ "<title>Results and discussion</title>", "<title>Assessment of efficiency of whole mount <italic>in-situ</italic> hybridisation and scanning with OPT as a method of detecting gene expression domains</title>", "<p>An initial technical issue we encountered with OPT scanning of WISH (whole mount <italic>in-situ</italic> hybridisation) specimens was that strong <italic>in-situ</italic> colour reaction staining can block autofluorescence and prevent the capture of portions of the anatomical data required for subsequent mapping to a reference model. To obviate this problem, we identified a particular depth of staining with the NBT–BCIP substrate, suitable for OPT scanning, which captures an extensive range of the expression pattern and allows the visualisation of dynamic gradients without causing a substantial dropout of the anatomical data necessary for mapping (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S</xref>1). To test our standard <italic>in-situ</italic> hybridisation protocol, monitor probe penetration of the embryonic limb, and assess the ability of the OPT system to identify graded patterns of expression within deep tissues, we focussed on <italic>Wnt5a</italic>. <italic>Wnt5a</italic> has been reported to have a proximo-distal gradient of expression based on both radioactive section <italic>in-situ</italic> hybridisation and northern blots of distinct portions of the limb (##REF##8297789##Dealy et al., 1993##) and is known to be expressed throughout mesodermal regions of the limb.</p>", "<p>We first assayed expression by WISH (method modified after ##REF##8722478##Nieto et al., 1996## see <xref rid=\"app1\" ref-type=\"sec\">supplementary data</xref>) ##FIG##0##Fig. 1##A). The <italic>Wnt5a</italic> whole mount was scanned using OPT and gene expression data mapped onto a reference limb (##FIG##1##Fig. 2##D), from which virtual sections were derived (##FIG##0##Fig. 1##B). These virtual sections were then compared with section <italic>in-situs</italic> (##FIG##0##Fig. 1##C) performed as in ##REF##11118473##Moorman et al. (2001)##. This comparison shows that the virtual section captures the extent and range of the <italic>Wnt5a</italic> expression pattern as accurately as the section <italic>in-situ</italic> with the exception of some apical ectodermal ridge (AER) expression (##FIG##0##Fig. 1##C arrowed). For a further illustration of the effective capture of expression patterns using OPT see <xref rid=\"app1\" ref-type=\"sec\">supplementary data</xref> (Fig. S3–5).</p>", "<p>We then measured the mean grey level signal intensity in all individual sections along the proximo-distal axis. The plot of these data (##FIG##0##Fig. 1##D) shows low levels of signal in the proximal region (Red, slice 1–28), either very low expression or background. The mean grey level intensity then climbs steeply in the medial region (Orange, slice 29–52) from ∼ 30 up to 100. In the distal region (Green, 53–75) the mean grey level intensity increases less steeply and then levels off at a mean intensity of around 160. In the final 5 slices the mean grey level intensity drops rapidly. This shows the capability of the OPT imaging method to allow a detailed analysis of graded patterns of expression.</p>", "<p>We also compared <italic>Wnt5a</italic> expression levels as measured from OPT scans of whole mount <italic>in-situs</italic> with real-time RT-PCR analysis (##FIG##0##Fig. 1##F). For both OPT and RT-PCR analyses the limb bud was divided into three regions of equal length along the proximo-distal axis designated proximal, medial and distal (##FIG##0##Fig. 1##E). For RT-PCR the sample tissue was dissected into the three regions of equal length and samples from 10 embryos were pooled. In the case of the OPT data this division was performed digitally using AMIRA’s segmentation software based upon guidance from the researcher who performed the initial microdissection. The real time RT-PCR data produced relative values for the expression of <italic>Wnt5a</italic> as follows; expression in the proximal region was taken as the reference expression level, the medial region had a 5 fold increase over the proximal region and the distal region a 19.7 fold increase. The OPT based analysis produced relative values for medial and distal regions of 4.5 fold and 6 fold increases over the value for the proximal region respectively. Therefore, WISH/OPT captures the graded nature of the expression along the proximo-distal axis, indeed the correspondence in the proximal and medial regions is striking, but not across the whole quantitative range captured using RT-PCR as the correspondence falls off dramatically at the higher levels seen in the distal region. The limitations in the captured range of expression may be due to limitations of the WISH detection method, <italic>i.e.</italic>, a nonlinear relationship between signal intensity and RNA quantity. This allows comparisons of the level of expression of a particular gene within a particular scan but not the comparison of absolute levels between different scans, although high and low regions of expression could be compared.</p>", "<p>The quality of data capture for the gradient of <italic>Wnt5A</italic>, both from selected domains and from serial virtual sections, suggests that WISH/OPT is suitable for examining complex patterns of graded expression in tissues within developing embryos, but not for quantification of signal with high accuracy and quantitative comparisons of mRNA levels between samples.</p>", "<title>Reference models for comparative analysis</title>", "<p>An initial requirement for meaningful comparison of gene expression patterns is a common spatial reference framework onto which different patterns can be mapped. We have produced a panel of such reference frameworks for several embryonic stages; whole embryos, isolated and fixed according to a standardised protocol, were collected at Hamburger Hamilton (HH) (##REF##24539719##Hamburger and Hamilton, 1951##) stages from HH18 through to HH25. Embryos were then scanned by OPT using autofluorescence stimulated by a UV lamp and reconstructed to produce 3D models. These models were rendered using the AMIRA software package to show morphology and gross anatomy of the embryos (##FIG##1##Figs. 2##A–H). This resulted in a clear 3D visualisation of the embryo and revealed details such as the AER – the thickened layer of epithelium that rims the distal limb buds – in the models from HH20 onwards (##FIG##1##Figs. 2##A–F, blue arrows indicate AER). This is best appreciated when the model is rotated (##FIG##1##Fig. 2##H″, blue arrows indicate AER) (to view models in 3D see movies in <xref rid=\"app1\" ref-type=\"sec\">supplementary data Fig. S6</xref>). Measurements of the length (<italic>L</italic>; along a line from anterior join of bud and trunk to posterior join) and width (<italic>W</italic>; a line from distal tip to trunk perpendicular to line <italic>L</italic>) of wing buds were made on the dorsal plan view (##FIG##1##Fig. 2##, Table 1) and the <italic>L</italic>/<italic>W</italic> ratio calculated in order to stage the reference embryo (<xref rid=\"app1\" ref-type=\"sec\">Fig. S2</xref>). The procedure was the same as one would use to stage a living chick embryo using the staging criteria of Hamburger and Hamilton (##FIG##1##Fig. 2##, Table 2) (<xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S2</xref>).</p>", "<p>Reference models for specific regions, such as the developing limb buds (##FIG##1##Figs. 2##A′–H′), can be extracted from whole embryo models and used for mapping of gene expression in these regions. Within the AMIRA program, such extracted models remain in register with the model of the whole embryo from which they are derived, thus allowing maintenance of a consistent positional system between all expression patterns mapped. These relative positions are also maintained when files are exported to the Wlz file format, which can be used to store 3 dimensional greyscale image data, using software tools developed by the MRC HGU. Subsequent mappings and analyses of gene expression reported here were performed on an extracted data set for the right wing bud of the late stage HH22 reference embryo (##FIG##1##Fig. 2##E′). Further reference sets for other stages can be easily generated.</p>", "<title>Reproducibility of 3D mapping of gene expression within and between labs</title>", "<p>To test the reliability of our <italic>in-situ</italic> protocol and 3D mapping, we focussed on the expression domain of <italic>Sonic hedgehog</italic> (<italic>Shh</italic>) in HH22 wing buds. <italic>Shh</italic> is expressed in the polarizing region at the posterior margin of the wing bud and <italic>Shh</italic> expression correlates with maps of polarizing activity (##REF##8269518##Riddle et al., 1993##). We compared data generated from wing buds in a single round of whole mount <italic>in-situ</italic> hybridisation experiments and from wing buds processed in three different labs.</p>", "<p>A round of <italic>in-situ</italic> hybridisations using standardised protocols for <italic>Shh</italic> expression were carried out on 4 embryos and the sense control probe was used on 2 embryos. All 4 embryos assayed using the Shh anti-sense probe were treated identically and detection was carried out in the same tube. Nevertheless there were differences in intensity of staining of <italic>Shh</italic> transcripts in the polarizing region (##FIG##2##Figs. 3##A–D) with wing buds of one embryo (##FIG##2##Fig. 3##C) showing very faint staining. Wing buds of control embryos (data not shown) had no visible background staining. <italic>In-situs</italic> of embryos from Edinburgh (##FIG##2##Fig. 3##E) and Dublin (##FIG##2##Fig. 3##F) showed similar localisation of <italic>Shh</italic> transcripts in the wing bud and one embryo from each site was then scanned together with the four embryos from the run carried out in Dundee.</p>", "<p>All six embryos were OPT scanned through different channels to capture a) autofluorescence to represent anatomy and b) the staining pattern under visible light. Having reconstructed 3D representations of each, we then digitally extracted the right wing bud and accompanying flank using the same spatial parameters. Co-visualisation of both anatomy (grey) and expression (orange) with volume rendering (##FIG##2##Figs. 3##A′–F′) shows the representation of the original <italic>in-situ</italic> data (##FIG##2##Figs. 3##A–F) following OPT scanning and reconstruction.</p>", "<p>The patterns of <italic>Shh</italic> expression in each of the six wing buds were then mapped in 3D to the HH22 reference wing (##FIG##1##Fig. 2##E). ##FIG##2##Figs. 3##A″–F″ shows heat maps of intensity of <italic>Shh</italic> expression in one section, taken across the antero-posterior/dorso-ventral (A-Po/Do-V) axes of the HH22 stage reference wing bud in a plane situated next to the AER (##FIG##2##Fig. 3##L), for the individual patterns of expression for the six source wing buds. Signal intensity of mapped gene expression data is represented by the heatmap in ##FIG##2##Fig. 3##A″ corresponding to grey scale values between 1 and 255, this measure of intensity is not suitable for precise quantitative comparisons between samples but allows visualisation of the differing levels of expression within a sample within the limits of the WISH methodology. Of the 4 wing buds from the Dundee laboratory one showed a weak signal (##FIG##2##Fig. 3##C″), with a maximal intensity of only 28. The sense controls were both very clean with no signal (data not shown). Scans for Edinburgh (##FIG##2##Fig. 3##E″) and Dublin (##FIG##2##Fig. 3##F″) had a localisation very similar to ##FIG##2##Fig. 3##A″ but an intensity of expression much closer to ##FIG##2##Fig. 3##D″.</p>", "<p>Both unique domains of expression and intersecting domains of expression can be derived for these data sets. Visualisation of the unique expression domains in 3 dimensions shows no unique domains for the specimens shown in A–D and only small peripheral regions for the higher intensity signal data sets from specimens in E and F (Data not shown). The intersect of the expression domains between all the scans (##FIG##2##Fig. 3##G) is restricted by the small domain of the outlying data set (##FIG##2##Fig. 3##C″), if we remove this outlying data from the calculation we have an intersect domain almost twice as large (##FIG##2##Fig. 3##H).</p>", "<p>Clearly, there is some variation between individual scans and an occasional extreme outlier, but our data suggest a clear common domain of gene expression is identifiable. To produce a reliable and robust domain which could compensate for the variations we see in individual <italic>in-situs</italic>, we incorporated the data from our multiple scans into one domain. We produced means of the data from the scans and corrected for background from the controls, using both raw and normalised data (##FIG##2##Figs. 3##I–J). The mean of the datasets (##FIG##2##Fig. 3##I) was heavily influenced by high intensity samples (compare ##FIG##2##Fig. 3##E″ and ##FIG##2##Fig. 3##I). To correct for the variation in signal intensity, whether as a result of differences in the <italic>in-situ</italic> itself or from the scanning steps, data sets were normalised by stretching their entire grey value range to cover the maximal range of 0–255. Such correction had almost no effect (##FIG##2##Fig. 3##J), although there was a small extension of the domain proximally. As an alternative to normalisation to account for variability and extreme outlying values we derived a median value for each voxel based on the grey levels of all of the scans (##FIG##2##Fig. 3##K). This median value seemed less dominated by outliers and a better representation of the range seen across the differing samples although it produced a more conservative domain than the simple mean since it removed areas where signal was not apparent in more than half of the samples.</p>", "<p>These results suggest that best practice for producing a reliable domain of expression for comparison is to perform several <italic>in-situs</italic> developed to suitable stain intensity and merge the resultant data. A minimum of four scans seems advisable to contribute to the merged data set and these may then be mapped to a common reference and a mean or median expression pattern derived. A mean of the patterns appears to better emphasise the extent of the expression domain while the median produces a more conservative domain less affected by outliers. As our analysis shows, more complicated treatment of the data seems to make little difference to the resulting domains although, for <italic>in-situs</italic> with persistently low signal, normalisation might help emphasise gradients of expression in some cases.</p>", "<p>This averaging or otherwise merging together of multiple samples is less necessary in the case of previously well characterised genes where the expected pattern of expression is already known and a representative sample can be confidently identified. This approach should be most valuable in the situation where the gene expression is either poorly characterised or unknown, as is likely to be the case in large scale screens.</p>", "<title>Comparative analysis of 3D Gene expression patterns</title>", "<p>OPT is a rapid method of capturing the 3D expression pattern and can allow data on multiple genes to be integrated into a common framework, therefore we used 3D warping of OPT data to our reference models to produce such an integrated data set. Expression patterns for the genes <italic>Shh</italic>, <italic>HoxD13</italic>, <italic>Fgf8</italic>, <italic>Msx1</italic>, <italic>Lmx1</italic>, <italic>Wnt5a</italic> and <italic>Tbx3</italic> (for representative <italic>in-situs</italic> see <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S6</xref>) were mapped to our HH22 reference model (##FIG##1##Fig. 2##E) using AMIRA’s 3D warping capabilities. This stage was chosen as it represents a well-developed limb bud but still consisting mainly of undifferentiated mesenchyme cells. These genes were chosen for particular characteristics of expression such as dorsal restriction, <italic>Lmx1</italic>, specific expression in the AER, <italic>Fgf8</italic>, specific expression in the mesoderm, <italic>Shh</italic>, or particular gradients of expression, such as the proximo-distal gradient of <italic>Wnt5a</italic>. Particular specimens for scanning were chosen based on <italic>in-situ</italic> quality in comparison to others processed with them, usually the best example from 5–6 <italic>in-situs</italic>. The resulting mappings were then visualised in 3D (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S8</xref>), virtual sections were derived along specific planes (##FIG##3##Fig. 4##A), gene expression patterns and intensity were visualised on the anterior–posterior/dorsal–ventral plane (##FIG##3##Fig. 4##A.i). The expression domains of several pairs of genes were co-visualised on the antero-posterior/dorso-ventral (A-Po/Do-V) (##FIG##3##Fig. 4##C, section plane in ##FIG##3##Fig. 4##A.i), antero-posterior/proximo-distal (A-Po/Pr-Di) (##FIG##3##Fig. 4##D, section plane in ##FIG##3##Fig. 4##A.ii) and dorso-ventral/proximo-distal (Do-V/Pr-Di) (##FIG##3##Fig. 4##E, section plane in ##FIG##3##Fig. 4##A.iii) planes to allow some specific comparisons to be drawn (##FIG##3##Figs. 4##C–E; for 3D visualisations see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S11–18</xref>). These mappings show both expected features, such as the dorsal expression pattern of <italic>Lmx1</italic>, and novel features such as an apparent gradient of expression from ventral to dorsal in <italic>Wnt5a</italic>. Indeed several dorso-ventral asymmetries were by far the most striking features to emerge.</p>", "<p>One such previously unappreciated asymmetry was in <italic>Shh</italic> expression (##FIG##3##Fig. 4##B), which shows that <italic>Shh</italic> expression extends further anteriorly on the ventral side of the limb. A transverse section through the limb along plane E shows that the asymmetry is more complex and the domain of <italic>Shh</italic> expression is skewed with the more proximal regions of the limb showing a more dorsal expression of <italic>Shh</italic> (##FIG##3##Fig. 4##E.ii). Planes taken at more anterior levels lose this obvious skewing (see <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S9</xref>) and elements of this distribution are confirmed by both normal <italic>Shh</italic> whole mount <italic>in-situ</italic> and a double <italic>in-situ</italic> for <italic>Shh</italic> and <italic>Fgf8</italic> (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S10</xref>). This may explain the observations of ##REF##7697724##Yang and Niswander (1995)## who reported no apparent dorso-ventral asymmetry in <italic>Shh</italic> expression at HH24 based on sectioned whole mount <italic>in-situ</italic> hybridisation data. Alternatively this may be due to the different stages assayed. A more dorsal distribution in the more proximal regions of the limb may suggest that a dorsally localised signal, such as <italic>Wnt7a</italic>, might be maintaining dorsal expression proximally while more distal expression would be maintained evenly across the dorso-ventral axis by signals from the apical ectodermal ridge. Indeed it is already known that Wnt7a plays a role in maintaining <italic>Shh</italic> expression in the polarising region (<xref rid=\"bib38 bib44\" ref-type=\"bibr\">Parr and McMahon, 1995; Yang and Niswander, 1995</xref>). Furthermore ##REF##10445500##Kawakami et al. (1999)## reported that <italic>Frizzled10</italic> (a Wnt receptor) colocalises with <italic>Shh</italic> dorsally and suggested that <italic>Shh</italic> expression in this part of the polarising region might be regulated by Wnt7a (##REF##11142678##Kawakami et al., 2000##).</p>", "<p>The pairwise comparisons similarly produced both expected and unexpected results. <italic>HoxD13</italic> shows striking asymmetrical dorso-ventral expression (##FIG##3##Figs. 4##C.v and E.v) and the ventral margin of <italic>HoxD13</italic> expression appears to coincide with that of <italic>Lmx1</italic> (##FIG##3##Figs. 4##C.viii and E.viii; Movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S18</xref>), suggesting a possible regulatory relationship. The dorso-ventral asymmetry in <italic>Hoxd13</italic> had been previously noted by Duboule et al. from 3D reconstructions based on radioactive section <italic>in-situs</italic> (##UREF##2##Olivo et al., 1993##) but at this time <italic>Lmx1</italic> was not known. Recent lineage tracing studies in the developing mouse limb have shown the existence of a dorsoventral lineage restriction compartment further suggesting that there is still considerable complexity in the dorsoventral organisation of the limb to be discovered (##REF##17715176##Arques et al., 2007##).</p>", "<p>Comparison of <italic>Shh</italic> and <italic>Fgf8</italic> expression (##FIG##3##Figs. 4##D.iii and E.iii; movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S13</xref>) shows an unexpected overlap of expression in the mesoderm. This shows that there are important limits of spatial resolution to the current mapping procedure given the well-characterised expression of these genes in mesoderm and apical ectodermal ridge respectively. Since the AER is one of the principle morphological features of the limb, it is heavily used in the landmarking process, which is the first stage of mapping expression data to the reference limb. Strong <italic>in-situ</italic> staining, as seen with <italic>Fgf8</italic>, can block autofluorescence and prevent the capture of the anatomical data for the ridge. A comparison of <italic>Wnt5a</italic> and <italic>Fgf8</italic> (Movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S11</xref>) also shows considerable overlap except for the most anterior region of <italic>Fgf8</italic> expression (##FIG##3##Fig. 4##C.i) and a persistent ‘leading edge’ of <italic>Fgf8</italic> expression in the most distal portion of the limb (##FIG##3##Fig. 4##E.i). In this case, we would expect to see considerable overlap due to expression of <italic>Wnt5a</italic> in the ridge, although it is not clear whether the common domain of expression accurately represents just ridge expression.</p>", "<title>OPT using Tyramide Signal Amplification</title>", "<p>To address the problem of the loss of anatomical landmarks when looking at strongly expressed ectodermal genes, such as <italic>Fgf8</italic>, we used a Tyramide Signal Amplification (TSA) kit (Invitrogen) to enhance a fluorescent colour reaction and avoid the blocking effect seen with chromagenic substrates such as NBT–BCIP. We compared the expression of <italic>Tbx3</italic> and <italic>Fgf8</italic> at HH24, mapped to the HH24 reference limb (##FIG##1##Fig. 2##G′), from a normal NBT–BCIP based colour reaction for <italic>Tbx3</italic> and a TSA fluorescent colour reaction for <italic>Fgf8</italic> (##FIG##4##Figs. 5##A, B). This approach provided a much more accurate localisation of <italic>Fgf8</italic> expression to the apical ridge compared to that seen in ##FIG##3##Fig. 4##. When we carried out a pairwise comparison between <italic>Fgf8</italic> and <italic>Tbx3</italic> expression patterns, the overlap was very much reduced compared to that seen between <italic>Fgf8</italic> and <italic>Shh</italic> in ##FIG##3##Fig. 4##D.iii and compared with <italic>Fgf8</italic> and <italic>Tbx3</italic> from our original data set in which we had used an NBT–BCIP reaction for both genes (##FIG##4##Fig. 5##C). While these mapped localisations are not sufficient for accurately discriminating expression from expressing tissues close together or very thin tissue layers a database containing such mapped data would be linked to the original 3D scans allowing visualisation of the data.</p>", "<title>Computational comparisons of mapped gene expression patterns</title>", "<p>Despite the shortcomings discussed above, our overall mapping strategy provides a valuable tool for analysing spatial and temporal relationships of multiple complex patterns.</p>", "<p>To begin to apply computational methods to analyse these multiple data sets at once and to look for similarities in patterns of gene expression, we utilised software produced by the MRC HGU to manipulate 3D image data in the Wlz format, a format used by EMAGE. AMIRA files were converted to Wlz format and a set of sub-regions of 10 × 10 × 10 voxels were defined for the early HH22 reference limb producing a coarse sampling of the 3D model. The expression data for the previously analysed genes and an additional gene, <italic>Wnt3a</italic>, were then used to derive mean expression values from the grey levels of each OPT scan for the newly defined volumes. This produced a matrix of common positional IDs and expression levels for each gene.</p>", "<p>These data were then analysed using hierarchical clustering for both the genes and the regions defined by the coarse sampling. This analysis was performed and visualised using the TMEV package (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.tm4.org/mev.html\">http://www.tm4.org/mev.html</ext-link>) (##FIG##5##Fig. 6##A). The clustering of these genes fits our expectations with <italic>Fgf8</italic> and <italic>Wnt3a</italic>, both known to be expressed in the apical ridge (<xref rid=\"bib4 bib25 bib35\" ref-type=\"bibr\">Barrow et al., 2003; Kengaku et al., 1998; McQueeney et al., 2002</xref>), treeing out together. Visualisation of the clustered regions shows that they largely form contiguous spatial domains (##FIG##5##Figs. 6##B–G; <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S19</xref>); some of these domains are associated with specific regions of the limb. Cluster B lies in a plane through the limb along the proximal–distal axis at the level of the anterior margin of <italic>Shh</italic> expression (##FIG##5##Fig. 6##B), cluster D is associated with the AER in the anterior of the limb (##FIG##5##Fig. 6##D) while cluster E is in the dorsal margin of the limb (##FIG##5##Fig. 6##E). None of the clusters visualised here corresponds simply to the expression pattern of one particular gene.</p>", "<p>This simple level of clustering computational analysis shows the potential for methods developed to study gene expression data from other sources, such as microarray data, to be applied to the study of 3D gene expression data. Not only can we identify similarly expressed genes using this method but it should also be possible to identify specific regions of the limb that may be important in the regulation of gene expression, such as novel signalling centres.</p>" ]
[ "<title>Results and discussion</title>", "<title>Assessment of efficiency of whole mount <italic>in-situ</italic> hybridisation and scanning with OPT as a method of detecting gene expression domains</title>", "<p>An initial technical issue we encountered with OPT scanning of WISH (whole mount <italic>in-situ</italic> hybridisation) specimens was that strong <italic>in-situ</italic> colour reaction staining can block autofluorescence and prevent the capture of portions of the anatomical data required for subsequent mapping to a reference model. To obviate this problem, we identified a particular depth of staining with the NBT–BCIP substrate, suitable for OPT scanning, which captures an extensive range of the expression pattern and allows the visualisation of dynamic gradients without causing a substantial dropout of the anatomical data necessary for mapping (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S</xref>1). To test our standard <italic>in-situ</italic> hybridisation protocol, monitor probe penetration of the embryonic limb, and assess the ability of the OPT system to identify graded patterns of expression within deep tissues, we focussed on <italic>Wnt5a</italic>. <italic>Wnt5a</italic> has been reported to have a proximo-distal gradient of expression based on both radioactive section <italic>in-situ</italic> hybridisation and northern blots of distinct portions of the limb (##REF##8297789##Dealy et al., 1993##) and is known to be expressed throughout mesodermal regions of the limb.</p>", "<p>We first assayed expression by WISH (method modified after ##REF##8722478##Nieto et al., 1996## see <xref rid=\"app1\" ref-type=\"sec\">supplementary data</xref>) ##FIG##0##Fig. 1##A). The <italic>Wnt5a</italic> whole mount was scanned using OPT and gene expression data mapped onto a reference limb (##FIG##1##Fig. 2##D), from which virtual sections were derived (##FIG##0##Fig. 1##B). These virtual sections were then compared with section <italic>in-situs</italic> (##FIG##0##Fig. 1##C) performed as in ##REF##11118473##Moorman et al. (2001)##. This comparison shows that the virtual section captures the extent and range of the <italic>Wnt5a</italic> expression pattern as accurately as the section <italic>in-situ</italic> with the exception of some apical ectodermal ridge (AER) expression (##FIG##0##Fig. 1##C arrowed). For a further illustration of the effective capture of expression patterns using OPT see <xref rid=\"app1\" ref-type=\"sec\">supplementary data</xref> (Fig. S3–5).</p>", "<p>We then measured the mean grey level signal intensity in all individual sections along the proximo-distal axis. The plot of these data (##FIG##0##Fig. 1##D) shows low levels of signal in the proximal region (Red, slice 1–28), either very low expression or background. The mean grey level intensity then climbs steeply in the medial region (Orange, slice 29–52) from ∼ 30 up to 100. In the distal region (Green, 53–75) the mean grey level intensity increases less steeply and then levels off at a mean intensity of around 160. In the final 5 slices the mean grey level intensity drops rapidly. This shows the capability of the OPT imaging method to allow a detailed analysis of graded patterns of expression.</p>", "<p>We also compared <italic>Wnt5a</italic> expression levels as measured from OPT scans of whole mount <italic>in-situs</italic> with real-time RT-PCR analysis (##FIG##0##Fig. 1##F). For both OPT and RT-PCR analyses the limb bud was divided into three regions of equal length along the proximo-distal axis designated proximal, medial and distal (##FIG##0##Fig. 1##E). For RT-PCR the sample tissue was dissected into the three regions of equal length and samples from 10 embryos were pooled. In the case of the OPT data this division was performed digitally using AMIRA’s segmentation software based upon guidance from the researcher who performed the initial microdissection. The real time RT-PCR data produced relative values for the expression of <italic>Wnt5a</italic> as follows; expression in the proximal region was taken as the reference expression level, the medial region had a 5 fold increase over the proximal region and the distal region a 19.7 fold increase. The OPT based analysis produced relative values for medial and distal regions of 4.5 fold and 6 fold increases over the value for the proximal region respectively. Therefore, WISH/OPT captures the graded nature of the expression along the proximo-distal axis, indeed the correspondence in the proximal and medial regions is striking, but not across the whole quantitative range captured using RT-PCR as the correspondence falls off dramatically at the higher levels seen in the distal region. The limitations in the captured range of expression may be due to limitations of the WISH detection method, <italic>i.e.</italic>, a nonlinear relationship between signal intensity and RNA quantity. This allows comparisons of the level of expression of a particular gene within a particular scan but not the comparison of absolute levels between different scans, although high and low regions of expression could be compared.</p>", "<p>The quality of data capture for the gradient of <italic>Wnt5A</italic>, both from selected domains and from serial virtual sections, suggests that WISH/OPT is suitable for examining complex patterns of graded expression in tissues within developing embryos, but not for quantification of signal with high accuracy and quantitative comparisons of mRNA levels between samples.</p>", "<title>Reference models for comparative analysis</title>", "<p>An initial requirement for meaningful comparison of gene expression patterns is a common spatial reference framework onto which different patterns can be mapped. We have produced a panel of such reference frameworks for several embryonic stages; whole embryos, isolated and fixed according to a standardised protocol, were collected at Hamburger Hamilton (HH) (##REF##24539719##Hamburger and Hamilton, 1951##) stages from HH18 through to HH25. Embryos were then scanned by OPT using autofluorescence stimulated by a UV lamp and reconstructed to produce 3D models. These models were rendered using the AMIRA software package to show morphology and gross anatomy of the embryos (##FIG##1##Figs. 2##A–H). This resulted in a clear 3D visualisation of the embryo and revealed details such as the AER – the thickened layer of epithelium that rims the distal limb buds – in the models from HH20 onwards (##FIG##1##Figs. 2##A–F, blue arrows indicate AER). This is best appreciated when the model is rotated (##FIG##1##Fig. 2##H″, blue arrows indicate AER) (to view models in 3D see movies in <xref rid=\"app1\" ref-type=\"sec\">supplementary data Fig. S6</xref>). Measurements of the length (<italic>L</italic>; along a line from anterior join of bud and trunk to posterior join) and width (<italic>W</italic>; a line from distal tip to trunk perpendicular to line <italic>L</italic>) of wing buds were made on the dorsal plan view (##FIG##1##Fig. 2##, Table 1) and the <italic>L</italic>/<italic>W</italic> ratio calculated in order to stage the reference embryo (<xref rid=\"app1\" ref-type=\"sec\">Fig. S2</xref>). The procedure was the same as one would use to stage a living chick embryo using the staging criteria of Hamburger and Hamilton (##FIG##1##Fig. 2##, Table 2) (<xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S2</xref>).</p>", "<p>Reference models for specific regions, such as the developing limb buds (##FIG##1##Figs. 2##A′–H′), can be extracted from whole embryo models and used for mapping of gene expression in these regions. Within the AMIRA program, such extracted models remain in register with the model of the whole embryo from which they are derived, thus allowing maintenance of a consistent positional system between all expression patterns mapped. These relative positions are also maintained when files are exported to the Wlz file format, which can be used to store 3 dimensional greyscale image data, using software tools developed by the MRC HGU. Subsequent mappings and analyses of gene expression reported here were performed on an extracted data set for the right wing bud of the late stage HH22 reference embryo (##FIG##1##Fig. 2##E′). Further reference sets for other stages can be easily generated.</p>", "<title>Reproducibility of 3D mapping of gene expression within and between labs</title>", "<p>To test the reliability of our <italic>in-situ</italic> protocol and 3D mapping, we focussed on the expression domain of <italic>Sonic hedgehog</italic> (<italic>Shh</italic>) in HH22 wing buds. <italic>Shh</italic> is expressed in the polarizing region at the posterior margin of the wing bud and <italic>Shh</italic> expression correlates with maps of polarizing activity (##REF##8269518##Riddle et al., 1993##). We compared data generated from wing buds in a single round of whole mount <italic>in-situ</italic> hybridisation experiments and from wing buds processed in three different labs.</p>", "<p>A round of <italic>in-situ</italic> hybridisations using standardised protocols for <italic>Shh</italic> expression were carried out on 4 embryos and the sense control probe was used on 2 embryos. All 4 embryos assayed using the Shh anti-sense probe were treated identically and detection was carried out in the same tube. Nevertheless there were differences in intensity of staining of <italic>Shh</italic> transcripts in the polarizing region (##FIG##2##Figs. 3##A–D) with wing buds of one embryo (##FIG##2##Fig. 3##C) showing very faint staining. Wing buds of control embryos (data not shown) had no visible background staining. <italic>In-situs</italic> of embryos from Edinburgh (##FIG##2##Fig. 3##E) and Dublin (##FIG##2##Fig. 3##F) showed similar localisation of <italic>Shh</italic> transcripts in the wing bud and one embryo from each site was then scanned together with the four embryos from the run carried out in Dundee.</p>", "<p>All six embryos were OPT scanned through different channels to capture a) autofluorescence to represent anatomy and b) the staining pattern under visible light. Having reconstructed 3D representations of each, we then digitally extracted the right wing bud and accompanying flank using the same spatial parameters. Co-visualisation of both anatomy (grey) and expression (orange) with volume rendering (##FIG##2##Figs. 3##A′–F′) shows the representation of the original <italic>in-situ</italic> data (##FIG##2##Figs. 3##A–F) following OPT scanning and reconstruction.</p>", "<p>The patterns of <italic>Shh</italic> expression in each of the six wing buds were then mapped in 3D to the HH22 reference wing (##FIG##1##Fig. 2##E). ##FIG##2##Figs. 3##A″–F″ shows heat maps of intensity of <italic>Shh</italic> expression in one section, taken across the antero-posterior/dorso-ventral (A-Po/Do-V) axes of the HH22 stage reference wing bud in a plane situated next to the AER (##FIG##2##Fig. 3##L), for the individual patterns of expression for the six source wing buds. Signal intensity of mapped gene expression data is represented by the heatmap in ##FIG##2##Fig. 3##A″ corresponding to grey scale values between 1 and 255, this measure of intensity is not suitable for precise quantitative comparisons between samples but allows visualisation of the differing levels of expression within a sample within the limits of the WISH methodology. Of the 4 wing buds from the Dundee laboratory one showed a weak signal (##FIG##2##Fig. 3##C″), with a maximal intensity of only 28. The sense controls were both very clean with no signal (data not shown). Scans for Edinburgh (##FIG##2##Fig. 3##E″) and Dublin (##FIG##2##Fig. 3##F″) had a localisation very similar to ##FIG##2##Fig. 3##A″ but an intensity of expression much closer to ##FIG##2##Fig. 3##D″.</p>", "<p>Both unique domains of expression and intersecting domains of expression can be derived for these data sets. Visualisation of the unique expression domains in 3 dimensions shows no unique domains for the specimens shown in A–D and only small peripheral regions for the higher intensity signal data sets from specimens in E and F (Data not shown). The intersect of the expression domains between all the scans (##FIG##2##Fig. 3##G) is restricted by the small domain of the outlying data set (##FIG##2##Fig. 3##C″), if we remove this outlying data from the calculation we have an intersect domain almost twice as large (##FIG##2##Fig. 3##H).</p>", "<p>Clearly, there is some variation between individual scans and an occasional extreme outlier, but our data suggest a clear common domain of gene expression is identifiable. To produce a reliable and robust domain which could compensate for the variations we see in individual <italic>in-situs</italic>, we incorporated the data from our multiple scans into one domain. We produced means of the data from the scans and corrected for background from the controls, using both raw and normalised data (##FIG##2##Figs. 3##I–J). The mean of the datasets (##FIG##2##Fig. 3##I) was heavily influenced by high intensity samples (compare ##FIG##2##Fig. 3##E″ and ##FIG##2##Fig. 3##I). To correct for the variation in signal intensity, whether as a result of differences in the <italic>in-situ</italic> itself or from the scanning steps, data sets were normalised by stretching their entire grey value range to cover the maximal range of 0–255. Such correction had almost no effect (##FIG##2##Fig. 3##J), although there was a small extension of the domain proximally. As an alternative to normalisation to account for variability and extreme outlying values we derived a median value for each voxel based on the grey levels of all of the scans (##FIG##2##Fig. 3##K). This median value seemed less dominated by outliers and a better representation of the range seen across the differing samples although it produced a more conservative domain than the simple mean since it removed areas where signal was not apparent in more than half of the samples.</p>", "<p>These results suggest that best practice for producing a reliable domain of expression for comparison is to perform several <italic>in-situs</italic> developed to suitable stain intensity and merge the resultant data. A minimum of four scans seems advisable to contribute to the merged data set and these may then be mapped to a common reference and a mean or median expression pattern derived. A mean of the patterns appears to better emphasise the extent of the expression domain while the median produces a more conservative domain less affected by outliers. As our analysis shows, more complicated treatment of the data seems to make little difference to the resulting domains although, for <italic>in-situs</italic> with persistently low signal, normalisation might help emphasise gradients of expression in some cases.</p>", "<p>This averaging or otherwise merging together of multiple samples is less necessary in the case of previously well characterised genes where the expected pattern of expression is already known and a representative sample can be confidently identified. This approach should be most valuable in the situation where the gene expression is either poorly characterised or unknown, as is likely to be the case in large scale screens.</p>", "<title>Comparative analysis of 3D Gene expression patterns</title>", "<p>OPT is a rapid method of capturing the 3D expression pattern and can allow data on multiple genes to be integrated into a common framework, therefore we used 3D warping of OPT data to our reference models to produce such an integrated data set. Expression patterns for the genes <italic>Shh</italic>, <italic>HoxD13</italic>, <italic>Fgf8</italic>, <italic>Msx1</italic>, <italic>Lmx1</italic>, <italic>Wnt5a</italic> and <italic>Tbx3</italic> (for representative <italic>in-situs</italic> see <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S6</xref>) were mapped to our HH22 reference model (##FIG##1##Fig. 2##E) using AMIRA’s 3D warping capabilities. This stage was chosen as it represents a well-developed limb bud but still consisting mainly of undifferentiated mesenchyme cells. These genes were chosen for particular characteristics of expression such as dorsal restriction, <italic>Lmx1</italic>, specific expression in the AER, <italic>Fgf8</italic>, specific expression in the mesoderm, <italic>Shh</italic>, or particular gradients of expression, such as the proximo-distal gradient of <italic>Wnt5a</italic>. Particular specimens for scanning were chosen based on <italic>in-situ</italic> quality in comparison to others processed with them, usually the best example from 5–6 <italic>in-situs</italic>. The resulting mappings were then visualised in 3D (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S8</xref>), virtual sections were derived along specific planes (##FIG##3##Fig. 4##A), gene expression patterns and intensity were visualised on the anterior–posterior/dorsal–ventral plane (##FIG##3##Fig. 4##A.i). The expression domains of several pairs of genes were co-visualised on the antero-posterior/dorso-ventral (A-Po/Do-V) (##FIG##3##Fig. 4##C, section plane in ##FIG##3##Fig. 4##A.i), antero-posterior/proximo-distal (A-Po/Pr-Di) (##FIG##3##Fig. 4##D, section plane in ##FIG##3##Fig. 4##A.ii) and dorso-ventral/proximo-distal (Do-V/Pr-Di) (##FIG##3##Fig. 4##E, section plane in ##FIG##3##Fig. 4##A.iii) planes to allow some specific comparisons to be drawn (##FIG##3##Figs. 4##C–E; for 3D visualisations see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S11–18</xref>). These mappings show both expected features, such as the dorsal expression pattern of <italic>Lmx1</italic>, and novel features such as an apparent gradient of expression from ventral to dorsal in <italic>Wnt5a</italic>. Indeed several dorso-ventral asymmetries were by far the most striking features to emerge.</p>", "<p>One such previously unappreciated asymmetry was in <italic>Shh</italic> expression (##FIG##3##Fig. 4##B), which shows that <italic>Shh</italic> expression extends further anteriorly on the ventral side of the limb. A transverse section through the limb along plane E shows that the asymmetry is more complex and the domain of <italic>Shh</italic> expression is skewed with the more proximal regions of the limb showing a more dorsal expression of <italic>Shh</italic> (##FIG##3##Fig. 4##E.ii). Planes taken at more anterior levels lose this obvious skewing (see <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S9</xref>) and elements of this distribution are confirmed by both normal <italic>Shh</italic> whole mount <italic>in-situ</italic> and a double <italic>in-situ</italic> for <italic>Shh</italic> and <italic>Fgf8</italic> (see <xref rid=\"app1\" ref-type=\"sec\">supplementary materials Fig. S10</xref>). This may explain the observations of ##REF##7697724##Yang and Niswander (1995)## who reported no apparent dorso-ventral asymmetry in <italic>Shh</italic> expression at HH24 based on sectioned whole mount <italic>in-situ</italic> hybridisation data. Alternatively this may be due to the different stages assayed. A more dorsal distribution in the more proximal regions of the limb may suggest that a dorsally localised signal, such as <italic>Wnt7a</italic>, might be maintaining dorsal expression proximally while more distal expression would be maintained evenly across the dorso-ventral axis by signals from the apical ectodermal ridge. Indeed it is already known that Wnt7a plays a role in maintaining <italic>Shh</italic> expression in the polarising region (<xref rid=\"bib38 bib44\" ref-type=\"bibr\">Parr and McMahon, 1995; Yang and Niswander, 1995</xref>). Furthermore ##REF##10445500##Kawakami et al. (1999)## reported that <italic>Frizzled10</italic> (a Wnt receptor) colocalises with <italic>Shh</italic> dorsally and suggested that <italic>Shh</italic> expression in this part of the polarising region might be regulated by Wnt7a (##REF##11142678##Kawakami et al., 2000##).</p>", "<p>The pairwise comparisons similarly produced both expected and unexpected results. <italic>HoxD13</italic> shows striking asymmetrical dorso-ventral expression (##FIG##3##Figs. 4##C.v and E.v) and the ventral margin of <italic>HoxD13</italic> expression appears to coincide with that of <italic>Lmx1</italic> (##FIG##3##Figs. 4##C.viii and E.viii; Movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S18</xref>), suggesting a possible regulatory relationship. The dorso-ventral asymmetry in <italic>Hoxd13</italic> had been previously noted by Duboule et al. from 3D reconstructions based on radioactive section <italic>in-situs</italic> (##UREF##2##Olivo et al., 1993##) but at this time <italic>Lmx1</italic> was not known. Recent lineage tracing studies in the developing mouse limb have shown the existence of a dorsoventral lineage restriction compartment further suggesting that there is still considerable complexity in the dorsoventral organisation of the limb to be discovered (##REF##17715176##Arques et al., 2007##).</p>", "<p>Comparison of <italic>Shh</italic> and <italic>Fgf8</italic> expression (##FIG##3##Figs. 4##D.iii and E.iii; movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S13</xref>) shows an unexpected overlap of expression in the mesoderm. This shows that there are important limits of spatial resolution to the current mapping procedure given the well-characterised expression of these genes in mesoderm and apical ectodermal ridge respectively. Since the AER is one of the principle morphological features of the limb, it is heavily used in the landmarking process, which is the first stage of mapping expression data to the reference limb. Strong <italic>in-situ</italic> staining, as seen with <italic>Fgf8</italic>, can block autofluorescence and prevent the capture of the anatomical data for the ridge. A comparison of <italic>Wnt5a</italic> and <italic>Fgf8</italic> (Movie in <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S11</xref>) also shows considerable overlap except for the most anterior region of <italic>Fgf8</italic> expression (##FIG##3##Fig. 4##C.i) and a persistent ‘leading edge’ of <italic>Fgf8</italic> expression in the most distal portion of the limb (##FIG##3##Fig. 4##E.i). In this case, we would expect to see considerable overlap due to expression of <italic>Wnt5a</italic> in the ridge, although it is not clear whether the common domain of expression accurately represents just ridge expression.</p>", "<title>OPT using Tyramide Signal Amplification</title>", "<p>To address the problem of the loss of anatomical landmarks when looking at strongly expressed ectodermal genes, such as <italic>Fgf8</italic>, we used a Tyramide Signal Amplification (TSA) kit (Invitrogen) to enhance a fluorescent colour reaction and avoid the blocking effect seen with chromagenic substrates such as NBT–BCIP. We compared the expression of <italic>Tbx3</italic> and <italic>Fgf8</italic> at HH24, mapped to the HH24 reference limb (##FIG##1##Fig. 2##G′), from a normal NBT–BCIP based colour reaction for <italic>Tbx3</italic> and a TSA fluorescent colour reaction for <italic>Fgf8</italic> (##FIG##4##Figs. 5##A, B). This approach provided a much more accurate localisation of <italic>Fgf8</italic> expression to the apical ridge compared to that seen in ##FIG##3##Fig. 4##. When we carried out a pairwise comparison between <italic>Fgf8</italic> and <italic>Tbx3</italic> expression patterns, the overlap was very much reduced compared to that seen between <italic>Fgf8</italic> and <italic>Shh</italic> in ##FIG##3##Fig. 4##D.iii and compared with <italic>Fgf8</italic> and <italic>Tbx3</italic> from our original data set in which we had used an NBT–BCIP reaction for both genes (##FIG##4##Fig. 5##C). While these mapped localisations are not sufficient for accurately discriminating expression from expressing tissues close together or very thin tissue layers a database containing such mapped data would be linked to the original 3D scans allowing visualisation of the data.</p>", "<title>Computational comparisons of mapped gene expression patterns</title>", "<p>Despite the shortcomings discussed above, our overall mapping strategy provides a valuable tool for analysing spatial and temporal relationships of multiple complex patterns.</p>", "<p>To begin to apply computational methods to analyse these multiple data sets at once and to look for similarities in patterns of gene expression, we utilised software produced by the MRC HGU to manipulate 3D image data in the Wlz format, a format used by EMAGE. AMIRA files were converted to Wlz format and a set of sub-regions of 10 × 10 × 10 voxels were defined for the early HH22 reference limb producing a coarse sampling of the 3D model. The expression data for the previously analysed genes and an additional gene, <italic>Wnt3a</italic>, were then used to derive mean expression values from the grey levels of each OPT scan for the newly defined volumes. This produced a matrix of common positional IDs and expression levels for each gene.</p>", "<p>These data were then analysed using hierarchical clustering for both the genes and the regions defined by the coarse sampling. This analysis was performed and visualised using the TMEV package (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.tm4.org/mev.html\">http://www.tm4.org/mev.html</ext-link>) (##FIG##5##Fig. 6##A). The clustering of these genes fits our expectations with <italic>Fgf8</italic> and <italic>Wnt3a</italic>, both known to be expressed in the apical ridge (<xref rid=\"bib4 bib25 bib35\" ref-type=\"bibr\">Barrow et al., 2003; Kengaku et al., 1998; McQueeney et al., 2002</xref>), treeing out together. Visualisation of the clustered regions shows that they largely form contiguous spatial domains (##FIG##5##Figs. 6##B–G; <xref rid=\"app1\" ref-type=\"sec\">supplementary Fig. S19</xref>); some of these domains are associated with specific regions of the limb. Cluster B lies in a plane through the limb along the proximal–distal axis at the level of the anterior margin of <italic>Shh</italic> expression (##FIG##5##Fig. 6##B), cluster D is associated with the AER in the anterior of the limb (##FIG##5##Fig. 6##D) while cluster E is in the dorsal margin of the limb (##FIG##5##Fig. 6##E). None of the clusters visualised here corresponds simply to the expression pattern of one particular gene.</p>", "<p>This simple level of clustering computational analysis shows the potential for methods developed to study gene expression data from other sources, such as microarray data, to be applied to the study of 3D gene expression data. Not only can we identify similarly expressed genes using this method but it should also be possible to identify specific regions of the limb that may be important in the regulation of gene expression, such as novel signalling centres.</p>" ]
[ "<title>Conclusion</title>", "<p>We have developed improved technology for the production of 3D atlases of gene expression (##REF##15043218##Baldock et al., 2003##). Specifically, we have shown that OPT is a reliable and efficient way of visualising 3D patterns of gene expression in the chick limb and have been able to directly compare different patterns on reference models using a 3D warping technique. This technique allows visualisation both of samples too large for confocal microscopy (##REF##18377226##Welten et al., 2006##) and those too small for good resolution with microMRI (see ##REF##18045352##Li et al., 2007## for visualisation of chick wings at later stages). Furthermore, visualisation of gene expression is still rudimentary with microMRI (##REF##17234603##Liu et al., 2007a##).</p>", "<p>As more 3D patterns of gene expression are mapped, simple pairwise comparisons will no longer be sufficient to analyse more complex relationships between groups of genes and then the computational approaches we have used here will be greatly beneficial, indeed similar methods have been used to study the expression of around 20,000 genes in the mouse brain (<xref rid=\"bib29 bib32\" ref-type=\"bibr\">Lein et al., 2007; Liu et al., 2007b</xref>). Although we have only compared 7 genes known to be expressed in chick limb development we have already revealed some previously unappreciated asymmetries and relationships, particularly with respect to the dorso-ventral axis. These techniques will also be more generally applicable to different developing structures and organisms.</p>" ]
[ "<p>Currently at the UK MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh, UK.</p>", "<p>Currently at the School of Biology, Bute Building, University of St. Andrews, St. Andrews, UK.</p>", "<p>Currently at the School of Biology and Biochemistry, Bath University, Bath, UK.</p>", "<p>Chick embryos are good models for vertebrate development due to their accessibility and manipulability. Recent large increases in available genomic data from both whole genome sequencing and EST projects provide opportunities for identifying many new developmentally important chicken genes. Traditional methods of documenting when and where specific genes are expressed in embryos using wholemount and section <italic>in-situ</italic> hybridisation do not readily allow appreciation of 3-dimensional (3D) patterns of expression, but this can be accomplished by the recently developed microscopy technique, Optical Projection Tomography (OPT). Here we show that OPT data on the developing chick wing from different labs can be reliably integrated into a common database, that OPT is efficient in capturing 3D gene expression domains and that such domains can be meaningfully compared. Novel protocols are used to compare 3D expression domains of 7 genes known to be involved in chick wing development. This reveals previously unappreciated relationships and demonstrates the potential, using modern genomic resources, for building a large scale 3D atlas of gene expression. Such an atlas could be extended to include other types of data, such as fate maps, and the approach is also more generally applicable to embryos, organs and tissues.</p>", "<title>Keywords</title>" ]
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[ "<title>Supplementary data</title>", "<p></p>", "<p></p>", "<title>Acknowledgments</title>", "<p>We thank Calum Thomson from the Light Microscopy facility at the University of Dundee for help with sectioning. This work was supported in part by funding from the Medical Research Council and by funding from The Royal Society, Cheryll Tickle is a Royal Society Professor.</p>", "<p>We would also like to thank Harris Morrison for maintaining the OPT facility at HGU, and for his help and advice, Venkat Shanmugasundaram for his help with warping expression domains using AMIRA and Peter Hohenstein for assistance with real time RT-PCR.</p>" ]
[ "<fig id=\"fig1\"><label>Fig. 1</label><caption><p>Comparison of WISH/OPT and other methods for detecting a gradient of expression. (A) Dorsal view of wholemount <italic>in-situ</italic> hybridisation of <italic>Wnt5a</italic> in a HH22 wing bud. (B) Virtual section of OPT data scanned from A mapped to a reference limb. (C) Section <italic>in-situ</italic> of Wnt5a from a HH22 wing bud, arrow indicates expression in the AER. (D) A plot of the mean signal intensity in virtual slices of the OPT data set taken along the proximo-distal axis of the limb with 0 representing the most proximal position and 75 the most distal. Colouring under the line represents the domain to which the slices belong, coloured as in panel E. (E) A surface rendering showing the early HH22 reference limb and the three assayed limb domains; proximal (red), medial (orange) and distal (green), arrows indicate the antero-posterior (A-Po) and proximo-distal (Pr-Di) axes of the limb. (F) Comparison of levels of expression in three domains assayed by OPT (purple) and real time RT-PCR (blue), error bars represent standard errors of ± 0.29, ± 1.4 and ± 1.6 for the RT-PCR measurements in the proximal medial and distal regions respectively. OPT values were based on the mean grey level intensity within the domain and standardised against the mean intensity value of the proximal domain to get a relative expression. Domain labels are coloured as in panel E.</p></caption></fig>", "<fig id=\"fig2\"><label>Fig. 2</label><caption><p>3D reference models of whole embryos over several stages. (A–H) Volume renderings of OPT scans of whole embryos at reference stages HH19 (A), HH20 (B), HH21 (C), Early stage HH22 (D), Late Stage HH22 (E), HH23 (F), HH24 (G), HH25 (H), scale bars (orange) for panels A–H indicate 1 mm. Blue arrows indicate where the AER is visible on rendered embryos. (A′–H′) Plan views of digitally extracted right wing buds from corresponding whole embryo scans. Panel A′ shows arrows indicating the antero-posterior (A-Po) and proximo-distal (Pr-Di) axes of the limb. (H″) Distal view showing AER (blue arrow), white arrows indicate the antero-posterior (A-Po) and dorso-ventral (Do-V) axes of the limb. Scale bars (orange) indicate 300 μm in panels A′–H′ and H″. (Table 1) Measurements of wing bud length to width ratios (<italic>L</italic>/<italic>W</italic>) made on the plan views. (Table 2) Stages according to the original <italic>L</italic>/<italic>W</italic> measurements of ##REF##24539719##Hamburger and Hamilton (1951)##. The measurements for later stages are not included in Table 1 as they are not covered by Hamburger and Hamilton.</p></caption></fig>", "<fig id=\"fig3\"><label>Fig. 3</label><caption><p>Reproducibility of 3D mapping of gene data. Photographs of right wing buds from four whole mount <italic>in-situ</italic> hybridisations for <italic>Shh</italic> from Dundee (A–D), one from Edinburgh (E) and one from Dublin (F). (A′–F′) OPT scans of the <italic>in-situs</italic> shown in panels A–F using both the fluorescence channel, for the anatomy (grey), and brightfield, for the signal (orange), visualised as volume renderings. (A″–F″) Gene expression data derived from the OPT scans in panels A′–F′ are displayed on a correspondingly labelled view of an A-Po/Pr-Di section (L) of the early HH22 wing bud (##FIG##1##Fig. 2##D), signal intensity is represented according to the heatmap in panel A″. (G) Intersect of domains of expression, in white, of panels A″–F″. (H) Intersect of domains of expression, in white, of panels A″, B″ and D″–F″, excluding the restrictive outlying data set C. (I) Mean of data in panels A″–F″. (J) Mean of data in panels A″–F″ after normalisation. (K) Median of data in panels A″–F″. The orange scale bar in all panels represents 300 μm, the scale shown in panel A″ is consistent for subsequent panels through to K. (L) A 3D model of the early HH22 wing bud showing the A-Po/Pr-Di plane of section, the nearby AER is indicated by blue arrows.</p></caption></fig>", "<fig id=\"fig4\"><label>Fig. 4</label><caption><p>Mapping of gene expression for <italic>Shh</italic>, <italic>HoxD13</italic>, <italic>Fgf8</italic>, <italic>Msx1</italic>, <italic>Lmx</italic>, <italic>Wnt5a</italic> and <italic>Tbx3</italic>. (A) 3 views of the late stage HH22 reference model showing the position of planes of section used for subsequent analyses. (A.i) A dorsal view of the limb showing the position of the A-Po/Do-V plane seen in panels B and C. (A.ii) A distal view of the limb showing the position of the A-Po/Pr-Di plane seen in panel D. (A.iii) A dorsal view of the limb showing the position of the Do-V/Pr-Di plane seen in panel E. (B) A virtual section through the reference limb along the A-Po/Do-V plane shown in panel A.i with mapped gene expression patterns for <italic>Shh</italic>, <italic>HoxD13</italic>, <italic>Fgf8</italic>, <italic>Msx1</italic>, <italic>Lmx1</italic>, <italic>Wnt5a</italic> and <italic>Tbx3</italic>. Sections from the three planes shown in panel A are (C) an A-Po/Do-V plane, (D) an A-Po/Pr-Di plane, (E) a Do-V/Pr-Di plane. Pairwise comparisons were visualised on these planes for expression domains of i) <italic>Fgf8</italic> (green) and <italic>Wnt5a</italic> (red)<italic>,</italic> ii) <italic>Shh</italic> (red) and <italic>Tbx3</italic> (green), iii) <italic>Fgf8</italic> (green) and <italic>Shh</italic> (red), iv) <italic>Fgf8</italic> (green) and <italic>Msx1</italic> (red), v) <italic>Wnt5a</italic> (green) and <italic>HoxD13</italic> (red), vi) <italic>HoxD13</italic> (red) and <italic>Tbx3</italic> (green), vii) <italic>HoxD13</italic> (red) and <italic>Shh</italic> (green), viii) <italic>HoxD13</italic> (red) and <italic>Lmx1</italic> (green). Comparison numberings are consistent between panels C and E<italic>.</italic> Regions of overlap are in yellow. All scale bars (orange) represent 300 μm.</p></caption></fig>", "<fig id=\"fig5\"><label>Fig. 5</label><caption><p>Improved localisation of <italic>Fgf8</italic> using fluorescent <italic>in-situ</italic> hybridisation. (A) An A-Po/Do-V plane through the HH24 reference limb (##FIG##1##Fig. 2##G′), (B) An A-Po/Pr-Di plane through the HH24 reference limb and (C) An A-Po/Pr-Di plane through the HH22 reference limb as in ##FIG##3##Fig. 4##D. All 3 panels show expression of both <italic>Fgf8</italic> (red) and <italic>Tbx3</italic> (blue) and the overlap of the domains of expression (yellow). Expression data for <italic>Tbx3</italic> are NBT–BCIP derived in all cases. <italic>Fgf8</italic> expression data in panels A and B are derived from Tyramide signal amplified FISH and NBT–BCIP derived in panel C. Note marked reduction in overlap in panels A and B compared with panel C.</p></caption></fig>", "<fig id=\"fig6\"><label>Fig. 6</label><caption><p>Computational analysis of gene expression data. (A) A hierarchical clustering of the coarse sampled gene expression pattern data for both genes and limb regions. Each column represents a gene expression pattern and each cell within a column of the matrix grid represents a discrete 10 × 10 × 10 spatial volume in the reference model. Both the gene and the spatial volume data have been hierarchically clustered and trees derived showing similarity relationships for genes at the top and for spatial volumes to the left. Cells in the matrix grid are coloured according to the accompanying heatmap for signal intensity and represent the mean signal intensity for a particular gene within a particular spatial volume. This matrix does not show data for all volumes in the reference model. To the right of the matrix are blocks of colour corresponding to clusters located at the end of particularly long branches of the hierarchical clustering tree to the left, the lettered clusters correspond to the visualisations in the subsequent panels. (B–G) Visualisations of the lettered clusters from panel A in a volume rendered view of the limb, anatomy is rendered in grey and the clusters are rendered in the colours corresponding to their labelling on the right hand side of the matrix (B) purple, (C) blue, (D) green, (E) Cyan,(F) pink, (G) red. (A movie showing these clusters is available in the supplemental materials Fig. S19). Scale bars in orange correspond to 300 μm; arrows to orient the 3 principal axes, A-Po axis (blue), Pr-Di axis (red) and Do-V axis (green). Intersect of arrows represents posterior, proximal and dorsal ends of the axes.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"d32e1381\"><caption><title>Fig. S1</title><p>Suggested standard depth of colour development for OPT scanning. HH22 <italic>Wnt5a in-situ</italic> hybridisation showing suggested stain development. B) A colourstrip with the suggested endpoint stain depth highlighted in red.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1392\"><caption><title>Fig. S2</title><p>This graph compares the measurements of wing bud length/width ratios from reference embryos (##FIG##1##Figs. 2##A′–F′) to the original values stipulated by Hamburger and Hamilton. The reference model measurements are shown in blue and the error bar represents the variation between the left and right wing buds of the reference embryos. The Hamilton–Hamburger measurements are shown in red and the error bars represent the range of values Hamburger and Hamilton associate with specific stages. Embryos with <italic>L</italic>/<italic>W</italic> values outside the range covered by the Hamburger Hamilton series are not shown on this graph.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1409\"><caption><title>Fig. S3</title><p>Efficiency of data capture using OPT. HH Stage 22 whole mount <italic>in-situ</italic> hybridisation showing <italic>Msx1</italic> gene expression. B) Raw image data of embryo during OPT data capture. C) Processed image data showing combined brightfield (Red) and fluorescence (Green) channels.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1423\"><caption><title>Fig. S4</title><p>Raw movie of embryo rotating during OPT data capture.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1432\"><caption><title>Fig. S5</title><p>Movie showing rotating combined data for the brightfield (Red) and fluorescence (Green) channels.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1440\"><caption><title>Fig. S6</title><p>Movie showing the rotation of the HH25 stage embryo from ##FIG##1##Fig. 2##H, the AER is readily appreciable on all limbs.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1451\"><caption><title>Fig. S7</title><p>Whole mount <italic>in-situs</italic> of HH22 embryos assayed for the expression of A) <italic>Msx1</italic>, B) <italic>Lmx1</italic>, C) <italic>Wnt5a</italic>, D) <italic>Wnt3a</italic>, E) <italic>Tbx3</italic>, F) <italic>HoxD13</italic>, G) <italic>Shh</italic> and H) <italic>Fgf8</italic>.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1487\"><caption><title>Fig. S8</title><p>A movie showing an initial surface render of the anatomy of the limb bud which then fades to reveal a surface rendering of the domain of <italic>Shh</italic> (Red) expression is shown, this is then joined by the domains of <italic>HoxD13</italic> (Purple), <italic>Fgf8</italic> (Green), <italic>Tbx3</italic> (Yellow), <italic>Msx1</italic> (Pink), <italic>Wnt5a</italic> (Cyan) and <italic>Lmx1</italic> (Blue) in sequence.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1517\"><caption><title>Fig. S9</title><p>A movie showing serial virtual sections along the A-Po/Do-V plane going from distal to proximal through the HH22 reference limb. The expression pattern of <italic>Shh</italic> is displayed on these sections according to the heatmap in ##FIG##2##Fig. 3##A″. The dorsal bias of <italic>Shh</italic> in more proximal sections is apparent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1534\"><caption><title>Fig. S10</title><p>Asymmetry of <italic>Shh</italic> expression. A) A distal view of a HH stage 23 <italic>in-situ</italic> showing the expression of <italic>Shh</italic>. The expression is predominantly in the dorsal region of the limb with respect to the AER, indicated by the blue arrow. B) A dorsal view of a HH Stage 23 double <italic>in-situ</italic> for <italic>Fgf8</italic> and <italic>Shh</italic>, both developed with NBT–BCIP. <italic>Shh</italic> expression in the ZPA indicated with red arrow. C) A posterior view of the same HH Stage 23 double <italic>in-situ</italic> for <italic>Fgf8</italic> and <italic>Shh</italic>. The domain of <italic>Shh</italic> expression, indicated by the red arrow, is clearly shifted to the dorsal side of the limb relative to the AER as marked by the expression of <italic>FGF8</italic>.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1580\"><caption><title>Fig. S11</title><p>A) Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Wnt5a</italic> (Cyan) and <italic>Fgf8</italic> (Green).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1594\"><caption><title>Fig. S12</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Tbx3</italic> (Yellow) and <italic>Shh</italic> (Red).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1608\"><caption><title>Fig. S13</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Shh</italic> (Red) and <italic>Fgf8</italic> (Green).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1622\"><caption><title>Fig. S14</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Msx1</italic> (Pink) and <italic>Fgf8</italic> (Green).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1636\"><caption><title>Fig. S15</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Wnt5a</italic> (Cyan) and <italic>HoxD13</italic> (Purple).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1651\"><caption><title>Fig. S16</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Tbx3</italic> (Yellow) and <italic>HoxD13</italic> (Purple).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1665\"><caption><title>Fig. S17</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Shh</italic> (Red) and <italic>HoxD13</italic> (Purple).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1679\"><caption><title>Fig. S18</title><p>Movie showing rotation of HH22 reference limb displaying expression domains of both <italic>Lmx1</italic> (Blue) and <italic>HoxD13</italic> (Purple).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1693\"><caption><title>Fig. S19</title><p>Movie showing volume renderings of domains representing clusters of gene expression. The clusters appear in alphabetical order relating to their labelling in ##FIG##4##Figs. 5##B–G and are coloured as in ##FIG##4##Fig. 5##.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"d32e1707\"><caption><title>Paper Materials and methods revised</title></caption></supplementary-material>" ]
[ "<fn-group><fn id=\"d32e1711\" fn-type=\"supplementary-material\"><label>Appendix A</label><p>Supplementary data associated with this article can be found, in the online version, at <ext-link ext-link-type=\"doi\" xlink:href=\"10.1016/j.ydbio.2008.01.031\">doi:10.1016/j.ydbio.2008.01.031</ext-link>.</p></fn></fn-group>" ]
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[{"surname": ["Bookstein"], "given-names": ["F."], "article-title": ["Principal warps: thin-plate splines and the decomposition of deformations"], "source": ["IEEE Trans. Pattern Anal. Mach. Intell."], "volume": ["11"], "year": ["1989"], "fpage": ["567"], "lpage": ["585"]}, {"surname": ["Maniatis", "Fritsch", "Sambroook"], "given-names": ["T.", "E.", "J."], "chapter-title": ["Molecular Cloning: A Laboratory Manual"], "year": ["1982"], "publisher-name": ["Cold Spring Harbor Laboratory Press"], "publisher-loc": ["New York"]}, {"surname": ["Olivo", "Izpis\u00faa Belmonte", "Tickle", "Boulin", "Duboule"], "given-names": ["J.", "J.C.", "C.", "C.", "D."], "article-title": ["Reconstruction from serial sections: a tool for developmental biology. Application to Hox genes expression in chicken wing buds"], "source": ["BioImaging"], "volume": ["1"], "year": ["1993"], "fpage": ["115"], "lpage": ["158"]}, {"surname": ["Verbeek", "den Broeder", "Boon", "Doerry", "van Raaij", "Zivkovic"], "given-names": ["F.J.", "M.J.", "P.J.B.B.", "E.", "E.J.", "D."], "article-title": ["Standard 3D digital atlas of zebrafish embryonic development for projection of experimental data"], "source": ["Proc. SPIE"], "volume": ["3964"], "year": ["1999"], "fpage": ["242"], "lpage": ["252"]}, {"surname": ["Yeh"], "given-names": ["J."], "article-title": ["The effect of miniaturized body size on skeletal morphology in frogs"], "source": ["Evol. Int. J. Org. Evol."], "volume": ["56"], "year": ["2002"], "fpage": ["628"], "lpage": ["641"]}]
{ "acronym": [], "definition": [] }
47
CC BY
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2022-01-12 20:25:41
Dev Biol. 2008 May 1; 317(1):13-23
oa_package/59/84/PMC2529376.tar.gz
PMC2529400
18802460
[ "<title>Introduction</title>", "<p>Kaposi's sarcoma-associated herpesvirus (KSHV, also known as human herpesvirus 8) is believed to be the etiologic agent for Kaposi's sarcoma (KS) ##REF##7997879##[1]##. KSHV infection is also linked to primary effusion lymphoma ##REF##9546789##[2]## and multicentric Castleman's disease, rare lymphoproliferative malignancies of B-cell origin ##REF##7700311##3##,##REF##7632932##4##. The KSHV genome encodes over 80 viral polypeptides, many of which are capable of promoting cell proliferation and/or modulating host responses, when expressed in gene transfer experiments (for review see reference ##REF##15263900##[5]##). One such gene product consistently detected in KS lesions is the viral G protein-coupled receptor (vGPCR, or open reading frame 74) ##REF##10364352##[6]##,##REF##11884567##[7]##.</p>", "<p>vGPCR is a homolog of the human interleukin-8 receptor and possesses promiscuous chemokine-binding activity ##REF##10531332##[8]##. In tissue culture, vGPCR expression activates various signaling pathways and up-regulates the transcription of numerous cellular and viral genes that encode cytokines, signaling molecules, and transcription factors that culminate in promoting cell proliferation and endothelial tube formation ##REF##12620408##[9]##–##REF##16904612##[15]##. Additionally, vGPCR transgenic mice developed tumors that resemble human KS lesions ##REF##12620408##[9]##,##REF##10662790##[16]##,##REF##12559173##[17]##. Although ligand binding is not required for vGPCR-mediated signaling, cognate chemokines appear to modulate vGPCR activity in tissue culture and in mice as well ##REF##10531332##[8]##,##REF##11748262##[18]##. Despite the fact that proliferative and prosurvival activities of vGPCR have attracted extensive attention in the past, accumulating evidence suggests that tightly regulated expression and signaling are important for vGPCR function in KSHV infection. Indeed, over-expression of vGPCR induced cell death in COS-1 cells and constitutive expression of vGPCR was toxic to PEL cells ##REF##10364352##[6]##,##REF##12477810##[19]##. Furthermore, vGPCR is predominantly translated from a bicistronic mRNA transcript downstream of K14 (vOX2), presumably reducing vGPCR protein expression ##REF##10364352##[6]##,##REF##11504542##[20]##. These observations suggest that KSHV likely has evolved mechanisms to achieve a temporary expression of the constitutively active vGPCR during lytic infection. A post-translational degradation is one of these mechanisms.</p>", "<p>Regulated protein degradation is important for a variety of cellular events including cell cycle, apoptosis, signal transduction, immune response, and development. Cellular GPCR can be degraded either by the ubiquitin-proteasome system (UPS) or by the lysosome. Within the lysomsome, proteins are cleaved by diverse acidic proteases upon fusion with endosomes or autophagosomes. For UPS substrates, proteins destined for destruction are tagged with ubiquitin through sequential actions of the E1 activating enzyme, E2 conjugating enzyme, and E3 ligase ##REF##11395416##[21]##. Relying on the UPS, the endoplasmic reticulum (ER)-associated degradation (ERAD) pathway is a major route to remove mis-folded proteins post-translationally, and plays an essential role for ER quality control. Indeed, alteration of ERAD pathways has been implicated in diverse clinical presentations such as neurodegeneration and cystic fibrosis. Furthermore, viruses usurp components of this pathway to evade host recognition and possibly modulate other host responses ##REF##16181332##[22]##–##REF##15215856##[24]##.</p>", "<p>We previously identified a small membrane protein, K7, which induces protein degradation of IκB and p53. K7 specifically interacts with the ubiquitin-associated domain of cellular <underline>p</underline>rotein <underline>l</underline>inking <underline>i</underline>ntegrin-associated protein and <underline>c</underline>ytoskeleton (PLIC1) and antagonizes PLIC1, thereby promoting protein degradation ##REF##15082787##[25]##. Additionally, K7 was shown to deregulate cellular apoptosis by targeting Bcl-2 and an ER resident calcium modulating cyclophilin ligand ##REF##12388711##[26]##,##REF##12032073##[27]##. Although these data imply that K7 inhibits apoptosis to facilitate viral replication, its biological roles in KSHV infection remain obscure. We report here that K7 interacts with vGPCR and induces its proteasomeal degradation. The knockdown of K7 by shRNA-mediated silencing increased vGPCR protein expression in BCBL-1 cells that are induced for KSHV lytic replication. Biochemical and confocal microscopy analyses support that K7 retains vGPCR in the ER, thereby facilitating the proteasome to degrade vGPCR. Consequently, K7 significantly reduces vGPCR transformation in vitro and tumorigenicity in nude mice. These data establish a negative regulation of vGPCR protein expression and tumorigenicity by KSHV K7.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Plasmids</title>", "<p>Unless specified, all constructs were derived from pcDNA5/FRT/TO (Invitrogen). A DNA fragment corresponding to the KSHV vGPCR was amplified from BCBL-1 genomic DNA by polymerase chain reaction (PCR) and cloned into pcDNA5/FRT/TO between BamHI and XhoI. For protein expression, either the HA epitope or the Flag epitope was inserted upstream or downstream of vGPCR coding sequence, respectively. Plasmids expressing wild-type and mutant K7 polypeptides were described in previous publications ##REF##15082787##[25]##,##REF##12388711##[26]##. For lentiviral expression, K7-Flag was cloned into pCDH-EF-puro (System Bioscience) between EcoRI and BamHI. HA-vGPCR was digested with EcoRI and BglII, and ligated to pCDH-EF-puro or pCDH-EF-CopGFP that was digested with EcoRI and BamHI. To generate the K7TM<sub>StpC</sub>, the K7 transmembrane (TM) domain was replaced with a heterologous TM segment of Stp C by PCR-based mutagenesis using overlapping PCR primers. All constructs were sequenced for verification.</p>", "<p>For the shRNA-mediated knockdown of K7, four pairs of synthetic DNA oligos were annealed and cloned into pLKO.1 (Sigma) that was digested with AgeI and EcoRI. The pLKO.1 expressing the scrambled shRNA was purchased from Sigma. Plasmids expressing HA-tagged wt and mutant ubiquitin were a kindly gift from Dr. James Z.J. Chen (UT Southwestern).</p>", "<title>Cell Culture and Transfection</title>", "<p>HEK293T (293T), HeLa, and NIH3T3 cells were grown in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum, 5 mM L-glutamine, 100 U/ml penicillin, and 100 µg/ml streptomycin. BJAB, JSC-1, BCBL-1, and BCBL-1/T-Rex_Rta cells were grown in RPMI 1640 supplemented with 10% fetal calf serum, 5 mM L-glutamine, 100 U/ml penicillin, and 100 µg/ml streptomycin. BCBL-1 cells were treated with phorbol-12-teradecanoate-13-acetate (TPA, 20 ng/ml) to induce lytic replication. HeLa cells were transfected with Fugene 6 (Roche), 293T cells were transfected with calcium phosphate (Clontech), ECV cells were transfected with lipofectamine (Invitrogen), and BJAB cells were transfected with electroporation at 220 V/975 µF. The stable BCBL-1/T-Rex_Rta inducible cell line was maintained and induced as previously described ##REF##12634378##[30]##.</p>", "<title>Immunoprecipitation and Immunoblot</title>", "<p>Immunoprecipitation and immuno-blot analyses were performed as previously described ##REF##12388711##[26]##. Immunoblot detection was performed with anti-V5 antibody (1∶5000, Invitrogen), anti-Flag M2 antibody (1∶5000, Sigma), anti-HA (1∶2000, Covance), anti-tubulin (1∶250, Santa Cruz), or anti-actin (1∶30,000, Abcam). Proteins were visualized with chemical luminescent detection reagent (Pierce) and a Fuji LAS-3000 camera.</p>", "<title>Reverse Transcriptase (RT)-PCR</title>", "<p>One million KSHV latently infected BCBL-1 or JSC-1 cells were treated with either TPA (20 ng/ml) to induce viral lytic replication and harvested at various time points. Alternatively, KSHV lytic replication was induced in BCBL-1/T-Rex_Rta stable cells with doxycline (1 µg/ml). Total RNA was extracted with RNAeasy column (Qiagen, CA) and digested with DNase I at 37°C for 1 h. After phenol/chloroform extraction, 1 µg of total RNA was used for first-strand cDNA synthesis using an oligo(dT) primer. Then, 1 µl of cDNA was added to 19 µl of PCR mixture and gene of interest was amplified using specific primers. PCR products were resolved on agarose gel and photographed. For each gene of interest, dilution of original cDNA and cycle number were determined to warrant that PCR products were generated within the linear range of PCR reaction. Total RNA from tumor tissues was extracted with triazol (Invitrogen, CA) and ethanol precipitation as previously described.</p>", "<title>Protein Stability</title>", "<p>Transiently transfected ECV cells were pulse labeled with <sup>35</sup>S-methionine/cysteine (Met/Cys) for 30 min. After extensive washing with phosphate buffered saline (137 mM NaCl, 2.7 mM KCl, 10 mM Na<sub>2</sub>HPO<sub>4</sub>, 2 mM KH<sub>2</sub>PO<sub>4</sub>, pH 7.4), cells were chased with cold medium up to 16 h. At various time points, cells were harvested, washed with cold PBS, resuspended in RIPA buffer (50 mM Tris-HCl [pH 7.4], 150 mM NaCl, 0.5% sodium deoxycholate, 0.1% SDS, 1% NP40, 5 mM EDTA/EGTA), and lysed by passing through 26-G syringe for 15 times. Centrifuged supernatant was pre-cleared with protein A/G agarose and mixed with 2 µg of anti-Flag M2 antibody. Incubation was carried out at 4°C for 4–6 h. Protein A/G agarose was added and incubation was further extended for 90 min. After extensive washing with RIPA buffer, precipitated proteins were resolved by SDS-PAGE and analyzed by autoradiography. The relative intensity of a selected protein band was quantified and its half-life was calculated. When vGPCR degradation route was investigated, 20 µM of lactacystin and MG132 (proteasome inhibitors) or 50 µM of chloriquine (a lysosome inhibitor) was added during the chase period. IP and autoradiography were performed similarly.</p>", "<title>Luciferase Reporter Assay</title>", "<p>The reporter cocktail consists of plasmids expressing fire fly luciferase (50 ng/µl) and β-galactosidase (100 ng/µl). While β-galactosidase expression is driven by a housekeeping glucophosphokinase promoter, the expression of fire fly luciferase is under control of response elements of NF-κB, NF-AT, and AP-1 transcription factor. 293T cells were transiently transfected with 2.5 µl of reporter cocktail, and 200 ng of plasmids expressing vGPCR and K7. For each transfection, the total amount of plasmid was balanced with an empty vector (pcDNA5/FRT/TO). At 36 h after transfection, cells were harvested and lysed on ice. Centrifuged supernatant was used to measure luciferase and β-galactosidase activity according to manufacturer's protocol (Promega).</p>", "<title>Apoptosis Assay</title>", "<p>NIH3T3 stable cells were treated with vehicle (DMSO), cyclohexamide (CHX, 1 µg/ml), or TNF-α (5 ng/ml) plus CHX (1 µg/ml) for 24 hours. Cells were harvested and live cells were scored by trypan blue staining as previously described ##REF##15082787##[25]##. Viable cells treated with drugs divided by viable cells treated by DMSO was used to obtain cell viability in percentage.</p>", "<title>Immunofluorescence Microscopy</title>", "<p>BJAB, HeLa, or BCBL-1 cells were fixed with paraformaldehyde and permeabilized with Triton X-100 (0.2% in PBS). After stained with primary and secondary antibodies, cells were analyzed by immunofluorescence microscopy as previously described ##REF##12388711##[26]##,##REF##18069888##[49]##. vGPCR in BCBL-1 cells was detected with a gift rabbit polyclonal antibody provided by Dr. Gary Hayward ##REF##11884567##[7]##. For commercial antibodies, mouse monoclonal anti-Flag antibody (1∶1500), rabbit polyclonal anti-Flag antibody (1∶400, Sigma), mouse monoclonal anti-V5 antibody (1∶500, Invitrogen), sheep anti-TGN46 (1∶200, Serotec), rabbit anti-PDI (1∶200, Calbiochem) were used. All conjugated secondary antibodies were obtained from Molecular Probes and diluted at 1∶1000 (Alexa 488-conjugated) or 1∶500 (Alexa 568 or Alexa 647-conjugated).</p>", "<title>Knockdown of K7 by shRNA-mediated silencing</title>", "<p>Four shRNA seuquences were designed using Dharmacon software and cloned into pLKO.1. These sequences are: <named-content content-type=\"gene\">5′ TCATCCGTATTGTGTATAT 3′</named-content>; <named-content content-type=\"gene\">5′ CATCGTGAGTTGGTTAATA 3′</named-content>; <named-content content-type=\"gene\">5′ TGGCTACTCTGCTCGATTA 3′</named-content>; <named-content content-type=\"gene\">5′ TGAAGGATGATGTTAATGA 3′</named-content>. Together with packaging plasmids DR8.9 and VSV-G, pLKO.1 plasmids expressing various K7 shRNA molecules were transfected into 293T cells with Fugene 6 (Roche). Lentivirus expressing the scrambled shRNA was produced similarly. Filtered lentivirus was used to infect BCBL-1 cells at 20 MOI in medium containing 10 µg/ml polybrene. To increase infection efficiency, cells were centrifuged at 1,800 rpm, 30°C for 1 h and incubation was further extended for up to 12 h. The infection was repeated once and cells were selected with puromycin at 1 µg/ml. At 48 h later, BCBL-1 cells were treated with TPA (20 ng/ml) to induce KSHV lytic replication.</p>", "<title>Cell Growth and Soft Agar Assay</title>", "<p>NIH3T3 cells were infected with lentiviruses to establish stable cell lines expressing K7 with puromycin selection. Then, NIH3T3/puro and NIH3T3/K7 cells were further infected with lentivirus expressing GFP or vGPCR. This lentiviral infection was repeated once to obtain stable cells expressing K7, vGPCR, or vGPCR and K7. Cells were cultured in complete DMEM medium containing puromycin (1 µg/ml). To measure the doubling time, 2×10<sup>5</sup> cells were plated and cells were counted at 24 h and 48 h later. The soft agar assay was performed as described by Liang et al ##REF##16799551##[50]##. Stable NIH3T3 cells (5×10<sup>4</sup>) were mixed with 1×10<sup>5</sup> normal NIH3T3 cells and cultured for two weeks in regular culture medium without puromycin.</p>", "<title>Tumor Formation In Vivo</title>", "<p>All animal experiments were performed according to the National Institutes of Health principles of laboratory animal care and approved by the University of Texas Southwestern Medical Center. Stable NIH3T3 cells (3×10<sup>6</sup>/site) expressing GFP, K7, vGPCR, or vGPCR and K7 were injected subcutaneously into the flanks of 6- to 8-wk-old mice (athymic, <italic>nu</italic>/<italic>nu</italic>, Jackson Laboratory).</p>" ]
[ "<title>Results</title>", "<title>K7 Interacts with KSHV vGPCR</title>", "<p>To understand K7's functions, we searched for cellular interacting proteins with K7 as bait using the yeast two-hybrid screen. One clone contained a partial sequence of a putative G protein-coupled receptor that encodes its last four transmembrane (TM) domains. Since the KSHV genome encodes a vGPCR, we speculated that K7 interacts with vGPCR. To test this possibility, whole cell lysates of 293T cells transiently transfected with plasmids expressing vGPCR-Flag and/or K7-V5 were precipitated with the M2 anti-Flag antibody and precipitates were analyzed by immunoblot with anti-V5 (K7) antibody. Indeed, K7 was readily detected in immune complexes containing vGPCR (##FIG##0##Figure 1A##, left panels). Notably, vGPCR expression greatly increases K7 protein expression and the glycosylated form (the slower migration band) is only detected in the presence of vGPCR. Reciprocally, vGPCR was also precipitated by anti-V5 (K7) antibody (##FIG##0##Figure 1A##, right panels). Of note, the interaction between K7 and vGPCR was also identified by the yeast two-hybrid screen with a high throughput approach ##REF##16339411##[28]##. To further characterize the vGPCR-K7 interaction, K7 mutants that contain various deletions as described in our previous publications ##REF##15082787##[25]##,##REF##12388711##[26]## were used for a co-immunoprecipitation (co-IP) assay. The internal hydrophobic region (amino acid 22–74) containing the putative TM domain was sufficient to interact with vGPCR (##FIG##0##Figure 1B##). Unfortunately, K7 mutants lacking the TM domain were expressed at an undetectable level compared to the wild type (wt) K7. Thus, we failed to obtain any deletion mutant that no long interacts with vGPCR. Nevertheless, these data indicate that K7 interacts with vGPCR and suggest that its predicted TM domain is important for this interaction.</p>", "<p>K7 contains a putative TM domain and vGPCR is a seven-membrane-spanning protein, therefore we examine whether the predicted K7 TM domain is necessary for this interaction. The K7 mutant whose putative TM region was replaced by a heterologous TM from the Saimiri transforming protein C (Stp C), designated K7TM<sub>Stp C</sub>, was constructed and expressed in 293T cells. We found that K7TM<sub>Stp C</sub> failed to interact with vGPCR under the same co-IP conditions (##FIG##0##Figure 1C##). Of note, K7TM<sub>Stp C</sub> was expressed and localized to intracellular organelles similarly to the wt K7 (unpublished data). Furthermore, appending the putative TM region (amino acids 23–45) of K7 to GFP renders it capable of binding vGPCR (##FIG##0##Figure 1D##). Thus, these data collectively support that K7 interacts with vGPCR and that the putative K7 TM region is necessary and sufficient for this interaction.</p>", "<p>K7 and vGPCR proteins are confined to distinct intracellular organelles. Particularly, vGPCR was reported to reside primarily in the trans-Golgi network (TGN) ##REF##11884567##[7]##, whereas K7 localizes to both the ER and mitochondrial compartments ##REF##12388711##[26]##,##REF##12032073##[27]##. To examine the intracellular distribution of vGPCR and K7, indirect immuno-fluorescence microscopy was performed. To this end, human lymphoid BJAB and HeLa cells were transfected with plasmids expressing vGPCR-Flag and K7-V5, and analyzed by confocal microscopy. In both HeLa and BJAB cells, vGPCR predominantly localizes to a subcellular structure reminiscent of the TGN, while K7 distributes throughout the cytoplasm mainly as punctate vesicles (##FIG##1##Figure 2A and 2B##). In support of the interaction between K7 and vGPCR, K7 had an intracellular staining pattern similar to that of vGPCR in both HeLa and BJAB cells (##FIG##1##Figure 2C##). Despite the overall colocalization between K7 and vGPCR, there are some regions that either K7 or vGPCR is predominant, likely reflecting their distinct intracellular compartments that vGPCR and K7 reside in when they are separately expressed (##FIG##1##Figure 2C##, insets of BJAB cells).</p>", "<title>Overlapped Expression of vGPCR and K7</title>", "<p>The fact that K7 interacts with vGPCR prompted us to investigate the temporal expression kinetics of K7 and vGPCR in KSHV lytic replication. Both K7 and vGPCR were reported to be expressed early during KSHV lytic reactivation and/or de novo infection ##REF##10364352##[6]##,##REF##11884567##[7]##,##REF##12032073##[27]##,##REF##15016882##[29]##; however, the relative temporal expression of vGPCR and K7 remains unclear. Our interaction study suggests that vGPCR and K7 are possibly expressed at the same time. Thus, we examined mRNA levels of vGPCR and K7 by reverse-transcriptase (RT)-polymerase chain reaction (PCR). The KSHV latently infected PEL cell lines BCBL-1 (KSHV only) and JSC-1 (KSHV and EBV co-infected) were treated with TPA to induce KSHV lytic replication. Alternatively, lytic replication was reactivated by Rta expression that was induced by doxycycline using the BCBL-1/T-Rex_Rta cell line ##REF##12634378##[30]##. RT-PCR analyses were performed using primers specific for vGPCR, K7, the polyadenylated nuclear RNA (PAN), and cellular β-actin. When treated with TPA (20 ng/ml), lytic replication was initiated in both BCBL-1 and JSC-1 cells which was indicated by potent induction of PAN transcripts (##FIG##2##Figure 3##). The residual PAN RNA in untreated BCBL-1 cells and BCBL-1/T-Rex_Rta cells (lane 3 of left two sets in ##FIG##2##Figure 3##) are likely due to spontaneous lytic replication of KSHV or leaky Rta expression in these PEL cells, respectively. Upon TPA induction, vGPCR transcripts peaked at 72 h, which coincided with the highest mRNA level of K7 in BCBL-1 cells. Upon Rta expression induced by doxycycline addition, vGPCR was highly expressed as early as 12 h post induction and was sustained for more than 24 h (##FIG##2##Figure 3##, middle panels), while K7 transcripts gradually increased and peaked at 36 h after TPA induction when vGPCR mRNA started to decline. This indicates that K7 expression predominantly overlaps with that of vGPCR in response to the KSHV lytic switch protein, Rta. Similar results were obtained for TPA-induced JSC-1 cells in which vGPCR was highly expressed at 12 and 24 h after treatment. Meanwhile, K7 was highly expressed at 24 h after induction (##FIG##2##Figure 3##, right panels). The most abundant lytic transcript of KSHV, PAN, was significantly induced by TPA and sustained high transcript levels in BCBL-1 and JSC-1 cells throughout the entire induction period. This was more pronounced by Rta induction (##FIG##2##Figure 3## third panel from top), while cellular β-actin transcript remained the same. Overall, these data indicated that K7 and vGPCR are expressed at the same time and suggest that the interaction between these two molecules is biologically relevant.</p>", "<title>K7 Reduces vGPCR Protein Expression</title>", "<p>We have consistently observed that K7 co-expression significantly reduces the protein level of vGPCR (##FIG##0##Figure 1A and 1B##), suggesting that K7 modulates vGPCR biosynthesis. Because our previous data implicate K7 in regulating protein degradation ##REF##15082787##[25]##, we speculated that K7 induces the degradation of vGPCR. To examine K7's effect on vGPCR protein expression, human endothelial ECV cells were transiently transfected with a plasmid expressing vGPCR-Flag and increasing amounts of a plasmid expressing K7-V5. Whole cell lysates were analyzed by immunoblot for vGPCR protein expression. The result shows that K7 reduces vGPCR protein in a dose-dependent manner (##FIG##3##Figure 4A##). The specificity of K7 is further supported by the observation that the K7TM<sub>Stp C</sub> chimera, a mutation that abolished its interaction with vGPCR, failed to suppress vGPCR protein expression (##FIG##3##Figure 4B##).</p>", "<p>Previous publications have convincingly shown that vGPCR activates a number of signaling pathways, leading to the activation of NF-AT, NF-κB, and AP-1 transcription factors ##REF##12477810##[19]##,##REF##11507211##[31]##,##REF##12719569##[32]##. To further correlate K7's effect on vGPCR protein expression, the transcription activation of NF-AT, NF-κB, and AP-1 response elements by vGPCR were measured by luciferase assays in transiently transfected 293T cells. Consistent with published data, vGPCR activated NF-κB, NF-AT, and AP-1 transcription factors by approximately 4, 25, and 4.5 fold, respectively. In contrast, K7 exhibited no effect on the transcription of NF-κB, NF-AT, and AP-1 (##FIG##3##Figure 4C##). In agreement with our observation that K7 reduces vGPCR protein, K7 suppressed the transcription activation by vGPCR to approximately two-fold for NF-κB and AP-1, and eight-fold for NF-AT, respectively (##FIG##3##Figure 4C##). These data indicate that K7 reduces vGPCR protein expression and mitigates vGPCR-activated downstream signaling.</p>", "<p>Although our studies clearly indicate that K7 reduces vGPCR protein expression, these experiments relied on exogenous protein expression. To corroborate K7-reduced vGPCR protein expression during KSHV infection, the shRNA-mediated silencing experiments were designed to knock down K7 expression and vGPCR protein level was examined by confocal microscopy. Both K7 and vGPCR are expressed in the lytic phase during KSHV infection. Given the fact that K7 open reading frame overlaps with the transcribed region of PAN (or T1.1), four pairs of short hairpin RNA (shRNA) molecules targeting the 5′ untranslated region of K7 transcripts were cloned (##FIG##3##Figure 4D##) and lentiviral particles were produced in 293T cells. Lentivirus was then used to infect KSHV-positive BCBL-1 cells that were subsequently treated with TPA to induce KSHV lytic replication. A scrambled shRNA was used as a control for all silencing experiments. Among the shRNAs, K7 shRNA#1 and #3 significantly reduced the level of K7 transcripts, while these two shRNA molecules had no discernable alteration on mRNA levels of PAN and vGPCR, when compared to BCBL-1 cells expressing the scrambled shRNA (##FIG##3##Figure 4D##, right panels). Densitometry of RT-PCR products showed that K7 shRNA#3 and shRNA#1 had a silencing efficiency of 60% and 50% (##FIG##3##Figure 4D##). Semi-quantitative PCR analyses using serial dilution of cDNA templates further support that K7 transcripts were reduced by 60%–70% (##SUPPL##0##Figure S1##). Notably, the knockdown of K7 did not significantly affect cell viability after lytic induction, suggesting that additional viral proteins such as vBcl-2 and vFLIP play a redundant antiapoptotic role. BCBL-1 cells infected with lentiviruses expressing K7 shRNA#1, shRNA#3, or the scrambled shRNA were induced with TPA for KSHV lytic replication. At 48 h after induction, cells were fixed and subjected to confocal microscopy analysis to examine vGPCR protein level. As shown in ##FIG##4##Figure 5##, the knockdown of K7 significantly increased vGPCR protein expression (second and third rows from the top), while the ER resident protein calreticulin was not affected. The vGPCR-positive cells increased from 20% in BCBL-1 cells expressing the scrambled shRNA to 65% in BCBL-1 cells expressing K7 shRNA#3 and 45% in BCBL-1 cells expressing K7 shRNA#1 (##FIG##4##Figure 5##, middle panels). Furthermore, merged images clearly indicate the increased vGPCR protein expression upon K7 knockdown, because image color shifted from red (calreticulin) in BCBL-1 cells expressing the scrambled shRNA to green (vGPCR) in BCBL-1 cells expressing K7 shRNA (##FIG##4##Figure 5##, right panels). Taken together, these findings support the conclusion that K7 suppresses vGPCR protein expression in tissue culture and in KSHV lytic infection.</p>", "<title>K7 Induces Proteasome-Dependent Degradation of vGPCR</title>", "<p>Our previous publication indicated that K7 induces protein degradation dependent on the UPS ##REF##15082787##[25]##. To investigate the mechanism by which K7 downregulates vGPCR protein expression, the half-life of vGPCR was measured by a pulse chase experiment. Transient transfection of ECV cells expressing vGPCR or vGPCR and K7 were pulse labeled with [<sup>35</sup>S]-methionine/cysteine (Met/Cys). After extensive washing, ECV cells were chased with cold medium. Precipitated vGPCR was quantified by autoradiography and its half-life was calculated. As shown in ##FIG##5##Figure 6A##, vGPCR has a half-life of about 6.5 h and K7 expression reduced its half-life to approximately 3.4 h, indicating that K7 promotes vGPCR degradation. Cellular GPCRs are 7-membrane-spanning proteins that can be degraded through the lysosome or the UPS ##REF##15217328##[33]##. To examine whether K7-induced vGPCR degradation is dependent on the proteasome or the lysosome, vGPCR protein stability was examined by a pulse chase experiment with either a lysosome inhibitor (chloroquine) or proteosome inhibitors (lactacystin and MG132). It was found that lactacystin and MG132, but not chloroquine, completely blocked K7-induced vGPCR degradation, indicating that this process relies on the proteolytic activity of the proteasome (##FIG##5##Figure 6B##).</p>", "<p>Proteasome substrates are often marked with polyubiquitin chains that facilitate delivery to and subsequent degradation by the proteasome. To further corroborate the proteasome-dependence of K7-induced vGPCR degradation, vGPCR ubiquitination was examined by immunoprecipitation and immunoblot. vGPCR was precipitated with anti-Flag sepharose and analyzed by immunoblot with anti-HA (ubiquitin) antibody. Consistent with the increased degradation of vGPCR, K7 promoted vGPCR polyubiquitination in the presence of a proteasome inhibitor, lactacystin (##FIG##5##Figure 6C##, first panel from left). Recent findings have shown that K48-linkage ubiquitin chains mediate protein degradation and K63-linkage ubiquitin chains are involved in signal transduction. Thus, these ubiquitin mutants were included in the vGPCR ubiquitination assay. Indeed, the K48R mutant, but not the K63R mutant, completely abolished vGPCR ubiquitination induced by K7 (##FIG##5##Figure 6C##). Of note, the protein level of precipitated vGPCR and vGPCR in whole cell lysate in the presence of K7 is significantly lower than vGPCR alone (##FIG##5##Figure 6C##, second panel, lanes 2–5, and ##SUPPL##1##Figure S2##). These data collectively support the conclusion that K7 increases vGPCR ubiquitination and promotes its proteasomeal degradation.</p>", "<title>K7 Retains vGPCR in the ER to Induce its Degradation</title>", "<p>To further define the molecular action of K7 in inducing vGPCR degradation, vGPCR intracellular localization was analyzed by confocal microscopy using human HeLa cells. Consistent with a previous report ##REF##11884567##[7]##, vGPCR primarily localized to the TGN stained by anti-TGN46 antibody (##FIG##6##Figure 7A##). Upon K7 expression, vGPCR localized to intracellular structures that resemble the ER and nuclear membrane (##FIG##6##Figure 7B##), suggesting that K7 retains vGPCR in the ER compartment. Indeed, HeLa cells expressing both K7 and vGPCR revealed that these two proteins colocalized significantly with protein disulfide isomerase (PDI), an ER resident protein (##FIG##6##Figure 7C##), supporting the notion that K7 retains vGPCR in the ER. Furthermore, K7 expression reduced vGPCR localization in the TGN when intracellular distribution of vGPCR and K7 was examined in relation to TGN46 (##FIG##6##Figure 7D##). These results clearly indicate that K7 retains vGPCR in the ER and suggest that K7 induces vGPCR degradation via the ER-associated degradation pathway.</p>", "<title>vGPCR and K7 Inhibit Cell Growth in vitro</title>", "<p>To examine K7's effect on vGPCR biological functions, NIH3T3 cell lines stably expressing K7, vGPCR, and vGPCR+K7 were established with lentivirus infection. As shown in ##FIG##7##Figure 8A##, K7 detectably reduced vGPCR protein expression without affecting its mRNA levels (##FIG##7##Figure 8B##). Of note, vGPCR did not further increase K7 protein after treatment by the proteasome inhibitor MG132 (##FIG##7##Figure 8A##). During the course to establish these stable cell lines, we noticed that NIH3T3 cells expressing vGPCR grow more slowly than the control NIH3T3 cells. In contrast to what was reported ##REF##9002520##[34]##, NIH3T3/vGPCR cells had a doubling time of approximately 31 h that is significantly longer than 22.6 h of NIH3T3/vector cells. RT-PCR analysis indicated that vGPCR is expressed at similar levels in NIH3T3, and reactivated BCBL-1 and JSC-1 cells (##SUPPL##2##Figure S3##). This observation rules out the possibility that the inhibitory effect on cell growth is due to over-expression. Interestingly, K7 expression also increased NIH3T3 doubling time to roughly 28.2 h. Consistent with K7-reduced vGPCR protein expression, K7 co-expression slightly decreases the doubling time of NIH3T3 cells to 30 h (##FIG##7##Figure 8C##). Due to K7's inhibitory effect on cell growth and vGPCR-increased K7 expression (unpublished data), the subtle difference in cell growth may be significant. Given the inhibitory effect of vGPCR on cell growth, we suspect that NIH3T3 cells expressing higher vGPCR will gradually decrease when continuously cultured without selection. To test this, NIH3T3/vGPCR and NIH3T3/vGPCR+K7 cells were passaged for a week and RT-PCR analyses were performed to assess the mRNA levels of vGPCR. Indeed, the vGPCR mRNA level significantly decreased after 1 wk of passage and K7 reduced the vGPCR loss (##FIG##7##Figure 8D##). Semi-quantitative RT-PCR and real-time PCR analyses revealed that the vGPCR mRNA in NIH3T3/vGPCR+K7 was approximately 5-fold of that in NIH3T3/vGPCR cells at day 7 (##FIG##7##Figure 8E## and ##SUPPL##3##S4##). The rapid loss of vGPCR transcripts suggests that NIH3T3 cells that lost vGPCR have a growth advantage.</p>", "<p>We and others have shown that K7 inhibits apoptosis induced by various stress stimulations ##REF##15082787##[25]##–##REF##12032073##[27]##. To examine whether vGPCR affects K7's antiapoptotic function, NIH3T3 stable cells were stimulated with TNF-α and cyclohexamide and cell viability was measured by trypan blue staining as described previously ##REF##15082787##[25]##. It was found that vGPCR expression had no significant effect on cell survival upon TNF-α stimulation, while K7 expression increased cell survival rate by 20% compared to NIH3T3/vector cells (##FIG##7##Figure 8F##). Interestingly, vGPCR co-expression with K7 further promotes cell survival rate by approximately 30%, indicating that vGPCR potentiates K7's antiapoptotic effect. This is consistent with our observation that vGPCR increases K7 protein expression (unpublished data). These results indicate that K7 reduces vGPCR-induced stress and suggest that K7 likely co-operates with vGPCR to promote cell survival during KSHV lytic replication.</p>", "<title>K7 Negatively Regulates vGPCR Tumorigenicity</title>", "<p>In a mouse pathogenesis model, vGPCR is sufficient to induce tumor formation in nude mice and vGPCR transgenic mice developed lesions that resemble human KS, suggesting its potential contribution to KSHV-associated malignancies ##REF##12559173##[17]##,##REF##11748262##[18]##,##REF##9002520##[34]##. To assess K7's effect on vGPCR tumorigenicity, NIH3T3 stable cells expressing K7, vGPCR, or vGPCR+K7 were mixed with NIH3T3 cells and colony formation on soft agar was examined. Similar to the human cytomegalovirus US28 ##REF##16924106##[35]##, vGPCR-expressing cells stimulated anchorage-independent growth of NIH3T3 cells, whereas neither NIH3T3/vector, nor NIH3T3/K7 cells supported colony formation (##FIG##8##Figure 9A##). In support of the observation that K7 suppressed vGPCR protein expression, NIH3T3/vGPCR+K7 cells formed smaller colonies than NIH3T3/vGPCR cells (##FIG##8##Figure 9A, left panels##). Furthermore, K7 expression also reduced the number of colonies from 258 of NIH3T3/vGPCR to 131 of NIH3T3/vGPCR+K7 (##FIG##8##Figure 9A##, right diagram). To further investigate K7's effect on vGPCR tumorigenicity in vivo, these stably transfected cells were injected into nude mice and tumor growth was assessed. Mice injected with NIH3T3/vGPCR developed visible tumors within two weeks and all mice harbored tumors after 6 wk. Neither NIH3T3/vector cells nor NIH3T3/K7 cells induced apparent tumor in nude mice. In agreement with results from the soft agar assay, K7 significantly reduced vGPCR capacity to promote tumor growth in nude mice as shown by the number of mice harboring tumor and tumor weight (##FIG##8##Figure 9B##). All four nude mice injected with NIH3T3/vGPCR developed tumors after 6 wk, whereas only two mice injected with NIH3T3/vGPCR+K7 developed tumors, which were substantially smaller (##FIG##8##Figure 9B##). The mean weight of tumors derived from NIH3T3/vGPCR cells is approximately 8-fold higher than that of tumors derived from NIH3T3/vGPCR+K7 cells (##FIG##8##Figure 9B## and unpublished data). Interestingly, we found that K7 transcripts were expressed at a higher level in the smaller tumor than the bigger tumor, suggesting that K7 inhibits the vGPCR-dependent tumor growth in vivo (##FIG##8##Figure 9C##). This result is consistent with the observation that K7 expression reduces vGPCR tumorigenicity (##FIG##8##Figure 9B##). In contrast, the vGPCR transcript was expressed more abundantly in tumors derived from NIH3T3/vGPCR+K7 cells than those derived from NIH3T3/vGPCR cells (##FIG##8##Figure 9C##). This likely represents the relative expression of vGPCR in stable NIH3T3 cells before mice injection. Overall, K7 negatively regulates vGPCR tumorigenicity in vitro by a soft agar assay and in vivo in nude mice.</p>" ]
[ "<title>Discussion</title>", "<p>We report here that KSHV K7 interacts specifically with vGPCR and induces the rapid degradation of vGPCR, thereby reducing vGPCR protein expression. The putative K7 TM domain is necessary and sufficient for its interaction with vGPCR, indicating a specific interaction between vGPCR and K7. However, the K7/vGPCR interaction may involve multiple residues within the putative TM domain of K7 because further mutational analyses within this domain failed to identify critical residues that are essential for this interaction (unpublished data). Alternatively, additional cellular components such as membrane proteins or lipids could be involved, as our co-IP procedure does not exclude this possibility. Nevertheless, these data support the conclusion that K7 interacts specifically with vGPCR.</p>", "<p>We have previously shown that K7 antagonizes cellular PLIC1, a factor that inhibits proteasome-mediated protein degradation, and induces rapid degradation of p53 and IκB ##REF##15082787##[25]##. Our current study enlists vGPCR as an additional proteasome substrate whose degradation is accelerated by K7. The specificity of K7-induced degradation appears to be derived from an interaction with either a proteasome substrate such as vGPCR or a key component of the UPS pathway such as PLIC1. It is possible that binding of K7 to cellular PLIC1 also contributes to K7-dependent reduced expression of vGPCR, given that PLIC1 has been shown to promote protein expression of multiple transmembrane proteins ##REF##11076969##[36]##,##REF##11528422##[37]##. Indeed, we have observed that PLIC1 overexpression increases vGPCR protein, while the knockdown of PLIC1 by shRNA-mediated silencing greatly reduces vGPCR protein expression. These data indicate that PLIC1 is a positive regulator for vGPCR expression (unpublished data). Future experiments will determine whether K7 binding to PLIC1 is sufficient for suppressing vGPCR protein expression.</p>", "<p>Confocal microscopy analyses and biochemical assays examining vGPCR protein degradation support the conclusion that K7 retains vGPCR in the ER and allows vGPCR to be removed by the proteasome. The rapid degradation of vGPCR induced by K7 also correlates with increased ubiquitination upon treatment with a proteasome inhibitor. vGPCR appears to carry polyubiquitin chains and K7-induced polyubiquitination of vGPCR is specifically inhibited by the K48R ubiquitin mutant, but not by the K63R ubiquitin mutant (##FIG##5##Figure 6C##). Interestingly, the K63R mutant significantly increased unmodified- as well as ubiquitinated-vGPCR protein. This is likely due to the inhibitory effect of K63R ubiquitin on vGPCR signaling that is presumably coupled to vGPCR degradation. For example, the K63R mutant may inhibit signaling downstream vGPCR such as NF-κB activation, therefore stabilizing vGPCR. Alternatively, vGPCR polyubiquitin chains may contain a mixture of K63- and K48-linkages. The fact that the K48R mutant abolished, while the K63R mutant increased vGPCR ubiquitination suggests that K48-linkage is necessary to initiate ubiquitination, whereas K63-linkage is important for degradation. These intriguing possibilities are not mutually exclusive and require further experimental investigation. Our data, however, do not exclude the possibility that vGPCR undergoes ubquitination-independent proteasomeal degradation. In transfected cells, K7 consistently altered vGPCR intracellular distribution, showing a more diffused ER/nuclear membrane pattern that was confirmed by staining with anti-PDI antibody. This observation suggests that K7 retains vGPCR in the ER in order to induce vGPCR degradation. This also implies that K7 likely engages the ERAD pathway to facilitate vGPCR degradation in similar ways employed by human cytomegalovirus US11 and murine γ-herpesvirus 68 mK3 ##REF##15215855##[23]##,##REF##15215856##[24]##,##REF##16186509##[38]##,##REF##16446359##[39]##. Future experiments will be directed to test whether K7-induced protein degradation is dependent on any critical components of the ERAD pathway.</p>", "<p>Interaction with K7 was found to reduce vGPCR protein, thereby dampening vGPCR-mediated signaling. Both vGPCR and K7 are expressed during KSHV lytic replication and it appears that K7 and vGPCR share an identical or overlapped expression profile. The observation that the K7 transcript peaks at a later time point than the vGPCR transcript raises the possibility that K7 serves as a negative regulatory factor to shut off vGPCR protein during KSHV lytic infection. Indeed, the knockdown of K7 by shRNA-mediated silencing increased vGPCR protein without altering vGPCR transcription level in BCBL-1 cells that are induced for KSHV lytic replication (##FIG##4##Figure 5##). Interestingly, K7 protein expression was substantially increased when co-expressed with vGPCR (unpublished data), revealing a negative feedback loop that culminates in dampening vGPCR protein expression. These observations are consistent with the notion that diverse regulatory mechanisms operate to achieve a temporary expression of vGPCR in KSHV infection. In addition to the K7-reduced vGPCR expression, known mechanisms also include the bicistronic translation and the vMIP-mediated regulation ##REF##11748262##[18]##,##REF##9918794##[40]##. Interestingly, modulation by its cognate chemokines is important for vGPCR tumorigenicity in transgenic mice ##REF##11748262##[18]##. Our findings that K7 interacts with vGPCR and directs it for proteasome-mediated degradation further support the notion that KSHV has evolved intricate mechanisms to regulate vGPCR activity. Additionally, K7 expression provides antiapoptotic activity under various conditions ##REF##15082787##[25]##–##REF##12032073##[27]## and vGPCR co-expression potentiates K7's antiapoptotic activity (##FIG##7##Figure 8F##). This implies that K7 can cooperate with vGPCR in the tumorigenesis of KSHV infection, analogous to the paradigm in which Bcl-2 cooperates with c-myc ##REF##1406976##[41]##. However, our transformation assay in vitro and tumor growth in nude mice ruled out this possibility. Together with the biscitronic translation and modulation by vMIP chemokines, vGPCR downregulation by K7 raises an intriguing speculation that KSHV has evolved these mechanisms to monitor vGPCR pathogenicity, permitting a persistent infection within its host.</p>", "<p>K7 expression suppressed vGPCR transformation on soft agar assay and more pronouncedly reduced vGPCR tumorigenicity in nude mice. Although K7 reduced vGPCR protein expression by approximately two-fold (##FIG##5##Figures 6A## and ##FIG##7##8A##), it was found that K7 inhibited vGPCR tumorigenicity by more than 8-fold (##FIG##8##Figure 9B##). This suggests that additional mechanisms, other than reduced protein expression, may contribute to K7's effect on vGPCR tumorigenicity. One likely mechanism is a K7-dependent retention of vGPCR in the ER, given that vGPCR predominantly localizes to the TGN and cell surface under normal circumstances. Conceivably, vGPCR functions in the TGN and on the cell surface are abolished by K7 expression. Interestingly, we have found that vGPCR is tyrosine sulfated in the TGN and tyrosine sulfation is important for vGPCR tumorigenicity (unpublished data). In addition to tyrosine sulfation, post-translational modifications in the ER (such as ubiquitination and glycosylation) altered by K7 may cause impaired vGPCR signaling and tumorigenicity. These mechanisms are not mutually exclusive and warrant further investigations of post-translational events underlying vGPCR tumorigenicity.</p>", "<p>Mounting evidence points to vGPCR expression inducing a stress in mammalian cells including KSHV infected PEL cells ##REF##10364352##[6]##,##REF##14724579##[13]##. Indeed, our vGPCR-expressing NIH3T3 cells have a longer doubling time than control NIH3T3/vector cells (##FIG##7##Figure 8C##). Furthermore, NIH3T3/vGPCR cells gradually lost vGPCR expression when continuously passaged in vitro, suggesting that NIH3T3 cells gain a growth advantage by reducing vGPCR expression. Indeed, K7 alleviated vGPCR-mediated inhibition of NIH3T3 growth and the rate of vGPCR transcript loss (##FIG##7##Figure 8C–8E##). In contrast, vGPCR expression was necessary for tumorigenicity in nude mice, and K7-reduced vGPCR expression correlated with less transformation in vitro and tumorigenicity in vivo (##FIG##8##Figure 9A and 9B##). Interestingly, the endothelial progenitor cell line containing Bac36 (a KSHV Bacmid) behaves similarly to NIH3T3/vGPCR cells, demonstrating reduced cell growth in vitro and increased tumor formation in vivo ##REF##17349582##[42]##. The seemingly paradox between in vitro stress and in vivo tumorigenicity may be explained by a paracrine mechanism supported by accumulating studies ##REF##12620408##[9]##,##REF##9422510##[43]##,##REF##10662787##[44]##. In fact, vGPCR-induced tumor formation is highly dependent on growth factors and chemokines that stimulate the angio-proliferation of neighboring cells ##REF##9422510##[43]##,##REF##10662787##[44]##. In KS lesions, vGPCR expressing cells presumably stimulate the proliferation of spindle cells that are latently infected by KSHV. The fact that slower growth of NIH3T3 stable cell lines in vitro correlates with higher tumorigenicity in vivo suggests that the nude mice model primarily assesses the paracrine function of vGPCR. This is also supported by our in vitro transformation assay where the proliferation of regular NIH3T3 cells was examined in the presence of NIH3T3/vGPCR cells (##FIG##8##Figure 9A##). Additionally, it is not unprecedented that oncogenic proteins exploit cellular stress responses to induce tumor formation. Perhaps, these stress responses represent various barriers that oncogenesis has to overcome. For example, H-RAS triggers the ER-associated unfolded protein response, cellular senescence and sensitizes cells to apoptosis ##REF##16964246##[45]##,##REF##16465287##[46]##. Similarly, the myc-mediated stress is overcome by Bcl-2 expression ##REF##1406976##[41]##. Taken together, the fact that the stress in tissue culture accompanies the tumorigenicity in vivo for many oncogenic proteins suggests that the stress response may serve as an indicator for tumorigenicity in vivo. Similar to vGPCR, K7 also reduces NIH3T3 growth and it will be interesting to examine K7's tumorigenicity in nude mice.</p>", "<p>All members of the beta- and gamma-herpesvirus family encode up to four GPCRs in their genomes. Some of them have been shown to constitutively activate signaling events downstream of various G proteins (for review see ##REF##18054964##[47]##). Although it was demonstrated that KSHV vGPCR can be uncoupled from downstream signal activation by overexpressed G protein-coupled receptor kinase 5 and arrestins ##REF##9480990##[48]##, it is largely unknown how these unconventional viral GPCRs are differentially regulated as opposed to cellular GPCRs under normal physiological conditions. This study established an example of post-translational regulation of vGPCR pathogenicity by which a viral factor-induced degradation greatly influences its tumorigenicity. Similar regulatory mechanisms may exist for other viral GPCRs of herpesviruses. Therefore, viral factors that modulate these viral GPCRs likely have a profound effect on various biological activities during herpesvirus infection.</p>" ]
[]
[ "<p>Conceived and designed the experiments: HF PF. Performed the experiments: HF XD AN PF. Analyzed the data: HF XD AN PF. Contributed reagents/materials/analysis tools: HF PF. Wrote the paper: HF PF.</p>", "<p>The Kaposi's sarcoma-associated herpesvirus (KSHV) genome encodes a G protein-coupled receptor (vGPCR). vGPCR is a ligand-independent, constitutively active signaling molecule that promotes cell growth and proliferation; however, it is not clear how vGPCR is negatively regulated. We report here that the KSHV K7 small membrane protein interacts with vGPCR and induces its degradation, thereby dampening vGPCR signaling. K7 interaction with vGPCR is readily detected in transiently transfected human cells. Mutational analyses reveal that the K7 transmembrane domain is necessary and sufficient for this interaction. Biochemical and confocal microscopy studies indicate that K7 retains vGPCR in the endoplasmic reticulum (ER) and induces vGPCR proteasomeal degradation. Indeed, the knockdown of K7 by shRNA-mediated silencing increases vGPCR protein expression in BCBL-1 cells that are induced for KSHV lytic replication. Interestingly, K7 expression significantly reduces vGPCR tumorigenicity in nude mice. These findings define a viral factor that negatively regulates vGPCR protein expression and reveal a post-translational event that modulates GPCR-dependent transformation and tumorigenicity.</p>", "<title>Author Summary</title>", "<p>Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiological agent of Kaposi's sarcoma. KSHV is also found in primary effusion lymphoma and multicentric Castleman's disease, rare lymphoproliferative diorders associated with immuno-suppression. The KSHV genome encodes a G protein-coupled receptor (vGPCR) that is believed to contribute to the KSHV-associated malignancies. vGPCR is a ligand-independent, constitutively active signaling molecule. It is not clear how vGPCR is negatively regulated. Here, we report that the KSHV small membrane K7 protein interacts with vGPCR through its putative transmembrane domain. Interaction with K7 retains vGPCR in the ER and facilitates its degradation by the proteasome, thereby reducing vGPCR protein expression. Consequently, K7 significantly reduces vGPCR-mediated transformation in vitro and tumor formation in nude mice. Our findings reveal that K7 functions as a viral factor to dampen vGPCR protein expression and negatively modulate the tumor-inducing capacity of vGPCR, implying that KSHV has evolved mechanisms to avoid deleterious effects and to permit persistent infection within its host.</p>" ]
[ "<title>Supporting Information</title>" ]
[ "<p>The authors wish to thank Drs. Nicholas Conrad, Julie Pfeiffer, and Neal Alto for critical reading of this manuscript. We thank Dr. Wenliang Li for technical advice on shRNA-mediated silencing, Dr. Gary Hayward for antibody to vGPCR, and Dr. Jason Huntley for assistance on cell inoculation into nude mice.</p>" ]
[ "<fig id=\"ppat-1000157-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g001</object-id><label>Figure 1</label><caption><title>KSHV K7 interacts with vGPCR.</title><p>(A) K7 interaction with vGPCR by co-IP. (Left) 293T cells were transfected with plasmids containing K7-V5 and vGPCR-Flag. Proteins precipitated with anti-Flag antibody were resolved by SDS-PAGE and analyzed by immunoblot with anti-V5 (top panel) and anti-Flag (middle panel) antibodies. WCLs were analyzed by immunoblot with anti-V5 (K7) antibody. Note: the K7 doublet indicates its glycosylated and unglycosylated forms. (Right) Proteins precipitated with anti-V5 antibody were analyzed by immunoblot with anti-Flag (top panel, peroxidase-conjugated) and anti-V5 (middle panel) antibodies. WCLs were analyzed by immunoblot with anti-Flag (vGPCR) antibody. H+L, the heavy and light chains of IgG; IB, immunoblot. (B) The K7 hydrophobic region is sufficient for its interaction with vGPCR. (Top) Diagram shows the structure of K7 protein and its residue numbers designed for the deletion analysis. Transfection of 293T cells with plasmids as indicated and IP with anti-Flag antibody were performed. Precipitated proteins were analyzed by immunoblot with anti-V5 (top panel) and anti-Flag (middle panel) antibodies. WCLs were analyzed with anti-V5 antibody. Δ3-21, deletion of amino acid 3 to 21; 5K/R, all lysine residues changed to arginine (for more details, please see ##REF##15082787##[25]##). (C) The putative K7 TM domain is necessary for its interaction with vGPCR. The putative K7 TM domain was replaced by the Stp C TM. Transfection of 293T cells with plasmids as indicated and IP were performed as in (A). Precipitated proteins were analyzed by immunoblot with anti-Flag (left panel) and anti-V5 (right top panel) antibodies. WCLs were analyzed by immunoblot with anti-V5 (right middle panel) anti-Flag (right bottom panel) antibodies. TM<sub>StpC</sub> denotes the K7 mutant that contains a StpC TM domain. To achieve equivalent protein expression, 3-fold more plasmid containing K7TM<sub>StpC</sub> than that containing the wt K7 was used for transfection. (D) The putative K7 TM domain is sufficient to interact with vGPCR. Transfection of 293T cells with plasmids as indicated and IP were performed as in (A). Precipitated proteins were analyzed by immunoblot with anti-GFP (top panel) and anti-Flag (middle panel) antibodies. WCLs were analyzed by immunoblot with anti-GFP antibody (bottom panel). L, the light chain of IgG.</p></caption></fig>", "<fig id=\"ppat-1000157-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g002</object-id><label>Figure 2</label><caption><title>Intracellular localization of vGPCR and K7.</title><p>Human HeLa and lymphoid BJAB cells were transfected as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. At 16 h after transfection, cells were fixed, permeabilized, and stained with rabbit anti-Flag and mouse anti-V5 antibodies. (A) vGPCR intracellular localization in human HeLa and BJAB cells. An inset in (A) and (B) represents an image of the other channel. (B) K7 intracellular localization in human HeLa and BJAB cells. (C) Intracellular co-localization of vGPCR (green) and K7 (red) in human HeLa and BJAB cells. Insets represent enlarged (3-fold) view of the boxed regions. Representative sections and their overlays (for panels in [C] only) are shown. Scale bar represents 12.5 µm.</p></caption></fig>", "<fig id=\"ppat-1000157-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g003</object-id><label>Figure 3</label><caption><title>Overlapped expression of K7 and vGPCR during KSHV lytic replication.</title><p>CBL-1, JSC-1, and BCBL-1/T-Rex_Rta cells were induced for KSHV lytic replication as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. RT-PCR analyses were performed using primers for vGPCR, K7, PAN, and cellular β-actin. No reverse transcriptase reaction was performed with total RNA of 36 h after treatment of each panel; h, hour after treatment.</p></caption></fig>", "<fig id=\"ppat-1000157-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g004</object-id><label>Figure 4</label><caption><title>K7 reduces vGPCR protein expression.</title><p>(A) K7 reduces vGPCR protein expression in a dose-dependent manner. Human endothelial ECV cells were transfected with plasmids containing vGPCR-Flag and K7-V5 as indicated. WCLs were analyzed by immunoblot with anti-Flag (vGPCR, top panel), anti-V5 (K7, middle panel), and anti-tubulin (bottom panel) antibodies. (B) The putative K7 TM domain is necessary for vGPCR downregulation. Transfection of ECV cells and immunoblot analyses were performed as in (A). Data represents three independent experiments. (C) K7 reduces vGPCR-mediated activation of NF-κB, NF-AT, and AP-1 transcription factors. 293T cells were transfected with reporter plasmid cocktail, and plasmids containing vGPCR and K7. Luciferase activity normalized against β-galactosidase activity is shown. Error bars denote standard deviation and data represent three independent experiments. (D) K7 knockdown by shRNA-mediated silencing. (Left Top) The relative genomic locations of K7 coding sequence and the transcribed region of PAN were shown. The numbers indicate the nucleotide position according to a published KSHV genome sequence (accession number: U75698). Bars represent relative location of sequences base paired with four shRNAs within the 5′ untranslated region of K7. The order of shRNAs on the diagram is: shRNA#2, #3, #1, and #4. (Left Bottom) A diagram shows the experimental design of lentivirus infection and KSHV lytic replication induced by TPA. (Right) RT-PCR analyses were performed using gene specific primers for K7, β-actin, vGPCR, and PAN as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. Numbers indicate intensity of K7 band measured by densitometry. Data represent two independent experiments.</p></caption></fig>", "<fig id=\"ppat-1000157-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g005</object-id><label>Figure 5</label><caption><title>Increased vGPCR protein expression by shRNA-mediated K7 knockdown.</title><p>Lentivirus infection and KSHV lytic replication induced with TPA were performed as in ##FIG##3##Figure 4D##. Cells were fixed and stained with anti-vGPCR (green) and anti-calreticulin (red) antibodies. vGPCR-positive and vGPCR-negative cells in 5 randomly selected fields were counted to obtain the percentage shown in the middle panels. For BCBL-1 cells induced with TPA, images to their right represent enlarged (2.5-fold) view of the boxed regions. Representative sections and their overlays are shown. Scale bar represents 12.5 µm.</p></caption></fig>", "<fig id=\"ppat-1000157-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g006</object-id><label>Figure 6</label><caption><title>K7 induces proteasome-dependent degradation of vGPCR.</title><p>(A) K7 reduces the half-life of vGPCR. ECV cells were transfected with plasmids expressing vGPCR-Flag and K7-V5. Pulse chase, IP, and autoradiography analyses were performed as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. The fully glycosylated vGPCR band was quantified and its half-life was calculated. Data (left panel) represent three independent experiments and error bars denote standard deviation. (B) K7-induced vGPCR degradation is dependent on the proteasome. Transfection and pulse chase experiments with ECV cells were performed as described in (A) except cells were harvested at time points as indicated. The numbers at the bottom indicate the relative intensity (top row) of vGPCR band compared to the initial chase time point and standard deviation (bottom row). Data represent three independent experiments. Lac: lactacystin (10 µM); MG: MG132 (20 µM); Ch: chloroquine (50 µM). (C) K7 increases vGPCR ubiquitination. NIH3T3/lenti-puro (Vec) or NIH3T3/lent-vGPCR-Flag (vGPCR) were transfected with plasmids expressing K7-V5 and HA-tagged Ubiquitin (wt), K48R (R48), or K63R (R63). At 36 h after transfection, cells were treated with lactacystin (20 µM) for 6 h. vGPCR was precipitated with anti-Flag sepharose and eluted with Flag peptide for immunoblot with anti-HA (ubiquitin, first panel from left), or eluted with loading buffer for immunoblot with anti-Flag (vGPCR, second panel). WCLs were analyzed by immunoblot with anti-HA (ubiquitin; third panel) and anti-V5 (K7; fourth panel) antibodies. Arrowheads indicated ubiquitinated vGPCR species (second panel).</p></caption></fig>", "<fig id=\"ppat-1000157-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g007</object-id><label>Figure 7</label><caption><title>K7 retains vGPCR in the ER.</title><p>(A) vGPCR localizes to the TGN. HeLa cells were transfected with plasmids containing vGPCR-Flag. At 16 h after transfection, cells were fixed and stained with mouse anti-Flag (vGPCR, green) and sheep anti-TGN46 (red) antibodies. For both (A) and (B), images at the bottom represent enlarged (3-fold) view of the boxed regions. Representative sections and their overlays are shown. Scale bar represents 12.5 µm. (B) K7 alters vGPCR intracellular localization. HeLa cells were transfected with plasmids containing vGPCR-Flag and K7-V5, and fixed as in (A). Cells were stained with rabbit anti-Flag (vGPCR, green) and mouse anti-V5 (K7, red) antibodies. (C) K7 retains vGPCR in the ER. HeLa cells were transfected with plasmids expressing HA-vGPCR and K7-V5, and fixed as described in (A). Cells were stained with rabbit antibody to protein disulfide isomerase (PDI, blue) and mouse anti-V5 (K7, red) antibody. After staining with corresponding secondary antibody and extensive washing, cells were further stained with Alexa 488-conjugated anti-HA antibody (vGPCR, green). Images on the right represent enlarged (3-fold) view of the boxed regions. Representative sections and their overlays are shown. Scale bar represents 12.5 µm. (D) The intracellular localization of K7 and vGPCR in relation to the TGN. HeLa cells were transfected with plasmids containing vGPCR-Flag and K7-V5. Cells were fixed and stained with mouse monoclonal anti-V5 (K7, green) and sheep anti-TGN46 (red) (left panels), or rabbit polyclonal anti-Flag (vGPCR, green) and sheep anti-TGN (red) (right panels). Representative images and their overlays are shown. Scale bar represents 12.5 µm.</p></caption></fig>", "<fig id=\"ppat-1000157-g008\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g008</object-id><label>Figure 8</label><caption><title>The effect of vGPCR and K7 on cell growth in vitro.</title><p>(A) K7 reduces vGPCR protein expression in stable NIH3T3 cells. Whole cell lysates of NIH3T3/vector, NIH3T3/vGPCR, NIH3T3/K7, and NIH3T3/vGPCR+K7 were precipitated with anti-HA and immunoblotted with anti-HA antibody (top panel). For K7 expression, above stable cells were treated with MG132 for 6 h before harvest, K7 was precipitated with anti-Flag and analyzed by immunoblot with anti-Flag antibody (bottom panel). (B) K7 does not reduce vGPCR mRNA level. The mRNA level of vGPCR in stable cell lines as described in (A) was analyzed by RT-PCR and β-actin PCR product serves as a loading control. (C) vGPCR and K7 inhibit cell growth. NIH3T3 stable cell lines described in (A) were cultured in complete DMEM containing puromycin (1 µg/ml) and counted at 24 h and 48 h. The doubling time was measured as derscribed in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>. Data represent three independent measurements and error bars denote standard deviation. (D) K7 reduces the loss of vGPCR transcripts in NIH3T3 cells. NIH3T3 stable cell lines as described in (A) were passaged up to 7 d and RT-PCR analyses were performed with primers specific for vGPCR, β-actin, and K7. (E) PCR analyses with serial dilution of cDNA templates from NIH3T3/vGPCR and NIH3T3/vGPCR+K7 were performed using vGPCR-specific primers. The ratio denotes fold of serial dilutions. (F) The effect of vGPCR and K7 on apoptosis. NIH3T3 stable cell lines were treated with TNF-α (5 ng/ml) and CHX (1 µg/ml) for 24 h; cell viability measured by trypan blue staining is shown. Data represent 3 independent experiments, and error bars denote standard deviation; *<italic>p</italic>&lt;0.03 relative to NIH3T3/vector cells as calculated by Student's <italic>t</italic>-test.</p></caption></fig>", "<fig id=\"ppat-1000157-g009\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000157.g009</object-id><label>Figure 9</label><caption><title>K7 negatively regulates vGPCR tumorigenicity.</title><p>(A) K7 reduces vGPCR activity to stimulate anchorage-independent growth of NIH3T3 cells. Colonies under microscope were photographed (left panels, 4×) or counted (right graph) after a 2-wk incubation. Data represent 3 independent experiments. Error bars denote standard deviation; *<italic>p</italic>&lt;0.02 relative to NIH3T3/vGPCR cells as calculated by Student's <italic>t</italic>-test. (B) K7 reduces vGPCR tumorigenicity in nude mice. Cells were injected into nude mice subcutaneously, and mice were killed and photographed (left panel) 6 wk later. Tumor weight was measured (right graph). The numbers in parenthesis indicate the number of mice developed tumor among 4 tested animals. Arrows indicate location of tumor and data represent 4 independent measurements for each group. (C) vGPCR and K7 expression in tumors. RT-PCR analyses were performed as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref> using primers specific for vGPCR, K7, and cellular β-actin. PCR products resolved on agarose gel were photographed. B, the bigger tumor; S, the smaller tumor; Pos, a positive control of K7.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"ppat.1000157.s001\"><label>Figure S1</label><caption><p>Knockdown of K7 During KSHV Lytic Reactivation. (A) The knockdown efficiency of K7 by shRNA-mediated silencing. BCBL-1 cells were infected with lentivirus and induced for lytic reactivation as diagrammed in ##FIG##3##Figure 4D##. RT-PCR analyses were performed with serial dilution of cDNA template (shown in ##FIG##3##Figure 4D##) as indicated by the ratio. (B) K7 knockdown on cell viability in KSHV lytic reactivated cells. Lentivirus infection and lytic reactivation were performed as in (A). Cells were harvested and cell viability was assessed by trypan blue staining.</p><p>(0.12 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"ppat.1000157.s002\"><label>Figure S2</label><caption><p>Reduced Expression of vGPCR by K7 Mutants and Lactacystin Treatment. (A) Glycosylation and ubiquitination are dispensable for K7’s ability to reduce vGPCR protein expression. Whole cell lysates of ECV cells transfected with plasmids containing vGPCR and K7 as indicated were analyzed by immunoblot with anti-Flag (vGPCR, top panel), anti-actin (middle panel), and anti-V5 (K7, bottom panel). Of note, the K7(5K&gt;R) and K7(N108Q) carry 6xHIS downstream of the V5 epitope that reduces their detection by immunoblot. Ub, ubiquitinated K7; gly, glycosylated K7. (B) The effect of lactacystin treatment on K7-reduced vGPCR expression. Human ECV cells were transfected with plasmids containing vGPCR or K7 and treated for 6 h with DMSO or lactacystin (20 μM). Whole cell lysates were analyzed by immunoblot with anti-Flag (vGPCR, top panel), anti-actin (middle panel), and anti-V5 (bottom panel).</p><p>(0.17 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"ppat.1000157.s003\"><label>Figure S3</label><caption><p>Relative Expression Levels of vGPCR in Reactivated BCBL-1 Cells and NIH3T3 Stable Cells. Total RNA was extracted and RT-PCR analyses were performed as described in <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref> using gene specific primers to vGPCR and β-actin. BCBL-1 cells were induced for lytic reactivation by TPA (20 ng/ml, 48 h) or BCBL-1/T-Rex_Rta cells were treated with doxycycline (1 μg/ml, 72 h) before harvest. No PCR product was etected for controls without RT (data not shown). The primers for β-actin locate within a highly conserved region of the human and mouse β-actin gene.</p><p>(0.11 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"ppat.1000157.s004\"><label>Figure S4</label><caption><p>vGPCR mRNA Levels in NIH3T3 Stable Cells by Real-Time PCR. The primers were designed using Primer Express v1.5 (Applied Biosystems). The efficiency and specificity of primers were validated and the real-time PCR using cDNA was performed with an ABI 7500 sequence detection system (Applied Biosystems). The vGPCR mRNA level at day 7 was arbitrarily set as 1. Data represent three independent experiments and error bars denote standard deviation.</p><p>(0.05 MB TIF)</p></caption></supplementary-material>" ]
[ "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This work is partly supported by National Institutes of Health (NIH) grant CA117809 and the University of Texas Southwestern Medical Center Endowed Scholar Program. PF is a special fellow of the Leukemia and Lymphoma Society.</p></fn></fn-group>" ]
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[ "<media xlink:href=\"ppat.1000157.s001.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"ppat.1000157.s002.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"ppat.1000157.s003.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"ppat.1000157.s004.tif\"><caption><p>Click here for additional data file.</p></caption></media>" ]
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{ "acronym": [], "definition": [] }
50
CC BY
no
2022-01-13 03:40:34
PLoS Pathog. 2008 Sep 19; 4(9):e1000157
oa_package/0e/69/PMC2529400.tar.gz
PMC2529401
18800168
[ "<title>Introduction</title>", "<p>Deposits of beta-amyloid (Aβ) and neurofibrillary tangles are the two pathological hallmarks of Alzheimer's disease. There is recent evidence that soluble Aβ aggregates can impair function, morphology and subsequently the viability of neuronal cells ##REF##17286590##[1]##. Based on NADH dependent reduction activity, cells are able to reduce the tetrazolium salt MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] into a formazane ##REF##10378224##[2]##. Thus, it is widely accepted that the amount of formazane production correlates with both the number and the viability of the cells. The MTT assay is well established for investigations of cellular viability in single cell cultures ##REF##14738150##[3]## and tissue slices ##REF##15963747##[4]##, ##REF##15256072##[5]##. The MTT assay is frequently used to evidence Aβ related changes in membrane properties and disturbed cellular viability ##REF##16391517##[6]##, ##REF##8108433##[7]##. The question how Aβ inhibits cellular MTT reduction is still a matter of debate. Based on their findings that Aβ potently inhibits cellular reduction of MTT in cultured rat hippocampal neurons and HeLa cell lines, Kaneko et al. (1995) have hypothesized that Aβ specifically suppresses mitochondrial succinate dehydrogenase ##REF##7595555##[8]##. Studies on rat brain tumor cells ##REF##9375659##[9]## and astrocytes ##REF##9809588##[10]##, on the other hand, indicated that Aβ decreases cellular MTT reduction by accelerating the exocytosis of MTT formazan.</p>", "<p>Although many <italic>in vitro</italic> findings on Aβ toxicity and competing, protective agents are based on the MTT assay ##REF##18078350##[11]##–##REF##16636106##[14]##, the influence of Aβ on MTT reduction has never been tested in more complex models than single cell cultures. Organotypic hippocampal slices (OHC) are an <italic>in vitro</italic> model that retains the three dimensional structure of <italic>in vivo</italic> systems and ranges in complexity between primary cell cultures and intact animals ##REF##16023058##[15]##. OHCs represent a well established tool for the investigation of brain damage due to oxygen glucose deprivation (OGD) ##REF##16473887##[16]## or epilepsy ##REF##16101559##[17]##. When OHCs were exposed to very high doses of Aβ (≥10 µM) neuronal apoptotic cell death ##REF##17560726##[18]##, ##REF##17908170##[19]## and a pronounced activation of astrocytes ##REF##10028921##[20]## occurred. More subtle submicromolar Aβ concentrations caused a retraction of neuronal dendrites and a degeneration of dendritic spines ##REF##17360908##[21]##. Although it has been shown that MTT is appropriate to evaluate the viability of brain tissue slices and its reduction is impaired after detrimental treatment, such as OGD ##REF##15256072##[5]##, ##REF##10936637##[22]##, the influence of Aβ on MTT reduction in OHCs has never been tested before.</p>", "<p>In this study, we compared OHCs and primary cell cultures for the effect of different Aβ species, varying in molecule length and aggregation status, on MTT reduction. We used freshly dissolved Aβ (25-35), which is frequently used and already shown to exert detrimental effects on brain function and MTT reduction of single cells well before aggregation occurs ##REF##17320856##[23]##, ##REF##9109514##[24]##. However, we can not exclude aggregation of Aβ (25-35) occurring during the experiment. Further, we used Aβ (1-40) fibrils, which are polypeptide aggregates, characterized by a fibrillar structure and the presence of a cross-β conformation ##REF##17530168##[25]##. These fibrils were shown to impair cellular MTT reduction ##REF##8108433##[7]##. The third species tested were so-called ‘oligomers’ of Aβ (1-42) ##REF##16135089##[26]## and Aβ (1-40) ##REF##12702875##[27]##. These oligomers are small non-fibrillar aggregates that are defined by an almost spherical shape and that have been discussed to be early mediators of cellular malfunction within the Alzheimer afflicted brain ##REF##17956317##[28]##. Moreover, we analyzed the influence of freshly dissolved Aβ (25-35), fibrillar Aβ (1-40) and oligomeric Aβ (1-40) on long-term potentiation (LTP) the cellular correlate for learning and memory ##REF##16919684##[29]## in acute hippocampal slices from rats and compared it with the influence on MTT activity. Surprisingly, in all tissue cultures we could not confirm the Aβ effects on MTT reduction known from primary cell cultures.</p>" ]
[ "<title>Methods</title>", "<title>Single cell culture</title>", "<p>The animals were maintained under constant environmental conditions, with an ambient temperature of 21±2°C, a relative humidity of 40%, a 12-h light–dark cycle and free access to food and water. All animal procedures have been approved by the ethics committee of the German federal state of Sachsen-Anhalt, and are in accordance with the European Communities Council Directive (86/609/EEC).</p>", "<p>Cells cultures from 1-day-old Wistar rats (Institute breeding stock) were prepared and cultured as described previously ##REF##16682083##[56]##. Briefly, newborn rats were decapitated, and the brains were removed and collected in ice-cold Hanks-buffer solution (Biochrom; Berlin, Germany). The brains were gently passed through nylon meshes of 250 mm and 136 mm pore width, in consecutive order. The cell suspension was centrifuged at 4°C for 5 min at 500g. The cells were resuspended in 10 ml growth medium (DMEM supplemented with 10% (v*v<sup>−1</sup>) fetal calf serum, 20 U*ml<sup>−1</sup> penicillin and 20 mg*ml<sup>−1</sup> streptomycin).</p>", "<p>Single cells from OHCs were isolated by gently removing the slices from the membrane and collecting them in ice-cold Hanks-buffer solution (Biochrom; Berlin, Germany). Then the protocol for cell culture preparation described above was applied. Preparation and cultivation of OHCs was done as described below.</p>", "<p>For astrocyte-enriched cultures (95% astrocytes), cells were seeded in 48 well plates at a starting density of 2*10<sup>4</sup> cells/ml in DMEM supplemented with 10% (v*v<sup>−1</sup>) fetal calf serum and incubated at 37°C in an atmosphere containing 5% CO<sub>2</sub>. The medium was changed every second day. For neuron-enriched culture (80% neurones), the DMEM was exchanged by Start V Medium (Biochrom) 24 h after seeding.</p>", "<p>The cell lineage BV2 microglia was cultured in DMEM supplemented with 10% FBS, 1% Pen/Strep (Biochrom), 1% l-Glutamin (Biochrom) at a density not exceeding 5*10<sup>5</sup> cells*ml<sup>−1</sup> and maintained in 5% CO<sub>2</sub> at 37°C.</p>", "<title>Aβ application/MTT assay</title>", "<p>Aβ (25-35) (Bachem) was freshly dissolved in bidistilled water to a concentration of 1 mg*ml<sup>−1</sup>. For fibril formation, recombinant Aβ (1-40) ##REF##15987892##[57]## was dissolved in bidistilled water to a concentration of 1 mg*ml<sup>−1</sup> and incubated for 5–7 days at 37°C. The formation of fibrils was verified by negative stain electron microscopy. Aβ (1-42) oligomers were generated as described ##REF##16135089##[26]##. The quality of the oligomer preparation was controlled by negative stain electron microscopy and Sodiumdodecylsulfate-Polyacrylamidgelelectrophoresis (SDS-PAGE). The Aβ species were added to the cell culture medium at a concentration of 0.5–10 µM (Aβ (1-42) oligomers) or 0.5–20 µM (Aβ (1-40) fibrils) and incubated for 1–3 days. Then MTT (Carl Roth) was added to the medium (0.5 mg*ml<sup>−1</sup>) and incubated for 3 hours. The medium was removed and the cells were diluted in 20% SDS/50% Dimethylformamid. The relative formazane concentration was measured by determination of the absorbance at 570 nm (well plate reader, Optima FluoStar).</p>", "<title>Organotypic cultures</title>", "<p>Organotypic hippocampal interface slice cultures from 10-day-old Wistar rats (Institute breeding stock) were prepared and cultured as interface slices as described previously ##REF##18042730##[59]##. Briefly, the slices were placed on membrane inserts in 6-well plates (NUNC, Wiesbaden, Germany) containing 1.2 ml of NB medium/well and were maintained in a humidified incubator for 12–15 days <italic>in vitro</italic> (DIV).</p>", "<title>Immunhistochemistry</title>", "<p>For the immunohistochemical staining of Aβ and GFAP, the slices were fixed in 0.1 M phosphate buffer containing 4% paraformaldehyde. The slices were stored in the fixative overnight. After cryoprotection in 30% sucrose, the slices were rapidly frozen in methylbutane at −80°C. The whole slices were cut on a sliding microtome and the 20 µm sections were stored at 4°C in cryoprotectant (CPS) containing 25% ethylene glycol, 25% glycerine in 0.1 M phosphate-buffered saline (PBS). The slices were transferred from CPS to 0.1 M phosphate buffer and washed overnight. Unspecific bindings were blocked for 2 h in the corresponding serum and then the slices were incubated with the primary antibodies and stored at 4°C overnight. All secondary antibodies were incubated at room temperature for 2 h. The slices were then coverslipped with 1,3-diethyl-8-phenylxanthine (DPX). The following primary antibodies and final dilutions were used: monoclonal mouse anti-GFAP (1∶200; Chemicon), polyclonal chicken anti-Aβ (1∶500; abcam), DAPI (1∶10000; MoBiTec). The primary antibodies were diluted in 0.1 M PBS/0.5% Triton X-100 and 3% donkey normal serum (Sigma, Deisenhofen, Germany). The following secondary antibodies and final dilutions were used: donkey anti-mouse Cy3 (1∶500; Dianova), donkey anti-chicken Cy2 (1∶100; Dianova). These secondary antibodies were diluted in 0.1 M PBS.</p>", "<title>Aβ application/MTT assay/PI staining</title>", "<p>The Aβ species were added to the slice culture medium at the respective concentrations (1–10 µM) and incubated for 3–6 days. For the application “on top of the slice”, 1 µl of the Aβ stock solution was directly applicated onto the surface of the slice. 1 µl of the solvent was applicated onto the control slices. Then MTT was applied to the medium (0,5 mg*ml<sup>−1</sup>) and incubated for 3 hours. The slices were quickly removed from the membrane and completely diluted in 20% SDS/50% dimethylformamid (incubation for 24 h at RT). After centrifugation, the relative formazane concentration of the supernatant was measured by determination of the absorbance at 570 nm (well plate reader, Optima FluoStar).</p>", "<p>Electron microscopy was done as previously described by ##REF##15464094##[60]##.</p>", "<p>Cell death was evaluated by cellular incorporation of propidium iodide (PI) 3d and 6d after Aβ treatment. Cultures were incubated with PI-containing medium (10 µM) for 2 h at 33°C. Fluorescent images were acquired semiautomatized (Nikon motorized stage; LUCIA software) and analyzed by densitometry to quantify necrotic cell death (LUCIA Image analysis software).</p>", "<title>Acute hippocampal slices/LTP</title>", "<p>Hippocampal slices (400 µm thick) were prepared from 7- to 8-week-old male Wistar rats (Institute breeding stock) as described previously ##REF##16125154##[61]##. Briefly, both hippocampi were isolated and transferred into a submerged-type recording chamber where they were allowed to recover for at least 1 h before the experiment started. The chamber was constantly perfused with artificial cerebrospinal fluid (ACSF) at a rate of 2.5 ml/min at 33±1°C.</p>", "<p>Synaptic responses were elicited by stimulation of the Schaffer collateral–commissural fibers in the stratum radiatum of the CA1 region using lacquer-coated stainless steel stimulating electrodes. Glass electrodes (filled with ACSF, 1–4 MΩ) were placed in the apical dendritic layer to record field excitatory postsynaptic potentials (fEPSPs). The initial slope of the fEPSP was used as a measure of this potential. The stimulus strength of the test pulses was adjusted to 30% of the EPSP maximum. During baseline recording, single stimuli were applied every minute (0,0166 Hz) and were averaged every 5 min. Once a stable baseline had been established, long-term potentiation was induced by applying 100 pulses at an interval of 10 ms and a width of the single pulses of 0.2 ms (strong tetanus) three times at 10 min intervals.</p>", "<p>Aβ (1-40) oligomers and fibrils were prepared as described previously ##REF##12702875##[27]## and visualized by negative stain electron microscopy. Immediately after the slice preparation, fibrillar Aβ (1-40) was persistently applied to the slices at a concentration of 1 µM. Aβ (1-40) oligomers and Aβ (25-35) were applied to the slice for 30 min before tetanus application at a concentration of 500 nM. The Aβ (1-40) solvent HFIP was removed from the ACSF by exposure to a gentle stream of carbogen for 1h. For control experiments we added the same amount of HFIP used for the Aβ (1-40) experiment to the ACSF and removed it by gasification. There was no difference between the potentiation in the HFIP-deprived ACSF and pure ACSF and, therefore, these experiments were pooled. In parallel to the experiments, some slices of the same preparation were separately exposed to Aβ for 3–4 hours and analyzed with MTT assay as described above.</p>", "<title>In vivo infusion of Aβ</title>", "<p>\n<italic>In vivo</italic> infusion was performed as described previously ##UREF##3##[62]##. Briefly, anaesthesia of 10-week-old male Wistar rats (Institute breeding stock) was induced with halothane in a mixture of nitrous oxide and oxygen (50∶50) and maintained with 2–3% halothane (Sigma, Deisenhofen, Germany) via a rat anaesthetic mask (Stölting). The animals were placed in a Kopf stereotaxic frame. Following a midline incision, a burr hole (1 mm in diameter) was drilled into the skull (coordinates: posterior, 0.9 mm from bregma; lateral, 1.7 mm to satura sagittalis) and a 29-gauge cannula was lowered to 4.5 mm below the skull, according to the rat brain atlas of Paxinos and Watson [63]. Aβ (25-35) (1 mg*ml<sup>−1</sup>) or Aβ (1-42) oligomer (1 mg*ml<sup>−1</sup>) was injected intracerebroventricularly in 3-µl sterile solvent over 5 min. After 5 min the cannula was slowly withdrawn. Aβ (35-25) (1 mg*ml<sup>−1</sup>) was used as inactive peptide control. After three days, acute hippocampal slices were prepared as described above, then directly placed on cell culture membranes and the MTT reduction activity analyzed as described above.</p>", "<title>Statistics</title>", "<p>Values of LTP recording are given as mean±S.E.M. Values of MTT reduction are given as mean±S.D. As indicated in <xref ref-type=\"sec\" rid=\"s2\">Results</xref>, the Mann–Whitney U-test or the analysis of variance (ANOVA) with repeated measures was used to compare the field potentials between two groups of differentially treated cells or slices, respectively (i.e., control vs. Aβ treatment), where appropriate.</p>" ]
[ "<title>Results</title>", "<title>Aβ impaired MTT reduction in neuronal, astroglia and microglia single cell cultures</title>", "<p>We extensively investigated different Aβ species, namely freshly dissolved Aβ (25-35), fibrillar Aβ (1-40), oligomeric Aβ (1-40) and oligomeric Aβ (1-42) for their effects on MTT reduction in neuronal, astroglia and microglia single cell cultures, representing the majority of cell types within the brain. In accordance with the literature ##REF##10378224##[2]##, ##REF##9809588##[10]##, each Aβ species led to a pronounced diminution of MTT reduction in all cell types tested (Neurons: control 100±4.4%, Aβ (25-35) 84.4±3.9%, fibrillar Aβ (1-40) 61.1±3.5%, oligomeric Aβ (1-40): 46.0±2.4%, Aβ (1-42) 77.7±5.1%; Microglia: control: 100±5.6%, Aβ (25-35) 25.9±6.2%, fibrillar Aβ (1-40) 42.3±6.5%, oligomeric Aβ (1-40): 49.1±2.5%, Aβ (1-42) 72.7±3.1% ##FIG##0##Figure 1A##). As we intended to investigate the effect of Aβ on MTT reduction in OHCs, where the most abundant cell type is astroglia, we determined the Aβ effect in detail in astroglia single cell cultures. Because OHCs and astroglial cultures are cultivated in different culture media we elucidated whether or not the Aβ mediated disruption of MTT reduction is influenced by the culture medium.</p>", "<p>All Aβ species tested acted in a dose dependent manner and Aβ (25-35) showed the highest activity (##FIG##0##Figure 1B##). In agreement with the literature ##REF##9359461##[30]##, the Aβ effect could be blocked by congo red (##FIG##0##Figure 1B##). In neurobasal (NB) medium (used for OHC cultivation) the Aβ effect on MTT activity was similar to the results obtained with DMEM (used for single cell culture; 500 nM Aβ (25-35) in DMEM: 43.7±3.7%, 500 nM Aβ (25-35) in NB: 39.4±2.2%; values were normalized to control; ##FIG##0##Figure 1B##).</p>", "<title>Aβ (25-35), Aβ (1-40) and Aβ (1-42) failed to impair MTT reduction in OHC</title>", "<p>Compared to single cells, the MTT reduction in OHCs was less frequently investigated. Therefore, we characterized the MTT assay in our system and examined its practicability to measure cell toxicity in OHCs. Similar to single cells, OHC produced the first formazane crystals immediately after MTT application and the reaction was saturated within 3 hours (##FIG##1##Figure 2A##). The MTT activity was diminished to 17.5±3.4% by application of 15 mM glutamate (##FIG##1##Figure 2B##). Since this is an approved model for excitotoxicity related cell damage ##REF##16101559##[17]##, we considered the MTT reduction assay to be suitable for the detection of cell damage in OHCs.</p>", "<p>As we intended to reproduce the Aβ effect from single cells in OHCs we applied high concentrations of freshly dissolved Aβ (25-35), fibrillar Aβ (1-40), oligomeric Aβ (1-40) and oligomeric Aβ (1-42). Surprisingly, no Aβ species caused an effect on MTT reduction, independent from the cell culture medium (Aβ (25-35) in NB: 93.4±22.1%; in DMEM: 100.9±19.1%; ##FIG##1##Figure 2B##) and the kind of application (in the medium: Aβ (25-35) 93.4±22.1%, fibrillar Aβ (1-40) 103.2%±17.5%, oligomeric Aβ (1-40) 103.4%±22.6% on top of the slice Aβ (25-35): 94.9%±25.3%; Aβ (1-42) 106.5%±19.3%); ##FIG##1##Figure 2B##). Similar results were obtained for slices that were cultivated for a longer time period (20 DIV), ruling out the possibility that older and less viable slices are more susceptible to Aβ (aged OHCs: Aβ (25-35) 96.7±24.1%, Aβ (1-40) 96.6%±34.5%; ##FIG##1##Figure 2B##).</p>", "<p>Succinate dehydrogenase activity ##REF##6058941##[31]## and exocytotic processes ##REF##11298798##[32]## are temperature-dependent and exocytosis is influenced by osmotic forces ##REF##6796595##[33]##. In order to exclude that temperature and osmolarity modify the Aβ effect on MTT reduction in OHCs, we scrutinized the effect of these two parameters. However, lowering the ambient temperature to 21°C or 4°C generally caused a decreased MTT reduction activity of the slices (absolute values not shown), but did not elicit an Aβ-induced diminution in MTT reduction. In addition, the MTT reduction under hypotonic (280 mosmol*kg<sup>−1</sup>-causes a cell swelling) and hypertonic (330 mosmol*kg<sup>−1</sup>-causes a cell shrinkage) conditions was also not significantly altered (Aβ (25-35) hypertone medium 112.9%±15.6%, Aβ (25-35) hypotone medium 107.3%±12.4% of control; ##FIG##1##Figure 2B##). Additionally, we confirmed the missing toxic effect of freshly dissolved Aβ (25-35) and fibrillar Aβ (1-40) in OHCs by an unchanged PI staining and by measuring the release of cytosolic enzyme lactate dehydrogenase (LDH) into the culture supernatant. There was no differences in the PI staining (##FIG##1##Figure 2C##) and the LDH release of Aβ (25-35) and fibrillar Aβ (1-40) treated slices, compared to control (LDH data not shown).</p>", "<p>In order to rule out that diffusion problems due to the size of the Aβ aggregates impede toxicity in the OHCs, we immunostained cross sections of OHCs after Aβ (1-40) treatment. Aβ was clearly marked within the slice (##FIG##1##Figure 2D##). Furthermore and in line with the literature ##REF##10028921##[20]##, ##REF##16175362##[34]##, Aβ (1-42) caused an activation of astroglia, as demonstrated by an increased expression of GFAP (##FIG##1##Figure 2E##). These results indicate that Aβ was able to affect the astroglia within the OHC, although Aβ failed to disturb the MTT reduction of the slice.</p>", "<title>Separation of single cells from OHCs and treatment with Aβ</title>", "<p>Considering our conflicting findings in single cells and OHCs it appeared likely that the susceptibility of cells to Aβ mediated diminution of MTT reduction activity depends on their environment. To address this matter, we split one OHC preparation into two groups. One group was cultivated further and the other group was separated into single cells. For the first time we prepared single cells from OHCs. Because of the matured state of the isolated cells only few neurons survived the isolation procedure and thus the cultures consisted largely of astrocytes (##FIG##2##Figure 3##).</p>", "<p>When we exposed the slices to Aβ (25-35) before the separation and measured the MTT reduction activity two hours after the preparation, there was no effect of Aβ (25-35) on the MTT reduction of both, the slices and the single cells (##FIG##2##Figure 3A##). In contrast, when the slices were first separated and then Aβ (25-35) was applied to the two groups for 2 days, Aβ diminished the MTT reduction in single cell cultures but not in OHCs (Aβ (25-35) 80.1%±1.0% control: 100%±1,1%; ##FIG##2##Figure 3B##).</p>", "<title>Aβ related impairment of LTP is restricted to a particular Aβ species and does not correlate with MTT reduction in acute hippocampal slices</title>", "<p>To further substantiate the assumption that cells within tissue-like structures react different to Aβ than single cells, we exposed acutely isolated hippocampal slices from adult rats to 500 nM Aβ (25-35) or 500 nM oligomeric Aβ (1-40) or 1 µM fibrillar Aβ (1-40) and measured the influence on LTP. When we exposed slices to Aβ (25-35) and oligomeric Aβ (1-40), 30 min before tetanus application, Aβ (25-35) did not influence the LTP, while application of oligomeric Aβ (1-40) significantly attenuated LTP (oligomeric Aβ (1-40): 139.5%±11.3% n = 8; Aβ (25-35): 184.2%±15.8% n = 8; control: 189.7%±15.9% n = 16 of baseline value 240 min after tetanus application; ##FIG##3##Figure 4A##). Because of their large molecule size Aβ (1-40) fibrils were expected to have limited and slow access to neuronal target structures. Therefore, we exposed slices to Aβ (1-40) fibrils persistently throughout the experiment and with a relatively high concentration of 1 µM. However, application of fibrillar Aβ (1-40) did not alter LTP (fibrillar Aβ (1-40): 187.1±16.6%, n = 8, of baseline value, 240 min after tetanus application; ##FIG##3##Figure 4A##). To investigate whether the disturbed LTP caused by Aβ (1-40) oligomers correlates with a diminished MTT reduction, we applied MTT to acute slices in parallel to LTP recording. There was no difference in the MTT reduction between control, Aβ (25-35) and Aβ (1-40) oligomer treated slices (ACSF control: 100.0±25.0%, Aβ (1-40) 107.9%±27.0%; Aβ (25-35) 106.7%±15.7% ##FIG##3##Figure 4B##).</p>", "<title>Aβ failed to diminish MTT reduction <italic>in vivo</italic>\n</title>", "<p>The short life span of acutely isolated slices from adult animals limits the exposure to Aβ aggregates. OHCs in contrast, allow long-time Aβ exposure but constitute of juvenile tissue. As we could not exclude that longer Aβ applications would indeed be able to reduce cellular viability in mature tissue we injected Aβ (25-35) and oligomeric Aβ (1-42) into the rat brain. Three days after Aβ application, the animals were sacrificed and we measured the MTT reduction in freshly prepared hippocampal slices. Aβ (25-35) and Aβ (1-42) did not influence the MTT reduction in this <italic>in vivo/ex vivo</italic> approach (untreated animal: 103.4±23.7%, Aβ (35-25) control: 100.0%±26.2%, Aβ (1-42) 99.4%±14.9%; Aβ (25-35) 108.0%±20.4%; ##FIG##3##Figure 4C##). To prove whether injected Aβ diffused into the hippocampus, we immunostained cross sections of the <italic>ex vivo</italic> slices. Aβ (1-42) oligomers were clearly marked within the slice (##FIG##3##Figure 4D##). Hence, an effect of Aβ on hippocampal cells could be expected. However, we could not observe a staining of Aβ (25-35), probably due to a wash out of that protein during the preparation procedure. But when injected Aβ (1-42) oligomers diffuse into the hippocampus, a successful diffusion of the smaller Aβ (25-35) peptide is likely. These data indicate that the missing Aβ effect on MTT reduction in OHC and acute isolated hippocampal slices represent the <italic>in vivo</italic> situation.</p>" ]
[ "<title>Discussion</title>", "<p>In this study we compared the effect of different Aβ species on the MTT reduction activity in hippocampal neurons, astrocytes, microglia, OHCs, acutely isolated hippocampal slices from adult animals and the hippocampal formation <italic>in vivo</italic>. We showed that all tested Aβ species impaired MTT reduction activity in all single cell cultures already at high nanomolar concentrations. These findings are in good agreement with various other studies investigating toxic or activating Aβ effects in hippocampal neurons ##REF##14769384##[35]##, astrocytes ##REF##9809588##[10]## and microglia ##REF##7720773##[36]##. In contrast to our findings in the single cell cultures none of the Aβ species affected cellular viability in OHCs, although we could confirm the presence of Aβ in the slices by immunostaining and GFAP upregulation. In line with our observations other studies in OHCs also showed no or, at very high concentrations, only very limited toxic effects of Aβ (25-35), Aβ (1-40) and Aβ (1-42) ##REF##17560726##[18]##, ##REF##17908170##[19]##, ##REF##11430902##[37]##, ##UREF##0##[38]##. In contrast to that and to our findings Lambert et al. published in 1998 that slice cultures could be injured with as little as 5 nM soluble Aβ (1-42) of so called Aβ derived diffusible ligands (ADDL) ##REF##9600986##[39]##. Later, Chong et al. described in 2006 neuronal cell death in hippocampal brain slices because of Aβ (1-42) oligomer treatment ##REF##16714296##[40]##. The reason for the difference to our results could be the kind of Aβ (1-42) preparation, as both groups used aggregation protocols which resulted in spheres of approximately similar size. However, their contrasting observations render it likely that their mode of preparation resulted in a different internal structure of the aggregates. Future studies should be carried out to extensively compare the different Aβ species for their potentially different effects. Nevertheless, we observed comparable detrimental effects of all investigated Aβ species on MTT reduction in single cell culture, which could not be seen in any complex tissue. That discrepancy between single cells and OHCs regarding the effect of Aβ is difficult to reconcile. As single cell cultures are almost exclusively prepared from embryonic tissue and as OHCs represent juvenile tissue one explanation could be that the respective cells are in different physiological states. Scrutinizing this assumption we show that single cells obtained from juvenile OHCs are only susceptible to Aβ effects after being cultured. Similarly, Yankner et al. (1990) reported that dissociated neurons maintained in cultures are resistant to Aβ (25-35) toxicity during the first days in culture and that Aβ neurotoxicity increases with the age of the culture ##REF##2218531##[41]##. This may indicate that cultured cells and cells that are embedded in the intact hippocampal synaptic circuitry and anatomy differ regarding cell properties which are crucial for Aβ toxicity or that the interaction between the neural elements in the relatively intact tissue enables a counteracting protective mechanism. Possible mechanisms may be alterations in the membrane lipid composition ##REF##15716586##[42]## or an altered accessibility of lipid rafts for Aβ ##UREF##1##[43]##. Similar reasons may account for the Aβ effects in studies where OHCs were cultured for several weeks ##REF##17452809##[44]##. These findings do not reflect the situation in adult tissue as we and others ##REF##8424453##[45]## did not observe a fast toxic effect of Aβ after <italic>in vivo</italic> application. Also consistent with our results Geula et al. (1998) did not observe a significant Aβ toxicity in aged rats but found age-dependent Aβ toxicity in aged monkeys [46]. This does not exclude that the hippocampal neurons in OHCs, acutely isolated slices and <italic>in vivo</italic> are physiologically impaired, as LTP was disturbed in the acutely isolated slice preparations at least after Aβ oligomer application. Recent studies increasingly indicated that soluble, pre-fibrillar Aβ assemblies rather than mature fibrils may induce early neuronal alterations, leading to physiological interruption before cell death is detectable [47]. Our LTP experiments elucidated the effects of distinct Aβ species on synaptic potentiation. We show that Aβ (1-40) oligomers disturbed LTP, whereas Aβ (1-40) fibrils did not impair LTP, although Aβ (1-40) fibrils where higher concentrated and permanently exposed to the slices. This is in good agreement with the current view that Aβ oligomers are responsible for the early disturbance of brain physiology ##REF##15608634##[48]##–##REF##16246051##[51]##. Whether or not LTP disturbances are a first sign of neuronal degeneration remains to be elucidated. If so, the MTT assay would evidently be unable to detect such early alterations in cellular physiology, as we demonstrated that Aβ (1-40) oligomer mediated LTP disruption was not reflected by MTT reduction in slices. On the other hand, studies utilizing primary neuronal and astroglial cultures showed an inhibition of MTT reduction already 2 h after Aβ application ##REF##9809588##[10]##, ##REF##10464339##[52]##. This may not necessarily reflect cell death, as Aβ-induced alterations in MTT reduction in human cortical cultures could not be confirmed with other cytotoxicity assays like LDH and alamarBlue ##REF##15689542##[53]##.</p>", "<p>Aβ (25-35) did not affect LTP in the present study, although a diminution in LTP was found by others ##UREF##2##[54]##. One possible explanation for this discrepancy is the strain dependence of the Aβ (25-35) effect, as Gengler (2007) showed that the influence of Aβ (25-35) on LTP in rat depends on their genetic background ##REF##17171334##[55]##.</p>", "<p>Taken together, we showed that single cell cultures are prone to impairment by Aβ, whereas cells embedded in the intact hippocampal synaptic circuitry and anatomy are quite resistant, suggesting that results obtained with cell cultures cannot be conferred directly to complex tissue. In addition, we demonstrated that Aβ mediated LTP disruption depends on the Aβ species and does not correlate with MTT reduction in acute isolated slices, relativizing the MTT assay as a reporter of early physiological disruption and drug testing. Thus, Aβ effects observed in single cell cultures should be interpreted cautiously regarding their relevance for more complex brain tissue, independently whether MTT reflects cellular viability or precedes cell death.</p>" ]
[]
[ "<p>Conceived and designed the experiments: MF KGR. Performed the experiments: RR AK JM. Analyzed the data: RR. Contributed reagents/materials/analysis tools: JM. Wrote the paper: RR UHS.</p>", "<p>The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazoliumbromide (MTT) reduction assay is a frequently used and easily reproducible method to measure beta-amyloid (Aβ) toxicity in different types of single cell culture. To our knowledge, the influence of Aβ on MTT reduction has never been tested in more complex tissue. Initially, we reproduced the disturbed MTT reduction in neuron and astroglia primary cell cultures from rats as well as in the BV2 microglia cell line, utilizing four different Aβ species, namely freshly dissolved Aβ (25-35), fibrillar Aβ (1-40), oligomeric Aβ (1-42) and oligomeric Aβ (1-40). In contrast to the findings in single cell cultures, none of these Aβ species altered MTT reduction in rat organotypic hippocampal slice cultures (OHC). Moreover, application of Aβ to acutely isolated hippocampal slices from adult rats and <italic>in vivo</italic> intracerebroventricular injection of Aβ also did not influence the MTT reduction in the respective tissue. Failure of Aβ penetration into the tissue cannot explain the differences between single cells and the more complex brain tissue. Thus electrophysiological investigations disclosed an impairment of long-term potentiation (LTP) in the CA1 region of hippocampal slices from rat by application of oligomeric Aβ (1-40), but not by freshly dissolved Aβ (25-35) or fibrillar Aβ (1-40). In conclusion, the experiments revealed a glaring discrepancy between single cell cultures and complex brain tissue regarding the effect of different Aβ species on MTT reduction. Particularly, the differential effect of oligomeric versus other Aβ forms on LTP was not reflected in the MTT reduction assay. This may indicate that the Aβ oligomer effect on synaptic function reflected by LTP impairment precedes changes in formazane formation rate or that cells embedded in a more natural environment in the tissue are less susceptible to damage by Aβ, raising cautions against the consideration of single cell MTT reduction activity as a reliable assay in Alzheimer's drug discovery studies.</p>" ]
[]
[ "<p>The authors wish to thank Katrin Böhm for expert technical assistance.</p>" ]
[ "<fig id=\"pone-0003236-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003236.g001</object-id><label>Figure 1</label><caption><title>Influence of Aβ on MTT reduction of single cell cultures.</title><p>A) Influence of Aβ on MTT reduction of neuron and microglia single cell cultures. When applied to cell cultures for 3 days, at 1 µM all Aβ species diminished the MTT reduction significantly in both cell types. The dashed line indicates the control level; * = p≤0.05, Mann–Whitney U-test, n = 10 per group B) Concentration dependent influence of Aβ on MTT reduction activity of astroglia single cell culture. When applied to cell culture for 3 days, any Aβ species diminished the MTT reduction significantly, compared to control. Congo red (2 µM) completely reverses the Aβ effect; Aβ (25-35) diminished the MTT reduction in NB medium, normally used for cultivation of OHC; the dashed line indicates the control level; * = p≤0.05, Mann–Whitney U-test, n = 10 per group C) Electron microscopic images (EMI) revealed that freshly dissolved Aβ (25-35) did not form aggregates. Moreover, EMI conformed the needle like structure of fibrillar Aβ (1-40) and the smaller, spherical shape of oligomeric Aβ (1-40) and oligomeric Aβ (1-42).</p></caption></fig>", "<fig id=\"pone-0003236-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003236.g002</object-id><label>Figure 2</label><caption><title>Influence of Aβ on MTT reduction, PI uptake and GFAP expression of OHC.</title><p>A) Time dependent MTT reduction activity of OHC. Numbers indicate the time after MTT application in minutes B) Influence of glutamate, freshly dissolved Aβ (25-35), fibrillar Aβ (1-40) and oligomeric Aβ (1-42) on MTT reduction activity of OHC under different conditions. Application of 10 µM Aβ for 3–6 days did not diminish the MTT reduction of OHC under different conditions; application of glutamate (15 µM) significantly reduced the MTT reduction, compared to control; the dashed line indicates the control level; * = p≤0.05, Mann–Whitney U-test, n≥12 per group C) PI staining of Aβ and glutamate treated OHCs. Application of 10 µM freshly dissolved Aβ (25-35) and 10 µM fibrillar Aβ (1-40) into the medium for 3 days did not cause cell death. Application of glutamate (15 µM) induced cell death D) Immunostaining of cross sections against fibrillar Aβ (1-40) revealed the presence of Aβ in the slice E) GFAP and DAPI staining of oligomeric Aβ (1-42) treated and control slice. Aβ (1-42) caused an activation of astroglia within the OHC, indicated by an increased GFAP expression.</p></caption></fig>", "<fig id=\"pone-0003236-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003236.g003</object-id><label>Figure 3</label><caption><title>Influence of Aβ on MTT reduction activity of OHC and single cells, generated from OHC.</title><p>A) Aβ (25-35) 1 µM was applied to the slice for 3 days. The MTT assay was done 2 hours after the preparation of the single cells out of the slice. In this case, 1 µM Aβ did not diminish the MTT reduction of OHC and single cells; B) 1 µM Aβ was applied to the slices and single cells after the preparation for 2 days. In this case, Aβ (25-35) 1 µM significantly reduced the MTT reduction of single cells, compared to control; * = p≤0.05, Mann–Whitney U-test, n = 10 per group.</p></caption></fig>", "<fig id=\"pone-0003236-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003236.g004</object-id><label>Figure 4</label><caption><title>Influence of Aβ on LTP and MTT reduction of acute isolated slices.</title><p>A) Influence of freshly dissolved Aβ (25-35), oligomeric Aβ (1-40) and fibrillar Aβ (1-40) on LTP of acute hippocampal slices. Oligomeric Aβ (1-40) significantly reduced the LTP, compared to control potentiation. Freshly dissolved Aβ (25-35) and fibrillar Aβ (1-40) did not effect the LTP; * = p≤0.05 ANOVA with repeated measures; The bar indicates the time of Aβ application. Tetanus was applied at time point 0; Analogue traces represent typical recordings of single experiments taken 20 minutes before tetanization (1), and 240 minutes after tetanization (2). B) Aβ treated acute slices did not differ from control slices in their MTT reduction activity. C) Influence of Aβ on MTT reduction activity of <italic>ex vivo</italic> slices. Injection of freshly dissolved Aβ (25-35) and oligomeric Aβ (1-42) for 3 days did not diminish the MTT reduction of the <italic>ex vivo</italic> slices, compared to untreated animals and the reverse control protein Aβ (35-25). D) Immunostaining of cross sections against Aβ revealed the presence of oligomeric Aβ (1-42) in the hippocampus.</p></caption></fig>" ]
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[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>Leibniz Institute for Neurobiology, Research Institute for Applied Neurosciences gGmbH</p></fn></fn-group>" ]
[ "<graphic xlink:href=\"pone.0003236.g001\"/>", "<graphic xlink:href=\"pone.0003236.g002\"/>", "<graphic xlink:href=\"pone.0003236.g003\"/>", "<graphic xlink:href=\"pone.0003236.g004\"/>" ]
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[{"label": ["38"], "element-citation": ["\n"], "surname": ["Ito", "Ito", "Takagi", "Saito", "Ishige"], "given-names": ["Y", "M", "N", "H", "K"], "year": ["2003"], "article-title": ["Neurotoxicity induced by amyloid beta-peptide and ibotenic acid in organotypic hippocampal cultures: protection by S-allyl-L-cysteine, a garlic compound."], "source": ["Brain Res"], "volume": ["19", "985"], "fpage": ["98"], "lpage": ["107"]}, {"label": ["43"], "element-citation": ["\n"], "surname": ["Williamson", "Usardi", "Hanger", "Anderton"], "given-names": ["R", "A", "DP", "BH"], "year": ["2007"], "article-title": ["Membrane-bound {beta}-amyloid oligomers are recruited into lipid rafts by a fyn-dependent mechanism."], "source": ["FASEB J"]}, {"label": ["54"], "element-citation": ["\n"], "surname": ["Lee", "Kuo", "Huang", "Hsu"], "given-names": ["CC", "YM", "CC", "KS"], "year": ["2007"], "article-title": ["Insulin rescues amyloid beta-induced impairment of hippocampal long-term potentiation."], "source": ["Neurobiol Aging"]}, {"label": ["62"], "element-citation": ["\n"], "surname": ["Paxinos", "Watson"], "given-names": ["G", "C"], "year": ["1998"], "source": ["The Rat Brain in Stereotaxic Coordinates (fourth ed.)"], "publisher-loc": ["New York"], "publisher-name": ["Academic Press"]}]
{ "acronym": [], "definition": [] }
62
CC BY
no
2022-01-13 07:14:34
PLoS One. 2008 Sep 18; 3(9):e3236
oa_package/3a/5f/PMC2529401.tar.gz
PMC2529402
18795103
[ "<title>Introduction</title>", "<p>Diabetes mellitus (DM) is one of the most common metabolic disorders in the world, in which more than 90% are grouped to type 2 diabetes mellitus (T2DM) ##REF##12851465##[1]##. Given the predicted explosion in the number of T2DM cases worldwide ##REF##14502096##[2]##, the biomedical researchers face much stronger challenges, particularly on understanding the pathogenesis of disease and discovering biomarkers for tracking the disease process.</p>", "<p>T2DM is characterized by abnormal glucose homeostasis leading to hyperglycemia, and the serum glucose has been used as a golden standard for diabetes diagnosis. However, T2DM is a kind of disease involving defects of multiple organs, which cannot be distinguished through the measurement of the serum-glucose level. In addition, T2DM is a multiple-stage disease, which usually covers several decades from impaired plasma glucose to various complications. The serum-glucose level only reflects the consequence of multiple physiological disorders in the given stage. Therefore, many efforts have been made to identify genetic and protein markers to reveal the molecular/cellular details or progression of diabetes ##REF##12916001##[3]##–##REF##17526982##[9]##. The genetic defects certainly render more probability to diabetes. On the other hand, the protein markers can track real-time status of diabetes. It has been found there are changes in the protein abundances of serum in diabetes progression ##REF##16305059##[10]##, ##UREF##2##[11]##. For instance, a number of studies suggest that the elevated circulating inflammatory biomolecules such as C-reactive protein and serum amyloid A can be used for predicting the development of T2DM ##REF##10335783##[12]##–##REF##11916936##[15]##. However, since the traditional strategy of diabetic diagnosis only relies on the individual molecules as the biomarkers, the sensitivity and accuracy of the biomarkers might be fluctuated due to ethnic or personal variance ##UREF##3##[16]##–##REF##16043703##[18]##. Proteomic technology might provide the new solutions for solving this problem, which can identify large set of the proteins in cells or tissues through high-throughput methods, and provide a globe view of the protein changes associated with diabetes.</p>", "<p>It is well known that serum severs the optimal resource for discovery of disease biomarkers. Up to now, a few proteomic analyses of serum related to diabetes have been reported. For example, Dayal B <italic>et al.</italic> used the protein-chip to identify the high-density lipoproteins apoA-I and apoA-II and their glycosylated products in patients with diabetes and cardiovascular disease ##REF##14730686##[19]##. Zhang <italic>et al.</italic> found that the protease inhibitors including clade A and C, alpha 2-macroglobulin, fibrinogen, and the proteins involved in the classical complement pathway such as complement C3, and C4 exhibited the higher expression-levels in insulin resistance/type-2 diabetes ##REF##12645894##[20]##. Bergsten <italic>et al.</italic> analyzed the serum proteins in T2DM by SELDI-TOF-MS and peptide-mass fingerprinting (PMF), and found the expression levels of apolipoprotein, complement C3 and transthyretin were over-represented, whereas albumin and transferrin were under-represented in T2DM ##REF##17163994##[21]##.</p>", "<p>However, none of these above works provided the real globe view for the protein profile of the diabetic serum, since the proteomic analysis of serum is a formidable challenge for its huge complexity and dynamic range ##UREF##2##[11]##, ##REF##11747208##[22]##. Recent advances in serum sample preparation such as a depletion of high abundance proteins can be coupled to 1D or 2D-LC-MS/MS analysis, which have provided the new ways for large-scale serum proteomic analysis ##REF##16047309##[23]##–##REF##15822942##[25]##. However, the step of the depletion of the high abundance proteins might cause some artifacts. In the present study, we used a label-free proteomic method with LC-MS/MS to investigate the protein profiling between the non-diabetic and diabetic serum without removing the high abundant proteins. After analyzing the proteomics data according to the stringent criteria, a total of 3,010 proteins and 3,224 proteins were identified from the non-diabetic and diabetic serum, respectively. In-depth bioinformatic analysis was employed for these differential proteins between the non-diabetic and diabetic serum.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Clinical sample collection and preparation</title>", "<p>Ten male adults were selected for this investigation, including five non-diabetic subjects (FPG 4.82±0.21 mmol/L; PG2H 4.78±1.54 mmol/L; BMI 21.67±0.81 kg/m2; HbA1c 5.68±0.54%; C-peptide 1.09±0.25 ng/mL; and homeostasis model assessment [HOMA] 1.04±0.67), and five type 2 diabetic patients (FPG 7.26±2.73 mmol/L; PG2H 12.2±1.21 mmol/L; BMI 27.03±4.23 kg/m2; HbA1c 7.14±0.42%; C-peptide 3.44±1.31 ng/mL; HOMA 5.67±3.96). The Homeostasis Model Assessment (HOMA) for insulin resistance and β-cell function was calculated from fasting plasma glucose and insulin concentrations. Informed consent was obtained from each person in written format and approved by Shanghai No. 6 People's Hospital Review Committee.</p>", "<p>Immediately after collection, fasting blood samples were allowed to clot at room temperature for four hours, and the serum were collected and centrifugated at 3000 rpm/min for 15 min. Before pooling the samples, the protein concentration of the serum samples was determined by Bradford assay on a Microplate Reader (Bio-Rad, Model 680). Five non-diabetic serum samples were mixed as control-pool sample, and five diabetic serum samples were also mixed as disease-pool sample. The two pooled serum samples were diluted respectively to ∼20 mg/mL with 100 mM phosphate buffer (pH 2.0, containing 5% ACN). Then, the pooled serum samples were filtered through 0.22 µm filters (Agilent technologies) by spinning at 10 000 g at 4°C for 30 min and dialyzed to 100 mM phosphate buffer (pH 2.0, containing 5% ACN).</p>", "<title>Gel electrophoresis and In-Gel Digestion</title>", "<p>The serum sample containing 1.8 mg proteins was reduced by adding 2 µL of 1 M DTT to 10 mM and incubated at 37°C for 2.5 hours. The mixture then was added with 10 µL of 1 M IAA and incubated for 40 min in darkness at room temperature. After these treatments, the samples were subjected to SDS-PAGE on a 7.5–17.5% gradient gel. The gel lane stained with Coomassie Blue was excised into 42 sections. Each excised section was cut into approx. 1 mm<sup>3</sup> pieces and destained using 30% acetonitrile/70% 100 mM ammonium bicarbonate solution, followed by dehydration in 100% acetonitrile for 5 min. Gel pieces were placed under vacuum centrifugation until completely dried. Each gel slice was then incubated in a 50 mM ammonium bicarbonate solution containing 10 ng/µL trypsin (Promega Biotech Co., Madison, WI, USA.) overnight. Peptides were extracted with 0.1% TFA/80% acetonitrile, dried by vacuum centrifugation, and stored at −80°C for further analysis with mass spectrometry.</p>", "<title>Label-free shotgun proteomic identification</title>", "<p>Each gel slice containing peptides was dissolved in 60 µL 0.1% formic acid, and then the half of this peptide-solution was loaded into the RP column. RP-HPLC was performed using an Agilent 1100 Capillary system (Agilent technologies) with C18 column (150 µm i.d., 100 mm length, Column technology Inc., Fremont, CA). The pump flow rate was 1.6 µL/min. Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile. The tryptic peptide mixtures were eluted using a gradient of 2–55% B over 135 min. The mass spectral data were acquired on a LTQ linear ion trap mass spectrometer (Thermo, San Jose, CA) equipped with an electrospray interface operated in positive ion mode. The temperature of heated capillary was set at 170°C. A voltage of 3.0 kV applied to the ESI needle. Normalized collision energy was 35.0. The number of ions stored in the ion trap was regulated by the automatic gain control. Voltages across the capillary and the quadrupole lenses were tuned by an automated procedure to maximize the signal for the ion of interest. The mass spectrometer was set as one full MS scan was followed by ten MS/MS scans on the ten most intense ions from the MS spectrum with the following Dynamic Exclusion™ settings: repeat count, 2, repeat duration, 0.5 min, exclusion duration, 1.5 min.</p>", "<title>Data analysis</title>", "<p>All .dta files were created using Bioworks 3.1, with precursor mass tolerance of 1.4 Da, threshold of 100, and minimum ion count of 15. The acquired MS/MS spectra were searched against the Human International Protein Index protein sequence database (version 3.07, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/IPI\">www.ebi.ac.uk/IPI</ext-link>) combined with sequences of real protein and reverse sequences of proteins, by using the TurboSEQUEST program in the BioWorks 3.1 software suite, with a mass tolerance of 3.0 Da. All cysteine residues were searched as carboxamidomethycystein (+57.02 Da). Up to one internal cleavage sites were allowed for tryptic searches. All output results were combined together using the in-house software named BuildSummary to delete the redundant data. Searches were conducted against the Human International Protein Index protein sequence database to control the false discovery rate at 2.5% and all spectral peptide count had a ΔCn score of at least 0.1. The proteins identified by two or more peptide counts in either non-diabetic or diabetic serum were used to the following bioinformatics analysis.</p>", "<title>Western bolt analysis of C3 and its fragments</title>", "<p>Each of 100 µg non-diabetic and diabetic serum-proteins was subjected to PAGE-gel electrophresis, and then proteins in the gel were transferred to a nitrocellulose membrane. The membranes were incubated first with the appropriate primary antibodies (C3b: ab11871, C3a: ab11872, purchased from Abcam Ltd, Cambridge, MA), respectively, and then incubated with HRP-conjugated secondary antibodies for 45 min. The proteins were detected by enhanced chemiluminescence (ECL-plus, Amersham Pharmacia Biotech).</p>", "<title>Validation of ficolin-3 over-representation in larger samples</title>", "<p>0.4 uL of each individual serum sample (non-diabetic and diabetic subjects, n = 24, respectively) diluted to 1/10 with 1.0 M Tris (pH 6.8) buffer was separated by SDS-PAGE, and electro-transferred to a nitrocellulose membrane (Whatman International Ltd., England.). The membrane was blotted with a mouse monoclonal antibody against human ficolin-3 (R&amp;D Systems, Inc., 1∶500). Signal detection was achieved with ECL Plus chemiluminescence system (Amersham Biosciences). Signal of bands from Western blot were scanned with PDQUEST GS-710 a flat-bed scanner and digitized with Gel-PRO Analyzer software (Media Cybernetics, Inc., USA). To decrease the system discrepancy, we used the serum of the same patient as the reference. Relative level of serum ficolin-3 was calculated by the proportion of density ratio of sample bands to that of the reference band. These density ratios were used for statistical analyses of serum ficolin-3 level between non-diabetic and diabetic subjects.</p>", "<title>Statistical analysis</title>", "<p>Data were expressed as means±standard deviation (SD) for normally distributed values. Differences between groups for normally distributed variables were tested using t-test (analysis of variance). All calculations were performed with GraphPad Prism software system (GraphPad San Diego, CA, USA) and SPSS13.0 statistical package (Statistical Software, Los Angeles, CA, USA). A P value below 0.05 was considered statistically significant.</p>" ]
[ "<title>Results</title>", "<title>Selection of non-diabetic subjects and diabetic patients</title>", "<p>Previous studies observed that T2DM might occurred at a greater frequency in adults who are younger than 65 years, suggesting that people who are old than 65 without diabetes mellitus usually do not anticipate the genetic susceptibility ##REF##15671192##[26]##. Therefore, we set age criteria for sample cohort that an adult in the present study must be old than 65 years (Non-diabetic subjects: age 67.6±1.67 years; type 2 diabetic patients: age 67±1.71 years) in order to reduce the genetic effects related to T2DM between non-diabetic and diabetic cohort. Furthermore, the careful selection of samples was performed based on the clinical parameters of non-diabetic and diabetic cohorts. Supplementary ##SUPPL##5##Table S1## summarized the clinical parameters of the selected non-diabetic subjects and diabetic patients, in which type 2 diabetic patients group had higher FPG, PG2H, WT, BMI, HOMA, HbA1c and C-peptide compared with control. To reduce the individual variance of serum proteins within the cohort, we pooled all the serum of each cohort for proteomic analysis, respectively.</p>", "<title>Semi-quantitative proteomic identification in non-diabetic and diabetic serum</title>", "<p>We analyzed differential protein profile in two cohorts using shotgun proteomics and label-free quantitative strategy. In order to reduce sample complexity, proteins in non-diabetic and diabetic serum were first separated on SDS-PAGE gel and the gel bands were excised and subjected to in-gel tryptic digestion, respectively (##FIG##0##Figure 1A##). The proteins were identified with criteria corresponding to an estimated false dicovery rate of 2.5%. After combining the MS/MS data generated from our experiment, we were able to assign 1,212,256 MS/MS spectra to 150,881 peptide counts, leading to identification of 5,882 unique peptides corresponding to 3,010 protein groups in non-diabetic serum, and 1,211,006 MS/MS spectra to 189,792 peptide counts, resulting in 5,960 unique peptides corresponding to 3,224 protein groups in diabetic serum (all these identified protein groups are called proteins in the text below for clarity). Supplementary ##SUPPL##0##Figure S1## showed the quite similar distributions of the identified peptides and proteins between non-diabetic and diabetic serum, indicating non-bias of the identified MS/MS spectra between non-diabetic and diabetic serum.</p>", "<p>Among the identified 3,010 proteins in non-diabetic serum and 3,224 proteins in diabetic serum, 942 (30.30%) and 1,046 (32.44%) proteins were selected respectively under the condition that each identified protein contained at least two peptide spectral counts. Totally 1,377 proteins were obtained according to these more stringent filter, resulting the false discovery rate of 1.6%. There were 888 identified proteins overlapped in non-diabetic and diabetic serum, whereas 223 proteins were identified uniquely from the non-diabetic serum and 266 proteins were found uniquely from the diabetic serum (##FIG##0##Figure 1B##, Supplementary ##SUPPL##6##Table S2##).</p>", "<title>Localized statistics of protein abundance distribution (LSPAD)</title>", "<p>Since the peptide-spectral-count distributions of identified 1377 serum-proteins were widely spread out to the range of 10<sup>5</sup> (Supplementary ##SUPPL##6##Table S2##), we developed M-A plotting referring to microarray analysis in order to display a relative protein-abundance distribution of each protein. First, for each protein, X<sub>1</sub> representing its peptide spectral counts in diabetic serum was transformed into Y<sub>1</sub> with formula <italic>f</italic>(<italic>X<sub>1</sub></italic>) = log<sub>2</sub>(<italic>X<sub>1</sub></italic>+1) as diabetic protein abundance, while the X<sub>2</sub> in non-diabetic serum was transformed into Y<sub>2</sub> with the same formula as a non-diabetic protein abundance. Then, we defined “M” as differential protein abundance between diabetic and non-diabetic serum by the formula of Y<sub>1</sub>−Y<sub>2</sub>, and “A” as an average protein abundance by the formula of (Y<sub>1</sub>+Y<sub>2</sub>)/2. Based on these formulas, total 1377 proteins were plotted as a scatter chart, in which the values of M were distributed on the Y-axis, and the values of A were distributed on the X-axis (##FIG##1##Figure 2A##).</p>", "<p>This scatter chart showed that the log2-ratio-range of the differential protein-abundances between non-diabetic and diabetic serum was considerably decreased along M-axis when the protein-abundances were increased along A-axis (##FIG##1##Figure 2A##). These observations indicated that the abundance ratio based on peptide spectral counts cannot be simply used as indicators for differential significance between diabetic and non-diabetic serum. For example, the significance of 2-fold change from 2 to 1 peptide spectral counts is not equal to the significance of 2-fold change from 20000 to 10000. In addition, we realized that the protein-distribution profiles at the low, middle and high level of protein abundance, respectively, were considerably different (##FIG##1##Figure 2B##), suggesting significance-calculation of particular differential proteins should be localized to a certain range of related abundance level. Therefore, we developed a computing method called Localized Statistics of Protein Abundance Distribution (LSPAD) to evaluate the statistical significance of protein-abundance bias between diabetic and non-diabetic serum, by which the differentia significance of a particular protein should be calculated through its local protein-abundance distribution-window rather than through whole distribution range from the lowest to highest protein-abundances. Since the whole distribution range of protein abundances could be generally subdivided into three parts (high, middle and low protein-abundances, see ##FIG##1##Figure 2## and Supplementary ##SUPPL##6##Table S2##), we postulated a width of the local window for statistics as 33%, i.e. only neighbored proteins with A value located within the 33% A-axis around a particular protein should be used for calculation.</p>", "<p>In detail, for a particular protein, all the average peptide spectral counts of neighbored proteins whose A value were within the 33% abundance-window of the target protein were calculated as a background to evaluate the statistical significance (p value) of over-representation or under-representation of the target protein by performing fisher's exact test on a following four-fold table:</p>", "<p>The p-values derived from the fisher's exact test were linearly transformed into p′ in order to evaluate the bias of each protein-abundance between diabetic and non-diabetic serum. (sgn = 1 indicates that a protein is over-represented in diabetic sample, and sgn = −1 indicates that a protein is over-represented in non-diabetic sample)</p>", "<p>To evaluate the reliability of LSPAD, we carried out the MA-plotting analyses to two duplicates of diabetic serum sample. First, the duplicates of one pooled diabetic-serum sample were separated by SDS-PAGE, and the entire gel was cut into 12 gel slices for LC-MS/MS analysis (Supplementary ##SUPPL##1##Figure S2A##). The results showed the consistent proteomic data from these two duplicates (Supplementary ##SUPPL##1##Figure S2B–E##). Then these data were subjected to LSPAD analysis. The result showed few protein-variants by comparing the protein-abundances between two duplicates of one pooled diabetic-serum sample with LSPAD method (Supplementary ##SUPPL##2##Figure S3A##). Furthermore, we analyzed the expression-differentiation significance of one diabetic-serum duplicate versus a non-diabetic serum (Supplementary ##SUPPL##2##Figure S3B##), and the other diabetic-serum duplicate versus the same non-diabetic serum (Supplementary ##SUPPL##2##Figure S3C##). The Supplementary ##SUPPL##2##Figure S3D## showed the high correlation coefficient of the M values between the significantly differential proteins in Supplementary ##SUPPL##2##Figure S3B and S3C##. Taken together, these results indicate that this LSPAD method is reliable for exploring the differentiation of the protein abundances between non-disease and disease serum.</p>", "<p>Accordingly, after 42 gel bands were analyzed in diabetic and non-diabetic serum respectively (##FIG##0##Figure 1##), 1377 identified proteins were analyzed by LSPAD approach. All the significant abundance-biases of 1377 proteins were calculated (Supplementary ##SUPPL##6##Table S2##). Furthermore, we marked the proteins with p′&lt;0.01 in red color as the significantly over-represented in diabetic serum, the proteins with p′&gt;0.99 in green color as the significantly under-represented in diabetic serum, and the non-significantly differential proteins in grey color (##FIG##1##Figure 2##).</p>", "<p>The 68 significant over-represented proteins in diabetic serum were listed in ##TAB##1##Table 1##. Many known risk factors of diabetes such as C-reactive protein, serum amyloid A and haptoglobin were over-represented in diabetic serum, in agreement with the observations by traditional approaches based on the analysis of individual proteins ##UREF##4##[27]##. In addition, a number of other factors including the novel proteins associated with diabetes were detected by this large-scale survey (##TAB##1##Table 1##). On the other hand, 74 proteins were found under-represented in diabetic serum (Supplementary ##SUPPL##6##Table S2##). As far as we know, some studies reported that Keratin and IgG were associated with diabetes ##REF##18533284##[28]##, ##REF##18383005##[29]##. In addition, a lot of keratins were also involved in the pathway of cell communication (Supplementary ##SUPPL##3##Figure S4##) in our results. According to our pathway-associated differential significance analysis, we found keratin associated pathway were significantly overall bias with diabetic serum, which might not result from the bias of sample preparation.</p>", "<title>Pathway-associated differential significance analysis</title>", "<p>To further reveal the significant bias of the protein abundances at the level of biological pathways in diabetic serum, we mapped those 1377 proteins into KEGG pathways ##REF##10592173##[30]##. Total 1377 identified proteins in the present study involved in 147 related pathways (Supplementary ##SUPPL##7##Table S3##). Then, we calculated these proteins with their p-values at the pathway level in order to discover overall bias of pathways associated with diabetic-serum. The calculation procedure was as follows: Supposing all the proteins are non-differential expressed and independent of each other, their p-values, <italic>p</italic>, should follow a uniform distribution between[<italic>0</italic>,<italic>1</italic>]. Thus, <italic>z</italic> = <italic>qnorm</italic>(<italic>p</italic>), should follow a standard normal distribution (here qnorm is normal inverse distribution function). After the normal inverse transformation of <italic>p<sub>i</sub></italic> to <italic>z<sub>i</sub></italic>, the summarized Z score for a certain pathway <italic>j</italic> was generated by the formula, . Here <italic>n<sub>j</sub></italic> was the number of the proteins involved in the pathway <italic>j</italic> in our experiments, and <italic>ix</italic> = {<italic>ix<sub>i</sub></italic>} denoted the index of these proteins. Because the proteins involved in the pathway <italic>j</italic> were supposed to be non-differential expressed and independent of each other, the summarized score for pathway <italic>j</italic>, <italic>Z<sub>j</sub></italic>, should also follow a standard normal distribution. In our case, for pathway <italic>j</italic>, the following hypothesis test was performed:</p>", "<p>H0: <italic>Z<sub>j</sub></italic> follows the standard normal distribution, indicating that the pathway is not un-biased in diabetic serum.</p>", "<p>H1: <italic>Z<sub>j</sub></italic> doesn't follow the standard normal distribution, indicating that the pathway is over-represented or under-represented in diabetic serum</p>", "<p>P value for pathway <italic>j</italic>, <italic>P<sub>j</sub></italic>, was transformed from <italic>Z<sub>j</sub></italic> by a normal cumulative function, <italic>p</italic> = <italic>pnorm</italic>(<italic>z</italic>). Under a statistic significance threshold <italic>α</italic>, an over-represented pathway in diabetic serum was identified with and under-represented pathway was identified with . If the P value is less than 0.01, it indicates that this pathway is significantly overall overrepresented in diabetic serum compared with non-diabetic serum. If the P value is more than 0.99, it means that this pathway is significantly overall overrepresented in non-diabetic serum.</p>", "<p>Among the 147 pathways, we selected 18 pathways, in which each pathway should have at least 5 identified proteins as well as more than 10% coverage of all the pathway-proteins in the database, to evaluate the pathway-bias between non-diabetic and diabetic serum. All the values of the protein-abundance biases in these 18 pathways were presented in Supplementary ##SUPPL##3##Figure S4##. Particularly, the pathways of complement system, PPAR system, cell communication and Alzheimer's disease showed the significantly overall over-representation in diabetes serum (p&lt;0.01), while insulin signaling, coagulation cascade, focal adhesion and long-term pathways presented significantly overall bias in non-diabetic serum (p&gt;0.99) (##FIG##2##Figure 3##).</p>", "<p>These significant differential pathways could be subdivided into two major categories: one had many significant-differential components in one pathway, and the other had a few highly significant-differential components in one pathway. For example, on the PPAR pathway, three apolipoproteins were all over-represented significantly in diabetic serum (##FIG##3##Figure 4A##). In Alzheimer's disease pathway, the apoliprotein E over-presentation also contributed the overall bias of this pathway to diabetic serum. Therefore, apolipoproteins could be considered as a kind of the important biomarkers associated with diabetes. As previous reports, many apolipoproteins were involved in lipid metabolism ##REF##9325276##[31]##–##REF##15060092##[43]##. These proteins were further selected to show their abundance biases between non-diabetic and diabetic serum. As shown in ##FIG##3##Figure 4B##, 8 proteins including apolipoprotein A-I, AII, C-II and C-III were significantly over-represented in diabetic serum, whereas 6 proteins were significantly under-represented in diabetic serum, which covered some regulatory factors such as paraoxonase 1 (PON1) in lipid metabolism.</p>", "<title>Over-representation of ficolin-related complement pathway in diabetic serum</title>", "<p>Our results showed that 12 proteins associated with complement system were significantly over-represented in diabetic serum (##FIG##4##Figure 5A##). It has been known that the complement system can be activated through three different ways, including lectin, classical and alternative pathways (##FIG##4##Figure 5B##) ##REF##9408965##[44]##, ##REF##7888067##[45]##. The present work showed that two trigger factors of lectin-complement activation, ficolin-2 and ficolin-3, were both over-represented significantly in the diabetic serum (##TAB##1##Table 1##), while the ficolin-3 was detected with much higher abundance than ficolin-2. Another kind of lectin related to complement initiation, mannose biding lectins (MBL), was not detected. These results indicate that ficolin-3 might be the major trigger of lectin-complement activation in diabetic patients.</p>", "<title>Validation of ficolin-3 related complement activation in diabetic serum</title>", "<p>When the complement system is activated, the complement C3 is cleaved to C3a and C3b, which is the common and crucial step in all complement activation pathways (as shown in ##FIG##4##Figure 5B##, ##REF##15996650##[46]##). To validate the level of C3 and its activation, Western blotting for C3, corresponding fragment C3a and C3b were performed in the non-diabetic and diabetic serum. It was confirmed that these proteins were over-represented in diabetic serum (##FIG##5##Figure 6##).</p>", "<p>It has been known that lectin is one of the trigger to complement activation ##REF##15996650##[46]##, ##REF##15677517##[47]##. Our studies identified 33 and 80 spectral peptide counts of ficolin-3 from non-diabetic and diabetic serum, respectively (##TAB##1##Table 1##). Among these detected peptides, two particular peptides (<named-content content-type=\"gene\">VVLLPSCPGAPGSPGEK</named-content> and <named-content content-type=\"gene\">YAVSEAAAHK</named-content>) were detected exclusively from diabetic serum (##FIG##6##Figure 7A and 7B##). Taken together, these findings indicate that ficolin-3 in diabetic serum are over-represented in diabetic serum. We further confirmed this observation by Western blotting (##FIG##5##Figure 6##).</p>", "<p>In order to evaluate the correlation of ficolin-3 with diabetes, the protein-abundance of ficolin-3 was validated by Western blotting in additional clinical sera from 24 non-diabetic subjects and 24 diabetic patients (Supplementary ##SUPPL##8##Table S4##). As shown in ##FIG##6##Figure 7C## and Supplementary ##SUPPL##4##Figure S5##, the level of serum ficolin-3 was 0.90±0.43 in non-diabetic sera and 1.43±0.87 in diabetic sera (p = 0.012). Taken together, these results suggest a ficolin-3 related complement activation in diabetic serum.</p>" ]
[ "<title>Discussion</title>", "<title>The strategy for analyzing the highly dynamical range of protein abundances</title>", "<p>In this study, LC-MS/MS coupled with a label-free quantitative strategy was applied to analyze the differential serum-protein abundance profile between non-diabetic and diabetic patients. The label-free quantitation based on peptide-spectral counts offers a high-coverage identification of proteins, and then gives a comprehensive and rapid comparison to the differential proteins, especially to the plasma proteins ##REF##17332893##[48]##. Since the distribution range of the peptide-spectral counts of the serum-proteins was up to 10<sup>5</sup> (Supplementary ##SUPPL##6##Table S2##), we applied M-A plotting method referring to microarray data-analysis for analyzing the effects of the different abundance-levels as well as the count-ratio of a particular protein between non-diabetic and diabetic serum (##FIG##1##Figure 2A##). From the ##FIG##1##Figure 2B##, we realized that the lower the abundance-level of the peptide-spectral counts, the higher the deviation of the count-ratio. In this regard, we cannot fix a count-ratio as a threshold covering low abundance-level to high abundance-level for evaluating the bias of the protein abundance of diabetic serum. In other words, the quantitative selection of differentia proteins based on the ratio of the particular protein-abundance, which is usually used in isotope-labeling proteomic methods, seems not suitable in the peptide-spectral counts quantification for the systems with the highly dynamic range of protein-abundances, i.e. serum proteome.</p>", "<p>Therefore, we developed a localized statistics of protein abundance distribution (LSPAD) for identifying the over- or under-represented proteins in diabetic serum. Based on this method, we can calculate the significance of the peptide-spectral-count bias for differentia proteins instead of using the count-ratio. Furthermore, we defined an abundance-window of 33% around a target protein as a localized background for calculating the statistical significance, by which we can evaluate the significant bias of a target protein-abundance compared to the abundance-distribution range of its neighbored proteins rather than to the abundance-distribution range of all identified proteins.</p>", "<title>Involvement of lipid metabolism and inflammation in type 2 diabetes</title>", "<p>In this study, many individual proteins associated with T2DM reported in previous studies were also identified. In the group of apolipoproteins, for example, many components were over-represented in diabetic serum including Apolipoprotein E, CII, CIII and serum amyloid. Apo E content of postprandial TG-rich lipoproteins in subjects with both T2DM and coronary artery disease was increased ##REF##8155085##[49]##. Serum amyloid A, a major apoprotein (45%) in high-density lipoproteins (HDL), was increased due to inflammation ##REF##11410071##[50]##. Apolipoprotein C III (apo C III) plays a central role in regulating plasma metabolism of triglyceride-rich lipoprotein (TRL). Previous studies suggested that apo C III might be an independent risk factor for atherosclerotic diseases in Chinese type 2 diabetes ##REF##15364160##[51]##. On the other hand, we identified some under-represented regulatory factors in lipid metabolism such as paraoxonase1 (PON1). PON1 is an anti-inflammatory enzyme, which participates in the prevention of low density lipoprotein (LDL) oxidation ##REF##17664137##[52]##, ##REF##16140307##[53]##. Recently, Mackness <italic>et. al</italic> reported that high C-reactive protein and low paraoxonase1 in diabetes might be used as risk factors of coronary heart disease ##REF##16140307##[53]##.</p>", "<p>We also found certain proteins associated with acute-phase response were over-represented in diabetic serum such as C-reactive protein ##REF##12523922##[54]##, ##REF##11978661##[55]##, serum amyloid A ##REF##9389420##[56]##, haptoglobin ##REF##2478861##[57]##, α-1-acid glycoprotein ##REF##10335783##[12]##, ceruloplasmin ##REF##12077727##[58]## and Von Willebrand factor ##REF##7569618##[59]##. Recently, abundant scientific evidences suggested the elevated circulating inflammatory markers such as C-reactive protein could be used for the prediction of the development of T2DM ##REF##10335783##[12]##–##REF##11916936##[15]##. Moreover, C- reactive protein was also as a biomarker for inflammation in uremia ##REF##11423586##[60]##. Studies also showed that haptoglobin and C-reactive protein were increased significantly in both diabetes and glucose intolerance ##REF##2478861##[57]##. There has been an explosion of interests that the chronic low-grade inflammation and the activation of the innate immune system were closely involved in the pathogenesis of T2DM ##REF##15161763##[61]##.</p>", "<title>Complement activation in type2 diabetes</title>", "<p>Cross-sectional study have demonstrated strong correlation between complement C3 and insulin resistance, which showed that C3 was associated with a increased risk of developing diabetes ##REF##15677517##[47]##. In the present study, the serum levels of C3 and its fragments C3a were over-represented in diabetic serum by western blot analysis, indicating the activation of complement system. Adipsin/complement factor D is a serine protease that is secreted by adipocytes into the bloodstream. Adipsin is deficient in several animal models of obesity ##REF##2734615##[62]##. In our study, the expressing level of adipsin was under-represented in diabetic serum. Lectin is also a trigger for complement activation. This process begins due to the binding of mannose-binding lectin (MBL) or ficolins with MBL-associated serine protease-2 (MASP-2), and leads to the formation of a C3 convertase ##REF##17892207##[63]##–##REF##9777418##[66]##. Up to now, only a few evidences showed that the increased level of MBL can provide prognostic information in patients with T2DM ##REF##17030835##[67]##. In the present work, MBL was not detected by mass spectrometry in serum, while both ficolin-2 and ficolin-3 were detected over-represented in diabetic serum. However, ficolin-2 was uniquely identified in diabetic serum with only 9 spectral counts while ficolin-3 was detected with much higher spectral counts. Therefore, it seems that ficolin-3 should be the major trigger and indicator of lectin-complement activation. The Western-blotting of serum ficolin-3 with a lager clinical population supports that serum ficolin-3 was significantly over-represented and positively correlated with T2DM. Thus, we argue that ficolin-3 triggers the lectin-complement pathway, which might play an important role in the chronic low-grade inflammation and activation of the innate immune system associated with T2DM.</p>", "<p>In summary, the LSPAD approach developed in this present work is well useful for analyzing proteomic data derived from biological complex systems such as plasma proteome, by which we disclosed the comprehensive distribution of the proteins associated with diabetes among high, medium and low abundant proteins. In addition, we found the involvement of the ficolin-related complement system in type 2 diabetes.</p>" ]
[]
[ "<p>Conceived and designed the experiments: WPJ RZ JRW. Performed the experiments: RXL HBC HZ SLZ JD QRL. Analyzed the data: RXL HBC KT SJL WPJ RZ JRW. Contributed reagents/materials/analysis tools: KT SJL YL. Wrote the paper: RXL HBC YL WPJ RZ JRW.</p>", "<title>Background</title>", "<p>Recent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease.</p>", "<title>Results</title>", "<p>In this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p&lt;0.01). In addition, a pathway-associated analysis was developed to obtain the overall pathway bias associated with type 2 diabetes, from which the significant over-representation of complement system associated with type 2 diabetes was uncovered. Moreover, an up-stream activator of complement pathway, ficolin-3, was observed over-represented in the serum of type 2 diabetic patients, which was further validated with statistic significance (p = 0.012) with more clinical samples.</p>", "<title>Conclusions</title>", "<p>The developed LSPAD approach is well fit for analyzing proteomic data derived from biological complex systems such as plasma proteome. With LSPAD, we disclosed the comprehensive distribution of the proteins associated with diabetes in different abundance levels and the involvement of ficolin-related complement activation in diabetes.</p>" ]
[ "<title>Supporting Information</title>" ]
[]
[ "<fig id=\"pone-0003224-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g001</object-id><label>Figure 1</label><caption><title>Overview of Idnetitication of proteins in non-diabetic and diabetic serum.</title><p>(A) Scheme of label-free strategy to differential protein identification in non-diabetic and diabetic serum. Pooled serum samples from five non-diabetic and five diabetic sera were separated respectively by gel electrophoresis. Each gel lane was divided into 42 regions and each section was processed for mass spectrometry. (B) 1377 proteins were identified by at-least two peptide spectral counts in either serum. 888 overlapped proteins were identified both in non-diabetic and diabetic serum, whereas 223 proteins were identified uniquely from the non-diabetic serum and 266 proteins were found uniquely from the diabetic serum.</p></caption></fig>", "<fig id=\"pone-0003224-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g002</object-id><label>Figure 2</label><caption><title>Quantitative strategy of proteins in non-diabetic and diabetic serum.</title><p>(A) M-A plotting of 1377 identified proteins. “M” was defined as differential protein abundance ratios of each protein between diabetic and non-diabetic serum, and “A” was defined as protein-abundance of each protein. In addition, ret dots represented statistically significant over-represented proteins in diabetic serum, green dots represented statistically significant under-represented proteins in diabetic serum, and grey dots were proteins without statistically-significant change in diabetic serum and non-diabetic serum. (B) The distribution profiles of 1377 identified proteins (black line), identified proteins with M less than 5 (red line), between 5 and 10 (green line), and more than 10 (blue line).</p></caption></fig>", "<fig id=\"pone-0003224-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g003</object-id><label>Figure 3</label><caption><title>The overall bias analysis of selected pathways found in non-diabetic and diabetic serum.</title><p>Proteins identified in non-diabetic and diabetic serum were mapped to known pathways using KEGG. The p value of each pathway was digitized to the length of the bar diagram.</p></caption></fig>", "<fig id=\"pone-0003224-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g004</object-id><label>Figure 4</label><caption><title>The identified proteins and abundance biases in specific pathways.</title><p>(A)PPAR system, (B) Apolipoproteins associated Lipid metabolism. The p value of identified protein was digitized to the length of the bar in each pathway.</p></caption></fig>", "<fig id=\"pone-0003224-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g005</object-id><label>Figure 5</label><caption><title>Overview of proteins associated with complement system.</title><p>(A) The identified proteins and the abundance biases in complement system. The p value of identified protein was digitized to the length of the bar in each pathway. (B) The three activation pathways of complement system: the classical, mannose-binding lectin, and alternative pathways. The three pathways converge at the point of cleavage of C3. Therefore, the C3 cleavage is the crucial step in activation of the three complement pathway. Molecules of C3 are cleaved to C3a and C3b by the C3 convertase. C3b binds covalently around the site of complement activation. Some of this C3b binds to the C4b and C3b in the convertase enzymes of the classical and alternative pathways, respectively, forming C5 convertase enzymes. This C3b acts as an acceptor site for C5, which is cleaved to form the anaphylatoxin C5a and C5b, which initiates the formation of the membrane-attack complex. Excitedly, ficolin-3 is a biologically active protein of the lectin-complement activation in association with MBL-associated serine protease (MASP). In this figure, significantly up-regulated proteins in diabetic serum were denoted with red color, slightly up-regulated proteins in diabetic serum were denoted with light red color, significantly up-regulated proteins in non-diabetic serum were denoted with blue color, and slightly up-regulated proteins in non-diabetic serum were denoted with light blue color. Not identified proteins or the fragment of the complement component were denoted with light grey color.</p></caption></fig>", "<fig id=\"pone-0003224-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g006</object-id><label>Figure 6</label><caption><title>Western blot confirmation of the serum level of C3 (∼187 kD), C3a (∼9 kD), C3b (alpha' chain, ∼104 kD) and Ficolin-3 (∼34 kD).</title><p>The Non-diabetic serum: the mixture of equal amount of serum from five non-diabetic subjects in ##TAB##1##Table 1##, Diabetic serum: the mixture of equal amount of serum from five diabetic patients in ##TAB##1##Table 1##.</p></caption></fig>", "<fig id=\"pone-0003224-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.g007</object-id><label>Figure 7</label><caption><title>MS/MS spectra of representative peptides from ficolin-3 and validation of ficolin-3 up-regulation in diabetic sera.</title><p>(A) VVLLPSCPGAPGSPGEK (B) YAVSEAAAHK (C) Western blot validation of the serum ficolin-3 level in the non-diabetic subjects and diabetic patients (n = 24, respectively) were conducted. N: non-diabetic serum; D: diabetic serum.</p></caption></fig>" ]
[ "<table-wrap id=\"d35e514\" position=\"float\"><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<underline>D</underline>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<underline>ND</underline>\n</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Peptide spectral counts of a target protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">X<sub>1</sub>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">X<sub>2</sub>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sum of counts of all the other proteins in the window</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">S<sub>1</sub>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">S<sub>2</sub>\n</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"pone-0003224-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003224.t001</object-id><label>Table 1</label><caption><title>Characterization of proteins significantly over-represented in diabetic serum compared to non-diabetic serum based on LSPAD method. (P&lt;0.01).</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI ID</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Protein name</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Diabetic peptide spectral count</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Non-diabetic peptide spectral count</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">P value</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00022434</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ALB protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">61457</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">47082</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.09E-91</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00514824</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement component C4B</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">875</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">183</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.44E-80</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00555805</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement component 4A</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.63E-69</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00032258</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement C4 precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3846</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2077</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.06E-69</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00453459</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement Component 4B preproprotein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3933</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2141</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.77E-69</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00418163</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">C4B1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3811</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2077</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.48E-66</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00384697</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ALB protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">47105</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37323</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.64E-37</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00556148</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement factor H</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2732</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1691</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.30E-30</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00465313</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Alpha 2 macroglobulin variant</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17016</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13013</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.50E-26</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00478003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Alpha-2-macroglobulin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">17344</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">13335</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.06E-24</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00385264</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ig mu heavy chain disease protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1614</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">880</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.42E-23</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00164623</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement C3 precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9754</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7267</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.64E-22</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00479708</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Immunoglobulin heavy constant mu (IGHM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1204</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.02E-21</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00549273</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Immunoglobulin heavy constant mu (IGHM)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1190</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.09E-21</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00019943</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Afamin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">553</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">221</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.57E-20</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00479169</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">65 kDa protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1932</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1181</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.35E-18</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00022488</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hemopexin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1952</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1268</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.99E-14</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00426051</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein DKFZp686C15213</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5203</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3835</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.18E-14</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021727</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">C4b-binding protein alpha chain precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">638</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">321</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.01E-13</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00478493</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Haptoglobin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4214</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.28E-12</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00550991</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Alpha-1-antichymotrypsin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1088</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">628</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.99E-11</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00019591</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Splice Isoform 1 of Complement factor B precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1183</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">696</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.42E-11</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021842</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Apolipoprotein E precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">394</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">181</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.28E-10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00019399</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Serum amyloid A-4 protein precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">143</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.21E-10</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021857</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Apolipoprotein C-III precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.87E-08</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00022392</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement C1q subcomponent, A chain precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">103</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25E-07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021841</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Apolipoprotein A-I precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4069</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3112</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.14E-07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00010865</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Casein kinase II beta subunit</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.70E-07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00396929</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PREDICTED: similar to immunoglobulin M chain</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">165</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">68</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.55E-06</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00410714</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Alpha 2 globin variant</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">433</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">244</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.33E-06</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00163446</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">The Human Immunoglobulin Heavy Diversity (IGHD)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">134</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.03E-06</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00171834</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">140</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.29E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00399007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein DKFZp686I04196</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5114</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4039</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.41E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00003590</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Quiescin Q6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.53E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00022389</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Splice Isoform 1 of C-reactive protein precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.53E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00015309</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">89</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.63E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00290077</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 15</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">142</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.21E-05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00217963</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">223</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">117</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000146102</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00418422</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">The Human Immunoglobulin Heavy Diversity (IGHD)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000152193</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00423461</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein DKFZp686C02220</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">828</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">548</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000223242</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00450768</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">147</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">69</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000275352</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00011261</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement component C8 gamma chain precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">266</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000391696</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00556567</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ficolin-3 protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">80</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000819734</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00441196</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3090</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2450</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000949718</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00386839</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Amyloid lambda 6 light chain variable region SAR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">180</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">98</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001229635</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00017601</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ceruloplasmin precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2260</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1816</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001476612</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00383953</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">VH4 heavy chain variable region precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">132</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001483067</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00009866</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">107</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">52</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001918932</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00470798</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein DKFZp686E23209</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4508</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3647</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002098573</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00017530</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ficolin-2 precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002266054</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021854</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Apolipoprotein A-II precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">853</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">582</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002359293</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00004550</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein FLJ20261</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">96</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.00238822</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00011252</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Complement component C8 alpha chain precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">81</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002415224</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00293898</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hepatocellular carcinoma associated protein TB6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002727717</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00384444</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type I cytoskeletal 14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">207</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.003122976</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021856</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Apolipoprotein C-II precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.00418406</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00219806</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">S100 calcium-binding protein A7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.004391148</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00446354</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Hypothetical protein FLJ41805</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.004391148</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00479762</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">115 kDa protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.004391148</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00022446</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Platelet factor 4 precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">82</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.00501026</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00300725</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type II cytoskeletal 6A</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">158</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">90</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005161139</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00242956</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Fc fragment of IgG binding protein</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.006549075</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00384401</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Myosin-reactive immunoglobulin kappa chain variable region</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.006595492</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00293665</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type II cytoskeletal 6B</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">141</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">79</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.00706398</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00299145</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type II cytoskeletal 6E</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">83</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.007903541</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00383603</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Anti-thyroglobulin light chain variable region</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.008537501</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00452748</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Serum amyloid A protein precursor</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.008537501</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IPI00021304</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Keratin, type II cytoskeletal 2 epidermal</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">810</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">575</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.009876282</td></tr></tbody></table></alternatives></table-wrap>" ]
[ "<disp-formula></disp-formula>", "<inline-formula></inline-formula>", "<inline-formula></inline-formula>", "<inline-formula></inline-formula>" ]
[]
[]
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[]
[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s001\"><label>Figure S1</label><caption><p>The distribution of proteins and peptides identified in 42 gel slices of non-diabetic serum and diabetic serum</p><p>(0.02 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s002\"><label>Figure S2</label><caption><p>Reproducibility of Gel-LC-MS/MS separations and identification.</p><p>(0.21 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s003\"><label>Figure S3</label><caption><p>Reproducibility and reliability of LSPAD method</p><p>(0.15 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s004\"><label>Figure S4</label><caption><p>The identified proteins and abundance biases in 18 pathways</p><p>(0.23 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s005\"><label>Figure S5</label><caption><p>Western blot analyses of the serum ficolin3 level in the non-diabetic subjects(n = 24)and diabetic patients(n = 24)</p><p>(0.14 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s006\"><label>Table S1</label><caption><p>Baseline characteristics of five non-diabetic subjects and five diabetic patients</p><p>(0.02 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s007\"><label>Table S2</label><caption><p>Proteins identified by two or more peptide spectral counts in non-diabetic and diabetic serum</p><p>(0.43 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s008\"><label>Table S3</label><caption><p>Pathway analysis by mapping 1377 proteins into KEGG pathways. Ratio (%): (100 Ã? Gene number found in pathway) / Totallygene number in pathway. P value: present overall bias of pathways associated with diabetic-serum or non-diabetic serum</p><p>(0.08 MB PDF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003224.s009\"><label>Table S4</label><caption><p>General and clinical parameters of non-diabetic subjects and type 2 diabetic patients</p><p>(0.05 MB PDF)</p></caption></supplementary-material>" ]
[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This work was supported by 973 Program #2006CB503900 to JR. Wu and WP Jia; a grant of Knowledge Innovation Program of the Chinese Academy of Sciences KSCX1-YW-02 to JR. Wu; the grants of National Natural Science Foundation of China 30521005 to JR. Wu and R. Zeng, #30425021 to R. Zeng; and National Basic Research Program of China #2006CB910700 to R. Zeng, and High-technology Project # 2007AA02Z334 to R. Zeng.</p></fn></fn-group>" ]
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[ "<media xlink:href=\"pone.0003224.s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s002.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s003.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s004.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s005.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s006.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s007.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s008.pdf\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003224.s009.pdf\"><caption><p>Click here for additional data file.</p></caption></media>" ]
[{"label": ["7"], "element-citation": ["\n"], "surname": ["Rao", "Sridhar", "Das"], "given-names": ["AA", "GR", "UN"], "year": ["2007"], "article-title": ["Elevated butyrylcholinesterase and acetylcholinesterase may predict the development of type 2 diabetes mellitus and Alzheimer's disease."], "source": ["Med Hypotheses"]}, {"label": ["8"], "element-citation": ["\n"], "surname": ["Rao", "Sridhar", "Srinivas", "Das"], "given-names": ["AA", "GR", "B", "UN"], "year": ["2007"], "article-title": ["Bioinformatics analysis of functional protein sequences reveals a role for brain-derived neurotrophic factor in obesity and type 2 diabetes mellitus."], "source": ["Med Hypotheses"]}, {"label": ["11"], "element-citation": ["\n"], "surname": ["Anderson", "Anderson"], "given-names": ["NL", "NG"], "year": ["2002"], "article-title": ["The human plasma proteome: history, character, and diagnostic prospects"], "fpage": ["845"], "lpage": ["867"]}, {"label": ["16"], "element-citation": ["\n"], "surname": ["Peeters", "Beckers", "Verrijken", "Roevens", "Peeters"], "given-names": ["A", "S", "A", "P", "P"], "year": ["2007"], "article-title": ["Variants in the FTO gene are associated with common obesity in the Belgian population."], "source": ["Mol Genet Metab"]}, {"label": ["27"], "element-citation": ["\n"], "surname": ["Gentleman", "Huber", "Irizarry", "Dudoit"], "given-names": ["RCV", "W", "R", "S"], "year": ["2005"], "article-title": ["Bioinformatics and Computational Biology Solutions Using R and Bioconductor: Statistics for Biology and Health"], "fpage": ["473"]}]
{ "acronym": [], "definition": [] }
67
CC BY
no
2022-01-13 07:14:34
PLoS One. 2008 Sep 16; 3(9):e3224
oa_package/fc/47/PMC2529402.tar.gz
PMC2529403
18802461
[ "<title>Introduction</title>", "<p>During meiosis, the diploid genome is segregated to form haploid nuclei through processes that include the close juxtaposition of homologous chromosomes and recombination between them. In most organisms, a proteinaceous structure called the synaptonemal complex (SC) forms between homologous chromosomes during meiotic prophase. The SC is required for synapsis, the intimate association of homologs along their entire length. The SC and its components are thought to play roles in regulating recombination and generally promoting the establishment of crossovers between the chromosomes ##REF##15473851##[1]##,##REF##10690419##[2]##.</p>", "<p>Examination of SCs by electron microscopy (EM) has defined distinct structures present in the SCs of most organisms. During early prophase, axial elements (AEs) form along the longitudinal axis of each pair of sister chromatids using a cohesin-based chromosome core as a scaffold for assembly ##REF##16518630##[3]##. As prophase progresses, the AEs of homologous chromosomes become physically connected by perpendicular transverse filaments (TFs) that span the SC central region (CR), which occupies the ∼100 nm space between the two homologous AEs ##REF##15473851##[1]##,##REF##10690419##[2]##. AEs within the mature SC are referred to as lateral elements (LEs). Finally, a central element (CE) is often observed as a structure overlapping the middle of the TFs and positioned parallel to the two LEs.</p>", "<p>Although homologous chromosomes undergo presynaptic pairing and alignment in some organisms ##REF##9334324##[4]##,##REF##11931237##[5]##, synapsis requires a fully formed CR that extends the length of the chromosomes. In this paper we will use the term “alignment” to describe the parallel association of homologs (or AEs) at a distance equal to or greater than the width of the SC and the term “pairing” to describe the close association of homologous sequences as determined by FISH.</p>", "<p>Components of TFs, such as ZIP1 (budding yeast), SYCP1 (mouse), SYP-1 (worms), and C(3)G (Drosophila), have been identified as proteins containing long coiled coil segments ##REF##12231631##[6]##–##REF##16421735##[11]##. Although these TF proteins are similar in predicted secondary structure, they share very little similarity in amino acid sequence. Despite this lack of sequence conservation, the proteins are all thought to form TFs across the CR of the SC by binding of their C-termini to the AEs with head-to-head orientation of their N-termini at the center of the CE ##REF##15767569##[12]##–##REF##8660935##[16]##. TFs are important for ensuring synapsis of homologs and normal levels of interhomolog exchange ##REF##12231631##[6]##, ##REF##11731477##[8]##, ##REF##16230536##[10]##, ##REF##15066280##[17]##–##REF##15545646##[20]##.</p>", "<p>In <italic>Drosophila melanogaster</italic> oocytes, the TFs are formed by the C(3)G protein ##REF##11731477##[8]##,##REF##15767569##[12]##. Like other TF proteins, C(3)G is comprised of a central coiled coil-rich domain flanked by N- and C-terminal globular domains. As shown by Jeffress <italic>et al.</italic>\n##REF##17947423##[21]##, C-terminal deletion of C(3)G results in its failure to attach to the AEs of each set of homologs. Instead, this C-terminal deletion protein forms a large cylindrical polycomplex structure. EM analysis of this structure reveals a polycomplex of concentric rings with alternating dark and light bands, presumably corresponding to long arrays of polymerized TFs. However, deletions of N-terminal regions completely abolished both SC and polycomplex formation. To explain these data, Jeffress <italic>et al.</italic>\n##REF##17947423##[21]## proposed that in Drosophila, the N- terminal globular domain of C(3)G is critical for the formation of anti-parallel pairs of C(3)G homodimers, and thus for assembly of complete TFs, while the C-terminus is required to affix these homodimers to the AEs.</p>", "<p>The question then arises as to how C(3)G molecules can be polymerized to form a linear array of TFs. The idea that such polymerization events are facilitated by the apposition of paired AEs seems unlikely given the finding that C-terminal deletions of C(3)G form polycomplexes ##REF##17947423##[21]##. The observation that the rat homolog of C(3)G (SYCP1) can form polycomplex-like structures in COS-7 cells ##REF##15496453##[22]## suggests that the process of TF polymerization may be self-promoting and sustaining, and thus requires no other components. However, in mice, significant extension of SYCP1 to form a full-length CR in meiotic cells requires the functions of the SYCE1, SYCE2, and TEX12 proteins, which localize to the CE of the SC ##REF##17339376##[23]##–##REF##18611960##[26]##. SYCE1 and SYCE2 physically associate with each other and the N-terminus of the TF protein SYCP1, while TEX12 binds to SYCE2 ##REF##15944401##[24]##,##REF##16968740##[25]##. Mice lacking the SYCE2 protein display defects in the formation of the TFs (SYCP1 accumulation), and thus in SC formation ##REF##17339376##[23]##. They appear to form only short and, at least in the case of <italic>Tex12<sup>−/−</sup></italic> mice, morphologically abnormal SCs ##REF##17339376##[23]##,##REF##18611960##[26]##. It remains to be determined whether or not functional homologs of SYCE2 and TEX12 might facilitate C(3)G polymerization, and thus CE formation in Drosophila oocytes.</p>", "<p>To discover additional components of the SC and genes involved in other critical processes in meiosis, we previously undertook a novel genetic screen for female meiotic mutants in Drosophila ##REF##18820465##[27]##. One of the genes identified in the screen, <italic>corona</italic> (<italic>cona</italic>), was found to have both a severe defect in meiotic recombination and a profound effect on the localization of C(3)G. Previous analyses of <italic>cona</italic> mutants demonstrated a failure of the SC protein C(3)G to localize correctly in the absence of CONA, demonstrating defective SC formation. As is the case for <italic>c(3)G</italic> mutants, the frequency of meiotic exchange in <italic>cona</italic> females was reduced 50- to 200-fold compared to wild-type ##REF##18820465##[27]## without a similar reduction in the number of DSBs [SLP and RSH, unpublished data]. Moreover, double mutants for <italic>c(3)G</italic> and <italic>cona</italic> displayed a defect in recombination that was comparable to either single mutant [SLP and RSH, unpublished data], and thus the two proteins likely function in a common pathway with respect to facilitating meiotic exchange. Like C(3)G, CONA protein is only conserved within the genus Drosophila, but CONA contains no predicted coiled coil domains or other characterized functional motifs ##REF##18820465##[27]##.</p>", "<p>In this study, we show that CONA is a new SC protein that co-localizes with C(3)G in a mutually-dependent fashion. We found that CONA accumulation is required for C(3)G localization into wild-type SC structures and formation of polycomplexes, but is not necessary for the formation of either the AEs or the chromosome cores from which they arise. Our results indicate that CONA is crucial for the assembly of the CR of the SC in Drosophila and may have a function similar to that of the vertebrate CE proteins TEX12 and SYCE2. However, the observation that pairing and alignment of AEs occurs in <italic>Tex12</italic> and <italic>Syce2</italic> mutant meiocytes, but not in <italic>cona</italic> oocytes, suggests that the SC plays a more critical role in the stable association of homologs in Drosophila than it does in mammalian cells.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Drosophila Stocks and Genetic Analyses</title>", "<p>Drosophila stocks and crosses were maintained on a standard medium at 25°C. Descriptions of genetic markers and chromosomes can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.flybase.org/\">http://www.flybase.org/</ext-link>\n##REF##16381917##[52]##. A <italic>w<sup>1118</sup></italic> stock was used as a wild-type stock for the immunofluorescence and FISH experiments, except for the experiment shown in ##FIG##0##Figure 1A##, in which a <italic>Canton-S</italic> strain was used. <italic>Df(3R)JDP</italic> was constructed by FLP-mediated recombination essentially as described by Parks <italic>et al.</italic>\n##REF##14981519##[53]## using FRT sequences in <italic>PBac{WH}cona<sup>f04903</sup></italic> and <italic>P{XP}d01968</italic>, inserted at coordinates 14,211,754 and 14,222,824, respectively, on the chromosome <italic>3R</italic> genome map (Release 5.6). The entire <italic>cona</italic> protein-coding region is deleted in <italic>Df(3R)JDP</italic>.</p>", "<p>The transgene construct <italic>P{UASP-cona::Venus}</italic> was constructed using the plasmid pPWV (obtained from the Drosophila Genomics Resource Center, Bloomington, IN) and the Gateway system (Invitrogen, Carlsbad, CA) using methods as recommended by the manufacturer. pPWV is identical to pUASP except that it contains a Gateway cassette followed by the Venus yellow fluorescent protein coding region ##REF##11753368##[54]##. The <italic>cona</italic> open reading frame was amplified from the <italic>cona</italic> cDNA bs15d10 (obtained from Geneservice, Ltd., Cambridge, UK) using primers tailed with <italic>att</italic>B1 and <italic>att</italic>B2 sequences and inserted into the vector pDONR221 in a BP Clonase (Invitrogen) reaction to form pDONR-<italic>cona</italic>. The <italic>cona</italic> cDNA insert from pDONR-<italic>cona</italic> was then transferred into pPWV in an LR Clonase (Invitrogen) reaction to form pP{UASP-<italic>cona::Venus</italic>}, with an open reading frame encoding a CONA::Venus fusion protein. After confirming the construct by sequence analysis, it was introduced into Drosophila by standard germline transformation methods (Genetic Services, Inc., Cambridge, MA).</p>", "<p>To observe GFP-ORD in chromosome spread experiments, <italic>P{gc(2)M-myc}II.5 P{GFP::ORD}48I ord<sup>10</sup> bw sp If/+</italic>; <italic>cona<sup>f04903</sup> e<sup>s</sup> ca /FRT82B cona<sup>A12</sup></italic> females were obtained by crossing <italic>y w/y<sup>+</sup>Y; (P{gc(2)M-myc}II.5 P{GFP::ORD}48I ord<sup>10</sup> bw sp If</italic>; <italic>cona<sup>f04903</sup> e<sup>s</sup> ca)/T(2;3)CyO-TM3</italic>, <italic>P{GAL4-Hsp70.PB}TR1</italic>, <italic>P{UAS-GFP.Y}TR1: P{GAL4-Hsp70.PB}TR2</italic>, <italic>P{UAS-GFP.Y}TR2</italic>, <italic>Ser<sup>1</sup></italic> males to <italic>y<sup>d2</sup> w<sup>1118</sup> P{ey-FLP.N}2 P{GMR-lacZ.C(38.1)}TPN1/Y</italic>; <italic>FRT82B cona<sup>A12</sup>/TM6B</italic>, <italic>P{y<sup>+</sup>}TPN1</italic>, <italic>Tb<sup>1</sup></italic> females. For chromosome spread experiments to observe C(2)M, homozygous <italic>cona<sup>A12</sup></italic> females were selected from the stock <italic>y<sup>d2</sup> w<sup>1118</sup> P{ey-FLP.N}2 P{GMR-lacZ.C(38.1)}TPN1/Y</italic>; <italic>FRT82B cona<sup>A12</sup>/TM6B, P{y<sup>+</sup>}TPN1</italic>, <italic>Tb<sup>1</sup></italic>.</p>", "<title>Antibody Production</title>", "<p>The full-length <italic>cona</italic> open reading frame was amplified from the <italic>cona</italic> cDNA bs15d10 and cloned into pET-19b (Novagen, San Diego, CA). After the construct was verified by sequencing, the 6XHis-tagged CONA protein was expressed in <italic>E. coli</italic> BL21 cells. The bacterial expressed protein was purified using ProBond Nickel-Chelating Resin (Invitrogen). Polyclonal antibody production in guinea pigs using purified 6XHis-CONA as antigen was performed by Cocalico Biologicals (Reamstown, PA). Pre-immune sera from the immunized guinea pigs did not stain Drosophila ovaries (data not shown).</p>", "<p>The anti-CONA antibody was specific to the CONA protein, as anti-CONA signals were not detected in ovaries from <italic>cona<sup>f04903</sup></italic> females (##SUPPL##0##Figure S1B##). Similar observations were made using ovaries from <italic>cona<sup>A12</sup></italic>/<italic>Df(3R)JDP</italic> females [SLP and WDW, unpublished data]. These observations suggested that little or no endogenous CONA protein is produced in the presence of the <italic>cona<sup>A12</sup></italic> or <italic>cona<sup>f04903</sup></italic> mutations.</p>", "<title>Immunofluorescence on Whole-Mount Ovarioles</title>", "<p>Immunofluorescence on whole ovarioles was performed as described previously and the ovarioles were mounted on coverslips by embedding in polyacrylamide gel in most experiments ##REF##11731477##[8]##. Primary antibodies used for staining whole-mount preparations were guinea pig anti-CONA (1∶125), mouse monoclonal anti-C(3)G 1A8-1G2 ##REF##15767569##[12]## (1∶500), mouse monoclonal anti-C(3)G 1G5-2F7 and 5G4-1F1 ##REF##15767569##[12]##,##REF##17947423##[21]## (1∶500 each), mouse monoclonal anti-ORB 6H4 and 4H8 ##REF##7523244##[55]## (1∶50 each), and rat anti-SMC1 ##REF##17702574##[56]## (1∶500). Secondary antibodies were Alexa 546 anti-mouse IgG (1∶500), Alexa 488 anti-mouse IgG (1∶500), Alexa 488 anti-guinea pig IgG (1∶500), Alexa 488 anti-rat IgG (1∶500) (Invitrogen), and Cy3 anti-mouse IgG (1∶500) (Jackson Immunoresearch, West Grove, PA).</p>", "<p>Microscopy was conducted using a DeltaVision RT restoration microscopy system (Applied Precision, Issaquah, WA) equipped with an Olympus IX70 inverted microscope and CoolSnap CCD camera. Image data were corrected and deconvolved using softWoRx v.2.5 software (Applied Precision). For some experiments, confocal images were collected using a Bio-Rad Radiance 2000 laser scanning confocal microscope and Zeiss LaserSharp2000 software. Maximum intensity projections were produced from confocal data using Zeiss LSM Image Browser v.4.2 software.</p>", "<title>Immunofluorescence on Chromosome Spreads</title>", "<p>Chromosome spread experiments were performed as described previously ##REF##17698920##[38]##. Primary antibodies used for immunofluorescence on chromosome spreads were affinity-purified guinea pig anti-SMC1 ##REF##17698920##[38]## (1∶500), rabbit anti-C(2)M ##REF##12593793##[39]## (1∶500), rabbit anti-GFP (Invitrogen) (1∶500), and mouse monoclonal anti-C(3)G 1A8-1G2 ##REF##15767569##[12]## (1∶500). Secondary antibodies were Alexa 488 anti-rabbit IgG (1∶400), Alexa 488 anti-mouse IgG (1∶400) (Invitrogen), Cy3 anti-guinea pig IgG (1∶400), Cy5 anti-guinea pig IgG (1∶400), and Cy5 anti-mouse IgG (1∶400) (Jackson Immunoresearch).</p>", "<p>For chromosome spreads, images were captured and processed as described previously ##REF##17698920##[38]##. Because the signal intensity varies considerably for different nuclei on the same slide, wild-type and mutant images were enhanced to different degrees during processing to render details visible. In general, the C(3)G signal on chromatin in <italic>cona</italic> nuclei is significantly weaker than in wild-type.</p>", "<title>Fluorescence In Situ Hybridization (FISH)</title>", "<p>FISH on ovarioles was performed as described elsewhere ##UREF##1##[57]## with simultaneous immunofluorescence detection of ORB protein. The probe for the FISH experiments was composed of three overlapping bacterial artificial chromosome (BAC) clones from the RP98 library ##REF##10731150##[58]## obtained from the BACPAC Resource Center, Children's Hospital Oakland Research Institute. The three BACs (and map locations on the <italic>X</italic> chromosome) were RP98-26N1 (9F4-10A2), RP98-17B23 (9F11-10A4), and RP98-26J12 (10A4-B1). BAC DNA was isolated using the Qiagen Midi Prep Kit. A DNA mixture containing 3.3 µg of DNA from each of the three BACs was labeled with Alexa 488 (Invitrogen) essentially as described by Dernburg ##UREF##2##[59]## and purified using a Qiaquick column (Qiagen). Immunofluorescence with anti-ORB primary antibodies and Cy3 anti-mouse IgG secondary antibodies was performed after hybridization under the same conditions as described above for whole mount ovarioles. The ovarioles were mounted in Prolong Gold antifade mountant (Invitrogen) ##REF##15965253##[60]##.</p>", "<p>Images were collected using a DeltaVision RT restoration microscopy system as described above. After image collection and processing, hybridization foci within pro-oocyte nuclei were scored for chromosome pairing. In nuclei with two foci, the distance between the pixels of highest fluorescence intensity within each focus was measured in three-dimensional image stacks using softWoRx Explorer software (Applied Precision). Nuclei containing a single hybridization focus or foci separated by 0.7 µm or less were defined as paired ##REF##16299588##[19]##, while those with foci separated by more than 0.7 µm were defined as unpaired.</p>" ]
[ "<title>Results</title>", "<title>Corona Protein Co-Localizes with the Synaptonemal Complex</title>", "<p>We previously showed that the <italic>cona</italic> gene corresponds to the transcription unit <italic>CG7676</italic> on the basis of the presence of a <italic>Doc</italic> transposon in the 3′ untranslated region of <italic>CG7676</italic> in <italic>cona<sup>A12</sup></italic> that was not present on the un-mutagenized parental chromosome and the isolation of a second allele, <italic>cona<sup>f04903</sup></italic>, which bears a <italic>PiggyBac</italic> insertion in sequence flanking the 5′ end of <italic>CG7676</italic>\n##REF##18820465##[27]##. Both <italic>cona<sup>A12</sup></italic> and <italic>cona<sup>f04903</sup></italic> drastically reduce the levels of meiotic recombination and produce high levels of nondisjunction (∼32–39%) ##REF##18820465##[27##, SLP and RSH, unpublished data].</p>", "<p>We raised an antibody against the CONA protein and used it to determine the localization of CONA in meiotic prophase cells in the germaria of Drosophila ovaries (see <xref ref-type=\"sec\" rid=\"s4\">Materials and Methods</xref>). Evidence that this antibody is specific to CONA (i.e., that no signal is observed in pro-oocytes homozygous for <italic>cona<sup>f04903</sup></italic>) is presented in ##SUPPL##0##Figure S1##. In wild-type ovaries, anti-CONA localization was observed within a subset of nuclei in regions 2A and 2B of the germarium and within the oocyte nucleus in region 3 and early egg chambers within the vitellarium. The distribution of CONA within nuclei was distinctly thread-like and strongly co-localized with the SC protein C(3)G (##FIG##0##Figure 1A##). These results demonstrate that CONA localizes along the SC.</p>", "<p>As an alternate strategy to localize the protein, we constructed a transgene, <italic>P{UASP-cona::Venus}</italic>, which expresses the full-length CONA protein fused to the yellow fluorescent protein Venus under the control of the GAL4/UAS system ##REF##8223268##[28]##. The CONA::Venus fusion protein was functional, as expression driven by <italic>nanos</italic>-GAL4::VP16 in the female germline rescued the nondisjunction phenotype in <italic>cona<sup>f04903</sup></italic> homozygotes. Control <italic>cona<sup>f04903</sup></italic> homozygotes lacking the <italic>P{UASP-cona::Venus}</italic> transgene showed 31.9% <italic>X</italic> chromosome nondisjunction, whereas <italic>cona<sup>f04903</sup></italic> homozygotes expressing CONA::Venus showed a nearly tenfold reduction in nondisjunction to just 3.4% (data not shown).</p>", "<p>We examined the pattern of <italic>nanos</italic>-GAL4::VP16-driven CONA::Venus localization during meiotic prophase. In a <italic>cona<sup>f04903</sup></italic> mutant background, strong Venus yellow fluorescent protein signal localized in a pattern very similar to that observed for CONA immunolocalization. Immunolocalization of C(3)G in these ovaries revealed extensive co-localization of CONA::Venus and C(3)G in thread-like patterns within nuclei (##FIG##0##Figure 1B–C##). Nuclear CONA::Venus fluorescence was strongest in a <italic>cona</italic> mutant background in which little or no wild-type CONA protein is present (##SUPPL##0##Figure S1##). When expressed in heterozygotes or homozygotes for a wild-type copy of the <italic>cona</italic> locus, CONA::Venus fluorescence was weaker in nuclei and increased diffuse fluorescence was often observed in the cytoplasm of germline cells in regions 1 and 2 of the germarium (data not shown). This may be the result of competition with wild-type CONA protein. A similar reduction in signal has been observed for localization of GFP-tagged ORD protein along the SC in the presence of wild-type ORD (RSK and SEB, unpublished data). These data confirm the immunolocalization of CONA and implicate CONA as a component of the SC.</p>", "<title>CONA Is Required for the Assembly of C(3)G into a Thread-Like SC</title>", "<p>When CONA::Venus was expressed under the control of a <italic>nanos</italic>-GAL4::VP16 driver in a <italic>cona<sup>f04903</sup></italic> heterozygote in which wild-type CONA protein was also present, C(3)G was detected as puncta and short threads within early prophase nuclei before CONA::Venus signal was detectable (##FIG##1##Figure 2A–B##). The spotty to thread-like pattern of C(3)G accumulation observed in ##FIG##1##Figure 2B## is also observed in early region 2A in <italic>cona<sup>f04903</sup>/+</italic> heterozygotes that lack the CONA::Venus transgene, and represents an early stage (zygotene) in SC assembly in which the short threads of C(3)G are associated with endogenous CONA (##SUPPL##0##Figure S1C##). As the intensity of CONA::Venus staining increased during the progression of meiotic prophase, CONA::Venus assumed a thread-like staining pattern that co-localized with C(3)G (##FIG##1##Figure 2E–F, 2I–J##).</p>", "<p>A different pattern of C(3)G localization was observed when CONA::Venus was expressed in the <italic>cona<sup>f04903</sup></italic> homozygote, and therefore was the only form of functional CONA protein present. In nuclei that contained very low or undetectable levels of CONA::Venus signal (##FIG##1##Figure 2C–D##), C(3)G staining exhibited a more diffuse appearance similar to that previously described for <italic>cona</italic> mutant pro-oocytes ##REF##18820465##[27]##. However, as CONA::Venus staining became more visible at slightly later stages, CONA::Venus and C(3)G co-localized in short thread-like segments and the diffuse C(3)G signal was no longer observed (##FIG##1##Figure 2G-H##). Eventually, CONA::Venus and C(3)G co-localization resembled that observed in the <italic>cona<sup>f04903</sup></italic> heterozygote in pachytene nuclei with fully assembled SC (##FIG##1##Figure 2K–L##). These data further demonstrate that the assembly of C(3)G into a thread-like SC requires the accumulation of CONA and involves co-localization with the CONA protein.</p>", "<title>Corona Localization Mimics that of C(3)G when AE Components Are Mutated</title>", "<p>The AEs are believed to form from chromosome core structures that contain sister chromatid cohesion proteins ##REF##16518630##[3]##. In mammals, AE-specific proteins such as SYCP2 and SYCP3 associate with components of the cohesin complex during the initial steps of SC assembly ##REF##7876343##[13]##, ##REF##10652260##[29]##–##REF##9933407##[37]##. Similarly, cohesin-based chromosomal cores/AEs form in Drosophila pro-oocytes ##REF##17698920##[38]##. Formation of the chromosomal core in Drosophila is dependent on the product of the <italic>c(2)M</italic> gene, which also localizes along this structure ##REF##15767569##[12]##,##REF##17698920##[38]##,##REF##12593793##[39]##. ORD protein also localizes along chromosome cores and is required for the maintenance of chromosome core integrity during meiotic prophase ##REF##17698920##[38]##,##REF##15007062##[40]##. Mutants in AE/LE proteins often result in recombination defects and the failure of synapsis, which indicates that properly formed AEs/LEs are required for the normal formation of the SC central region ##REF##11463847##[31]##, ##REF##12593793##[39]##, ##REF##12967565##[41]##–##REF##10485848##[44]##.</p>", "<p>To better understand the association of CONA with the SC, we examined the localization of the CONA protein in mutants that disrupt different components of the AE. Mutations in the <italic>c(2)M</italic> gene result in incomplete SC formation, as indicated by only very short segments of nuclear C(3)G localization, in contrast to the long, thread-like localization observed in wild-type ##REF##12593793##[39]##. In contrast, in <italic>ord</italic> mutants, the thread-like C(3)G staining appears to disassemble earlier than in wild-type due to the dissolution of cohesin-based chromosome cores along the chromosome arms ##REF##17698920##[38]##,##REF##15007062##[40]##.</p>", "<p>Analysis of CONA localization in <italic>c(2)M<sup>EP2115</sup></italic> homozygous pro-oocytes demonstrated that CONA was localized in numerous short segments that corresponded to sites of C(3)G localization (##FIG##2##Figure 3A##). CONA was consistently co-localized with C(3)G and was not seen to localize elsewhere in the germarium except to the dot-like short segments of C(3)G. The observed localization of CONA in <italic>c(2)M<sup>EP2115</sup></italic> homozygotes could indicate a robust association with C(3)G and/or an inability to localize to abnormally formed AEs except at sites stabilized by C(3)G accumulation. Nonetheless, the dependency on AEs for localization is similar for both CONA and C(3)G.</p>", "<p>CONA localization was also analyzed in <italic>ord<sup>5</sup>/ord<sup>10</sup></italic> trans-heterozygotes, in which no <italic>ord</italic> activity is present ##REF##15007062##[40]##. In agreement with published data ##REF##15007062##[40]##, we found that C(3)G formed extensive thread-like patterns of localization in pro-oocyte nuclei in region 2A (##FIG##2##Figure 3B##), but appeared as shorter segments in older germline cysts beginning in late region 2B (##FIG##2##Figure 3C##). Oocyte nuclei in region 3 displayed C(3)G signals that were further shortened or dot-like, indicating early SC disassembly. At all stages, CONA was always observed to co-localize with C(3)G within the germarium. The initial co-localization of CONA with C(3)G in region 2A was thread-like, similar to wild-type, but older germline cysts did not reveal differences in the extent of localization of the two proteins, suggesting that removal of CONA occurred contemporaneously with C(3)G removal. These results indicate that CONA and C(3)G behave similarly in both <italic>c(2)M</italic> and <italic>ord</italic> mutant backgrounds and suggest that CONA and C(3)G may comprise parts of the same SC sub-structure.</p>", "<title>CONA Requires C(3)G for Localization to the SC</title>", "<p>The consistent co-localization of CONA and C(3)G in wild-type and mutant backgrounds and the requirement of CONA for proper C(3)G localization prompted the question of whether C(3)G is required for CONA localization. If CONA is a protein of the AE/LE that is required for C(3)G attachment, it would be expected to localize to chromosomes regardless of whether C(3)G is present or not. When CONA localization was examined in females homozygous for the null mutation <italic>c(3)G<sup>68</sup></italic>, we found no evidence of CONA antibody staining in pro-oocyte nuclei (##FIG##2##Figure 3D##). This result is unlike that observed for the AE/LE component C(2)M ##REF##17947423##[21]##,##REF##12593793##[39]## and suggests that CONA does not act as an AE/LE component that localizes independently of C(3)G. Instead, these data are consistent with a role for CONA within the CR of the SC, which would not be expected to form in the absence of C(3)G.</p>", "<title>SMC1, ORD, and C(2)M Localize to Chromosome Cores in <italic>cona</italic> Mutant Pro-Oocytes</title>", "<p>To further investigate the role of CONA in SC formation, we investigated whether the SC protein C(2)M and the cohesion proteins ORD and SMC1 are able to localize normally in the absence of wild-type <italic>cona</italic>. All three proteins associate with the AEs/LEs of the SC in wild-type ##REF##15767569##[12]##,##REF##15007062##[40]##. In these experiments, we considered two aspects of ORD, SMC1, and C(2)M localization: first, whether the proteins localized to chromosomes and second, whether the localization appeared equivalent to that observed in wild-type in which normal SC is present. We utilized chromosome spread preparations in which soluble nuclear proteins are removed and only chromosome-associated proteins remain ##REF##17698920##[38]##. As shown in ##FIG##3##Figure 4A##, SMC1 and ORD are able to stably associate with the meiotic chromosomes in <italic>cona</italic> mutant pro-oocytes. Both cohesion proteins accumulate normally at the centromeres as evidenced by the bright foci present in both wild-type and <italic>cona</italic> mutant nuclei. Although distinct thread-like staining along the chromosome cores is also visible, the threads of staining appear to be thinner and more numerous than those in wild-type. A similar pattern was also observed for C(2)M localization (##FIG##3##Figure 4B##). These data suggest that ORD, SMC1, and C(2)M localize to chromosomes and form chromosome cores/AEs in the absence of CONA. However, their localization does not appear equivalent to wild-type, most likely because the AEs do not align and pair. A similar localization pattern for AE/LE proteins has been reported for <italic>c(3)G</italic> mutant oocytes ##REF##17698920##[38]##,##REF##12593793##[39]##.</p>", "<p>We also examined C(3)G localization to determine whether C(3)G protein can associate with chromatin in the absence of CONA. Although the C(3)G signal on <italic>cona</italic> spreads is diminished compared to wild-type, and long continuous thread-like staining is absent, puncta and short fragments of chromosome-associated C(3)G are visible. In many cases, these short stretches coincide with C(2)M, SMC1, and ORD (##FIG##3##Figure 4A–B##). Together, these results argue that CONA is not required for the localization of ORD, SMC1, or C(2)M to chromosomes or for the formation of the AEs. However, our data suggest that in the absence of CONA activity, association of C(3)G with AEs is insufficient for assembly of a normal SC central region and the pairing/alignment of AEs.</p>", "<title>Corona Localizes to C(3)G<sup>Cdel</sup> Polycomplexes and Is Required for Their Formation</title>", "<p>The co-localization with C(3)G, the profound effect on C(3)G localization, and the minor effect on AE protein localization led us to postulate that CONA localizes within the CR of the SC rather than along AEs. Based on this hypothesis, we predicted that CONA would co-localize with C(3)G protein that is prevented from binding to AEs. C(3)G is thought to interact with AEs via its C-terminal globular domain, which is normally oriented toward the AEs ##REF##15767569##[12]##. Jeffress and colleagues ##REF##17947423##[21]## found that a deletion of amino acids 651–744 at the C-terminal end of C(3)G (known as C(3)G<sup>Cdel</sup>) abolished the ability for C(3)G to form normal SC along chromosomes, but instead the protein accumulated into aggregates called polycomplexes (PCs). The PCs formed by the C(3)G<sup>Cdel</sup> protein often take on a hollow cylindrical shape, and may form in either the presence or absence of wild-type C(3)G protein.</p>", "<p>We analyzed CONA localization in C(3)G<sup>Cdel</sup> PCs by immunofluorescence in females expressing the C(3)G<sup>Cdel</sup> protein in the absence of wild-type C(3)G. As expected, the C(3)G<sup>Cdel</sup> protein was detected in sub-cellular bodies of varying size, which correspond to the PCs, and not in a thread-like pattern along chromosomes. Similarly, strong CONA immunofluorescence was detected on the PCs, but not along chromosomes (##FIG##4##Figure 5A##). This demonstrates that amino acids 651–744 at the C-terminus of C(3)G are dispensable for CONA co-localization and that CONA does not localize along AEs or chromosome cores in the absence of wild-type C(3)G.</p>", "<p>Since CONA is necessary for the assembly of wild-type C(3)G into normal SC, and CONA co-localizes with both C(3)G in wild-type and with C(3)G<sup>Cdel</sup> in PCs, we tested whether CONA is required for the formation of the PCs. Using antibodies specific to the coiled coil region of C(3)G to detect both wild-type C(3)G and C(3)G<sup>Cdel</sup> (##FIG##4##Figure 5B## and ##SUPPL##1##Figure S2A##), we examined germaria from females expressing C(3)G<sup>Cdel</sup> in a <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> double mutant background. Expression of the C(3)G<sup>Cdel</sup> protein results in PC formation in a <italic>c(3)G<sup>68</sup></italic> single mutant background (##FIG##4##Figure 5C## and ##SUPPL##1##Figure S2B##). However, when CONA was absent in the <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> double mutant, no anti-C(3)G immunofluorescence was visible above background levels, even though pro-oocyte nuclei could be detected by anti-SMC1 staining (##FIG##4##Figure 5D## and ##SUPPL##1##Figure S2D##). The diffuse C(3)G staining observed in <italic>cona</italic> mutants was also not visible in this experiment, possibly due to differences in expression or stability of wild-type C(3)G compared to the C(3)G<sup>Cdel</sup> protein. As a positive control to ensure that the transgenes encoding GAL4::VP16 and C(3)G<sup>Cdel</sup> were both present and functioning in the experiment, and that the anti-C(3)G staining was successful, ovaries from sibling <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> heterozygote females were stained and analyzed at the same time. This control, in which both <italic>c(3)G</italic> and <italic>cona</italic> were heterozygous over wild-type alleles, revealed PC formation indicative of C(3)G<sup>Cdel</sup> expression, as well as thread-like C(3)G staining expected for a <italic>c(3)G cona</italic> double heterozygote (##SUPPL##1##Figure S2C and S2E##).</p>", "<p>The failure to detect PC formation in <italic>cona</italic> homozygotes demonstrates that CONA is required for C(3)G<sup>Cdel</sup> PC formation, similar to the requirement of CONA for SC formation. This observation and the localization of CONA to C(3)G<sup>Cdel</sup> PCs support the hypothesis that CONA is involved in CR formation in SCs. In these experiments we observed the disorganization of chromosomal cores/AEs along chromosome arms when the CR is abrogated by mutations in <italic>c(3)G</italic> and/or <italic>cona</italic>. Chromosomal cores/AEs detected using anti-SMC1 antibodies in wild-type appeared long and thread-like, closely matching C(3)G localization (##FIG##4##Figure 5E##). In the absence of wild-type C(3)G or CONA, however, SMC1 was detected in less intensely stained linear segments that were more numerous (##FIG##4##Figure 5F–G##). As noted above, this suggests that assembly of chromosome cores/AEs occurs along the sister chromatids but disruption of the CR of the SC results in disorganization of these structures compared to wild-type.</p>", "<title>Corona Is Necessary for Meiotic Chromosome Pairing</title>", "<p>The SC is known to play a role in homologous chromosome pairing in Drosophila oocytes ##REF##16299588##[19]##,##REF##15545646##[20]##, and defects in this process could contribute to the disorganization of AEs and the reduction in exchange in <italic>cona</italic> mutants. To determine whether <italic>cona</italic> is required for homologous chromosome pairing, we examined the association of homologous euchromatic DNA sequences in pro-oocytes and oocytes from germarium regions 2A, 2B and 3 using fluorescence <italic>in situ</italic> hybridization (FISH). Using a FISH probe that hybridizes near the middle of the <italic>X</italic> chromosome euchromatin, we found paired homologs in 97.7% (85/87) of the wild-type cells examined (##FIG##5##Figure 6A##). In contrast, paired homologs were detected in only 46.0% (40/87) of <italic>cona<sup>f04903</sup></italic> homozygous pro-oocytes and oocytes (##FIG##5##Figure 6B##). This demonstrates a dramatic decrease in the ability of homologous chromosomes to associate in the absence of CONA.</p>", "<p>Testing for homolog pairing in females homozygous for <italic>c(3)G<sup>68</sup></italic> demonstrated that homologs were paired in only 36.5% (19/52) of cells examined (##FIG##5##Figure 6C##), which is consistent with previously published results that show a role for C(3)G, and thus the SC, in homolog pairing ##REF##16299588##[19]##,##REF##15545646##[20]##. In <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> double mutant females, homologs were paired in 29.8% (14/47) of the pro-oocytes and oocytes examined (##FIG##5##Figure 6D##), a figure not significantly different than that for <italic>c(3)G<sup>68</sup></italic> alone (χ<sup>2</sup> = 0.506; p = 0.477). Since CONA is required for normal C(3)G localization, the pairing defect in the <italic>cona</italic> mutant may be a result of abnormal C(3)G localization. We noticed that there was a slight, but not significant (χ<sup>2</sup> = 3.324; p = 0.068), elevation in pairing frequency in <italic>cona<sup>f04903</sup></italic> homozygotes compared to <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> double homozygotes, which could possibly be explained by a low level of homolog pairing promoted by the small amount of C(3)G that localizes to chromosomes in the <italic>cona<sup>f04903</sup></italic> single mutant (##FIG##3##Figure 4##). These data demonstrate that both <italic>c(3)G</italic> and <italic>cona</italic> are necessary for normal levels of homolog pairing, and are consistent with CONA functioning within the CR of the SC to promote synapsis.</p>" ]
[ "<title>Discussion</title>", "<title>Corona Is Critical for Polymerization of C(3)G to Form the Central Region of the SC</title>", "<p>We have characterized Corona (CONA), a novel SC-associated protein that is critical for the higher-order assembly of TFs into the CR of the SC. The normal localization of CONA and C(3)G is mutually-dependent – in the absence of CONA, C(3)G is visible as only spots or short threads along meiotic chromosome cores, and in the absence of C(3)G, CONA appears to be absent from the meiotic nucleus. Three lines of evidence suggest that CONA plays a critical role in the stable assembly of C(3)G into the CR of the SC. First, <italic>cona</italic> mutant oocytes fail to form long stretches of continuous SC, and only short threads or spots of C(3)G are visible within the pro-oocyte nucleus (##FIG##3##Figure 4## and ##SUPPL##0##Figure S1##). Second, the dependence of SC assembly (as assayed by C(3)G polymerization) on CONA::Venus expression in the absence of endogenous CONA, as well as the co-localization of CONA and C(3)G in <italic>c(2)M</italic> and <italic>ord</italic> mutants (##FIG##1##Figure 2## and ##FIG##2##Figure 3##) suggest that CONA is required to polymerize C(3)G into long stretches required to form the CR. Third, the requirement for CONA to facilitate C(3)G polymerization is also demonstrated by the fact that CONA localizes to the C(3)G PCs created by expressing C(3)G proteins that lack their C-termini and thus cannot bind chromosomes (##FIG##4##Figure 5##). Moreover, CONA also is required for the formation of these PCs, demonstrating that CONA has a functional role necessary for the connection of C(3)G<sup>Cdel</sup> molecules in PCs.</p>", "<p>The phenotypes of <italic>cona</italic> mutants make it clear that the CONA-mediated assembly of C(3)G into polymerized TFs is required for most, if not all, aspects of C(3)G function. Despite being present in <italic>cona</italic> mutants, C(3)G protein is unable to promote homolog synapsis or exchange. Defects in meiotic pairing, synapsis, and recombination are similar in <italic>cona</italic>, <italic>c(3)G</italic> and <italic>c(3)G cona</italic> mutant pro-oocytes (##FIG##5##Figure 6##, SLP and RSH, unpublished data).</p>", "<title>How Might CONA Function?</title>", "<p>In terms of its role in the formation of the CR of the SC, CONA may have a role similar to the mouse CE proteins SYCE1, SYCE2, and TEX12 ##REF##17339376##[23]##–##REF##18611960##[26]##. These proteins co-localize extensively with the TF protein SYCP1, though SYCE2 and TEX12 were reported to have a more punctate appearance. Moreover, SYCE1 and SYCE2 also remain co-localized with SYCP1 when AEs/LEs are disrupted in <italic>Sycp3</italic>\n<sup>−/−</sup> spermatocytes and oocytes, and are unable to localize to meiotic chromosomes in the absence of SYCP1 ##REF##15944401##[24]##,##REF##16968740##[25]##. Mutation of SYCE2 or TEX12 disrupts synapsis and greatly reduces the amount of SYCP1 that localizes to chromosomes, yet AE proteins localize normally. In <italic>Syce2<sup>−/−</sup></italic> and <italic>Tex12<sup>−/−</sup></italic> meiotic cells, synapsis appears to be initiated at multiple sites along the paired homologs, but they fail to extend and form full-length SC ##REF##17339376##[23]##,##REF##18611960##[26]##. These findings are quite similar to the <italic>cona</italic> mutant phenotype, in which only a small amount of C(3)G is found on chromosomes, while the C(2)M, SMC1, and ORD proteins are still localized properly.</p>", "<p>SYCE1 has been proposed to stabilize head-to-head interactions between SYCP1 dimers, while SYCE2 and TEX12 have been proposed to connect SYCP1-SYCE1 complexes to form higher-order structures ##REF##17339376##[23]##,##REF##18611960##[26]##. Either of these roles of CE proteins is consistent with the activities of CONA, in that the N-terminus of C(3)G is localized to the CE and required for normal SC formation ##REF##15767569##[12]##,##REF##17947423##[21]## and the formation of higher order SC or PC structures fails in the absence of CONA. Moreover, the phenotype exhibited by <italic>cona</italic> mutants parallels that documented for N-terminal deletions of C(3)G ##REF##17947423##[21]##; only spots or short stretches of chromosomally-associated C(3)G are visible. These data suggest that either one large or multiple small domains deleted in these N-terminus-deficient C(3)G proteins may define regions of C(3)G that interact with CONA.</p>", "<title>\n<italic>cona</italic> and <italic>c(3)G</italic> Mutations Both Abolish Alignment of the AEs</title>", "<p>Localization of C(2)M, SMC1, and ORD in <italic>cona</italic> mutant pro-oocytes indicates that chromosome core/AE structures are present, although they are more numerous and appear thinner than in wild-type. This disorganized pattern resembles that observed for C(2)M and cohesin SMC proteins when C(3)G is absent ##REF##17698920##[38]##,##REF##12593793##[39]## and argues that AEs cannot align in the absence of synapsis in Drosophila oocytes. In addition, FISH analysis demonstrates that pairing of homologous sequences is severely disrupted in <italic>cona</italic> (this study) and <italic>c(3)G</italic> oocytes ##REF##16299588##[19]##,##REF##15545646##[20]##.</p>", "<p>Disruption of homolog pairing and alignment in <italic>cona</italic> and <italic>c(3)G</italic> mutants contrasts sharply with what is observed in mammalian meiocytes lacking the TF protein SYCP1 or CE proteins SYCE2 or TEX12. Although homologous chromosomes fail to synapse in <italic>Sycp1<sup>−/−</sup></italic>, <italic>Syce2<sup>−/−</sup></italic>, and <italic>Tex12<sup>−/−</sup></italic> meiotic cells, AEs lie in close proximity along their entire length ##REF##15937223##[18]##,##REF##17339376##[23]##,##REF##18611960##[26]##. Presumably, the formation of DSBs at multiple sites along the chromosomes establishes axial associations and these are sufficient to hold homologous chromosomes in close proximity even when the SC fails to propagate ##REF##15937223##[18]##,##REF##17339376##[23]##,##REF##18611960##[26]##. Axial associations likely form the basis for the assembly of the short regions of SC observed in <italic>Syce2<sup>−/−</sup></italic> and <italic>Tex12<sup>−/−</sup></italic> meiotic cells, which could further secure the alignment of homologs. While we cannot rule out the possibility that similar short regions of “synapsis” exist in <italic>cona</italic> oocytes, it seems likely that even a small number of these along the length of the chromosome would result in at least some examples in which AEs lie as “parallel tracks” in chromosome spreads, a phenomenon that we did not observe.</p>", "<p>Our analysis of <italic>cona</italic> mutant oocytes suggests that, unlike mammals, the SC is critical for early events governing the pairing/alignment of homologous chromosomes in Drosophila. We can envision at least three different models that might explain why homolog alignment is dependent on SC in Drosophila. In the first model, homologous chromosomes enter meiotic prophase already paired and aligned as a result of the persistence of pairing established during preceding cell cycles and the rapid formation of SC is required to maintain these associations ##REF##9334324##[4]##. Although this model has been favored in the past, two published reports refute the argument that homologous chromosomes enter meiosis already paired and aligned. As noted by Fung and colleagues ##REF##9531544##[45]## as well as Csink and Henikoff ##REF##9763417##[46]##, the pairing of homologous chromosomes in Drosophila somatic cells is disrupted during both replication and mitosis. Therefore, any pairing that exists prior to meiotic S phase would be lost and need to be re-established, most likely during meiotic prophase.</p>", "<p>The second model posits that the different effects on homolog pairing and alignment observed in flies and mammals reflect differences in the ability of CE proteins to stabilize short stretches of SC. In contrast to flies, DSBs are required for synapsis in mice ##REF##11106739##[47]##–##REF##9452390##[49]##. The short stretches of SC resulting from the formation of DSBs and early recombination intermediates in mouse meiocytes lacking SYCE2 or TEX12 may maintain the alignment of AEs in the absence of full synapsis. If the requirement for CE function is sufficiently more stringent in flies than in mammals, then short regions of synapsis similar to those observed in <italic>Tex12<sup>−/−</sup></italic> and <italic>Syce2<sup>−/−</sup></italic> meiocytes may be unstable or never form in <italic>cona</italic> mutant flies. In the absence of such stretches of SC or DSB-induced axial associations, the Drosophila homologs would be expected to quickly dissociate.</p>", "<p>Our third model is based on the different temporal relationship between DSB formation and SC assembly in flies and mammals. In mammals, DSB formation and the formation of early recombination intermediates occur commensurate with, and are required for SC formation ##REF##11106739##[47]##,##UREF##0##[48]##. In contrast, DSB initiation occurs after the completion of SC assembly in Drosophila and is not required for synapsis ##REF##9452390##[49]##–##REF##17166055##[51]##. Because SC assembly in flies occurs via a DSB-independent pathway, pairing/alignment of AEs may be abolished in mutant oocytes in which higher-order assembly of TFs is prevented. According to this model, initial pre-synaptic associations of homologs may be maintained either by the formation of early recombination intermediates and axial associations that lead to the initiation of short stretches of SC (the mammalian paradigm), or by the establishment of extensive synapsis (the Drosophila paradigm). In both cases, the initial event (formation of recombination intermediates or SC formation) is eventually followed and perhaps ‘locked-in’ by the other. One could hypothesize that mammalian CE mutants can maintain alignments because of the earlier formation of recombination intermediates and axial associations. In contrast, lack of SC assembly in <italic>cona</italic> and <italic>c(3)G</italic> mutants would compromise the essential early step that maintains the alignment of homologous chromosomes in Drosophila oocytes. If the homologs are already apart by the time DSBs occur in <italic>cona</italic> and <italic>c(3)G</italic> mutants, DSBs would be too late to stabilize homolog associations and maintain AE alignment.</p>", "<p>In summary, our data demonstrate an essential requirement for CONA in the polymerization of C(3)G that is required for SC formation. Understanding the mechanism by which CONA performs that role will require the identification of CONA-interacting proteins, which we expect will include the N-terminal globular domain of C(3)G and perhaps other CE proteins as well. Elucidating the function of these proteins in SC assembly and the consequences of their loss by mutation may also help us understand the role of the SC in establishing or maintaining the pairing and alignment of homologs in early prophase.</p>" ]
[]
[ "<p><bold>¤:</bold> Current address: Institute of Molecular Biology, University of Oregon, Eugene, Oregon, United States of America</p>", "<p>Conceived and designed the experiments: SLP RSK CML SEB RSH. Performed the experiments: SLP RSK CML RJN JKJ. Analyzed the data: SLP RSK CML SEB RSH. Contributed reagents/materials/analysis tools: JKJ WDW. Wrote the paper: SLP RSK CML RJN JKJ WDW SEB RSH.</p>", "<p>The synaptonemal complex (SC) is an intricate structure that forms between homologous chromosomes early during the meiotic prophase, where it mediates homolog pairing interactions and promotes the formation of genetic exchanges. In <italic>Drosophila melanogaster</italic>, C(3)G protein forms the transverse filaments (TFs) of the SC. The N termini of C(3)G homodimers localize to the Central Element (CE) of the SC, while the C-termini of C(3)G connect the TFs to the chromosomes via associations with the axial elements/lateral elements (AEs/LEs) of the SC. Here, we show that the Drosophila protein Corona (CONA) co-localizes with C(3)G in a mutually dependent fashion and is required for the polymerization of C(3)G into mature thread-like structures, in the context both of paired homologous chromosomes and of C(3)G polycomplexes that lack AEs/LEs. Although AEs assemble in <italic>cona</italic> oocytes, they exhibit defects that are characteristic of <italic>c(3)G</italic> mutant oocytes, including failure of AE alignment and synapsis. These results demonstrate that CONA, which does not contain a coiled coil domain, is required for the stable ‘zippering’ of TFs to form the central region of the Drosophila SC. We speculate that CONA's role in SC formation may be similar to that of the mammalian CE proteins SYCE2 and TEX12. However, the observation that AE alignment and pairing occurs in <italic>Tex12</italic> and <italic>Syce2</italic> mutant meiocytes but not in <italic>cona</italic> oocytes suggests that the SC plays a more critical role in the stable association of homologs in Drosophila than it does in mammalian cells.</p>", "<title>Author Summary</title>", "<p>Meiosis is a specialized type of cell division that is needed to produce sperm and egg cells, which carry only half the number of chromosomes of other cells in the body. Meiosis is required for reproduction, but abnormalities in chromosome number caused by errors in the process of meiosis are responsible for many birth defects and mental retardation syndromes in humans. The fruit fly, <italic>Drosophila melanogaster</italic>, is an excellent organism in which to study meiosis because of the powerful genetic and microscopic techniques that can be implemented with it. Early in meiosis, homologous chromosomes are joined together by an elaborate protein structure called the synaptonemal complex (SC) that plays a critical role in both holding homologous chromosomes together and in facilitating a process known as meiotic recombination. In this study, we have found a protein called Corona that is required for the formation of the SC. Our data show that Corona is required for the proper localization of the SC protein C(3)G. In the absence of Corona, C(3)G fails to polymerize and form the central region of the SC. Increasing our understanding of SC assembly and function will lead to a better understanding of the mechanism for proper chromosome segregation during meiosis.</p>" ]
[ "<title>Supporting Information</title>" ]
[ "<p>We thank Kathy Teeter, Kristen Dean, Danny Agne, Kathy Wright, and Jeremy Ong for their assistance on the genetic screen that produced <italic>corona</italic>. We also wish to thank the Bloomington Drosophila Stock Center and the Harvard Exelixis Stock Collection for providing fly stocks, and Nicolas Malmanche, the Drosophila Genomics Resource Center and the Developmental Studies Hybridoma Bank for reagents.</p>" ]
[ "<fig id=\"pgen-1000194-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g001</object-id><label>Figure 1</label><caption><title>CONA protein co-localizes with C(3)G.</title><p>(A) Wild-type pro-oocytes stained with anti-CONA and anti-C(3)G, showing CONA (green) and C(3)G (red) co-localization. (B) Images of a single deconvolved optical section of a pair of pro-oocytes showing that CONA::Venus (green) and C(3)G (red) co-localize extensively. (C) Maximum intensity projections of the entire nuclei from B. Scale bars, 5 µm.</p></caption></fig>", "<fig id=\"pgen-1000194-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g002</object-id><label>Figure 2</label><caption><title>CONA is required for the thread-like localization of C(3)G.</title><p>Shown is the localization of CONA::Venus (green), C(3)G (red), and DAPI (blue) in region 2A of germaria from <italic>P{nos-GAL4::VP16}/+</italic>; <italic>P{UASP-cona::Venus}/+</italic> ; <italic>cona<sup>f04903</sup>/+</italic> (A, B, E, F, I, J, left) and <italic>P{nos-GAL4::VP16}/+</italic> ; <italic>P{UASP-cona::Venus}/+</italic> ; <italic>cona<sup>f04903</sup></italic> (C, D, G, H, K, L, right). The top, middle and bottom rows show pro-oocytes in which C(3)G was present but CONA::Venus was visible at very low to undetectable (A, B, C, D), moderate (mod.) (E, F, G, H), or high (I, J, K, L) levels. When one functional copy of the endogenous <italic>cona<sup>+</sup></italic> gene is present, the localization of C(3)G takes on a punctate to thread-like pattern (arrowheads) in very early cysts, even when CONA::Venus is not readily detected (A, B). The spotty to thread-like pattern of C(3)G accumulation observed in panel B is also observed in early region 2A in <italic>cona<sup>f04903</sup>/+</italic> heterozygotes that lack the CONA::Venus construct, and represents an early stage (zygotene) in SC assembly (##SUPPL##0##Figure S1C##). When CONA::Venus is expressed in a <italic>cona</italic> homozygous mutant background and is the only functional CONA protein present, the initial localization of C(3)G resembles that of a <italic>cona</italic> mutant homozygote (C, D), with diffuse and spotty regions of C(3)G localization (arrowheads). C(3)G takes on a thread-like appearance only when CONA::Venus begins to be detected (G, H, K, L), showing that the assembly of C(3)G into a thread-like SC coincides with and requires the accumulation of CONA::Venus protein. Scale bars, 5 µm.</p></caption></fig>", "<fig id=\"pgen-1000194-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g003</object-id><label>Figure 3</label><caption><title>CONA co-localizes with C(3)G in disrupted SCs and requires C(3)G for localization.</title><p>(A) Wild-type control pro-oocytes stained with anti-CONA and anti-C(3)G, showing CONA and C(3)G co-localization. (B) <italic>c(2)M<sup>EP2115</sup></italic> homozygous pro-oocyte stained to detect CONA and C(3)G co-localization. (C) <italic>ord<sup>5</sup>/ord<sup>10</sup></italic> pro-oocytes from germarium region 2A showing CONA and C(3)G co-localization. (D) <italic>ord<sup>5</sup>/ord<sup>10</sup></italic> pro-oocytes from germarium region 2B (anterior tip of germarium oriented toward the top) stained to detect CONA and C(3)G co-localization in pro-oocytes experiencing early SC disassembly (arrowhead). (E) <italic>c(3)G<sup>68</sup></italic> homozygote germarium showing the absence of CONA signal in pro-oocytes (arrowheads) marked by high levels of cytoplasmic ORB protein. Scale bars, 5 µm.</p></caption></fig>", "<fig id=\"pgen-1000194-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g004</object-id><label>Figure 4</label><caption><title>Cohesion and AE proteins localize to chromosomes and form chromosome cores during early prophase in the absence of CONA activity.</title><p>(A) Localization of SMC1, GFP-ORD, C(3)G and DNA (DAPI) on chromosome spread preparations from wild-type and <italic>cona<sup>A12</sup>/cona<sup>f04903</sup></italic> mutant ovaries. As in wild-type, SMC1 and GFP-ORD are enriched at centromeres (bright regions, arrows) and localize along the chromosome cores in the <italic>cona</italic> mutant. However, the threads of SMC1 and GFP-ORD localization appear thinner and more numerous than in wild-type, giving them a somewhat disorganized appearance. C(3)G is associated with the chromatin but does not form long thread-like stretches. Although the coincidence of the three proteins is less obvious than in wild-type, short stretches of C(3)G co-localization with the chromosome cores are visible (arrowheads). (B) Localization of SMC1, C(2)M, C(3)G and DNA (DAPI) on chromosome spread preparations of <italic>cona<sup>A12</sup></italic> ovaries. Like SMC1 and GFP-ORD, C(2)M localizes along the chromosome cores/AEs and co-localization of C(2)M with SMC1 and C(3)G is visible (arrows). The C(3)G signal in <italic>cona</italic> mutants is weaker than in wild-type and has been significantly enhanced to ensure that the details of the staining pattern are visible. The disorganized appearance of cores in both A and B is consistent with absence of AE alignment and synapsis and is similar to that observed for SMC1 and C(2)M localization in <italic>c(3)G</italic> mutant oocytes ##REF##17698920##[38]##,##REF##12593793##[39]##. All panels are single optical sections. Scale bars, 2 µm.</p></caption></fig>", "<fig id=\"pgen-1000194-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g005</object-id><label>Figure 5</label><caption><title>CONA localizes to C(3)G<sup>Cdel</sup> polycomplexes (PCs) and is required for their formation.</title><p>(A) A <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup></italic> pro-oocyte stained to detect CONA (green) and the coiled coil region of C(3)G (red) shows that CONA localization is restricted to the C(3)G<sup>Cdel</sup> PC (arrowhead). (B) Maximum intensity projections of a wild-type pro-oocyte stained to detect SMC1 (green) and the coiled coil region of C(3)G (red), showing a wild-type pattern of SC. (C) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup></italic> pro-oocyte stained to detect SMC1 (green) and the coiled coil region of C(3)G (red). Large arrowheads indicate the major C(3)G<sup>Cdel</sup> PC visible in the nucleus. (D) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> pro-oocyte stained to detect SMC1 (green) and the coiled coil region of C(3)G (red), demonstrating the lack of PC formation in the absence of CONA. (E) SMC1 localization (white) in a single optical section of the pro-oocyte shown in panel B. (F) SMC1 localization (white) in a single optical section of the pro-oocyte shown in panel C. (G) SMC1 localization (white) in a single optical section of the pro-oocyte shown in panel D. Small arrowheads in E–G indicate thread-like SMC1 localization. Scale bars, 5 µm.</p></caption></fig>", "<fig id=\"pgen-1000194-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000194.g006</object-id><label>Figure 6</label><caption><title>Homologous chromosome pairing is disrupted in <italic>cona</italic> mutants.</title><p>Shown are pro-oocytes from wild-type (A), <italic>cona<sup>f04903</sup></italic> (B), <italic>c(3)G<sup>68</sup></italic> (C), and <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> (D) germaria identified by ORB localization (red) and hybridized with a FISH probe (green) specific for polytene bands 9F4-10B1 of the <italic>X</italic> chromosome. DAPI-stained DNA is shown in blue. In contrast to wild-type (A), in which the FISH signals usually appeared as a single focus or closely spaced foci, FISH signals in <italic>cona<sup>f04903</sup></italic> were often observed as widely separated foci (B), indicating a disruption in homologous chromosome pairing. Scale bars, 1 µm. (E) Quantified results of the FISH analysis on pro-oocytes and oocytes from germarium regions 2A, 2B, and 3 are shown as percent of nuclei with paired chromosomes (blue bars) and unpaired chromosomes (dark grey bars) in each genotype shown. The number of nuclei observed in each category is shown above each bar. (Nuclei containing a single hybridization focus or foci separated by 0.7 µm or less were defined as paired ##REF##11483963##[33]##, while those with foci separated by more than 0.7 µm were defined as unpaired.)</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"pgen.1000194.s001\"><label>Figure S1</label><caption><p>CONA and C(3)G localization in <italic>cona</italic> mutant pro-oocytes. (A) Wild-type control pro-oocytes showing CONA and C(3)G co-localization. (B) <italic>cona<sup>f04903</sup></italic> homozygous pro-oocytes showing CONA is not detected and C(3)G localization is more diffuse than in wild-type nuclei with threads that are less distinct. Similar observations were made using ovaries from <italic>cona<sup>A12</sup></italic>/<italic>Df(3R)JDP</italic> females (SLP and WDW, unpublished data). These observations indicate that little or no endogenous CONA protein is produced in the presence of the <italic>cona<sup>A12</sup></italic> or <italic>cona<sup>f04903</sup></italic> mutations. (C) <italic>cona<sup>f04903</sup>/+</italic> pro-oocytes in early region 2A showing CONA is present and co-localizes with the polymerizing C(3)G in early zygotene stage pro-oocytes (arrow) that show spotty C(3)G localization. (D) <italic>cona<sup>f04903</sup>/+</italic> pro-oocytes in late region 2A showing that CONA is present and co-localized with C(3)G, similar to wild-type. Pro-oocytes were stained with anti-CONA (green) and anti-C(3)G (red). Each image represents a single deconvolved optical section. Scale bars, 2.5 µm (A, C, D) and 5 µm (B).</p><p>(5.2 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000194.s002\"><label>Figure S2</label><caption><p>CONA is required for C(3)G<sup>Cdel</sup> polycomplex (PC) formation. (A) Maximum intensity projections of a wild-type germarium stained to detect SMC1 (green) and the coiled coil region of C(3)G (red). Arrowheads indicate pro-oocytes with thread-like C(3)G localization. (B) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup></italic> germarium stained to detect SMC1 (green) and the coiled coil region of C(3)G (red). Arrowheads indicate PCs visible in pro-oocyte nuclei. (C) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup>/TM3</italic>, <italic>Ser</italic> germarium stained to detect SMC1 (green) and the coiled coil region of C(3)G (red). Arrowheads indicate PCs visible in pro-oocyte nuclei that also have thread-like C(3)G localization due to heterozygosity for <italic>c(3)G<sup>68</sup></italic> and <italic>cona<sup>f04903</sup></italic>. (D) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup></italic> germarium stained to detect SMC1 (green) and the coiled coil region of C(3)G (red), which demonstrates the lack of PC formation in the absence of CONA. (E) Maximum intensity projections of a <italic>y w/y w P{nos-GAL4::VP16}</italic>; <italic>P{UASP-c(3)G<sup>Cdel</sup>}4/+</italic>; <italic>c(3)G<sup>68</sup> cona<sup>f04903</sup>/TM3</italic>, <italic>Ser</italic> pro-oocyte stained to detect SMC1 (green) and the coiled coil region of C(3)G (red). Large arrowheads indicate the major PC visible in the nucleus. Small arrowheads indicate thread-like C(3)G localization also present due to heterozygosity for <italic>c(3)G<sup>68</sup></italic> and <italic>cona<sup>f04903</sup></italic>. Scale bars, 50 µm (A-D), 5 µm (E).</p><p>(3.9 MB TIF)</p></caption></supplementary-material>" ]
[ "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>RSH is an American Cancer Society Research Professor. This work was supported by funds from the Stowers Institute for Medical Research, National Institutes of Health grant GM-59354 to SEB, and grants to SLP from the James Cook University Faculty of Medicine, Health and Molecular Sciences.</p></fn></fn-group>" ]
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[ "<media xlink:href=\"pgen.1000194.s001.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000194.s002.tif\"><caption><p>Click here for additional data file.</p></caption></media>" ]
[{"label": ["48"], "element-citation": ["\n"], "surname": ["Romanienko", "Camerini-Otero"], "given-names": ["PJ", "RD"], "year": ["2000"], "article-title": ["The mouse Spo11 gene is required for meiotic chromosome synapsis."], "source": ["Mol Cell"], "volume": ["6"], "fpage": ["975"], "lpage": ["987"]}, {"label": ["57"], "element-citation": ["\n"], "surname": ["Dernburg", "Sullivan", "Ashburner", "Hawley"], "given-names": ["AF", "W", "M", "RS"], "year": ["2000"], "article-title": ["In situ hybridization to somatic chromosomes."], "source": ["Drosophila Protocols"], "publisher-loc": ["Cold Spring Harbor, NY"], "publisher-name": ["Cold Spring Harbor Laboratory Press"], "fpage": ["25"], "lpage": ["55"]}, {"label": ["59"], "element-citation": ["\n"], "surname": ["Dernburg", "Bickmore"], "given-names": ["AF", "WA"], "year": ["1999"], "article-title": ["Fluorescence "], "italic": ["in situ"], "source": ["Chromosome Structural Analysis: A Practical Approach"], "publisher-loc": ["New York"], "publisher-name": ["Oxford University Press"], "fpage": ["125"], "lpage": ["145"]}]
{ "acronym": [], "definition": [] }
60
CC BY
no
2022-01-12 23:38:08
PLoS Genet. 2008 Sep 19; 4(9):e1000194
oa_package/55/8b/PMC2529403.tar.gz
PMC2529404
18797507
[ "<title>Introduction</title>", "<p>Almost two-thirds of American adults are either overweight or obese and the incidence of childhood obesity is rising rapidly ##REF##15199035##[1]##–##REF##17510091##[4]##. The cause of the recent and dramatic rise in obesity in western societies is not fully understood, but its appearance in a genetically stable population indicates that environmental factors play a critical role ##REF##10391020##[5]##–##REF##16263145##[7]##. In western societies, the increased availability of tasty, energy-rich foods together with a reduced requirement for energy expenditure is believed to promote a net positive energy balance resulting in increased body mass and obesity ##REF##10391020##[5]##, ##REF##13937884##[8]##. The availability of these foods, however, does not entirely account for their widespread (over-)consumption. Palatable, high-calorie foods are widely available because there is a demand for them, though marketing practices undoubtedly contribute to this, especially among children ##REF##16720538##[9]##–##REF##17766531##[11]##. Moreover, the widespread availability of high fat and high sugar foods does not mean that healthier alternatives are unavailable, though this may vary by region and socioeconomic status ##REF##17884566##[12]##–##UREF##1##[18]##. Human beings, though influenced by environmental factors ##REF##16263145##[7]##, respond to their environment with motivated, goal-directed behavior ##REF##17122359##[19]##–##REF##17307205##[21]##, often seeking out foods they know to be poor choices, sometimes even when they resolve to eat better or less. These adult behaviors are often resistant to change ##REF##11194216##[22]##. Understanding the factors that shape adult food-related motivation and its contribution to weight gain would greatly aid in the development of preventative measures in tackling the problem of obesity.</p>", "<p>Efforts to understand the recent rise in obesity focus on discerning the relative contributions of, or interactions between, genetic and environmental factors in adulthood ##REF##9603719##[6]##, ##REF##16263145##[7]##, ##REF##17122359##[19]##, ##REF##9284671##[23]##, ##REF##10889787##[24]##. Developmental contributions and interactions are only recently being investigated and have focused almost exclusively on maternal over- and under-nutrition affecting the fetal or neonatal nutritional environment. Arising from the fetal origins hypothesis, termed the ‘thrifty phenotype,’ first proposed by Hales, Barker and colleagues ##REF##1644236##[25]##, ##REF##11809615##[26]##, this work proposes that pre- and peri-natal nutritional status developmentally programs the organism's adult metabolism and energy balance to form a “predictive adaptive response” ##REF##16881892##[27]##. This work has clearly demonstrated that early nutritional status can alter later energy balance behavior and body weight ##REF##15992360##[28]##–##REF##16684802##[36]##. The window during which such programming occurs, however, is not well defined. Whether a developmental window remains open during childhood and adolescence allowing nutritional experience during this time to substantively shape subsequent adult energy balance remains an open question. As children's diets in contemporary western society tend to be replete with high fat and sugar foods, such developmental effects could potentially compound the obesity epidemic or, alternatively, offer a potential approach to addressing it.</p>", "<p>Human studies examining childhood diet and obesity are equivocal, providing some evidence that childhood diet can contribute to obesity risk ##REF##17414504##[37]## but also showing that restricting children's diets increases obesity risk ##REF##17442696##[38]##, though these studies generally do not examine adult outcome. Given the difficulty inherent in human studies, including the challenge of maintaining adequate experimental control and the expense and delay inherent in human longitudinal studies, an animal model would greatly facilitate investigations into the relationship between early dietary experience and adult obesity.</p>", "<p>Like many humans, C57BL/6 mice show a strong preference for sugar and fat and become obese and develop diabetes when given chronic access to a high-sugar/high-fat diet ##REF##15996693##[39]##–##REF##1621856##[42]##. The C57BL/6 mouse shares with humans the ‘thrifty genotype,’ a putative genetic predisposition to store calories whenever food is readily available ##REF##13937884##[8]##, ##REF##7752914##[40]##. Here we used C57Bl/6 mice to directly test the effect of early post-weaning experience on adult motivated behavior and obesity risk. We focused on sucrose exposure in early life since sucrose is a potent natural reinforcer ##REF##17668074##[43]## and because the complex mechanisms that have evolved to regulate sugar metabolism interact with motivational and reward systems ##REF##17137609##[44]##. We hypothesized that early experience with sucrose can alter adult motivated behavior and thereby may constitute an important factor determining adult feeding behavior and energy balance.</p>", "<p>We show that unlimited access to sucrose early in life reduces motivation to acquire sucrose, but only when work is required to obtain it. When high-sugar/high-fat foods were made freely-available, mice exposed to sucrose early in life preferred and consumed this food as much as non-exposed animals, but in this environment gained more weight than controls. These data provide clear empirical support for the often asserted but rarely demonstrated link between childhood diet and later adult feeding behavior and body weight and suggest that the impact of early diet on adult obesity risk may be contingent upon the adult environment. These findings additionally suggest that the window for developmental programming in response to nutritional environment extends beyond gestation and suckling.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Animals</title>", "<p>Ten breeding pairs of C57BL/6 mice (Jackson Laboratories, Ben Harbor) were set up and each litter was weaned at 21 days and distributed evenly between the experimental early environmental conditions (unlimited sucrose exposure and no sucrose exposure). Animals were housed under standard conditions throughout. The sucrose exposure group, upon weaning, had 20 mg sucrose pellets (Bio-Serv, Frenchtown, NJ) continuously and freely available within their homecage. There were two cohorts born 3 weeks apart. The sucrose was removed from all sucrose exposure mice at the same time, resulting in two cohorts with either 4 or 7 weeks exposure to sucrose. They were maintained on standard <italic>ad libitum</italic> chow thereafter, except during the dietary challenge in which they were offered a high sugar/high fat option in addition to standard chow. Both males and females were included and distributed approximately equally.</p>", "<title>Behavior Tests</title>", "<p>All experiments were carried out during the light period (06:00–18:00). When food restriction was used, mice were given 2 hours per day access to food immediately following the behavior test. Water was available <italic>ad libitum</italic>.</p>", "<title>Open field</title>", "<p>Each mouse was placed in an acrylic open field chamber 40 cm long×40 cm wide×37 cm high (Med Associates, St. Albans, VT). Illumination was 21 lux. Infrared beams recorded the animals' locations and paths (locomotor activity) as well as the number of rearing movements (vertical activity). Data were collected in 5 minute bins during 60 min trials. The chambers were cleaned with 70% ethanol between all trials.</p>", "<title>Wheel running</title>", "<p>Mice were singly housed each with a 4.5″ wire mesh wheel (Mini Run-a-Round, Pets International, Ltd., Elk Grove Village, IL). Two counter-balanced magnets (Digi-key, Thief River Falls, MN) were placed on 3/8 inch stainless steel strips attached to the wheel (McMaster Carr Supply Co, Chicago, IL). The wheel was situated in the cage such that a magnetic switch closes (Digi-key) at every pass of a magnet. Data were collected using Vitalview acquisition software, QA-4 activity input modules, and DP-24 data ports, (Mini-mitter Co., Sun River, OR). Data were collected in minute bins throughout the one week experiment.</p>", "<title>Operant tasks</title>", "<p>The progressive ratio (PR) operant task and the concurrent choice task were conducted in operant conditioning chambers (Med Associates, St Albans, VT), 5 days per week in 90 min sessions for the progressive ratio task and 30 min sessions for the concurrent choice task. In the PR experiment, mice were first trained under a fixed ratio one schedule (FR1, every press rewarded) with only the active lever extended. During the first two days of training, food pellets (20-mg sucrose pellets, Bio-Serv, Frenchtown, NJ) were also delivered into the food receptacle on a random interval 60 s schedule with intervals ranging between 0 and 120 seconds. When mice reached a criterion of 30 lever presses in less than 45 min on two consecutives days they were shifted to a PR3 schedule for two days before PR7 testing began. In progressive ratio, the number of lever presses required to earn a pellet is incremented by 3 (PR3) or 7 (PR7) after each reward so that each subsequent pellet becomes more costly. Mice were tested under both food-restricted and non-food-restricted conditions. Three parameters were recorded: the breakpoint, defined as the last ratio completed, and number of lever presses on the active and inactive levers. The concurrent choice task was adapted from the protocol used by Cousins and Salamone ##UREF##2##[45]## in rats. Tests were conducted under food restriction. Mice had the choice between lever pressing for a more preferred food (FR35, every 35 lever presses delivers a 20 mg sucrose pellet) or consuming a less preferred standard rodent chow that was concurrently and freely available on the floor of the operant box. This experimental arrangement (“choice condition”) was used on day 1, 3 and 5 of each week; and on days 2 and 4, only the FR35 was available (“no choice condition”). Testing lasted 1 week. The number of lever presses, the total amount of food consumed (pellet and lab chow in g) and the percentage of food obtained by lever pressing were calculated.</p>", "<title>Sucrose preference and extinction</title>", "<p>Mice were singly housed in cages that included two identical water bottles (15 ml round bottom polypropylene tubes with rubber stopper on the mouth and a sipper tube with ball-bearing), one of which contained sucrose solution at varying concentrations in the course of the experiment (0%, 0.2%, 5%, 10% and 15%). Each concentration of sucrose was provided for four days. The positions of the bottles were rotated daily to counter-balance potential position preferences. The bottles were weighed daily and the amount consumed from each recorded. A cage without mice was maintained and weighed daily to track spillage, which remained minimal and is not reported. Preference as a percent was calculated by dividing the amount consumed from the sucrose bottle from the total amount consumed from both bottles. After the final 15% concentration, all bottles were washed thoroughly and water placed in all bottles and the test was continued for an additional two days to determine the rate of extinction of the preference for the previously sucrose filled bottle.</p>", "<title>Glucose Challenge</title>", "<p>Mice were fasted for 21 hours. Tail bleeds were used to sample blood glucose and measured using the Accu-Check Active Meter (Roche). After a baseline fasting glucose level was obtained, mice were challenged with 2 g/kg dextrose prepared in 0.9% saline and blood glucose checked at 30, 65 and 90 minutes.</p>", "<title>High-Sugar/High-Fat Dietary Challenge</title>", "<p>Mice were singly housed and provided both standard chow and equal amounts of various Nestlé® chips (butterscotch, milk chocolate, white chocolate, peanut butter) <italic>ad libitum</italic>. The mice were weighed and consumption measured weekly during the three week dietary challenge. Mice had <italic>ad libitum</italic> access to water. Subsequent to the challenge, mice were returned to the standard chow diet and group housing. Metabolic efficiency was calculated by dividing the total weight gained during the challenge by the kcal consumed to yield weight gain per kcal. Caloric content of chow and Nestlé® chips were obtained from the manufacturers' websites.</p>", "<p>All animal procedures were approved by the Institutional Animal Care and Use Committee at The University of Chicago.</p>" ]
[ "<title>Results</title>", "<p>Thirty male and female C57BL/6 mice were weaned onto standard chow and half of the animals were given concurrent exposure to unlimited amounts of sucrose (20 mg pellets) in their homecage for four weeks (n = 7) or seven weeks (n = 8; ##FIG##0##Figure 1a##). This post-weaning period of development approximately corresponds to childhood through adolescence or early adulthood. Following this manipulation, there was no difference in weights between the two groups (4 wk group, F<sub>(1,9)</sub> = 2.96, p = 0.1; 7 wk group, F<sub>(1,12)</sub> = 0.35, p = 0.6) as expected since sucrose consumption alone does not generally cause weight gain in rodents ##REF##7752914##[40]##, ##REF##14522747##[46]##, ##REF##16484528##[47]## or humans ##REF##8889626##[48]##–##REF##11093293##[50]##. Thereafter, all animals were maintained under standard conditions with <italic>ad libitum</italic> access to standard chow. The groups were tested as indicated in ##FIG##0##Figure 1a##.</p>", "<title>Behavioral effects of early sucrose exposure</title>", "<p>Five weeks after the termination of sucrose exposure, adult animals were tested for their willingness to work to acquire sucrose rewards in the progressive ratio test. In this instrumental task, the number of active lever presses required to obtain a sucrose reward is increased incrementally after each sucrose pellet earned. The maximal number of presses for which the animal earned a reward is termed the breakpoint and reflects an animal's willingness to work for a reinforcer. Mice given unlimited access to sucrose early in life exhibited a lower breakpoint than animals not exposed to sucrose (##FIG##0##Figure 1b##; F<sub>(1,14)</sub> = 4.61, p = 0.05). This effect was only observed if mice were food-restricted (##FIG##0##Figure 1c##; sated condition: F<sub>(1,14)</sub> = 2.11, p = 0.2). Interestingly, though not statistically significant, the unlimited sucrose exposure group tended to press less on the inactive lever as well, which does not yield reward, than did the no sucrose exposure group (food- restricted condition, F<sub>(1,14)</sub> = 3.38, p = 0.09, data not shown). Inactive lever-pressing can be interpreted as either an indicator of general activity or as an expression of the animals' exploratory strategy; that is, how often they check the ‘other lever’ to see if reward contingencies have changed. Baseline locomotor activity measured in the open field test (##FIG##0##Figure 1d##; F<sub>(1,12)</sub> = 0.52, p = 0.5) or in homecage wheel-running (##FIG##0##Figure 1e##; F<sub>(1,13)</sub> = 0.28, p = 0.6) was not affected by sucrose exposure, suggesting no difference between the two groups in basal locomotion and goal-directed energy-expenditure (wheel running), although these activity measures were not taken under food restriction. Taken together, these behavior results indicate that mice given unlimited access to sucrose in early life exhibit reduced sucrose-seeking behavior in adulthood as indicated by a lower breakpoint.</p>", "<p>Given that unlimited sucrose exposure early in life reduced sucrose-seeking in adulthood, we asked how the mice would behave given a choice between freely available, standard chow and sucrose that had to be earned. In this concurrent choice paradigm, mice were tested in two conditions termed ‘choice’ and ‘no choice’ on alternating days. In the choice condition, mice could either work (lever-press) for sucrose pellets or eat standard chow freely available. In the no-choice condition, there was no freely available chow. There was no difference in total food consumption (chow plus sucrose) between the groups in the choice condition (##FIG##1##Figure 2c##; F<sub>(1,14)</sub> = 0.59, p = 0.5). Consistent with the results from the food-restricted progressive ratio test, mice exposed to unlimited sucrose in early life worked less to obtain sucrose in both the choice and no choice conditions (Choice Condition, ##FIG##1##Figure 2a##; F<sub>(1,14)</sub> = 26.25, p&lt;0.001; No Choice Condition, ##FIG##1##Figure 2b##; F<sub>(1,14)</sub> = 5.67, p = 0.03) and showed enhanced preference for freely-available standard chow in the choice condition (##FIG##1##Figure 2d##; F<sub>(1,14)</sub> = 38.07, p&lt;0.0001). Thus, unlimited access to sucrose early in life reduced motivation for sucrose rewards and preferentially enhanced consumption of freely-available food over more palatable food that required work to obtain.</p>", "<p>Following the concurrent choice test, we evaluated glucose metabolism in this cohort of mice. After 21 hours of fasting, there were no differences in weight between the groups (##FIG##2##Figure 3a##; F<sub>(1,14)</sub> = .011, p = 0.917) and no difference in either fasting glucose levels (##FIG##2##Figure 3b##; baseline, F<sub>(1,12)</sub> = .552, p = 0.472) or response to glucose challenge (##FIG##2##Figure 3b##; group X timepoint, F<sub>(3,36)</sub> = 1.15, p = 0.340). These results indicate that sucrose exposure early in life does not alter glucose tolerance when animals are maintained on standard chow in adulthood.</p>", "<p>Both the progressive ratio and concurrent choice tests evaluate motivation for sucrose when work is required to obtain the reward. We also tested baseline sucrose preference with no explicit work requirement. Singly-housed mice were presented with two bottles, one filled with sucrose and the other with water. Increasing concentrations of sucrose (0.2%, 5%, 10%, and 15%) were presented in one bottle for four days at each concentration. Each day, consumption was measured and the position of the bottles switched to control for position preferences. Across subsequent concentrations, mice exposed to sucrose early in life showed a reduced preference relative to mice raised without sucrose (##FIG##3##Figure 4a##; Treatment: F<sub>(1,30)</sub> = 6.0, p = 0.03). However, there were no significant differences in sucrose (##FIG##3##Figure 4c##; Treatment, F<sub>(1,33)</sub> = 1.60, p = 0.2) or water (##FIG##3##Figure 4d##; Treatment, F<sub>(1,36)</sub> = 1.0, p = 0.3) consumption between groups. Thus, in comparison with the no sucrose exposure group, the mice exposed to unlimited sucrose in early life show mildly reduced sucrose preference resulting in little change in overall consumption. To further assess sucrose-seeking behavior, we tested the mice in extinction conditions. Sucrose bottles were washed, refilled with water, and preference testing was continued for two days. While no overt cues were associated with either bottle, rodents can discriminate bottles based on tactile characteristics ##REF##2281949##[51]##. Although both groups showed preference for high doses of sucrose (10 and 15%), mice given unlimited access to sucrose early in life more readily extinguished their preference for the sucrose bottle than animals not exposed to sucrose when they were young (##FIG##3##Figure 4b##; F<sub>(1,12)</sub> = 11.54, p = 0.005). Together with the instrumental data described above, these results show a clear reduction in sucrose-seeking behavior in mice given early unlimited exposure. The expression of this effect, however, appears to be contingent upon the costs associated with obtaining the sucrose. In the instrumental tasks where the cost of food is relatively high due to an explicit work requirement and food scarcity, i.e., food-restriction, the effect of sucrose exposure early in life is robust. In contrast, in the sucrose preference test where the work requirement is low and food is freely available, the effect of early sucrose exposure on adult sucrose-seeking is diminished.</p>", "<title>Effects of sucrose exposure on weight gain in adulthood</title>", "<p>To directly assess vulnerability to obesity, we measured weight gain when mice had access to freely available high-sugar/high-fat (HS/HF) dietary options. Prior to testing, there was no difference in weight between the groups with no exposure and unlimited exposure to sucrose during early life (F<sub>(1,10)</sub> = 0.14, p = 0.72). We singly housed the mice and after a one-week acclimation period provided both standard chow as well as HS/HF options consisting of Nestlé® butterscotch, peanut butter, milk and white chocolate chips for three weeks. We found that mice that had unlimited exposure to sucrose when they were young gained more weight in this environment than those animals that did not have access to sucrose during development (##FIG##4##Figure 5a##; HS/HF weight gain, F<sub>(1,10)</sub> = 5.84, p = 0.0362; ##FIG##4##Figure 5b##; group X week, F<sub>(3,30)</sub> = 3.78, p = 0.0206). Though both male and female mice exposed to unlimited sucrose gained more weight than controls in this condition, the effect may be more robust in females (17% and 12% increase over controls in females and males, respectively). While there was no difference between groups in consumption of either the HS/HF food (##FIG##4##Figure 5d, dashed lines##, F<sub>(1,10)</sub> = 1.0, p = 0.34) or standard chow (##FIG##4##Figure 5d, solid lines##, F<sub>(1,10)</sub> = 0.332, p = 0.577), the sucrose-exposed mice exhibited greater efficiency at storing energy as indicated by weight gained per kcal consumed (##FIG##4##Figure 5c##, F<sub>(1,10)</sub> = 5.326, p = 0.0437). Consistent with the sucrose preference test, in an environment where little cost was associated with acquiring the high sugar option, both sucrose exposed and non exposed groups equally preferred the HS/HF diet (as percentage of total consumption, unlimited, 67.1%; no sugar, 71.0%; F<sub>(1,10)</sub> = 0.551, p = 0.474). Singly housing mice during the dietary challenge is unlikely to have suppressed behavioral differences between the groups as mice were also singly housed during sucrose preference testing where they exhibited behavioral differences.</p>" ]
[ "<title>Discussion</title>", "<p>Our data indicate that a single factor in early, post-weaning development– sucrose exposure– has persistent effects on adult motivated behavior and weight gain. A study in rats published in 1978 ##REF##451607##[52]## found that early exposure to different concentrations of sucrose solution did not alter subsequent adult appetite for sweet foods. Although the authors drew a conclusion opposite ours— that early experience with sucrose does not significantly affect adult consumption and preference— their data are consistent with the present study. Like the Wurtman study, we did not observe an effect of early sucrose exposure on preference for and consumption of sweet food in adulthood when it was freely available. However, their study did not examine whether there was a difference in willingness to work to obtain sweet foods nor did it report the body weights of rats when given access to high-sugar food. Thus, although our study replicates their findings, the more extensive examination of behavior and inclusion of body weight measurement in this study yield a more complex picture of the impact of early experience on adult feeding behavior and body weight and demonstrate that early dietary experience does alter adult consumption and vulnerability to obesity.</p>", "<p>Current views on the cause of the recent increase in obesity center on an interaction between genetics and adult environment ##REF##10391020##[5]##, ##REF##17122359##[19]##, ##REF##10889787##[24]##, ##REF##8889626##[48]##. The “thrifty genotype” hypothesis proposes that humans are predisposed to store calories in times of plenty in order to survive later times of scarcity. In contemporary society, however, where energy rich foods are readily available without intervening periods of scarcity, this genetic propensity is thought to result in obesity. This view does not consider the effect that dietary experience during development may have on expression of a putative thrifty genotype. In our study, four to seven weeks of exposure to sucrose post-weaning altered adult sucrose-seeking and weight gain among mice that shared an identical “thrifty genotype” and were exposed to identical environments as adults. These data emphasize the importance of a developmental perspective extending beyond gestation and nursing and suggest that children's diets can intensify or ameliorate the impact of a putative thrifty genotype.</p>", "<p>In human studies, unlimited soft drink consumption during childhood is correlated with an increase in obesity risk (see ##REF##17414504##[37]## for review), consistent with our finding in mice that unlimited access to sucrose early in life resulted in increased weight gain when exposed to freely available HS/HF dietary options. Importantly, however, our study shows that the impact of early sucrose exposure on adult obesity risk depends upon the dietary environment in which the adult finds itself. Sucrose-exposed animals do not seek out sucrose to the extent that naïve animals do when there is a significant cost associated with acquiring it; however, they are more vulnerable to weight gain in an environment with freely-available (low-cost), palatable, energy rich foods. Our study suggests that individuals exposed to sugary diets in early life will be less likely to seek these foods if there are costs associated with obtaining them in adulthood. Consequently, making high sugar and high fat foods less readily available within individual environments, such as the workplace, schools and home, may contribute to effective weight management. These results suggest that with a population raised on a diet high in sugar, environmental manipulation of the costs associated with energy-dense foods is likely to be an efficacious obesity-reducing intervention during adulthood.</p>", "<p>These data might suggest that reducing a child's intake of sucrose might diminish future obesity risk. However, human studies have also found that parental restriction of a child's diet also increases obesity risk (see ##REF##17442696##[38]## for review). Several potential explanations of these findings have been proposed, all suggesting that increases in sucrose/fat-seeking behavior and consumption follow restriction ##REF##17442696##[38]##. The increased motivation to obtain sucrose in the mice not exposed to sucrose in early life― an extreme form of restriction not possible in human studies― supports this notion. The differences in motivation we observe between groups in the behavior tests are not evident when the animals are sated. This, together with the observation of no weight difference while the mice are maintained on standard chow suggest that the motivational differences we observe between the groups reflect alterations in incentive motivation ##REF##12948663##[53]##, ##REF##4424766##[54]## for preferred, sweet food rather than changes in primary motivation, hunger. This is demonstrated clearly in the concurrent choice test where both groups consumed the same amount of total food but the sucrose-exposed mice consumed less sucrose— available, but associated with a cost— showing that the sucrose-exposed mice exhibit less incentive motivation to work for sucrose. This suggests that early experience can alter the incentive motivational processes that determine goal-directed behavior in response to hunger resulting in different behavioral choices and consumption.</p>", "<p>The present study cannot determine the mechanisms underlying the observed effects of early sucrose experience. In the behavior tests, differences in sucrose-seeking may arise as a result of (a) different learned valuation of the preferred food arising from early exposure, (b) different metabolic responses to food restriction between sucrose-exposed and non-exposed animals, with the sucrose-exposed mice protecting energy stores to a greater extent or (c) different motivational responsiveness to metabolic signals such as leptin or insulin, which have been shown to interact with midbrain dopamine systems involved in reward-seeking behavior ##REF##17137609##[44]##, ##REF##16982425##[55]##, ##REF##16982424##[56]##. The glucose challenge data suggests that the two groups respond to acute food deprivation (21 hour fast) and acute glucose increases similarly when maintained on standard chow, consistent with the observation that sucrose-exposure does not effect weight gain when animals eat standard chow. However, differences in energy metabolism and storage may arise in response to chronic food deprivation which may contribute to the observed differences in sucrose-seeking. Similarly, sucrose-exposed and non-exposed mice likely have different metabolic and/or behavioral responses to HS/HF dietary options that promote weight gain, as evidence by their increased feed efficiency. The potential influence of sucrose exposure in early life on the interaction between metabolic signals and motivational systems are currently being investigated.</p>", "<p>Although our study indicates the importance of a developmental perspective in studying obesity, with obvious policy implications, many questions remain. If early experience shapes later food-seeking behavior and obesity risk, the question naturally arises as to the time window during which these developmental processes are active and how susceptible the developmental outcomes are to change after that window closes. Establishing and characterizing these developmental phenomena will facilitate investigation into their biological substrates increasing our understanding of how genes and environments interact to produce the current epidemic of obesity.</p>" ]
[]
[ "<p>Conceived and designed the experiments: JAB. Performed the experiments: CRMF JAB. Analyzed the data: CRMF PM XZ JAB. Contributed reagents/materials/analysis tools: XZ. Wrote the paper: CRMF JAB. Provided feedback on design and implementation of experiments, analysis and interpretation of data and feedback on manuscript preparation: XZ PM.</p>", "<p>The cause of the current increase in obesity in westernized nations is poorly understood but is frequently attributed to a ‘thrifty genotype,’ an evolutionary predisposition to store calories in times of plenty to protect against future scarcity. In modern, industrialized environments that provide a ready, uninterrupted supply of energy-rich foods at low cost, this genetic predisposition is hypothesized to lead to obesity. Children are also exposed to this ‘obesogenic’ environment; however, whether such early dietary experience has developmental effects and contributes to adult vulnerability to obesity is unknown. Using mice, we tested the hypothesis that dietary experience during childhood and adolescence affects adult obesity risk. We gave mice unlimited or no access to sucrose for a short period post-weaning and measured sucrose-seeking, food consumption, and weight gain in adulthood. Unlimited access to sucrose early in life reduced sucrose-seeking when work was required to obtain it. When high-sugar/high-fat dietary options were made freely-available, however, the sucrose-exposed mice gained more weight than mice without early sucrose exposure. These results suggest that early, unlimited exposure to sucrose reduces motivation to acquire sucrose but promotes weight gain in adulthood when the cost of acquiring palatable, energy dense foods is low. This study demonstrates that early post-weaning experience can modify the expression of a ‘thrifty genotype’ and alter an adult animal's response to its environment, a finding consistent with evidence of pre- and peri-natal programming of adult obesity risk by maternal nutritional status. Our findings suggest the window for developmental effects of diet may extend into childhood, an observation with potentially important implications for both research and public policy in addressing the rising incidence of obesity.</p>" ]
[]
[ "<p>We thank Zhen Fang Huang Cao for technical assistance.</p>" ]
[ "<fig id=\"pone-0003221-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003221.g001</object-id><label>Figure 1</label><caption><title>Experimental timeline, progressive ratio and activity.</title><p>(a) Timeline indicating the order of experimental manipulations and tests. Two cohorts of mice were used and administered the experimental manipulation and testing procedures as indicated above and below the timeline. Breakpoint in progressive ratio (PR7) for the sucrose exposed and non-sucrose exposed mice tested under (b) food restriction and (c) sated conditions (n = 8). (d) Total distance traveled in the open field (1 hr) (n = 7). (e) Total number of wheel turns in 1 week (n = 7, 8). Box plots: Middle lines represent the median values, the top and bottom of the boxes represent the 25th and 75th percentiles, the whiskers represent the 10th and 90th percentiles and the dots represent mean values ; * p = .05.</p></caption></fig>", "<fig id=\"pone-0003221-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003221.g002</object-id><label>Figure 2</label><caption><title>Concurrent choice.</title><p>Average lever presses during (a) choice and (b) no choice conditions (n = 8). (c) Total food consumed (earned sucrose+freely available chow) during choice sessions (n = 8). (d) Percentage of total intake comprised of earned sucrose pellets during choice sessions (n = 8). Box plots: as described in ##FIG##0##figure 1##; * p&lt;.05; **p&lt;.005; *** p&lt;.0001.</p></caption></fig>", "<fig id=\"pone-0003221-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003221.g003</object-id><label>Figure 3</label><caption><title>Glucose challenge.</title><p>(a) Weights after a 21 hour fast and prior to glucose challenge. (b) blood glucose levels at fasting baseline (timepoint 0) and following i.p. injection of dextrose (2 g/kg). N = 6 (no sucrose) and 8 (unlimited).</p></caption></fig>", "<fig id=\"pone-0003221-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003221.g004</object-id><label>Figure 4</label><caption><title>Sucrose preference.</title><p>(a) Average preference expressed as sucrose consumed (g) divided by total sucrose and water consumption (g) (n = 7). Dashed line indicates no preference. (b) Average preference for the bottle previously paired with sucrose during extinction (n = 7). (c) Average sucrose and (d) water consumption (n = 7) ±SEM; * p&lt;.05, ** p = .005.</p></caption></fig>", "<fig id=\"pone-0003221-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003221.g005</object-id><label>Figure 5</label><caption><title>High sugar/high fat dietary challenge.</title><p>(a) Percent weight gain 3-weeks prior to (left) and during (right) the 3-week HS/HF exposure period in adulthood. (b) Body weight at the beginning and subsequent three weeks of HS/HF dietary options. (c) Metabolic efficiency as gram body weight increase per kcal consumed across the HS/HF dietary challenge. (d) Weekly consumption of standard chow (solid lines) and HS/HF options (dashed lines). N = 5–7; ±SEM, * p&lt;.05.</p></caption></fig>" ]
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[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This work is supported by NIMH MH66216 (XZ), NIDA 1 F32 DA020427-01 (JB), USPHS T32-DA-07255 (CF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p></fn></fn-group>" ]
[ "<graphic xlink:href=\"pone.0003221.g001\"/>", "<graphic xlink:href=\"pone.0003221.g002\"/>", "<graphic xlink:href=\"pone.0003221.g003\"/>", "<graphic xlink:href=\"pone.0003221.g004\"/>", "<graphic xlink:href=\"pone.0003221.g005\"/>" ]
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[{"label": ["3"], "element-citation": ["\n"], "surname": ["Ogden", "Fryar", "Carroll", "Flegal"], "given-names": ["CL", "CD", "MD", "KM"], "year": ["2004"], "article-title": ["Mean body weight, height, and body mass index, United States 1960\u20132002."], "source": ["Adv Data"], "fpage": ["1"], "lpage": ["17"]}, {"label": ["18"], "element-citation": ["\n"], "surname": ["Moore", "Diez Roux", "Nettleton", "Jacobs"], "given-names": ["LV", "AV", "JA", "DR"], "suffix": ["Jr"], "year": ["2008"], "article-title": ["Associations of the Local Food Environment with Diet Quality\u2013A Comparison of Assessments based on Surveys and Geographic Information Systems: The Multi-Ethnic Study of Atherosclerosis."], "source": ["Am J Epidemiol"]}, {"label": ["45"], "element-citation": ["\n"], "surname": ["Cousins", "Sokolowski", "Salamone"], "given-names": ["MS", "JD", "JD"], "year": ["1993"], "article-title": ["Different effects of nucleus accumbens and ventrolateral striatal dopamine depletions on instrumental response selection in rats."], "source": ["Pharmacology, Biochemistry and Behavior"], "volume": ["46"], "fpage": ["943"], "lpage": ["951"]}]
{ "acronym": [], "definition": [] }
56
CC BY
no
2022-01-13 07:14:34
PLoS One. 2008 Sep 17; 3(9):e3221
oa_package/0c/ce/PMC2529404.tar.gz
PMC2529405
18800169
[ "<title>Introduction</title>", "<p>The most of neuronal nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels composed of α and β subunits that assemble to form pentamers with a variety of physiological and pharmacological properties. Two major subtypes exist in the brain, those comprised of α4β2 and those comprised of α7 subunits ##REF##10836143##[1]##, ##REF##17009926##[2]##. The former contribute &gt;90% of the high affinity binding sites for nicotine in the rat brain, and the low affinity binding sites (α7 subunits) for nicotine are recognized by their nanomolar affinity for α-bungarotoxin. Several lines of evidence suggest that α7 nAChRs play a role in the pathophysiology of neuropsychiatric diseases such as schizophrenia, Alzheimer's disease, anxiety, depression, and drug addiction, and that α7 nAChRs are the most attractive therapeutic targets for these diseases ##REF##9384901##[3]##–##REF##16472157##[11]##. Studies using postmortem human brain samples have demonstrated alterations in the levels of α7 nAChRs in the brains of patients with schizophrenia ##REF##7548469##[12]##, ##REF##11470559##[13]## and Alzheimer's disease ##REF##10095081##[14]##–##REF##11230868##[16]##. It is thus of great interest to examine whether α7 nAChRs are altered in the living brain of patients with neuropsychiatric diseases such as schizophrenia and Alzheimer's disease. It is also of interest to measure the receptor occupancy of potential therapeutic α7 nAChR drugs in the intact human brain.</p>", "<p>The distribution, density, and activity of receptors in the living brain can be visualized noninvasively by radioligands labeled for positron emission tomography (PET), and the receptor binding can be quantified by appropriate tracer kinetic models, which can be modified and simplified for particular applications ##REF##10942041##[17]##–##REF##17073685##[19]##. The PET ligands ([<sup>11</sup>C]nicotine and 2-[<sup>18</sup>F]fluoro-A85380) for α4β2 nAChRs have been used in clinical studies ##REF##15695782##[20]##–##REF##16832659##[22]##. However, there have been no clinical studies using PET ligands for α7 nAChRs in the human brain. Therefore, it is very important to develop a safe PET ligand for quantification of α7 nAChRs in the human brain. Very recently, researchers at Sanofi-Aventis developed the novel selective α7 nAChR agonist SSR180711 (4-bromophenyl 1,4-diazabicyclo(3.2.2) nonane-4-carboxylate)(##FIG##0##Figure 1##) ##REF##17019409##[23]##, ##REF##16936709##[24]##, which is under clinical study.</p>", "<p>Here, we developed two novel PET ligands, [<sup>76</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA-1001, for <italic>in vivo</italic> imaging of α7 nAChRs in the human brain. Using conscious monkeys, we evaluated the two PET ligands for <italic>in vivo</italic> imaging of α7 nAChRs in the non-human primate brain. Furthermore, we evaluated the usefulness of [<sup>11</sup>C]CHIBA-1001 in a non-human primate model of schizophrenia.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Synthesis of the precursor and CHIBA-1001</title>", "<p>SSR180711, CHIBA-1001 and the precursor, 4-(tributylstannyl)phenyl 2,5- diazabicyclo[3.2.2]nonane -2-carboxylate (##FIG##0##Figure 1##), were synthesized as described in the Supplemental ##SUPPL##1##Method S1##.</p>", "<title>[<sup>125</sup>I]α-Bungarotoxin binding</title>", "<p>The binding assay using [<sup>125</sup>I]α-bungarotoxin was performed as described in a previous report ##REF##9164577##[45]## with a slight modification (See Supplemental ##SUPPL##2##Method S2##).</p>", "<title>Synthesis of [<sup>75</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA-1001</title>", "<p>[<sup>76</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA-1001 were synthesized by bromination and methylation of the precursor, respectively (See Supplemental ##SUPPL##3##Method S3##).</p>", "<title>Subjects</title>", "<p>Eleven young-adult male rhesus monkeys (<italic>Macaca mulatta</italic>) weighing from 4 to 6 kg were used for PET measurements. The monkeys were maintained and handled in accordance with the recommendations of the US National Institutes of Health and also the guidelines of the Central Research Laboratory, Hamamatsu Photonics (Hamamatsu, Shizuoka, Japan). The animal experimental procedure was approved by the Animal Care and Use Committee of Hamamatsu Photonics and Chiba University. Over the course of 3 months, the monkeys were trained to sit on a chair twice a week. The magnetic resonance images (MRI) of all monkeys were obtained with a Toshiba MRT-50A/II (0.5T) under anesthesia with pentobarbital. The stereotactic coordinates of PET and MRI were adjusted based on the orbitomeatal (OM) line with monkeys secured in a specially designed head holder ##REF##7715827##[46]##. At least 1 month before the PET study, an acrylic plate, with which the monkey was fixed to the monkey chair, was attached to the head under pentobarbital anesthesia as described previously ##REF##7874501##[47]##.</p>", "<title>PET scans</title>", "<p>PET data were collected on a high-resolution PET scanner (SHR-7700; Hamamatsu Photonics K.K., Hamamatsu, Japan) with a transaxial resolution of 2.6-mm full-width at half-maximum (FWHM) and a center-to-center distance of 3.6 mm ##UREF##4##[48]##. The PET camera allowed 31 slices for imaging to be recorded simultaneously. After an overnight fast, animals were fixed to the monkey chair with stereotactic coordinates aligned parallel to the OM line. A cannula was implanted in the posterior tibial vein of the monkey for administration of [<sup>76</sup>Br]SSR180711 or [<sup>11</sup>C]CHIBA-1001. [<sup>76</sup>Br]SSR180711 or [<sup>11</sup>C]CHIBA-1001 was injected through the posterior tibial vein cannula 30 min after administration of saline (control), SSR180711 (5.0 mg/kg, i.v.), or A85380 (1.0 mg/kg, i.v.; Sigma-Aldrich Co., Ltd., St Louis, MO). PET images were acquired over 91 min (10 s×6 frames, 30 s×6 frames, 1 min×12 frames, and 3 min×25 frames). Summation images from 70 to 91 min postinjection were constructed. PET scans were reconstructed using filtered backprojection in a 100×100 matrix, with a voxel size of 1.2 mm×1.2 mm×3.6 mm. Each MRI was coregistered to a summation image. Due to the very short half-life of <sup>11</sup>C (20.4 min), a time lag of at least 3 hr between scans provided sufficient decay time of radioactivity in monkeys (approximately 1/400 of the injected dose). Therefore, the level of radioactivity associated with the previous injection of labeled compound would not interfere with the next scan as previously reported ##REF##15199373##[49]##, ##REF##16712806##[50]##.</p>", "<p>Next, we examined the effects of subchronic administration of the NMDA receptor antagonist phencyclidine (PCP: 0.3 mg/kg, i.m., twice a day for 13 days) on the distribution of [<sup>11</sup>C]CHIBA-1001 binding in the monkey brain. In the control (n = 4), PET scans were performed before PCP administration. One day after subchronic administration of PCP, PET scans were performed as described above.</p>", "<p>To assess the semi-quantitative analysis of PET data, arterial samples were obtained every 8 s from injection to 64 s, and then again at 1.5, 2.5, 4, 6, 10, 20, 30, 45, 60, and 90 min after [<sup>11</sup>C]CHIBA-1001 injection. Blood samples of [<sup>11</sup>C]CHIBA-1001 were centrifuged to separate the plasma, weighed, and subjected to radioactivity measurement. For metabolite analysis, methanol was added to some plasma samples, the resulting solutions were centrifuged, and the supernatants were developed with a thin-layer chromatography (TLC) plate (AL SIL G/UV; Whatman, Kent, UK) using a mobile phase of dichloromethane:diethyl ether:ethanol:triethylamine (20:20:2:2). At each sampling time point for analysis, the ratio of radioactivity in the unmetabolized fraction to that in the total plasma (metabolite plus unmetabolite) was determined using a phosphoimaging plate (BAS-1500 MAC; Fuji Film Co., Tokyo, Japan). The metabolite-corrected plasma curve was obtained.</p>", "<title>Kinetic analysis</title>", "<p>Time-activity curves of radioactivity in each region of interest (ROI) in the brain and metabolite-corrected arterial plasma were determined. Analysis of the Logan plot provides the linear function of the free receptor concentration, which is known as the distribution volume ##REF##2384545##[51]##. In reversibly labeled compounds, the Logan plot becomes linear after a certain period of time with a slope (<italic>K</italic>) that is equal to the steady-state distribution volume. In the preliminary semi-quantitative analysis, the ratios of <italic>K</italic> in each ROI (<italic>K</italic> (ROI)) to <italic>K</italic> in the cerebellum (<italic>K</italic> (CE)) were calculated to determine the binding of α7 nAChRs in the monkey brain.</p>", "<title>Statistical analysis</title>", "<p>Statistical analysis of the control (baseline) and drug (SSR180711 or A85380) -treated groups was performed by paired t-test. Statistical analysis of the control (baseline) and PCP-treated groups was also performed by paired t-test. Significance was set at p&lt;0.05.</p>" ]
[ "<title>Results</title>", "<title>Receptor affinity and specificity</title>", "<p>SSR180711 displaced specific binding of [<sup>3</sup>H]α-bungarotoxin to the rat and human α7 nAChRs with K<sub>i</sub> values of 22 and 14 nM, respectively ##REF##17019409##[23]##, and SSR180711 (10 µM) was found to be devoid of activity (inhibition lower than 50%) for a 100 standard receptor binding profile ##REF##17019409##[23]##. In our assay, the IC<sub>50</sub> values of SSR180711 and CHIBA-1001 for [<sup>125</sup>I]α-bungarotoxin (0.5 nM) binding to the rat brain homogenates were 24.9 and 45.8 nM, respectively. Furthermore, CHIBA-1001 (1 µM) was found to be devoid of activity (inhibition lower than 50%) for a 28 standard receptor binding profile (See Supplemental ##SUPPL##4##Table S1## and ##SUPPL##5##S2##).</p>", "<title>Synthesis of [<sup>76</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA1001</title>", "<p>[<sup>76</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA-1001 were synthesized by bromination and methylation of the precursor, respectively (##FIG##0##Figure 1##). The radiochemical purity and specific activity of [<sup>76</sup>Br]SSR180711 were approximately 100% and 8.11±1.65 GBq/µmol (mean±SD of 9 experiments), respectively. The radiochemical yields and yields of [<sup>76</sup>Br]SSR180711 were 16.7±6.14% and 0.21±0.09 GBq (mean±SD of 9 experiments), respectively. The radiochemical purity and specific activity of [<sup>11</sup>C]CHIBA-1001 were 98.6±1.68% (mean±SD of 12 experiments) and 343.7±36.1 GBq/µmol (mean±SD of 12 experiments), respectively. The radiochemical yields and yields of [<sup>11</sup>C]CHIBA-1001 were 9.49±1.45% and 1.88±0.33 GBq (mean±SD of 12 experiments), respectively.</p>", "<title>Conscious monkey PET studies</title>", "<p>Baseline PET scans showed rapid brain penetration and accumulation of [<sup>76</sup>Br]SSR180711 in the monkey brain (##FIG##1##Figures 2##–\n##FIG##3##4##). The peak time of radioactivity in the hippocampus was about 60 min after administration of the radioligand. Furthermore, the peak time of radioactivity in the other brain regions (occipital cortex, temporal cortex, frontal cortex, striatum, thalamus, and cerebellum) was about 30–40 min after administration of the radioligand. The distribution of radioactivity in the brain regions after administration of the radioligand was consistent with the distribution of α7 nAChRs in the monkey brain ##REF##12722104##[25]##–##REF##16817863##[27]##. Uptake of radioactivity in the brain regions after intravenous administration of [<sup>76</sup>Br]SSR180711 was significantly decreased by pretreatment with the α7 nAChR agonist SSR180711 (5.0 mg/kg, i.v., 30 min)(##FIG##1##Figures 2##–\n##FIG##3##4##). Uptake of radioactivity (during 70–91 min) in the brain regions except the cerebellum (low receptor density) after intravenous administration of [<sup>76</sup>Br]SSR180711 was significantly decreased by pretreatment with the α7 nAChR agonist SSR180711 (5.0 mg/kg, i.v., 30 min)(##FIG##3##Figures 4A##). However, the distribution of radioactivity in the brain regions after intravenous administration of [<sup>76</sup>Br]SSR180711 was not altered by pretreatment with the selective α4β2 nAChR agonist A85380 (1.0 mg/kg, i.v., 30 min)##REF##8887981##[28]##, ##REF##16958984##[29]##(##FIG##1##Figures 2##, ##FIG##2##3## and ##FIG##3##4B##).</p>", "<p>Baseline PET scans showed rapid brain penetration and accumulation of [<sup>11</sup>C]CHIBA-1001 in the monkey brain (##FIG##4##Figures 5##–\n##FIG##6##7##). The peak time of radioactivity in the other brain regions (occipital cortex, temporal cortex, frontal cortex, striatum, thalamus, and cerebellum) was about 10 min after administration of [<sup>11</sup>C]CHIBA-1001, whereas the peak time of radioactivity in the hippocampus was about 30 min after administration. The distribution of radioactivity in the striatum, thalamus, hippocampus, occipital cortex, temporal cortex, and frontal cortex 40–60 min after administration of the radioligand was higher than that in the cerebellum, consistent with the distribution of α7 nAChRs in the monkey brain ##REF##12722104##[25]##–##REF##16817863##[27]##. Uptake of radioactivity (during 70–91 min) in the brain regions except the cerebellum (low receptor density) after intravenous administration of [<sup>11</sup>C]CHIBA-1001 was decreased by pretreatment with SSR180711 (5.0 mg/kg) although these differences failed to reach statistical significance because of small number (n = 3) of monkey (##FIG##6##Figures 7A##). Furthermore, a preliminary study indicated that the uptake of radioactivity in the brain regions after intravenous administration of [<sup>11</sup>C]CHIBA-1001 was also decreased by pretreatment with another α7 nAChR agonist A844606 (5.0 mg/kg, i.v., 30 min before) ##REF##18157163##[30]## (Supplemental ##SUPPL##0##Figure S1##). However, the uptake of radioactivity in the brain regions after intravenous administration of [<sup>11</sup>C]CHIBA-1001 was not altered by pretreatment with the selective α4β2 nAChR agonist A85380 (1.0 mg/kg, i.v., 30 min)##REF##8887981##[28]##, ##REF##16958984##[29]##(##FIG##4##Figures 5##, ##FIG##5##6##, and ##FIG##6##7B##).</p>", "<p>In the described in <xref ref-type=\"sec\" rid=\"s3\">discussion</xref> section, it is likely that [<sup>11</sup>C]CHIBA-1001 is superior to [<sup>76</sup>Br]SSR180711 because of high brain uptake and lower half-life of [<sup>11</sup>C]. Therefore, [<sup>11</sup>C]CHIBA-1001 was used in the subsequent studies.</p>", "<title>Phencyclidine (PCP)-treated monkeys</title>", "<p>The N-methyl-D-aspartate (NMDA) receptor antagonist phencyclidine (PCP) has been used as an animal model of schizophrenia, since it has been shown to cause schizophrenia-like symptoms in humans ##REF##8876245##[31]##–##REF##17601496##[35]##. We performed two PET scans, one before (baseline) and one 1-day after subchronic administration of PCP (0.3 mg/kg, twice a day for 13 days). Subchronic administration of PCP did not alter the time-curve of the radioactivity or the percentage of unmetabolized fraction in the plasma of monkeys (##FIG##7##Figure 8##). Interestingly, subchronic administration of PCP decreased the binding of [<sup>11</sup>C]CHIBA-1001 in several regions (frontal cortex, temporal cortex, occipital cortex, striatum, thalamus, and hippocampus) of the monkey brain; the difference of binding in the frontal cortex was statistically significant (t = 5.73, df = 3, p = 0.011) between the two groups (##FIG##7##Figure 8C##), consistent with a previous report using mice ##REF##17601496##[35]##.</p>" ]
[ "<title>Discussion</title>", "<p>In the present study, we have developed two PET ligands, [<sup>76</sup>Br]SSR180711 and [<sup>11</sup>C]CHIBA-1001. It is likely that [<sup>11</sup>C]CHIBA-1001 is superior to [<sup>76</sup>Br]SSR180711 for the following reasons. First, [<sup>11</sup>C]CHIBA-1001 can be synthesized using an in house cyclotron, whereas [<sup>76</sup>Br]SSR180711 cannot. Second, the radiation exposure dose in humans by [<sup>11</sup>C]CHIBA-1001 PET study is lower than that of [<sup>76</sup>Br]SSR180711 because of the short half-life (the half-lives of [<sup>11</sup>C] and [<sup>76</sup>Br] are 20.4 min and 16.2 hours, respectively). Third, the short half life allows several repetitions of [<sup>11</sup>C]CHIBA-1001 PET in a single day. Fourth, brain uptake of [<sup>11</sup>C]CHIBA-1001 is higher than that of [<sup>76</sup>Br]SSR180711.</p>", "<p>We have demonstrated that [<sup>11</sup>C]CHIBA-1001 is a novel PET ligands for <italic>in vivo</italic> imaging of α7 nAChRs in the non-human primate brain. First, an <italic>in vitro</italic> receptor binding study showed that CHIBA-1001 is a highly selective ligand at α7 nAChRs, since this ligand was found to be devoid of activity for the standard receptor binding profile. Second, an <italic>in vivo</italic> PET study using conscious monkeys demonstrated a high accumulation into the brain after intravenous administration of [<sup>11</sup>C]CHIBA-1001. The regional distribution of radioactivity in the monkey brain after intravenous administration of [<sup>11</sup>C]CHIBA-1001 is consistent with the distribution of α7 nAChRs in the monkey brain ##REF##12722104##[25]##–##REF##16817863##[27]##. Furthermore, the uptake of radioactivity in the monkey brain regions was blocked by pretreatment with the selective α7 nAChR agonist SSR180711 and A844606, but not the selective α4β2 nAChR agonist A85380. Third, we found a reduction of [<sup>11</sup>C]CHIBA-1001 binding in the frontal cortex of the monkey brain after subchronic administration of PCP.</p>", "<p>Recently, we reported that the repeated administration of PCP (10 mg/kg/day for 10 days) significantly decreased the density of α7 nAChRs in the frontal cortex of the mouse brain ##REF##17601496##[35]##, consistent with our monkey data. The precise mechanism(s) underlying how repeated PCP administration could modulate α7 nAChRs in the brain are currently unknown. It has been reported that the immunoreactivity of α7 nAChRs in the prefrontal cortex of schizophrenics was significantly decreased compared to that in normal controls ##REF##14643090##[36]##. Interestingly, α7 nAChR agonists can increase the release of glutamate from the presynaptic terminals, resulting in stimulation of the NMDA receptors on the postsynaptic neurons, suggesting that stimulation at α7 nAChRs may potentiate the NMDA receptors ##UREF##0##[7]##, ##UREF##1##[37]##, ##UREF##2##[38]##. Taken together, these findings suggest that α7 nAChRs may interact with the NMDA receptors in the brain, although further study on the cross-talk between α7 nAChRs and NMDA receptors in the brain is necessary ##UREF##0##[7]##, ##UREF##1##[37]##, ##UREF##2##[38]##.</p>", "<p>A postmortem human brain study demonstrated decreased expression of hippocampal α7 nAChRs in schizophrenic patients ##REF##7548469##[12]##, suggesting that schizophrenic patients have fewer α7 nAChRs in the hippocampus, a condition which may lead to the failure of cholinergic activation of the inhibitory interneurons, manifesting clinically as decreased gating of responses to sensory stimulation ##REF##7548469##[12]##. Deficient inhibitory processing of the P50 auditory evoked potential is a pathophysiological feature of schizophrenia ##REF##9384901##[3]##, ##REF##2405807##[39]##–##REF##12470124##[41]## and Alzheimer's disease ##REF##11481170##[42]##, and it has been suggested that α7 nAChRs play a critical role in this phenomenon ##REF##9012828##[40]##, ##REF##12470124##[41]##, ##UREF##3##[43]##, ##REF##16754836##[44]##. In the present study, using [<sup>11</sup>C]CHIBA-1001 and PET, we could detect the reduction of α7 nAChRs in the frontal cortex in a non-human primate PCP model of schizophrenia although semi-quantitative analysis using Logan plot analysis was performed in this study. Taken together, these results suggest that it would be of great interest to examine whether α7 nAChRs are altered in the intact brain of patients with schizophrenia and Alzheimer's disease by using [<sup>11</sup>C]CHIBA-1001 and PET.</p>", "<p>Based on the above findings, α7 nAChRs are the most attractive target for potential therapeutic drugs in several neuropsychiatric diseases ##REF##9384901##[3]##–##REF##16472157##[11]##, ##UREF##3##[43]##, ##REF##16754836##[44]##. A number of pharmaceutical industries have developed selective α7 nAChR agonists for the treatment of neuropsychiatric diseases, including schizophrenia and Alzheimer's disease, and clinical trials of some drugs have been started. Using [<sup>11</sup>C]CHIBA-1001 and PET, it will be possible to measure the relationship between the receptor occupancy and the dose of α7 nAChR agonists in the human brain, since this radioligand can be used for quantitative occupancy assessment of α7 nAChRs.</p>", "<p>In conclusion, the present study presents the successful <italic>in vivo</italic> characterization of α7 nAChRs in the conscious monkey brain using [<sup>11</sup>C]CHIBA-1001 and PET. Therefore, <italic>in vivo</italic> PET imaging of α7 nAChRs in the intact human brain provides a method for quantitative study of α7 nAChR-related pathophysiology in neuropsychiatric diseases. In addition, the <italic>in vivo</italic> determination of receptor occupancy allows for the demonstration of target engagement and assessment of titration for potential dose regimens. A clinical PET study in healthy human subjects using [<sup>11</sup>C]CHIBA-1001 is currently underway.</p>" ]
[]
[ "<p>Conceived and designed the experiments: KH HT. Performed the experiments: KH SN HO MM TK MT MI TK HT. Analyzed the data: KH. Contributed reagents/materials/analysis tools: KH MM TK MT TK HT. Wrote the paper: KH.</p>", "<title>Background</title>", "<p>The α7 nicotinic acetylcholine receptors (nAChRs) play an important role in the pathophysiology of neuropsychiatric diseases such as schizophrenia and Alzheimer's disease. However, there are currently no suitable positron emission tomography (PET) radioligands for imaging α7 nAChRs in the intact human brain. Here we report the novel PET radioligand [<sup>11</sup>C]CHIBA-1001 for <italic>in vivo</italic> imaging of α7 nAChRs in the non-human primate brain.</p>", "<title>Methodology/Principal Findings</title>", "<p>A receptor binding assay showed that CHIBA-1001 was a highly selective ligand at α7 nAChRs. Using conscious monkeys, we found that the distribution of radioactivity in the monkey brain after intravenous administration of [<sup>11</sup>C]CHIBA-1001 was consistent with the regional distribution of α7 nAChRs in the monkey brain. The distribution of radioactivity in the brain regions after intravenous administration of [<sup>11</sup>C]CHIBA-1001 was blocked by pretreatment with the selective α7 nAChR agonist SSR180711 (5.0 mg/kg). However, the distribution of [<sup>11</sup>C]CHIBA-1001 was not altered by pretreatment with the selective α4β2 nAChR agonist A85380 (1.0 mg/kg). Interestingly, the binding of [<sup>11</sup>C]CHIBA-1001 in the frontal cortex of the monkey brain was significantly decreased by subchronic administration of the N-methyl-D-aspartate (NMDA) receptor antagonist phencyclidine (0.3 mg/kg, twice a day for 13 days); which is a non-human primate model of schizophrenia.</p>", "<title>Conclusions/Significance</title>", "<p>The present findings suggest that [<sup>11</sup>C]CHIBA-1001 could be a novel useful PET ligand for <italic>in vivo</italic> study of the receptor occupancy and pathophysiology of α7 nAChRs in the intact brain of patients with neuropsychiatric diseases such as schizophrenia and Alzheimer's disease.</p>" ]
[ "<title>Supporting Information</title>" ]
[ "<p>The authors would like to thank to Ms. Yuko Fujita for her technical assistance on the receptor binding assays.</p>" ]
[ "<fig id=\"pone-0003231-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g001</object-id><label>Figure 1</label><caption><title>Synthesis of [<sup>76</sup>Br[SSR180711 and [<sup>11</sup>C]CHIBA-1001.</title></caption></fig>", "<fig id=\"pone-0003231-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g002</object-id><label>Figure 2</label><caption><title>Representative PET images in the brains of a rhesus monkey after intravenous administration of [<sup>76</sup>Br]SSR180711.</title><p>Upper: Control monkey (saline pre-treated). Middle: Pretreatment with SSR180711 (5.0 mg/kg, 30 min before). Lower: Pretreatment with A85380 (1.0 mg/kg, 30 min before)</p></caption></fig>", "<fig id=\"pone-0003231-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g003</object-id><label>Figure 3</label><caption><title>Representative time-activity curves of radioactivity (expressed as % Dose/mL) in several brain regions of a rhesus monkey after intravenous administration of [<sup>76</sup>Br[SSR180711 in control (saline pre-treated) monkey, SSR180711 (5.0 mg/kg, 30 min before)-pretreated monkey, and A85380 (1.0 mg/kg, 30 min before)-pretreated monkey.</title></caption></fig>", "<fig id=\"pone-0003231-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g004</object-id><label>Figure 4</label><caption><title>Effects of SSR180711 and A85380 on the uptake of the radioactivity in the monkey brain after intravenous administration of [<sup>76</sup>Br[SSR180711.</title><p>(A): Uptake values (expressed as % Dose/mL) of [<sup>76</sup>Br[SSR180711 in several brain regions under control (saline pre-treated) group (during 70–91 min post-injection) and SSR180711 (5.0 mg/kg, 30 min before) treated groups. Data were the mean±S.D. of three monkeys. *p&lt;0.05, **p&lt;0.01 as compared to control group (Paired t-test). (B): Uptake values (expressed as % Dose/mL) of [<sup>76</sup>Br[SSR180711 in several brain regions under control (saline pre-treated) group (during 70–91 min post-injection) and A85380 (1.0 mg/kg, 30 min before) treated groups. Data were the mean±S.D. of three monkeys. CERE: cerebellum, HIPP: hippocampus, OCC: occipital cortex, STR: striatum, THA: thalamus, TEM: temporal cortex, FC: frontal cortex</p></caption></fig>", "<fig id=\"pone-0003231-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g005</object-id><label>Figure 5</label><caption><title>Representative PET images in the brains of a rhesus monkey after intravenous administration of [<sup>11</sup>C]CHIBA-1001.</title><p>Upper: Control monkey (saline pre-treated). Middle: Pretreatment with SSR180711 (5.0 mg/kg, 30 min before). Lower: Pretreatment with A85380 (1.0 mg/kg, 30 min before)</p></caption></fig>", "<fig id=\"pone-0003231-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g006</object-id><label>Figure 6</label><caption><title>Representative time-activity curves of radioactivity (expressed as % Dose/mL) in several brain regions of a rhesus monkey after intravenous administration of [<sup>11</sup>C]CHIBA-1001 in control (saline pre-treated) monkey, SSR180711 (5.0 mg/kg, 30 min before)-pretreated monkey, and A85380 (1.0 mg/kg, 30 min before)-pretreated monkey.</title></caption></fig>", "<fig id=\"pone-0003231-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g007</object-id><label>Figure 7</label><caption><title>Effects of SSR180711 and A85380 on the uptake of the radioactivity in the monkey brain after intravenous administration of [<sup>11</sup>C]CHIBA-1001.</title><p>(A): Uptake values (expressed as % Dose/mL) of [<sup>11</sup>C]CHIBA-1001 in several brain regions under control (saline pre-treated) group (during 70–91 min post-injection) and SSR180711 (5.0 mg/kg, 30 min before) treated groups. Data were the mean±S.D. of three monkeys. (B): Uptake values (expressed as % Dose/mL) of [<sup>11</sup>C]CHIBA-1001 in several brain regions under control (saline pre-treated) group (during 70–91 min post-injection) and A85380 (1.0 mg/kg, 30 min before) treated groups. Data were the mean±S.D. of three monkeys. CERE: cerebellum, HIPP: hippocampus, OCC: occipital cortex, STR: striatum, THA: thalamus, TEM: temporal cortex, FC: frontal cortex</p></caption></fig>", "<fig id=\"pone-0003231-g008\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pone.0003231.g008</object-id><label>Figure 8</label><caption><title>Effects of subchronic administration of PCP on the binding in monkey brain after intravenous administration of [<sup>11</sup>C]CHIBA-1001.</title><p>(A): Radioactivity in the plasma of control (baseline; n = 4) and PCP-treated (n = 4) groups after intravenous administration of [<sup>11</sup>C]CHIBA-1001. Data were the mean±S.D. of four monkeys. (B): Percentage of unmetabolized fraction in the plasma of control and PCP-treated groups after intravenous administration of [<sup>11</sup>C]CHIBA-1001. Data were the mean±S.D. of four monkeys. (C): Receptor binding in the several brain regions of control and PCP-treated groups. Data were the mean±S.D. of four monkeys. *p&lt;0.05 as compared to control (baseline) group (Paired t-test).</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s001\"><label>Figure S1</label><caption><p>Effects of the another alpha7 nAChR agonist A844606 on the uptake of the radioactivity in the monkey brain after intravenous administration of [11C]CHIBA-1001. Representative time-activity curves of radioactivity (expressed as % Dose/mL) in several brain regions of a rhesus monkey after intravenous administration of [11C]CHIBA-1001 in control (saline pre-treated) monkey, and A844606 (1.0 mg/kg, 30 min before)-pretreated monkey.</p><p>(0.14 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s002\"><label>Method S1</label><caption><p>Preparation of SSR180711, CHIBA-1001 and precursor.</p><p>(0.08 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s003\"><label>Method S2</label><caption><p>[125I]alpha-Bungarotoxin binding</p><p>(0.03 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s004\"><label>Method S3</label><caption><p>Synthesis of [75Br]SSR180711 and [11C]CHIBA-1001</p><p>(0.03 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s005\"><label>Table S1</label><caption><p>Inhibition effect of CHIBA-1001 (10 uM) on radioligand binding to various receptors</p><p>(0.07 MB DOC)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pone.0003231.s006\"><label>Table S2</label><caption><p>Inhibition effect of CHIBA-1001 (1 µM) on radioligand binding to various receptors</p><p>(0.04 MB DOC)</p></caption></supplementary-material>" ]
[ "<fn-group><fn fn-type=\"COI-statement\"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding: </bold>This study was supported in part by grants from the Program for Promotion of Fundamental Studies in Health Sciences of the National Institute of Biomedical Innovation of Japan (to K.H.), Research Program on Development of Innovative Technology, Japan Science and Technology Agency (JST), and CREST, JST (to H.T.). These funders had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report, and in the decision to submit the paper for publication.</p></fn></fn-group>" ]
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[ "<media xlink:href=\"pone.0003231.s001.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003231.s002.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003231.s003.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003231.s004.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003231.s005.doc\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pone.0003231.s006.doc\"><caption><p>Click here for additional data file.</p></caption></media>" ]
[{"label": ["7"], "element-citation": ["\n"], "surname": ["Hashimoto", "Koike", "Shimizu", "Iyo"], "given-names": ["K", "K", "E", "M"], "year": ["2005"], "article-title": ["\u03b17 Nicotinic receptor agonists as potential therapeutic drugs for schizophrenia."], "source": ["Curr Med Chem\u2013CNS Agents"], "volume": ["5"], "fpage": ["171"], "lpage": ["184"]}, {"label": ["37"], "element-citation": ["\n"], "surname": ["Hashimoto", "Shimizu", "Iyo"], "given-names": ["K", "E", "M"], "year": ["2005"], "article-title": ["Dysfunction of glia-neuron communication in pathophysiology of schizophrenia."], "source": ["Curr Psychiatry Rev"], "volume": ["1"], "fpage": ["151"], "lpage": ["163"]}, {"label": ["38"], "element-citation": ["\n"], "surname": ["Hashimoto", "Hattori", "Sawa", "McInnis"], "given-names": ["K", "E", "A", "M"], "year": ["2007"], "article-title": ["Candidate genes and models: Chapter 6. Neurotransmission."], "source": ["Neurogenetics of Psychiatric Disorders"], "publisher-loc": ["New York"], "publisher-name": ["Informa Healthcare"], "fpage": ["81"], "lpage": ["100"]}, {"label": ["43"], "element-citation": ["\n"], "surname": ["Koike", "Hashimoto", "Takai", "Shimizu", "Komatsu"], "given-names": ["K", "K", "N", "E", "N"], "year": ["2005"], "article-title": ["Tropisetron improves deficits in auditory P50 suppression in schizophrenia."], "source": ["Schizophrenia Res"], "volume": ["76"], "fpage": ["67"], "lpage": ["72"]}, {"label": ["48"], "element-citation": ["\n"], "surname": ["Watanabe", "Okada", "Shimizu", "Omura", "Yoshikawa"], "given-names": ["M", "H", "K", "T", "E"], "year": ["1997"], "article-title": ["A high resolution animal PET scanner using compact PS-PMT detectors."], "source": ["IEEE Trans Nucl Sci"], "volume": ["44"], "fpage": ["1277"], "lpage": ["1282"]}]
{ "acronym": [], "definition": [] }
51
CC BY
no
2022-01-13 07:14:34
PLoS One. 2008 Sep 18; 3(9):e3231
oa_package/2b/31/PMC2529405.tar.gz
PMC2529406
18802462
[ "<title>Introduction</title>", "<p>Recent advances in mapping array technology and the increasing content from SNP databases ##UREF##0##[1]##,##REF##17943122##[2]## have expanded the capacity for large-scale genotyping. With mapping arrays for more than one million SNPs now available ##UREF##1##[3]##,##UREF##2##[4]##,##REF##16768648##[5]##,##UREF##3##[6]##, genome-wide association studies carry the promise of identifying replicable associations between important genetic risk factors and complex diseases. One of the major hurdles that needs to be addressed in order to make genome-wide association studies successful is the multiple comparison problem. Hundreds of thousands of SNPs are genotyped and examined for potential associations with multiple phenotypes, resulting in possibly millions of statistical tests. The small number of SNPs that contain “true” signals must be identified among the thousands of false-positive results. The success of genome-wide association studies will depend upon whether it will be possible to overcome this obstacle and translate the increase in genotype information into the identification of novel disease loci, or whether the increased genetic information will be diluted by the multiple testing problem.</p>", "<p>A brute-force way to address the multiple comparison problem is to design studies with sample sizes large enough to test all genotyped SNPs with standard association tests and adjust for multiple comparison using the Bonferroni correction ##UREF##4##[7]##. However, while sample sizes of several thousand subjects will certainly be feasible for common phenotypes (e.g., BMI, height), such a strategy carries the risk that the increase in sample size is accompanied by an increase in study heterogeneity, mitigating the positive effects of a larger sample size. Further, for many diseases, recruiting the theoretically required sample size may not be feasible, prohibited either by the costs for recruitment or phenotype assessment, or by the prevalence of the disease. An alternative approach is to develop novel statistical methodology to address the multiple comparison problem with realistic sample sizes.</p>", "<p>For the analysis of quantitative traits in family-based designs, Van Steen et al. ##REF##15937480##[8]## proposed a new class of two-stage testing strategies that uses the same data set twice, first for genomic screening and then for genetic association testing. The approach proved to be a very powerful way to address the multiple testing problem in genetic association studies ##REF##15937480##[8]##,##REF##17701906##[9]##,##REF##17653107##[10]##,##REF##17310127##[11]##. Van Steen type testing strategies take advantage of a unique property of family-based data in that it can be partitioned into two statistically independent components. By exploiting the information about the genetic association that is not used in the second stage when the association tests are computed, the first stage prioritizes “promising” SNPs for the second stage.</p>", "<p>Van Steen type testing strategies have three key advantages: 1.) The method achieves statistical power levels which can be substantially higher than those of standard approaches ##REF##15937480##[8]##,##REF##17701906##[9]##, and is thereby able to establish genome-wide significance within one study ##REF##15937480##[8]##,##REF##17701906##[9]##,##REF##17653107##[10]##,##REF##17310127##[11]##. 2.) The Van Steen algorithm maintains the separation between the multiple testing problem and the replication process. Replication attempts in different studies are reserved for the generalization of the established associations and assessment of heterogeneity between study populations. 3.) Since genome-wide significance is established in the first data set, the number of SNPs that are pushed forward for replication testing in other populations is generally very small and does not require a large budget, which makes simultaneous replication attempts in multiple samples feasible.</p>", "<p>Although the approach has recently been significantly improved and now allows family studies to achieve power levels that are comparable to population-based studies with the same number of probands ##REF##17701906##[9]##, its applicability is limited. While extensions of the testing strategy are available for arbitrary family structures and for case/control designs ##REF##17653107##[10]##,##REF##17310127##[11]##, the approach cannot be applied in situations in which there is no phenotypic variation in the phenotypes of the probands, i.e., all probands are affected with the disease or trait of interest. This prevents the utilization of the approach in trio designs (i.e., affected probands and their parents). Since this original trio/TDT design is frequently used, this limitation of the testing strategy poses a major disadvantage for family-based designs.</p>", "<p>In this manuscript, we propose an extension of Van Steen type testing strategies to family-based designs in which all probands are affected. The strategy also uses the same data set for both stages, which we will refer to as the rank-weighting step and the testing step. In the first stage of the testing strategy, the genetic relative risk effect sizes are estimated for each SNP. We show that it is possible to derive four estimating equations that depend only on the observed parental mating types, but not on any unknown parameters. The estimating equations can be solved analytically, allowing for the construction of effect size estimators that do not depend on the marker allele frequency or offspring genotypes. This is in contrast to effect size estimators/association test statistics for study designs with only affected subjects in population-based studies ##UREF##5##[12]##,##REF##9867708##[13]##,##REF##14686606##[14]##, where the allele frequency must be specified.</p>", "<p>Based on the genetic effect size estimates obtained from the estimating equations, we compute the conditional power of the FBAT/TDT for all SNPs. The relative rank of the SNPs by conditional power is then used in a weighted Bonferroni approach ##REF##17701906##[9]## to assign each SNP an individually adjusted significance level. The weights are constructed so that the overall type-1 error is maintained. In the second step of the testing strategy, the FBAT/TDT statistic is computed for each SNP and genome-wide significance is established based on its individually adjusted significance level.</p>", "<p>Using extensive simulation studies, the statistical power of the testing strategy is assessed for over a range of genetic effect sizes, different numbers of trios, when the mode of inheritance is known and unknown, and in the absence and presence of linkage disequilibrium (LD). The practical relevance of the approach is illustrated by an application to a genome-wide association study of childhood asthma.</p>" ]
[ "<title>Methods</title>", "<title>An Overview of Partitioning Family-Based Data into Independent Components</title>", "<p>Van Steen testing strategies for genome-wide association studies partition the data set into two statistically independent, but overlapping parts ##REF##15937480##[8]##,##REF##17701906##[9]##,##REF##17653107##[10]##,##REF##17310127##[11]##,##UREF##6##[15]##,##REF##14614234##[16]##. In family-based designs, the first component contains information about the SNP-trait association at a population level, which is assessed based on the proband's phenotype, <italic>Y</italic>, and the parental genotypes, <italic>P</italic>\n<sub>1</sub>, <italic>P</italic>\n<sub>2</sub>\n##UREF##6##[15]##,##UREF##7##[17]##. In our application, we use the offspring phenotype and parental genotypes to construct effect size estimates of the genetic relative risk. The second component of the data characterizes the SNP-trait association at the family level, i.e., the allele transmissions from the parents to their offspring ##REF##10782012##[18]##,##UREF##8##[19]##,##REF##8447318##[20]##. Family-based association tests such as the TDT or FBAT are therefore conditional tests that treat the offspring genotype, <italic>X</italic>, as random, conditioning upon the offspring phenotype, <italic>Y</italic>, and the parental genotypes <italic>P</italic>\n<sub>1</sub>, <italic>P</italic>\n<sub>2</sub>. The evidence for SNP-trait association is evaluated by comparing the observed offspring genotype with the expected offspring genotype, which are computed by conditioning upon the parental genotypes, assuming Mendelian transmissions. Since the offspring genotype is the only random component of the FBAT/TDT statistic, the implication is that other information in the FBAT/TDT statistic (i.e., the offspring phenotype and parental genotypes) may be used to assess the evidence for association without biasing the significance level of the FBAT/TDT statistic.</p>", "<p>Based on the two information sources about association in family-based designs, the density of the joint distribution for <italic>X</italic>, <italic>Y</italic>, and <italic>P</italic>\n<sub>1</sub>, <italic>P</italic>\n<sub>2</sub> can then be partitioned into two statistically independent components ##UREF##9##[21]##,Since the density for the first step of the testing strategy, the rank-weighting step, is given by <italic>p</italic>(<italic>Y</italic>, <italic>P</italic>\n<sub>1</sub>, <italic>P</italic>\n<sub>2</sub>), and the density of the second step, the FBAT/TDT testing step, is <italic>p</italic>(<italic>X</italic>|<italic>P</italic>\n<sub>1</sub>, <italic>P</italic>\n<sub>2</sub>, <italic>Y</italic>), likelihood decomposition (Equation 1) implies that the two steps of the testing strategy are independent. The “evidence of association” (i.e., the genetic effect size estimate) for each marker from the rank-weighting step can be utilized in the second stage without having to adjust the overall significance level for the estimation of the genetic effect size in the first stage. There are various ways in which the information from the rank-weighting step can inform the application of the FBAT/TDT statistic in the second step. The effect size estimate from the screening step can be used to select a small subset of “very promising” markers for FBAT/TDT testing ##REF##15937480##[8]## or to assign each marker with an individual significance level that reflects the rank of the marker's effect size estimate relative to the other markers ##REF##17701906##[9]##. Another possibility is to have the information from the screening step define the “tuning parameters” of the FBAT statistic ##UREF##10##[22]##,##UREF##11##[23]##.</p>", "<title>The Rank-Weighting Step: Estimating the Power of the FBAT Statistic under <italic>H<sub>A</sub></italic> When Trio Data Are Given and All Probands Are Affected</title>", "<p>We assume that trios are given (i.e., affected probands and parents), and that SNP data are analyzed. If the parental data are missing/unavailable, the parental genotypes can be replaced in all equations below by the sufficient statistic by Rabinowitz &amp; Laird ##REF##10782012##[18]##,##UREF##8##[19]##. The sufficient statistic for each nuclear family is defined by all family configurations that lead to consistent inference about the missing parents, given the observed genotypes. When parental data are given, the parental genotypes represent the sufficient statistic. Like the parental genotypes, the sufficient statistic allows for the computation of the offspring genotype distribution within each family, independent of the unknown allele frequency. For a more detailed discussion, we refer to the original paper ##REF##10782012##[18]##.</p>", "<p>For each marker locus of interest, let <italic>x<sub>i</sub></italic> be the coded genotype of the <italic>i</italic>\n<sup>th</sup> proband, counting the number of minor alleles for the SNP of interest. The variables <italic>p<sub>i</sub></italic>\n<sub>1</sub> and <italic>p<sub>i</sub></italic>\n<sub>2</sub> denote the parental genotypes for both parents at the locus. The phenotype of the <italic>i</italic>\n<sup>th</sup> proband is defined by <italic>y<sub>i</sub></italic>. For trio samples in which all probands are affected, the phenotype is coded as “y = 1”. The FBAT statistic, , ##UREF##8##[19]##,##REF##8447318##[20]## is then given by:and has a chi-square distribution with one degree of freedom. Assuming an additive coding function for the genotype, this FBAT statistic and the original TDT statistic ##REF##8447318##[20]## are equivalent.</p>", "<p>In order to develop a Van Steen type testing strategy ##UREF##6##[15]##,##REF##14614234##[16]## for the classical TDT design, the conditional power ##UREF##10##[22]##,##REF##12181775##[24]## of the FBAT/TDT statistic, , has to be computed in the first step of the testing strategy. This requires the specification of the conditional marker density under the alternative hypothesis:where affected probands are coded as “<italic>y<sub>i</sub></italic> = 1”. The parameter <italic>f<sub>x</sub></italic> denotes the penetrance probability (i.e., <italic>f<sub>x</sub></italic> = <italic>Pr</italic>(<italic>y<sub>i</sub></italic> = 1|<italic>x</italic>)), and Ψ<italic><sub>x</sub></italic>, the genotype relative risk (i.e., Ψ<italic><sub>x</sub></italic> = <italic>f<sub>x</sub></italic>/<italic>f</italic>\n<sub>0</sub>). The probability <italic>Pr</italic>(<italic>x</italic>|<italic>p<sub>i</sub></italic>\n<sub>1</sub>, <italic>p<sub>i</sub></italic>\n<sub>2</sub>) is defined by Mendelian transmission and can be computed straightforwardly, conditional on parental genotypes, without any additional knowledge/assumptions. The penetrance probabilities are unknown and have to be estimated based on the information that is available in the rank-weighting step, i.e., the offspring phenotype and the parental genotypes.</p>", "<p>In the original Van Steen approach ##REF##15937480##[8]##, the parental genotypes are used to compute the expected/predicted marker scores of the offspring. By regressing the offspring phenotype on its expected marker score, an estimate for the genetic effect size is obtained that allows us to specify the penetrance probability, <italic>Pr</italic>(<italic>y<sub>i</sub></italic> = 1|<italic>x<sub>i</sub></italic>) ##UREF##6##[15]##,##REF##14614234##[16]##. However, when there is no phenotypic variation in the data (i.e., all probands are affected), this approach is not applicable and an alternative approach has to be developed. In order to simplify the notation, our derivation will be based on the parameterization of the marker distribution (Equation 3) in terms of the genotype relative risks, Ψ<italic><sub>x</sub></italic>.</p>", "<p>Due to the lack of variation in the phenotype, the only variation that can be utilized for the estimation of the relative risk probabilities are the parental genotypes. In the trio design, there are six distinct parental mating types: (<italic>p</italic>\n<sub>1</sub> = 2, <italic>p</italic>\n<sub>2</sub> = 2), (<italic>p</italic>\n<sub>1</sub> = 2, <italic>p</italic>\n<sub>2</sub> = 1), (<italic>p</italic>\n<sub>1</sub> = 2, <italic>p</italic>\n<sub>2</sub> = 0), (<italic>p</italic>\n<sub>1</sub> = 1, <italic>p</italic>\n<sub>2</sub> = 1), (<italic>p</italic>\n<sub>1</sub> = 1, <italic>p</italic>\n<sub>2</sub> = 0) and (<italic>p</italic>\n<sub>1</sub> = 0, <italic>p</italic>\n<sub>2</sub> = 0), where 0, 1, and 2 denote the number of copies of the minor allele for the marker of interest. The frequencies of the parental mating types in the ascertained sample (<italic>y<sub>i</sub></italic> = 1) can be computed using Bayes' rule,where the parameter, <italic>p</italic>, denotes the minor allele frequency for the marker in the general population, and again, as above, the probabilities are defined by Mendelian transmissions. The probabilities <italic>Pr</italic>(<italic>p</italic>\n<sub>1</sub> = <italic>k</italic>, <italic>p</italic>\n<sub>1</sub> = <italic>l</italic>) are the paternal mating type frequencies in the general population, and <italic>k</italic> and <italic>l</italic> are given by one of the six distinct mating types defined above. Under the assumption of random mating and Hardy-Weinberg equilibrium at the marker locus in the general population, the probabilities <italic>Pr</italic>(<italic>p</italic>\n<sub>1</sub> = <italic>k</italic>, <italic>p</italic>\n<sub>1</sub> = <italic>l</italic>) will be defined by the actual mating type and the minor allele frequency, <italic>p</italic>.</p>", "<p>Based on these assumptions, the likelihood of the parental mating types in the ascertained sample is given by , where the probability of a mating type is denoted as and the observed number of mating types is . In order to obtain maximum likelihood estimates for the genotype relative risks Ψ<sub>1</sub> and Ψ<sub>2</sub>, one has to maximize the likelihood function <italic>l</italic>(Ψ<sub>1</sub>, Ψ2, <italic>p</italic>) over all unknown parameters, i.e., the genotype relative risks, Ψ<sub>1</sub> and Ψ2, and the minor allele frequency of the marker, <italic>p</italic>. However, due to the structure of the likelihood function, the Fisher information matrix is ill conditioned ##UREF##12##[25]## and a numerical solution of the likelihood maximization is non-trivial. This is particularly challenging in the context of genome-wide association studies in which the numerical implementation must be fast and reliable. In addition to the technical issues related to the likelihood maximization, the estimation of the allele frequency at the marker locus is also problematic in the presence of population admixture.</p>", "<p>To avoid issues related to the estimation of the allele frequency, we will construct estimators for the genotype relative risks, Ψ<sub>1</sub> and Ψ<sub>2</sub>, that are independent of the minor allele frequency, <italic>p</italic>, and have a closed analytical form, facilitating a numerically fast and robust implementation in genome-wide association studies. We consider the following four possible ratios of parental mating types:Under the assumption of Hardy-Weinberg equilibrium in the general population, using (Equation 4), the minor allele frequency, <italic>p</italic>, drops out of the mating type ratios, and one can show that the ratios <italic>R</italic>\n<sub>1</sub>, <italic>R</italic>\n<sub>2</sub>, <italic>R</italic>\n<sub>3</sub>, and <italic>R</italic>\n<sub>4</sub> are given by:\n</p>", "<p>It is important to note that the four ratios <italic>R</italic>\n<sub>1</sub>, <italic>R</italic>\n<sub>2</sub>, <italic>R</italic>\n<sub>3</sub>, and <italic>R</italic>\n<sub>4</sub> do not depend on the unknown minor allele frequency, <italic>p</italic>, and can be estimated based on the parental genotypes, e.g., . It is also important to note that, if a likelihood approach for the parental mating types had been implemented, the minor allele frequency, <italic>p</italic>, would have to be estimated.</p>", "<p>If a genetic model is specified (e.g., under an additive mode of inheritance, Ψ<sub>1</sub> = (1+Ψ<sub>2</sub>)/2), each equation in (Equation 6) will depend only on one unknown genotype relative risk parameter. Each equation can then be solved for the unknown parameter and four estimates for the genotype relative risk are obtained. Alternatively, an overall effect size estimate can be constructed by averaging over all four estimates for the genetic effect size. The selected estimate for the genotype relative risk can then be used to calculate the marker distribution under the alternative hypothesis (Equation 3), which is the final component needed in calculating the conditional power of the FBAT/TDT statistic. Using simulation studies, we will assess which of the four ratios (or the average) for the proposed testing strategy generally achieves the highest and most stable power estimates.</p>", "<p>Since the proposed estimators for the genotype relative risk only depend on the parental genotypes, they fulfill the decomposition condition (Equation 1) and can be used in the rank-weighting step of the testing strategy without biasing the significance level of the FBAT/TDT statistic in the second stage. The independence of the mating type ratios from the allele frequency makes the approach particularly attractive in the presence of population admixture.</p>", "<p>While we have outlined the concept of genotype relative risk estimation in the context of ascertained family samples for the trio designs, the genetic effect size estimators can be constructed in the same way for more complex nuclear family structures. Using the algorithm by Rabinowitz &amp; Laird ##REF##10782012##[18]##, all possible parental mating types can be derived for nuclear families with missing parental information and/or multiple offspring. The mating type probabilities can then be computed based on Bayes' rule, as for the trio design (Equation 4). By examining all possible mating type ratios, the ratios that depend only on the genotype relative risk, but not on the allele frequency, can be identified and used to construct direct estimators of the genetic effect size. While we are not able to provide a general rule of thumb on how to construct mating type ratios that do not depend on the allele frequency other than to evaluate all possible ratios, such ratios appear to exist for most nuclear family-types. Since the identification process of the suitable mating type ratios can be automated by using software packages such as Maple and Mathematica, the proposed concept of genotype relative risk estimation is not specific to the trio design and should be applicable to general nuclear family-types.</p>", "<p>It is important to note that the proposed genetic effect size estimators are derived under the assumption of Hardy-Weinberg equilibrium at the marker locus in the general population, but not in the ascertained sample. Since it is common practice to filter out SNPs that are strongly out of Hardy-Weinberg equilibrium when the genotype data are cleaned prior to analysis, only SNPs with mild to moderate violations of the Hardy-Weinberg assumption will reach the association analysis step. The effects of SNPs with Hardy-Weinberg violations on the proposed testing strategy are thereby limited. However, the genetic effect size estimation in the first step will be biased for such SNPs. In the presence of SNPs that are out of Hardy-Weinberg equilibrium and that are not associated with affection status, the proposed testing strategy is likely to have reduced power. If the Hardy-Weinberg assumption does not hold at the disease susceptibility locus (DSL), the power of the proposed testing strategy can be either increased or decreased, depending on whether the signal that is caused by the true genetic effect at the DSL locus is amplified by the Hardy-Weinberg violation or not. Further, it is important to note that, while violations of the Hardy-Weinberg assumption will have an effect on the rank-weighting step, the validity of the FBAT/TDT-testing step and, consequently, the validity of the entire approach will not be affected by departures from Hardy-Weinberg.</p>", "<title>The Testing Step: Testing for Family-Based Association with Weighted Bonferroni Significance Levels</title>", "<p>In the first phase of the testing strategy, the genetic effect size estimates for each marker are used to compute the conditional power at each locus, and all markers are ranked by power. A weighted Bonferroni approach ##REF##17701906##[9]## is implemented that assigns individual significance levels, denoted as <italic>α<sub>i</sub></italic>, to each marker locus based on its conditional power ranking. Essentially, <italic>α<sub>i</sub></italic> is the type 1 error apportioned to the <italic>i</italic>\n<sup>th</sup> test on the basis of its power ranking relative to all of the other tests. The individual significance levels are selected so that the overall significance level is maintained, e.g., . Using the FBAT/TDT statistic, each marker is then tested in the second stage at the individual significance level <italic>α<sub>i</sub></italic>, and its association with affection status is declared as genome-wide significant if its FBAT/TDT statistic p-value is less than the individual significance level <italic>α<sub>i</sub></italic>.</p>", "<p>In order to determine the individual significance levels <italic>α<sub>i</sub></italic>, we must select a weighting scheme to apply to the weighted Bonferroni method ##REF##17701906##[9]##. Essentially, the weighted Bonferroni method partitions the SNPs into bins and assigns each bin a weight, where the bin and weight sizes vary depending on the relative power ranking of the SNPs in the bin. Each SNP within a bin is assigned an equal weight, which represents a fraction (or individual significance level, <italic>α<sub>i</sub></italic>) of the overall significance level, <italic>α</italic>. Many different weighting schemes to select bin/weight sizes may be applied, as long as <italic>α</italic> is maintained. We selected an exponential weighting scheme, which uses weights that decrease exponentially and bin sizes that increase exponentially as the power rankings decrease ##REF##17701906##[9]##. To define the exponential weighting scheme, let <italic>k<sub>j</sub></italic> be the size of the <italic>j<sup>th</sup></italic> partition, and let <italic>k</italic> and <italic>r</italic> be user-defined partitioning parameters with an integer value. Then the sizes of the subsequent partitions can be defined by <italic>k</italic>\n<sub>1</sub> = <italic>k</italic> and <italic>k<sub>j</sub></italic> = <italic>k</italic>*<italic>r</italic>\n<sup>(<italic>j</italic>−1)</sup>. The exponential weight, <italic>w<sub>j</sub></italic>, for the <italic>j<sup>th</sup></italic> bin is given by , with . Finally, the individual significance level for the <italic>j<sup>th</sup></italic> partition/bin is . With these parameter specifications, it is straightforward to see that , thus the overall alpha level is maintained. Further discussion of the weighted Bonferroni method and weighting schemes is given in Ionita-Laza et al. ##REF##17701906##[9]##. The optimal choices for the initial partition size <italic>k</italic> and the partitioning parameter <italic>r</italic> will be determined by simulation studies.</p>", "<title>Simulation Studies</title>", "<p>Using simulation studies, we compare the proposed testing strategy to the standard approach, FBAT/TDT testing with Bonferroni corrected p-values. Both approaches are contrasted under various scenarios with differing trio sample sizes and minor allele frequencies. We simulate trio data under the assumption that all offspring are affected and the genotypes of both parents are known. The minor allele frequencies are drawn from <italic>β</italic> distributions that resemble the 550 K Illumina HumanHap array.</p>", "<p>The data were simulated under two separate scenarios. In the first scenario, independence among all markers (i.e., no linkage disequilibrium (LD)) is assumed. In the second scenario, we simulated local LD between the SNPs. In order to obtain realistic local LD patterns, we utilized a 550 K scan in the CAMP study (see Data Analysis section) that consists of 400 trios. Based on the observed local LD patterns in CAMP, we simulated the correlated SNPs for the second scenario. Specifically, we applied a ‘moving window’ algorithm, where the observed correlation (<italic>r</italic>\n<sup>2</sup>) between the SNP to be simulated and the SNP immediately preceding the SNP that is simulated (in terms of physical location) was used to recapitulate local LD patterns on a genome-wide scale.</p>", "<p>In each simulation, one locus/SNP is assumed to be the DSL, while the other SNPs that are not in LD with the DSL are considered null loci. For the null loci, under the independence scenario, the parental genotypes are generated by drawing from a Binomial distribution with the selected marker's minor allele frequency. When SNPs are correlated, the moving window approach described above is used to generate parental genotypes. Based on the parental genotypes, the offspring genotype is obtained by simulated Mendelian transmissions from the parents. At the DSL, the configuration of genotypes in the proband and parents is simulated based on their theoretical distribution under the specified alternative hypothesis, as outlined in Knapp ##REF##10090903##[26]## and Lange &amp; Laird ##UREF##10##[22]##,##REF##12181775##[24]##.</p>", "<p>For the considered scenarios, we assessed the performance of the proposed approach when the genetic effect size is estimated either based on one of four mating type ratios (R1–R4, Equation 6) or by the average of the four estimates. In simulation studies comparing the performance of the estimators (data not shown), we observed that the genotype relative risk estimator based on equation R4 consistently generated the highest power estimates (for minor allele frequencies (MAFs) &gt;0.1), and was stable, even with modest effect sizes (e.g., OR = 1.25) and lower allele frequencies (e.g., MAF≤0.2). Thus, all estimated power levels for the proposed method that are shown here are based on the genotype relative risk estimator for mating type ratio R4.</p>", "<p>In the first set of simulations, we assume an additive mode of inheritance at the DSL. The genetic effect size is defined in terms of an odds ratio and ranges between 1.25 and 2.5, depending on the number of trios. A disease prevalence (<italic>K</italic>) of 10% is selected throughout the simulations. The trio sample size varies between 500–2000 trios. To accurately depict the degree of LD between markers, 500,000 markers are simulated. Under the independence scenario, the power was assessed as the proportion of replicates where the FBAT test statistic p-value was less than the required weighted Bonferroni alpha level, based on its power ranking from the rank-weighting step. Under the LD scenario, the power was computed in two ways. First, we defined a positive result identically to the procedure used for the independence scenario (i.e., a significant result for the DSL only). Secondly, we more broadly defined a positive result to include a significant finding in the DSL or in any markers in strong LD (<italic>r</italic>\n<sup>2</sup>&gt;0.8) and within the same physical region, (i.e., within five SNPs) with the DSL. For the standard Bonferroni correction, power was defined as the proportion of replicates with an FBAT statistic p-value&lt;10<sup>−7</sup> (i.e., 0.05/500,000).</p>", "<title>Estimated Power Levels for n = 500–2000 Trios, under an Additive Genetic Model</title>", "<p>The results of the first set of simulations are displayed in ##TAB##0##Table 1##. The number of trios is presented in column 1 and the odds ratio (OR) for the DSL is specified in column 2. The minor allele frequency (MAF) of the DSL is displayed in Column 3. Columns 4, 6, and 8, denoted as “Weighted,” present the power estimates using the weighted Bonferroni method by Ionita-Laza et al. ##REF##17701906##[9]##, with an exponential weighting scheme and partitioning parameters of K = 7 and r = 2. The choice of K = 7 and r = 2 tended to have the highest power among a range of partitioning (K = 3–10, r = 2–5) parameters, although decreases in power were minimal within these ranges (data not shown). Columns 5, 7, and 9, denoted as “Standard,” display the results for the standard approach in which all SNPs are equally weighted when applying the Bonferroni correction, and a significance level of 10<sup>−7</sup>, (i.e., 0.05/500,000) is required for genome-wide significance. Columns 4–5 (Independence scenario”) reflect the scenario in which all markers are independent (i.e., adjacent <italic>r</italic>\n<sup>2</sup> = 0). Columns 6–9 (“LD scenario”) display the power estimates when LD is present among markers, where the power represents either detecting the DSL only (Column 6–7), or the DSL/markers in strong LD with the DSL (Columns 8–9). The power estimates are based on at least 1,000 replicates for each (DSL) minor allele frequency and odds ratio.</p>", "<p>For genome screens of 500 K SNPs, regardless of the sample size or degree of correlation among markers, the use of power-driven weights from the rank-weighting step shows a considerable improvement in power over the standard methodology. For the lowest power estimates (&lt;40% power for the standard Bonferroni), the power estimates for the weighted method are typically at least twofold greater than the standard approach. For low to moderate power estimates, (40–70% power for Bonferroni), the weighted method outperforms the standard correction by to 15–40%. For SNPs with greater than 70% power with the standard approach, the improvement ranges between 7 and 11%, unless the power estimates are near one. However, even in these scenarios, the power estimates for the weighted Bonferroni method are always higher, though the differences between the two methods are more modest.</p>", "<p>With respect to trio sample size, we note that even with smaller sample sizes (e.g., n = 500), there is still power to detect a DSL (or SNP in LD with the DSL), and the power gains over standard Bonferroni correction are maintained, although a more pronounced effect size is required (OR = 2.25–2.5) to achieve adequate power. Based on the results of our simulation studies, we would not recommend genome-wide association studies of fewer than 300 trios unless extremely large effect sizes (OR&gt;3) were anticipated.</p>", "<p>To verify that the proposed testing strategy maintains the overall alpha level, the simulations were repeated under the null hypothesis of no linkage/no association, with a sample size of 500 trios. Based on over 10,000 replicates, the observed overall type 1 error rate was maintained at 4.66%.</p>", "<p>Finally, in examining the impact that LD has on power, when considering a positive finding to be the detection of the DSL only, the power of the approach was slightly reduced in comparison to the scenario in which the SNPs were independent. However, the proposed testing strategy still outperforms the standard approach by differences that are of practical relevance. When the definition of a positive finding is extended to those SNPs that are in LD with the DSL, the power estimates are higher than the independence scenario. This is a significant finding, given that some array platforms for genome-wide genotyping do not employ LD-tagging methods, and as chip density increases (i.e., one million SNP arrays), linkage disequilibrium will have a greater impact on the analysis of genome-wide association studies.</p>", "<title>Estimated Power Levels for n = 2000 Trios, When the Genetic Model Is Unknown</title>", "<p>Since in practice the underlying mode of inheritance is unknown, we ran a second set of simulations to reflect this reality and assess the impact on the power of the proposed method and the standard approach. In the data analysis step of the following simulation, the true genetic model was considered to be “unknown.” We simulated three scenarios, where the true (but unknown) generating model was either additive, dominant, or recessive, and conducted separate FBAT analyses under all three genetic models. To evaluate the power for the weighted Bonferroni method ##REF##17701906##[9]##, we estimated the conditional power for each SNP under all three genetic models. For each SNP, the result for the genetic model with highest power was selected and the lower powered results (without evaluating the FBAT statistic p-value) were discarded. This resulted in 500,000 SNPs/power estimates across the three genetic models, that were ranked overall by power and evaluated for association using weighted Bonferroni significance levels. The weighted Bonferroni significance levels were computed in the same way as previously described. We then compared the power obtained from the weighted method to standard Bonferroni correction, which computed the FBAT statistic under all three genetic models at each SNP, thus requiring a correction for 1.5 million comparisons (500,000 markers * 3 genetic models) and an FBAT p-value &lt;3.3×10<sup>−8</sup> for significance (i.e., 0.05/1,500,000). For simplicity, we ran these simulations for 2000 trios.</p>", "<p>The results of the second set of simulations are displayed in ##TAB##1##Table 2##. The data are presented in an identical format to the simulations under the additive model (including partitioning parameters of K = 7 and r = 2), except that column 1 reflects the “true” underlying genetic model rather than the number of trios.</p>", "<p>For the additive model, in comparison to the simulations where the genetic model is known, the power estimates tend be slightly lower. In the independence scenario, for an odds ratio of 1.5 and MAF of 0.2, when the genetic model is known, the weighted Bonferroni method has 91% power versus 85% for the standard, whereas, when the genetic model is unknown, the power estimates are 80% and 57%, respectively. However, our new method seems much more robust to analysis under multiple models in comparison to the standard correction. For an effect size of 1.5, the power loss in the unknown model ranges from 7 to 15%, depending on MAF, while power loss under the standard method ranges from 15 to 63%. Similar observations are made for the power comparisons between the weighted and standard methods for the LD scenarios. The overall power is reduced relative to the situation where the generating genetic model is known, but the difference in power between the weighted and standard methods is more striking. In comparing the independence scenario to the LD scenarios, the patterns observed when the genetic model is known hold here as well: when LD is present and the DSL or SNPs in LD with the DSL are considered, the power is highest, followed by the independence scenario. The lowest overall power is noted when LD is present and only the DSL is examined for significant association. In summary, while the overall power drops, the benefits of our methodology versus the standard are more pronounced when the genetic model is unknown and multiple analyses are conducted.</p>", "<p>In comparing our method with weighted Bonferroni significance levels to the standard under dominant and recessive models, our procedure consistently demonstrates greater power, regardless of the degree of LD, effect size, or MAF. However, under a recessive model, a MAF of 0.3 or greater is required to achieve adequate power for the range of effect sizes that we examined (OR = 2–2.5).</p>", "<p>Overall, our new methodology has the greatest impact for the low to moderately powered markers. For SNPs with standard Bonferroni power estimates ranging between 40% and 70%, the new method generally boosts power by an absolute difference of 10–15%, potentially providing marginally powered SNPs with a better chance of detection.</p>", "<title>Summary</title>", "<p>Our simulation studies illustrate that the application of the proposed testing strategy is not limited by the number of trios analyzed, the degree of correlation among SNPs, the genetic model, or the size of the genetic effect. When standard approaches fail to provide sufficient power, the proposed testing strategy maintains acceptable power levels for small to moderate effect sizes (n = 2000) for the additive generating models, and moderate effect sizes under the dominant or recessive models or designs with fewer trios (n = 500–1000). As a general rule of thumb, our simulation experiments suggest that the testing strategy achieves optimal power levels for partitioning parameters of K = 7 and r = 2 for 500,000 markers, though power estimates were similar for K = 5–10 and r = 2–3. A comparison of the achieved power levels for differing number of trios and various genetic models illustrates that the impact of the multiple testing problem on a genome-wide association study can be minimized by the use of the proposed testing strategy.</p>", "<title>Data Analysis: A Genome-Wide Screen of Children Asthmatics</title>", "<p>Asthma is a complex respiratory disorder, likely due to both genetic and environmental influences that affect the developing respiratory system. Asthma has been shown to have substantial heritability ##REF##16117840##[27]##,##REF##11668102##[28]##,##UREF##13##[29]## and a comprehensive review of the literature in 2003 reported more than 200 studies with an association between asthma and its related phenotypes ##UREF##14##[30]##.</p>", "<p>Thus, we applied our methodology to a family-based genome-wide association study of asthma. The families were originally recruited through the Childhood Asthma Management Program (CAMP) ##REF##10027502##[31]## Genetics Ancillary Study. All of the families were ascertained through asthmatic probands between 5 and 12 years old with mild to moderate asthma. All of the probands are affected, making it impossible to apply methodologies that require phenotype variation.</p>", "<p>SNP genotyping was performed using Illumina HumanHap 550v3 arrays. Of 547,645 SNPs, 2.5% were removed during data cleaning due to genotype completion rates &lt;95%, parent-offspring Mendelian errors, or because the assay sequence could not be aligned to one genomic locus, which resulted in 534,290 autosomal markers for analysis. Genotyping was conducted on 1215 subjects in 422 families. After removing 43 subjects with inadequate data, 1172 subjects comprising 403 families were analyzed. We applied the new power rank-weighting methodology, under an additive genetic model, to all 534,290 SNP, using equation R4 (Equation 6) to estimate genetic effect sizes, which had consistently had the highest power in the simulation studies. The power rankings were used to individually weight the family-based association test (also assuming an additive model) for each marker, using the method of Ionita-Laza et al. ##REF##17701906##[9]##. ##TAB##2##Table 3## displays the results for the CAMP data analysis. Based on the results of the simulation studies, the partitioning parameters of K = 7 and r = 2 were used.</p>", "<p>From the analysis, two SNPs were identified as genome-wide significant with a global alpha level of 0.05. These SNP were also the top two by power. Thus, the Top K Method by Van Steen et al. ##REF##15937480##[8]##, with a modest choice of ‘Top’ markers selected for analysis, would have also detected these SNPs. However, the weighted Bonferroni method by Ionita-Laza et al. ##REF##17701906##[9]## allows for the evaluation of all SNP. Most strikingly, neither of these SNPs would have been detected after standard Bonferroni ##UREF##4##[7]## or FDR-type ##UREF##15##[32]## correction. These significant markers reside on chromosomes 1 (rs10863712) and 14 (rs1294497). In both markers, the minor allele is over-transmitted to the affected proband. These markers are currently under further study. These results provide proof of concept for our new method in that the top-ranked markers by power also showed evidence of association, strongly suggesting consistency of association in the independent population level and family level components of family-based data.</p>" ]
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[ "<title>Discussion</title>", "<p>With the current genotyping capabilities, genome-wide association studies have become a reality. In order to utilize the wealth of SNP data obtained in such studies to identify genes for complex diseases, new statistical approaches are needed that can handle the multiple comparisons problem on an increasingly large scale. For population-based studies, multi-stage designs have been suggested. In each stage of the design, the “most promising” SNPs (top 1–10% of all genotyped SNPs) are pushed forward to the next level in which they are genotyped in another sample. Overall significance is established by combining the evidence from all stages into a single analysis. While this is a cost-effective approach, it is not as powerful as genotyping all subjects ##REF##16415888##[33]##.</p>", "<p>Testing strategies that use the same data set for genomic screening (i.e., rank-weighting) and testing ##REF##15937480##[8]##,##REF##17701906##[9]##,##REF##17653107##[10]##,##REF##17310127##[11]## establish genome-wide significance within one data set. They usually identify only a handful of SNPs (typically fewer than 20) which are then genotyped in other studies in order to generalize the significant findings ##REF##16614226##[34]##,##REF##18387595##[35]##. In contrast to multi-stage designs, genotyping the identified SNPs in other samples does not serve the purpose of establishing genome-wide significance. The effects of study heterogeneity are thereby limited. However, thus far, such testing strategies have only been available for the small subset of family-based studies in which the primary phenotype is quantitative, but not for the most popular family design, the classical trio design. The lack of phenotypic variation has prevented the genetic effect size estimation by the conditional mean model in the rank-weighting step.</p>", "<p>In this manuscript, we have developed an approach that makes such testing strategies available for the commonly used TDT design. Our simulation studies show that our method outperforms standard methodology substantially. The effect size estimators that we suggest allow for the assessment of the genotype relative risk at a population level in ascertained family samples. In contrast to association tests for affected-only designs in population-based studies ##UREF##5##[12]##,##REF##9867708##[13]##,##REF##14686606##[14]##, here it is possible to estimate the genetic effect size independent of the unknown allele frequency. While we have discussed only the construction of such effect size estimators for the trio design, the concept of identifying probability ratios of mating types that depend on the genetic effect size, but not on the unknown allele frequency, is generally applicable to all family-based designs.</p>", "<title>URL</title>", "<p>The testing strategy as well as the corresponding power and sample size calculations has been fully implemented in the software package <italic>PBAT</italic>, which is freely available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biostat.harvard.edu/~clange/default.htm\">http://www.biostat.harvard.edu/˜clange/default.htm</ext-link>\n##REF##14740322##[36]##,##REF##15814068##[37]##.</p>" ]
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[ "<p>Conceived and designed the experiments: AM STW CL. Performed the experiments: AM. Analyzed the data: AM. Contributed reagents/materials/analysis tools: STW. Wrote the paper: AM CL.</p>", "<p>For genome-wide association studies in family-based designs, we propose a powerful two-stage testing strategy that can be applied in situations in which parent-offspring trio data are available and all offspring are affected with the trait or disease under study. In the first step of the testing strategy, we construct estimators of genetic effect size in the completely ascertained sample of affected offspring and their parents that are statistically independent of the family-based association/transmission disequilibrium tests (FBATs/TDTs) that are calculated in the second step of the testing strategy. For each marker, the genetic effect is estimated (without requiring an estimate of the SNP allele frequency) and the conditional power of the corresponding FBAT/TDT is computed. Based on the power estimates, a weighted Bonferroni procedure assigns an individually adjusted significance level to each SNP. In the second stage, the SNPs are tested with the FBAT/TDT statistic at the individually adjusted significance levels. Using simulation studies for scenarios with up to 1,000,000 SNPs, varying allele frequencies and genetic effect sizes, the power of the strategy is compared with standard methodology (e.g., FBATs/TDTs with Bonferroni correction). In all considered situations, the proposed testing strategy demonstrates substantial power increases over the standard approach, even when the true genetic model is unknown and must be selected based on the conditional power estimates. The practical relevance of our methodology is illustrated by an application to a genome-wide association study for childhood asthma, in which we detect two markers meeting genome-wide significance that would not have been detected using standard methodology.</p>", "<title>Author Summary</title>", "<p>The current state of genotyping technology has enabled researchers to conduct genome-wide association studies of up to 1,000,000 SNPs, allowing for systematic scanning of the genome for variants that might influence the development and progression of complex diseases. One of the largest obstacles to the successful detection of such variants is the multiple comparisons/testing problem in the genetic association analysis. For family-based designs in which all offspring are affected with the disease/trait under study, we developed a methodology that addresses this problem by partitioning the family-based data into two statistically independent components. The first component is used to screen the data and determine the most promising SNPs. The second component is used to test the SNPs for association, where information from the screening is used to weight the SNPs during testing. This methodology is more powerful than standard procedures for multiple comparisons adjustment (i.e., Bonferroni correction). Additionally, as only one data set is required for screening and testing, our testing strategy is less susceptible to study heterogeneity. Finally, as many family-based studies collect data only from affected offspring, this method addresses a major limitation of previous methodologies for multiple comparisons in family-based designs, which require variation in the disease/trait among offspring.</p>" ]
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[ "<p>We thank all subjects for their ongoing participation in this study. We acknowledge the CAMP investigators and research team, supported by NHLBI, for collection of CAMP Genetic Ancillary Study data. All work on data collected from the CAMP Genetic Ancillary Study was conducted at the Channing Laboratory of the Brigham and Women's Hospital under appropriate CAMP policies and human subject's protections. We would like to thank Dr. Nan Laird for her helpful comments on an earlier version of this manuscript.</p>" ]
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[ "<table-wrap id=\"pgen-1000197-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000197.t001</object-id><label>Table 1</label><caption><title>Power for 500–2000 trios and 500K markers, using mating type ratio equation R4, under an additive genetic model.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Number</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Odds</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAF</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Independence scenario</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">LD scenario (DSL only)</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">LD scenario (DSL+)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">of Trios</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ratio</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td></tr></thead><tbody><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>2000</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.066</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.127</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.017</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.241</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.039</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.168</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.391</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.147</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.295</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.203</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.031</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.513</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.300</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.270</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.165</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.048</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.504</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.366</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.375</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.226</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.078</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.154</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.371</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.195</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.591</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.388</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.454</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.212</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.800</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.665</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.744</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.591</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.590</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.397</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.921</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.857</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.764</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.666</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.591</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.465</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.930</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.893</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.517</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.357</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.390</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.225</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.722</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.604</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.908</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.846</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.827</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.703</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.985</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.964</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.976</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.952</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.931</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.874</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.992</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.979</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.969</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.940</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.902</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td></tr><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>1000</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.072</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.170</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.084</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.354</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.189</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.271</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.113</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.520</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.352</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.470</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.336</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.360</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.220</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.667</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.555</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.456</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.371</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.333</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.248</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.660</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.571</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.75</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.438</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.324</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.345</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.236</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.581</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.488</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.859</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.777</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.770</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.658</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.940</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.901</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.932</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.881</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.819</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.976</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.960</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.936</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.904</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.881</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.839</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.976</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.964</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.825</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.759</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.750</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.669</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.918</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.876</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.992</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.985</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.984</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.970</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.998</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.996</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.994</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.990</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.994</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.989</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.998</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td></tr><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>500</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.184</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.128</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.132</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.276</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.205</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.573</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.480</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.490</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.382</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.693</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.606</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.711</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.628</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.635</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.538</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.805</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.740</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.665</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.590</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.591</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.505</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.771</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.707</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.447</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.350</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.367</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.278</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.551</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.473</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.849</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.787</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.797</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.720</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.916</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.878</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.905</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.868</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.869</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.811</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.954</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.928</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.894</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.856</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.849</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.805</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.934</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.900</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.694</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.612</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.624</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.542</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.793</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.729</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.957</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.934</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.935</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.895</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.981</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.967</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.978</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.964</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.966</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.943</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.991</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.982</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.965</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.949</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.945</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.919</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.983</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.975</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000197-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000197.t002</object-id><label>Table 2</label><caption><title>Power for 2000 trios and 500K markers, using mating type ratio equation R4, under an “unknown” genetic model.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">True Gen.</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Odds</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAF</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">Independence scenario</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">LD scenario (DSL only)</td><td colspan=\"2\" align=\"left\" rowspan=\"1\">LD scenario (DSL+)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Model</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ratio</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Weighted</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Standard</td></tr></thead><tbody><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>Add.</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.033</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.001</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.140</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.265</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.055</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.175</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.022</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.320</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.122</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.137</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.305</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.174</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.375</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.140</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.026</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.098</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.256</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.092</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.414</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.171</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.316</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.085</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.623</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.430</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.537</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.332</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.373</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.166</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.777</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.644</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.532</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.404</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.376</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.241</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.793</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.711</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.354</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.183</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.281</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.107</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.546</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.385</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.790</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.646</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.669</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.466</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.928</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.876</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.910</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.844</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.802</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.694</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.984</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.967</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.916</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.878</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.817</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.742</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.985</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.973</td></tr><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>Dom.</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.207</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.099</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.135</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.053</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.360</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.230</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.376</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.257</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.271</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.154</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.597</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.490</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.306</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.218</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.200</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.129</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.522</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.443</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.145</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.104</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.072</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.046</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.263</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.204</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.75</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.760</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.690</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.642</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.548</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.896</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.856</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.937</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.910</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.862</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.808</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.988</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.979</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.906</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.868</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.821</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.758</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.967</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.951</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.693</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.624</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.577</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.503</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.830</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.784</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.989</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.984</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.970</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.959</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.993</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.992</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.999</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.998</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.965</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.950</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.935</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.911</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.987</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.982</td></tr><tr><td colspan=\"9\" align=\"left\" rowspan=\"1\">\n<bold>Rec.</bold>\n</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.007</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.217</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.165</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.147</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.104</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.335</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.267</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.767</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.723</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.657</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.598</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.887</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.867</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.25</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.003</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.039</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.014</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.029</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.562</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.463</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.450</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.373</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.704</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.620</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.971</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.959</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.949</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.927</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.991</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.985</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.004</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.007</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.000</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.103</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.053</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.068</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.036</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.155</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.087</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.850</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.784</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.783</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.709</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.926</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.884</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.997</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.995</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.991</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.000</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000197-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000197.t003</object-id><label>Table 3</label><caption><title>CAMP results: SNPs meeting genome-wide significance at α = 0.05.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Marker</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">MAF</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">H-W Equil.</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Num. Info. Families</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">FBAT p-value</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Power Rank</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Required Significance Level</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">rs10863712</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.471</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.813</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">275</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0032</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">rs1294497</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.490</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.882</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">276</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.0047</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.005</td></tr></tbody></table></alternatives></table-wrap>" ]
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[ "<table-wrap-foot><fn id=\"nt101\"><p>Estimated power levels to detect the DSL using 500–2000 trios, assuming a 10% disease prevalence and additive mode of inheritance. The significance level is set to 5%. For the weighted Bonferroni method (Weighted), the partitioning parameters are <italic>K</italic> = 7 and <italic>r</italic> = 2. MAF denotes minor allele frequency. The power reflects the proportion of times the p-value of the DSL (Independence scenario and LD scenario (DSL only)) or a SNP in LD with the DSL (LD scenario (DSL+)) met the weighted Bonferroni (Weighted) or standard Bonferroni corrected (Standard) significance level. The standard Bonferroni correction adjusts for 500 K comparisons.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt102\"><p>Estimated power levels to detect the DSL using 2000 trios, assuming a 10% disease prevalence. The significance level is set to 5%. For the weighted Bonferroni method (Weighted), the partitioning parameters are <italic>K</italic> = 7 and <italic>r</italic> = 2. “Under True Gen. Model”, Add. refers to the scenario where the true (but “unknown”) model is additive (as the results are analyzed using all three genetic models). Similar scenarios are provided for the dominant (Dom.) and recessive (Rec.) genetic models. MAF denotes minor allele frequency. The power reflects the proportion of times the p-value of the DSL (Independence scenario and LD scenario (DSL only)) or a SNP in LD with the DSL (LD scenario (DSL+)) met the weighted Bonferroni (Weighted) or standard Bonferroni corrected (Standard) significance level. The standard Bonferroni correction adjusts for 1.5 M comparisons (500 K markers <sup>*</sup> 3 genetic models).</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"nt103\"><p>Results of the CAMP analysis with 402 families, 534,290 SNPs, assuming an additive mode of inheritance. Num. Info. Families indicates the number of families that were informative (i.e., at least one parent was heterozygous) for the marker of interest, and MAF denotes minor allele frequency. Markers with fewer than 20 families were removed from the analysis, as the asymptotic properties required for the test statistic may not hold. The power ranks are obtained from the conditional power of the test, calculated using our new technique with mating type ratio equation R4. The required significance level is obtained using the Ionita-Laza method ##REF##17701906##[9]## with <italic>K</italic> = 7, <italic>r</italic> = 2, and α = 0.05.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>The CAMP Genetics Ancillary Study is supported by U01 HL075419, U01 HL65899, P01 HL083069, R01 HL086601, and T32 HL07427 from the National Heart, Lung and Blood Institute, National Institutes of Health. CL is supported by the National Institutes of Health grant R01 59532.</p></fn></fn-group>" ]
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[{"label": ["1"], "element-citation": ["\n"], "collab": ["The International HapMap Consortium"], "year": ["2005"], "article-title": ["A haplotype map of the human genome."], "source": ["Nature"], "volume": ["427"], "fpage": ["1299"], "lpage": ["1320"]}, {"label": ["3"], "element-citation": ["\n"], "surname": ["Matsuzaki", "Dong", "Loi", "Di", "Liu"], "given-names": ["H", "S", "H", "X", "G"], "year": ["2004"], "article-title": ["Genotyping over 100,000 snps on a pair of oligonucleotide arrays."], "source": ["Nat Meth"], "volume": ["11"], "fpage": ["109"], "lpage": ["11"]}, {"label": ["4"], "element-citation": ["\n"], "surname": ["Di", "Matsuzaki", "Webster", "Hubbell", "Liu"], "given-names": ["X", "H", "TA", "E", "G"], "year": ["2005"], "article-title": ["Dynamic model based algorithms for screening and genotyping over 100 k snps on oligonucleotide microarrays."], "source": ["Bioinf"], "volume": ["21"], "fpage": ["1958"], "lpage": ["63"]}, {"label": ["6"], "element-citation": ["\n"], 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{ "acronym": [], "definition": [] }
37
CC BY
no
2022-01-12 23:38:08
PLoS Genet. 2008 Sep 19; 4(9):e1000197
oa_package/f2/cf/PMC2529406.tar.gz
PMC2529407
18802463
[ "<title>Introduction</title>", "<p>The fixation of beneficial mutations can strongly reduce levels of closely linked neutral variation – the so-called genetic hitchhiking effect ##REF##4407212##[1]##. This prediction has been used to search for positive selection by looking for regions of the genome with reduced variability ##REF##12351680##[e.g., 2]##. The hitchhiking model most often used is of a single selective sweep, where the location and timing of selection are assumed to be known ##UREF##0##[3]##. This single sweep model has been of great value in understanding the effect that a single selective event has on patterns of polymorphism, as a function of the strength of selection and location of the beneficial mutation ##REF##4407212##[e.g., 1]##,##REF##8536987##[4]##,##REF##10880498##[5]##. However, this model is somewhat disconnected from the problem of detecting selective sweeps in the genome, for which locations and timings are not known <italic>a priori</italic>, and should be treated as random variables.</p>", "<p>Kaplan <italic>et al.</italic> (1989) described a “recurrent hitch-hiking” (RHH) model, where the expected number of sweeps (per base pair, per 2<italic>N</italic> generations) is <italic>2Nλ</italic> with sweeps occurring at random locations in the genome ##UREF##1##[6]##. The RHH model is most commonly considered for the case of genic selection on new mutations entering the population ##UREF##1##[e.g., 6]##–##REF##7498754##[8]##. Under this model, several patterns expected under the single sweep model no longer apply. For example, the single sweep model predicts coalescent histories with long internal branches, as some lineages may escape the recent coalescent event via recombination. This results in the widely employed prediction of an excess of high-frequency derived alleles flanking the fixed site ##REF##10880498##[5]##. Under RHH models however, the probability of such a history is small, as sweeps are on average old and high frequency derived mutations have thus likely drifted to fixation ##REF##11901132##[9]##.</p>", "<p>Wiehe and Stephan (1993) showed that under a RHH model, for a given recombination rate, the expected level of heterozygosity at linked sites relative to neutral expectations is dependent upon the compound parameter (<italic>s</italic>)(2<italic>Nλ</italic>), where 2<italic>Nλ</italic> is the rate of fixation of beneficial mutations and <italic>s</italic> is the average strength of selection ##REF##8355603##[7]##. This result implies that that the two parameters are confounded (much like the effective population size, <italic>N<sub>e</sub></italic>, and mutation rate, <italic>μ</italic>, in <italic>θ</italic> = 4<italic>N<sub>e</sub>μ</italic>) as their effect on expected levels of diversity depends on their product. In <italic>D. melanogaster</italic> and <italic>D. simulans</italic>, lower than expected levels of nucleotide diversity are observed in regions of reduced recombination ##REF##1560824##[10]## and in the coding sequences of rapidly evolving proteins ##REF##17989248##[11]##,##REF##18073425##[12]##. These findings are compatible with either strong but infrequent positive selection (<italic>i.e.</italic>, large <italic>s</italic> and small 2<italic>Nλ</italic>) or weak but common positive selection (<italic>i.e.</italic>, small <italic>s</italic> and large 2<italic>Nλ</italic>) ##REF##8355603##[7]##, ##REF##17989248##[11]##–##REF##16361239##[13]##.</p>", "<p>A number of methods have been proposed for quantifying <italic>s</italic> and 2<italic>Nλ</italic> (separately) using divergence and polymorphism data ##REF##17989248##[e.g., 11]##–##REF##18073425##[12]##, ##REF##1459433##[14]##–##REF##17409186##[17]##. These approaches typically make strong assumptions regarding the possible distribution of selection coefficients, the number of adaptive substitutions between species, or the timing of selection. For example, Li and Stephan (2006) examined 250 non-coding regions from an East African population of <italic>D. melanogaster</italic>\n##REF##17040129##[18]##. Using a likelihood approach, they estimate that approximately 160 beneficial mutations have fixed in this population over the last ∼60,000 years (corresponding to ), with mean selection coefficient <italic>ŝ</italic>∼0.002. This inference is achieved by effectively assuming that the timing of all sweeps is known (and the time since the sweep, <italic>τ</italic> = 0). Under a recurrent sweep model, this assumption may bias the estimation of <italic>s</italic> and <italic>2Nλ</italic>. Additionally, as this method relies on first fitting a demographic model to non-coding DNA polymorphisms, it is possible that the effects of purifying selection on the site frequency spectrum of non-coding DNA ##REF##16237443##[19]##–##REF##17028331##[20]## may strongly affect the estimates.</p>", "<p>Using synonymous polymorphism data in <italic>D. melanogaster</italic>, and divergence to <italic>D. simulans</italic>, at 137 X-linked loci, Andolfatto (2007) employed a maximum likelihood approach to estimate the joint parameter <italic>2Nλs</italic>, followed by a McDonald-Kreitman-based method to separately estimate <italic>2Nλ</italic> and <italic>s</italic>\n##REF##17989248##[11]##. Based on these calculations, Andolfatto estimated that most beneficial amino acid substitutions are very weakly advantageous on average (with average <italic>ŝ</italic>∼1.2E−5 and ). Macpherson <italic>et al.</italic> (2007), using polymorphism data from <italic>D. simulans</italic> (and divergence to <italic>D. melanogaster</italic>), propose a method to infer the rate and strength of selection from the spatial scale of variation in polymorphism and divergence ##REF##18073425##[12]##. In contrast to Andolfatto's estimates, Macpherson <italic>et al.</italic> estimate a much stronger average selection coefficient (<italic>ŝ</italic>∼0.01) and less frequent selection (). However, they note that their method is more likely to detect strong selection, so the effects of many weakly beneficial mutations may be missed.</p>", "<p>By evaluating a wide array of recurrent selection models across a variety of sampling schemes, with parameters relevant for both Drosophila and human populations, we demonstrate here that there are differences in the predictions of weak and strong selection models, both in the spatial distribution of variability levels and the distribution of polymorphism frequencies (also called the site frequency spectrum, hereafter SFS). We propose a polymorphism-based approximate Bayesian (ABC) estimator that is most closely allied to the approach of Macpherson <italic>et al.</italic> (2007), but is also applicable to sub-genomic multi-locus data of the kind that has most often been collected ##REF##17989248##[e.g., 11]##, ##REF##15987874##[21]##–##UREF##2##[22]##, and incorporates more information from the data. Fundamentally, this estimation procedure is based on the principle that while models may predict the same average affects, the variance of many common summary statistics varies greatly between models. We show that highly accurate estimation will be possible with large-scale genome polymorphism data, and that the approach is robust to both mutation and recombination rate heterogeneity.</p>" ]
[ "<title>Methods</title>", "<title>Simulation of the Recurrent Hitchhiking Model</title>", "<p>We use the recurrent selective sweep coalescent simulation machinery described in ##REF##17565955##[24]##, with a modification to account for the stochastic trajectories of positively selected mutations in finite populations ##REF##17989248##[11]##, ##REF##15465123##[35]##–##UREF##4##[36]##. Briefly, sweeps are occurring in the genome at a rate determined by 2<italic>Nλ</italic> = <italic>Λ</italic>, where λ is the rate of sweeps per generation ##UREF##1##[6]##,##REF##7498754##[8]##. Following ##REF##17565955##[24]##, selective sweeps are allowed both within the sampled region, as well as at linked sites. This distinction is significant, because for large simulated regions the probability of a sweep within the region may not be negligible for large <italic>Λ</italic>. The rate of sweeps within a region is thus M<italic>Λ</italic>, and as each sweep may affect up to <italic>s/r<sub>bp</sub></italic> (from ##UREF##1##[6]##,##REF##15302222##[37]##; which is equivalent to 4<italic>Ns</italic>/<italic>ρ<sub>bp</sub></italic>), the rate considering both the sequenced and flanking regions becomes , where <italic>ρ<sub>bp</sub></italic> is the scaled recombination rate between base pairs and <italic>M</italic> is the size of the region in base pairs (see ##UREF##1##[6]##,##REF##15302222##[37]## for details). With this, the expected waiting time between sweeps is in 2<italic>N</italic> generations.</p>", "<p>For the purposes of testing the proposed estimator, we evaluated models for <italic>N<sub>e</sub></italic> = 10<sup>6</sup>, <italic>θ</italic> = 4<italic>Nμ</italic> = 0.01/site, and <italic>ρ</italic> = 4<italic>Nr</italic> = 0.2/site (<italic>r</italic> = 5E−08 per site per generation) and 0.1/site (<italic>r</italic> = 2.5E−08 per site per generation) in order to replicate Drosophila-like parameters (##REF##16299396##[32]##; corresponding to values of <italic>ρ/θ</italic> = 20 and 10, respectively). The product <italic>sλ</italic> was set at 2.5E−13 in the case of <italic>ρ/θ</italic> = 10, and to 5E−13 for <italic>ρ/θ</italic> = 20. To replicate human-like parameters, we consider <italic>N<sub>e</sub></italic> = 10<sup>4</sup>, <italic>θ</italic> = 0.002/site, and <italic>ρ</italic> = 0.002/site (<italic>r</italic> = 5E−08 per site per generation; corresponding to <italic>ρ/θ</italic> = 1) and <italic>sλ</italic> was set at 5E−11. In all cases, the sample size (<italic>n</italic>) = 25, and neutral variation is reduced to 60% of the neutral expectation. These calculations may be made from Eq.(5) of ##REF##8355603##[7]##, which predicts the expected heterozygosity at linked neutral sites,where <italic>θ</italic> is the neutral population mutation rate, <italic>r</italic> is the unscaled recombination rate in Morgans per base pair per generation, <italic>κ</italic> is a constant ∼0.075, <italic>γ</italic> = 2<italic>N<sub>e</sub>s</italic> (where <italic>s</italic> is the selection coefficient), and <italic>λ</italic> is the rate of adaptive substitutions per site per generation. In most cases, simulated datasets consist of 10 50 kb regions or 1000 500 bp regions (which correspond to the same number of surveyed sites). 10,000 replicate datasets were generated under each model.</p>", "<p>When simulating distributed rather than fixed values of <italic>s</italic>, 2<italic>Nλ</italic>, <italic>θ</italic>, and <italic>ρ</italic>, values for each region are drawn from a distribution (exp(<italic>s</italic>), exp(2<italic>Nλ</italic>), N(<italic>ρ</italic>, <italic>ρ</italic>/2) or exp(<italic>ρ</italic>). Thus, the value is fixed for an individual locus, but varies among loci. An alternative model was additionally examined, in which <italic>s</italic> is not fixed per locus, but rather is drawn from an exponential distribution for each selective event. These two separate models were chosen for two distinct purposes: 1) an exp(<italic>s</italic>) per locus is chosen for the performance simulations as it results in a large variance between loci. Thus, alongside the fixed parameter model, these comparisons represent two extremes; 2) an exp(<italic>s</italic>) per sweep is chosen when analyzing the empirical and demographic data, as we believe it better approximates biological reality (representing a model first introduced by Fisher). While the true underlying distributions are unknown, there is some biological data to draw from. For instance, observed <italic>K<sub>a</sub></italic> among genes ##REF##17989248##[11]## is nearly exponentially distributed, implying that an exp(2<italic>Nλ</italic>) is a reasonable approximation. We model a normally distributed recombination rate for Drosophila-like parameters since heterogeneity in recombination rates is not believed to be large ##REF##17160365##[38]##. Additionally, recombination rate variation is minimized in the Andolfatto (2007) dataset analyzed here, as high recombination regions of the X were surveyed. For human-like parameters, we model an exponential recombination rate because recombination rate heterogeneity is more extreme ##REF##17146469##[39]##. When comparing between fixed and distributed models, a fixed value of <italic>s</italic> = 0.01 for example, is compared with a distributed model in which 0.01 is the mean of the exponential distribution from which the loci are drawn. In order to assess any bias which may be associated with variable mutation rates between regions, models were tested in which <italic>θ</italic>/locus is drawn from a Γ-distribution. Two Γ-distributions are examined, one matching the observed CV of synonymous site divergences among loci in the Andolfatto (2007) dataset analyzed here (Γ(200,2.5)), and one in which <italic>θ</italic> is very widely dispersed (Γ(10,50)).</p>", "<p>In order to consider the performance of our method under non-equilibrium demographic models, we fit a simple bottleneck and growth model to the empirical data based on observed values of and the average Tajima's <italic>D</italic> (0.025/site and −0.28, respectively). Under both models, simulation parameters are thus scaled to mimic the observed values of these two statistics. As with above, <italic>n</italic> = 12, <italic>ρ</italic> = 0.1, <italic>θ</italic> = 0.01 and <italic>N<sub>e</sub></italic> = 10<sup>6</sup>. Course grids under both models were simulated using the program <italic>ms</italic>\n##REF##11847089##[40]##. We estimate a growth model in which growth rates were set to <italic>α</italic> = 50 at time <italic>t</italic> = 0.5 4<italic>N</italic> generations in the past, where <italic>N(t)</italic> = <italic>N<sub>0</sub></italic>exp<sup>−αt</sup>. We estimate a bottleneck model that posits a stepwise reduction to 0.0001 of the population's former size beginning at <italic>t<sub>b</sub></italic> = 0.5 and lasting 0.01 4<italic>N</italic> generations (BN1). In addition, a bottleneck model was selected to fit another feature of the data, the observed CV(<italic>π</italic>) (population reduced to 5.1% of its former size at time <italic>t<sub>b</sub></italic> = 0.19 and lasting 0.01 4<italic>N</italic> generations; BN2). Estimation is performed using priors generated under a model in which parameters are distributed between loci (and <italic>s</italic> is distributed per sweep), as we argue that to be a more biologically relevant scenario compared to fixed parameter models.</p>", "<title>Parameter Estimation</title>", "<p>To estimate the parameters <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic>, we relied upon their relationship with the means and standard deviations of common summary statistics. We take an approximate Bayesian (ABC) approach ##REF##10605120##[41]##–##REF##12930770##[44]## to obtain marginal posterior distributions (estimation is also possible using joint posterior distributions, an example of this is discussed in the <xref ref-type=\"sec\" rid=\"s2\">Results</xref> and given as a Supplement). Calculating our summary statistics (the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>) from the observed data, and from simulated data with parameters drawn from uniform priors, we implement the regression approach of ##REF##12524368##[42]##. Briefly, this involves fitting a local-linear regression of simulated parameter values to simulated summary statistics, and substituting the observed statistics into a regression equation. The prior distributions used were <italic>s</italic>∼Uniform (1.0E−06, 1.0), 2<italic>Nλ</italic>∼Uniform (1.0E−07, 1.0E−01), and <italic>θ</italic>∼Uniform (0.0001, 0.1), and the tolerance, δ = 0.001. Under a fixed selection parameter model, each draw from the prior represents the parameter value that is in common among all loci in a given dataset (<italic>i.e.</italic>, 1000 500 bp regions, or 10 50 kb regions). Under a distributed parameter model, each draw from the prior represents the mean of the distribution from which each locus in a given dataset will be drawn (or in the case of the alternative for modeling selection coefficients, a value of <italic>s</italic> is drawn for each sweep – see ‘simulation of the recurrent hitchhiking model’).</p>", "<p>In order to determine the optimal combination of information, estimation was performed using all combinations of the mean and standard deviations of <italic>π</italic>, the number of segregating sites (<italic>S</italic>), <italic>θ<sub>H</sub></italic>, Tajima's <italic>D</italic>, Fay and Wu's <italic>H</italic>, and <italic>ZnS</italic>. The combination of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic>, and <italic>ZnS</italic> was found to result in highly accurate and unbiased MAP estimates. Two statistics were utilized to evaluate the MAPs of <italic>ŝ</italic>, and . First, in order to measure any biases, the relative bias (RB) was determined from 1000 MAP estimates, as RB = Mean(<italic>Xˆ</italic>−<italic>X</italic>)/<italic>X</italic>. Second, in order to measure deviations from the expected values, the relative mean square error (RMSE) was determined as RMSE = Mean (<italic>Xˆ</italic>−<italic>X</italic>)<sup>2</sup>/<italic>X</italic>\n<sup>2</sup>. The necessary code, and instructions for performing estimation, can be found at: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molpopgen.org/\">http://www.molpopgen.org/</ext-link>.</p>", "<title>Empirical Data</title>", "<p>We use the137 X-linked coding loci surveyed in ##REF##17989248##[11]##; Genbank accession numbers EU216760-EU218523. All loci were surveyed in 12 lines of <italic>D. melanogaster</italic> from a Zimbabwe population. For this analysis, only synonymous sites were considered. We summarized the mean average pairwise diversity, , its standard deviation, SD(π), and the coefficient of variation, , as well as the means and SDs of the number of segregating sites, <italic>S</italic>, <italic>θ<sub>H</sub></italic>\n##REF##8722804##[25]##, Tajima's <italic>D</italic>\n##UREF##5##[45]##, Fay and Wu's <italic>H</italic>\n##REF##10880498##[5]##, and <italic>ZnS</italic>\n##UREF##3##[26]##, for synonymous sites across loci. Levels of synonymous polymorphism positively correlate with rates of divergence at synonymous sites ##REF##17989248##[11]##. To account for this, we also used partial regression corrected values of π at synonymous sites that account for variation in K<sub>s</sub>\n##REF##17989248##[11]##. We found that this had very little effect on and SD(π) in this particular case.</p>" ]
[ "<title>Results/Discussion</title>", "<title>Distinguishing Models of Weak and Strong Recurrent Selection</title>", "<p>As pointed out by Macpherson <italic>et al.</italic> (2007), there is reason to anticipate that region size may be key in uncoupling the strength of selection (<italic>s</italic>) from the rate of beneficial fixation (2<italic>Nλ</italic>) (see ##TAB##0##Table 1## for a summary of terms). Intuitively, because only a very strong sweep is capable of severely reducing larger regions - on the order of 100 kb for instance - regions may be observed with very little variation under this model. However, because selection is rare, other regions will appear close to neutral. Conversely, weak selection serves to homogenize variation as it occurs with much greater frequency. For example, for an effective population size of 10<sup>6</sup> and <italic>ρ</italic> = 4<italic>Nr</italic> = 0.1/bp, the expected waiting time between sweeps is 68,000 generations, for <italic>s</italic> = 1E−04 and 2<italic>Nλ</italic> = 5E−04, for a region size of 10<sup>4</sup> base pairs. For the same population parameters, but <italic>s</italic> = 0.01 and 2<italic>Nλ</italic> = 5E−06, the expected waiting time between sweeps is 532,000 generations. Considering that most signatures of selection are dissipated by 400,000 generations for these parameters ##REF##11901132##[9]##,##REF##11861577##[23]##, this demonstrates that if selection is strong and rare on average, there will likely be a large variance across the genome, from strongly swept to essentially neutral looking regions (##FIG##0##Figure 1##). Capturing this variance is dependent upon the size of the sampled region as, while many values of <italic>s</italic> may reduce a 500 bp region for instance, only large selection coefficients are capable of reducing a 100 kb region, suggesting that larger region sizes should afford greater discriminatory power.</p>", "<p>In order to more precisely determine this ‘region size’ effect, we examined 500 bp, 1 kb, 2 kb, 5 kb, 10 kb, 25 kb, 50 kb, and 100 kb regions using simulated data (##FIG##1##Figure 2A##). First examining <italic>L</italic> = 500 bp regions (matching existing empirical datasets, <italic>e.g.</italic>, ##REF##17989248##[11]##,##REF##15987874##[21]##), we observe that there is relatively little difference in the coefficient of variation (CV) of <italic>π</italic> between RHH models of strong and weak selection (##FIG##1##Figure 2##), consistent with previous observations that <italic>s</italic> and <italic>2Nλ</italic> are difficult to estimate separately with data of this kind ##REF##16361239##[13]##.</p>", "<p>Examining larger regions, the CV is essentially unchanged under weak selection models once regions larger than 25 kb have been sequenced. Conversely, the CV continues to grow rapidly under a strong selection model, producing a four-fold difference in the CV at 50 kb of sequence relative to weak selection models, and over a five-fold difference at 100 kb for these parameters, for Drosophila-like parameters (<italic>θ</italic> = 0.01/site; <italic>ρ/θ</italic> = 10). The difference between strong and weak selection models in ##FIG##1##Figure 2## does not appear to be attributable to the total amount of surveyed sequence between the 100 kb and 500 bp regions. By comparing the distribution observed when considering ten 100 kb regions vs. two thousand 500 bp regions (and thus the same number of segregating sites on average) we still observe a large difference in CV at the scale of 100 kb, and little difference between models at the scale of 500 bp (results not shown).</p>", "<p>We found that the relative point at which the region size benefit plateaus is a function of <italic>θ</italic>, <italic>ρ</italic>/<italic>θ</italic>, 2<italic>Nλ</italic> and <italic>s</italic>. We examined the effect of doubling the recombination rate (such that <italic>ρ/θ</italic> = 20), and find that the CV is reduced under all models relative to <italic>ρ/θ</italic> = 10, and that the models begin to differentiate at smaller region sizes (##FIG##1##Figure 2B##). These effects are a result of the fact that the expected size of the swept region will decrease as the recombination rate increases ##UREF##1##[6]##. Additionally, using human-like parameters (<italic>θ</italic> = 0.002/site, <italic>ρ/θ</italic> = 1), we find that the pattern of an increasing CV with region size is still observed to some extent. However, the CV is much larger on average even under neutrality when <italic>ρ/θ</italic> = 1, and the models are more similar to one another with human-like parameters (##FIG##1##Figure 2C##) than with Drosophila-like parameters (##FIG##1##Figures 2A and B##). This implies that weak and strong selection models will be more difficult to distinguish in humans.</p>", "<p>It is noteworthy that for large surveyed regions, more strongly negative values of Fay and Wu's <italic>H</italic>-statistic (<italic>i.e.</italic>, SFS skewed towards high-frequency derived alleles) and Tajima's <italic>D</italic>-statistic (<italic>i.e.</italic>, SFS skewed towards rare alleles) are observed under strong selection models (##FIG##2##Figure 3##), suggesting that differences in the polymorphism site frequency spectrum may also be used to distinguish between models if large enough regions are surveyed. Though this differs qualitatively from the conclusions of Przeworski (2002), simulations demonstrate that this is attributable to a modeling difference (results not shown), as we here allow sweeps within the sampled region (following ##REF##17565955##[24]##). This discrepancy between modeling approaches will thus only become greater as region sizes increase.</p>", "<title>Estimating Recurrent Selection Parameters: An Approximate Bayesian Approach</title>", "<p>The above results suggest that focusing on variability across loci may distinguish models of strong, rare sweeps from those of frequent, weak sweeps. Thus, we here implement an approximate Bayesian (ABC) approach to estimate the strength of selection (<italic>s</italic>), the rate of fixation of beneficial mutations (2<italic>Nλ</italic>) and the neutral population mutation rate (<italic>θ = 4N<sub>e</sub>u</italic>) under a recurrent hitchhiking model. We begin by employing the observed mean and standard deviation of heterozygosity (<italic>π</italic>), which is closely related to previously published estimation procedures ##REF##17989248##[e.g., 11]##–##REF##18073425##[12]##. In order to evaluate this approach, we tested the performance using simulated data. ##SUPPL##0##Figure S1## shows distributions of maximum <italic>a posteriori</italic> (MAP) estimates of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> under two different models (strong rare and weak frequent selection), for 50 kb and 500 bp regions. In these simulations, <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> have fixed values indicated with the vertical dotted line.</p>", "<p>We find that this <italic>π</italic>-based estimation performs reasonably well, particularly when the size of surveyed regions is large and selection is strong. For 500 bp regions, MAP estimates are accurate within an order of magnitude. However, distributions of MAP estimates are typically widely dispersed, particularly when selection is weak (##SUPPL##0##Figure S1##; ##SUPPL##4##Table S1##). Additionally, estimation of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> is generally upwardly biased. Under the best conditions - large region sizes and strong selection - the performance of the estimator is greatly improved (RMSE(<italic>ŝ</italic>) = 0.179, and the relative bias, RB(<italic>ŝ</italic>) = −0.281).</p>", "<p>Given the computational efficiency of ABC, it is straightforward to explore multiple combinations of test statistics, in order to determine whether incorporating additional information from the site frequency spectrum or spatial distribution of sites may significantly improve the accuracy of estimation. We found that the incorporation of the mean and variance of several common summary statistics did not significantly improve or alter estimation, owing to correlations with <italic>π</italic> (results not shown). However, other statistics such as <italic>θ<sub>H</sub></italic>\n##REF##8722804##[25]##, and <italic>ZnS</italic>\n##UREF##3##[26]## are only weakly correlated with <italic>π</italic> (results not shown). As such, it may be anticipated that the addition of these statistics may provide additional information, which would allow for further discrimination between models.</p>", "<p>This intuition appears to be accurate. The addition of the mean and SD of <italic>ZnS</italic> and <italic>θ<sub>H</sub></italic> particularly, and the number of segregating sites (<italic>S</italic>) to a lesser extent, appear to improve the performance of the method considerably. For strong selection, even at the 500 bp scale, the addition of multiple summary statistics reduces the bias and RMSE by half relative to <italic>π</italic>-based estimation (##SUPPL##4##Table S1##), thereby improving the accuracy of estimation (##FIG##3##Figure 4##). This result suggests a distinct advantage to utilizing these additional summary statistics, particularly when surveying larger regions.</p>", "<title>The Effect of Variation in Model Parameters</title>", "<p>Though the parameters <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>ρ</italic> are fixed in the above simulations, these parameters likely vary among genomic regions in real data. While it is attractive to assume a fixed parameter model given its simplicity, if the true model is in fact one in which parameters are drawn from distributions, this may lead to a bias in estimation owing to misspecification of the model. We consider a variety of examples – those in which <italic>s</italic> and 2<italic>Nλ</italic> are drawn from exponentials, and <italic>ρ</italic> is drawn from an exponential or normal. When comparing between fixed and distributed models – the mean of the distribution is equal to the fixed value used previously (<italic>i.e.</italic>, if in the fixed model <italic>s</italic> = 0.01, the distribution model to which it would be compared would have <italic>s</italic> exponentially distributed with mean 0.01). ##SUPPL##1##Figure S2## documents the effect of modeling parameters drawn from distributions on the relative CV of π (compare to ##FIG##1##Figure 2##). As expected the relative CV is inflated compared to the fixed parameter model, which may lead to biases in estimation if unaccounted for.</p>", "<p>In order to consider the effect of model misspecification on parameter estimation, datasets are simulated under a model where parameters were drawn from distributions, yet priors are constructed assuming that these parameters have fixed values. Misspecification of the model in this way leads to an upward bias in the estimate of selection coefficients, and a downward bias in the estimated rate of selection (##FIG##4##Figure 5##). To account for this misspecification, the priors must be appropriately constructed, by allowing each locus within a given replicate dataset to also be drawn from distributions (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). As shown in ##FIG##4##Figure 5##, while the distribution of MAP estimates are more greatly dispersed when compared with ##FIG##3##Figure 4## (<italic>e.g.</italic>, under a fixed model the RMSE(<italic>ŝ</italic>) = 7.9E−06 for strong selection and large regions, and under a distributed model the RMSE(<italic>ŝ</italic>) = 1.11), the mean of the distribution nonetheless accurately reflects the means of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> (for the above two models, the RB(<italic>ŝ</italic>) are 0.12 and 0.57, respectively; ##SUPPL##4##Table S1##). Additionally, for all estimated parameters, the relative bias is reduced for 50 kb relative to 500 bp regions.</p>", "<p>For comparison, an alternate distributed parameter model was considered. As opposed to <italic>s</italic> being drawn from an exponential distribution for each locus, we model <italic>s</italic> being drawn from an exponential distribution for each selective event. Results between the two models are similar, though this case results in consistently smaller RMSEs (results under this alternative model, mirroring ##FIG##4##Figure 5##, are given in ##SUPPL##4##Table S1##). This result suggests that this alternative distribution model is intermediate between the two extreme cases examined here - fixed models and distributed locus-by-locus models. Despite the overall improvement gained by modeling distributed parameters in general, an important limitation is the assumption that the shape of the underlying distribution of each parameter is known.</p>", "<p>The above simulations however, continue to assume a constant mutation rate among regions. In reality, the mutation rate may vary among loci, which may be a potential source of bias for the method ##REF##17989248##[11]##–##REF##18073425##[12]##. Thus, in order to consider the possible effects of mutation rate variation, the distribution of variation at synonymous sites among loci in the Andolfatto (2007) dataset (see below) was taken as a proxy for mutation rate variation. We estimated the parameters for a Γ-distribution using the distribution of synonymous site divergence estimates across loci. Modeling this observed distribution with simulated data (<italic>i.e.</italic>, Γ(200,2.5); ##SUPPL##2##Figure S3##), we found that the estimation was not affected and results resemble those of a fixed <italic>θ</italic> model (##SUPPL##0##Figure S1##, ##FIG##3##Figures 4##–##FIG##4##5##). This result suggests that the variation in mutation rate observed in <italic>D. melanogaster</italic> is not widely dispersed enough to impact estimation, and is thus not likely to be biasing our estimated parameter values.</p>", "<p>As there is relatively little variance at synonymous sites observed among regions in the Andolfatto (2007) dataset, data was simulated in which <italic>θ</italic> is much more widely dispersed (<italic>i.e.</italic>, Γ (10,50)), in order to determine the possible bias introduced by more extreme mutation rate variation. Importantly, under this model, estimation based upon and SD(<italic>π</italic>) becomes strongly biased in the direction of estimating larger selection coefficients, as heterogeneity in mutation rate is artificially inflating the variance among loci (##SUPPL##2##Figure S3##). However, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic>, and <italic>ZnS</italic>, results appear robust to mutation rate variation (for <italic>π</italic>-based estimation, the RB of <italic>ŝ</italic> = 8.95, for all statistics the RB = 0.51; ##SUPPL##4##Table S1##). This is owing to the fact that while <italic>π</italic> is greatly impacted by this heterogeneity, other statistics, such as <italic>ZnS</italic>, have standard deviations that vary greatly between RHH models, yet are largely unaffected by mutation rate variation within any given model. Importantly, we only here consider regional variation in mutation rates and not site-to-site variation within genes (<italic>e.g.</italic>, CpG in mammals).</p>", "<p>In summary, we propose that our estimator of recurrent hitchhiking model parameters that incorporates information from multiple summary statistics performs reasonably well. This method is preferable to a <italic>π</italic>-based approach both because it is more accurate and more robust to variation in mutation rate. The overall performance of the method will be greatly improved by the availability of genome-scale polymorphism data. An important point relevant to all of these models is that relatively simple adaptive models have been considered, and additional complexities such as recently increased or decreased rates of adaptation, variation in dominance of beneficial mutations, or selection from standing variation, have yet to be incorporated.</p>", "<title>An Application to Multi-Locus Data from <italic>D. melanogaster</italic>\n</title>", "<p>Here we apply our approach to the multi-locus data set of Andolfatto (2007), who surveyed 137 X-linked regions from an East African population of <italic>D. melanogaster</italic>\n##REF##17989248##[11]##. Though our performance evaluation of the method suggests that regions of this size are not ideal for estimation (the average region length in this dataset is 680 bps), they indicate at least the possibility of distinguishing weak from strong selection models, though such small regions cannot assure accurate parameter estimation. We estimated selection parameters both from 1) priors where these parameters are drawn from distributions (exp(<italic>s</italic>), exp(2<italic>Nλ</italic>) and N(<italic>ρ</italic>, <italic>ρ</italic>/2), and 2) in order to compare to previous estimation methods, priors that assume fixed values of <italic>s</italic>, <italic>2Nλ</italic> and <italic>ρ</italic>. The strength of selection for each sweep, <italic>s</italic>, is drawn from an exponential distribution (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). We ignore variation in <italic>θ</italic> among loci as we have shown that this is not expected to significantly impact estimation (see above).</p>", "<p>Shown in ##FIG##5##Figure 6## are marginal posterior distributions for selection parameters (assuming distributed parameters, <italic>ŝ</italic> = 2E−03, , and per site). Consistent with simulated data, parameter estimations assuming fixed values leads to considerably larger estimates of <italic>ŝ</italic>, and reduced estimates of (##FIG##5##Figure 6##, <italic>ŝ</italic> = 0.01, , and per site). It is thus important to emphasize that estimation will be sensitive to the underlying models chosen for the priors. Given that we expect these parameters to vary among loci, we consider the former estimate to perhaps be better, with the caveat that we lack precise knowledge of how these parameters are actually distributed (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> for more details). Interestingly, the large estimate of compared to previous studies ##REF##17989248##[11]##–##REF##18073425##[12]## suggests a stronger mean reduction in genome variation due to hitchhiking (∼50%). Finally, it is additionally noteworthy that estimation does not necessarily need to be performed using the marginal posteriors as we have implemented here. For example, ##SUPPL##3##Figure S4## compares estimation between joint and marginal posteriors for our empirical dataset, and finds that while the estimates are similar, they are not identical. Understanding these differences, and better determining if estimation based upon joint posteriors may have any advantages, is a topic of future investigation.</p>", "<title>The Effect of Demography on the Estimator</title>", "<p>An important consideration we have not addressed thus far is the impact of non-equilibrium demography, which may closely resemble sweep-like patterns of variation and may be expected to bias the estimator ##REF##15911584##[e.g., 27]##–##REF##17473869##[28]##. For instance, a strong population bottleneck exhibits many characteristics of a selection model – greatly increasing the variance of summary statistics, and specifically producing very negative values of the <italic>H</italic>-statistic ##REF##12399406##[29]##–##REF##16299396##[32]##. In order to assess the potential bias induced by demography on the proposed estimator, we model two simple bottleneck models (BN1 and BN2) and a growth model (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). BN1 and the growth model were fit to match the observed mean <italic>π</italic> and Tajima's <italic>D</italic>. BN2 was chosen specifically to match the observed CV(π). Under all three models, the posterior distributions are localized around weaker selection coefficients, and larger rates, than we estimate from the observed data, with estimation based upon distributed priors (MAP estimates given in ##TAB##1##Table 2##).</p>", "<p>This result suggests both that, while the estimator is obviously sensitive to non-equilibrium demography, our empirical data is not easily explained by any of the demographic models considered (with the empirical estimates falling outside of the 95% CIs for the demographic models considered). This is particularly encouraging given that one of the bottleneck models, BN2, was chosen specifically to match the CV(π) that was observed for this dataset. Clearly, to minimize demographic effects, populations should be carefully chosen when possible. The dataset we have analyzed is from a putatively ancestral East African population that is believed to have been relatively demographically stable compared to non-African populations, which show signatures of a recent and severe bottleneck ##REF##17040129##[18]##, ##REF##15930491##[31]##–##REF##16299396##[32]##. Characterizing biases induced from a wider range of demographic models is a topic of future study, and will be important before performing estimation in other populations and species. One promising direction will likely take advantage of the observed correlation between <italic>π<sub>s</sub></italic> and <italic>K<sub>a</sub></italic>\n##REF##17989248##[11]##–##REF##18073425##[12]##, which is difficult to explain under neutral demographic models. The incorporation of divergence data of this sort may increase the robustness of the estimator to non-equilibrium perturbations ##REF##18073425##[12]##.</p>", "<title>Comparison with Existing Estimates of Recurrent Hitchhiking Parameters</title>", "<p>Several other studies have attempted to estimate parameters under a recurrent hitchhiking model, and a discussion of how our estimates compare with those studies is of considerable interest. As previous studies assumed fixed values of <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic>, it is most appropriate to first compare these estimates with our “fixed value” estimation. Li and Stephan (2006) employed a sliding window likelihood ratio test using multi-locus polymorphism data and estimate that <italic>ŝ</italic>∼0.002 and \n##REF##17040129##[18]##, which is similar to our estimates (##TAB##2##Table 3##). Their approach has a number of notable differences with ours: they co-estimate a growth model within their estimation procedure, use non-coding DNA rather than synonymous sites, and assume that all detectable sweeps have fixed immediately prior to sampling (<italic>i.e.</italic>, <italic>τ</italic> = 0). Given that our values of 2<italic>Nλs </italic>are quite similar, so is the expected level of reduction in genome variability (##TAB##2##Table 3##). Macpherson <italic>et al.</italic> (2007) used large-scale polymorphism data from six lines of <italic>D. simulans</italic> and estimate a strong average selection coefficient (<italic>ŝ</italic>∼0.01) ##REF##18073425##[12]##, which is identical to our fixed value estimate. The bigger difference is in our estimates of 2<italic>Nλ</italic>, with our estimate being ∼4× larger. However, given that the dataset examined here is from <italic>D. melanogaster</italic>, there is no reason to necessarily anticipate that these estimates should match.</p>", "<p>It is noteworthy that our estimated selection coefficient is an order of magnitude smaller (and our estimate of the rate an order of magnitude larger) when we assume that <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> are drawn from distributions rather than taking fixed values. Despite this, our estimated selection coefficient under the distributed model is still almost two orders of magnitude larger than Andolfatto's (2007) estimate ##REF##17989248##[11]##. Andolfatto's estimates of <italic>s</italic> and 2<italic>Nλ</italic> are particularly relevant, as we here examine the same dataset and arrive at quite different conclusions. The discord between estimates may arise from the fact that Andolfatto's estimate of <italic>s</italic> relies on estimating 2<italic>Nλ</italic> using the McDonald-Kreitman statistical framework ##REF##1904993##[33]##–##REF##15044594##[34]##. However, we note that with short surveyed fragments, our estimator of <italic>s</italic> is somewhat upwardly biased (##FIG##4##Figure 5##) so it will be interesting to apply our method to larger genomic regions when that data becomes available.</p>", "<p>Additionally, while Andolfatto (2007) and Macpherson <italic>et al.</italic> (2007) estimate a 20% average reduction in genome-wide variability, we estimate a considerably larger reduction (50%), which is more consistent with the estimate of 2<italic>Nλs</italic> of Li and Stephan (2006). This may to some extent explain Andolfatto's observation that the observed Tajima's <italic>D</italic> at synonymous sites is more negative than predicted by his estimates of <italic>s</italic> and 2<italic>Nλ</italic>. When we model a recurrent hitchhiking model with our estimated parameters, the average Tajima's <italic>D</italic> is −0.3, which is close to the observed average (−0.28). While a negative mean Tajima's <italic>D</italic> is usually interpreted in the context of demographic models (such as population growth, see for example ##REF##17040129##[18]##), it may instead imply that recurrent hitchhiking may be having a larger genome wide impact than previously appreciated.</p>", "<title>Conclusions</title>", "<p>While common/weak and rare/strong recurrent positive selection result in similar average levels of genome variation on average (for 2<italic>Nλs</italic> = constant), rare/strong selection greatly increases the variance of common summary statistics relative to common weak selection. We demonstrate, using an ABC approach based upon this observation, that the rate and the strength of selection may accurately be estimated jointly. Though there is some power to differentiate parameters using existing data, our results strongly suggest that genome scale data will afford much better discriminatory power. Our study also highlights that learning more about how parameters such as <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> are distributed among loci will be crucial for accurate parameter estimation.</p>" ]
[ "<title>Results/Discussion</title>", "<title>Distinguishing Models of Weak and Strong Recurrent Selection</title>", "<p>As pointed out by Macpherson <italic>et al.</italic> (2007), there is reason to anticipate that region size may be key in uncoupling the strength of selection (<italic>s</italic>) from the rate of beneficial fixation (2<italic>Nλ</italic>) (see ##TAB##0##Table 1## for a summary of terms). Intuitively, because only a very strong sweep is capable of severely reducing larger regions - on the order of 100 kb for instance - regions may be observed with very little variation under this model. However, because selection is rare, other regions will appear close to neutral. Conversely, weak selection serves to homogenize variation as it occurs with much greater frequency. For example, for an effective population size of 10<sup>6</sup> and <italic>ρ</italic> = 4<italic>Nr</italic> = 0.1/bp, the expected waiting time between sweeps is 68,000 generations, for <italic>s</italic> = 1E−04 and 2<italic>Nλ</italic> = 5E−04, for a region size of 10<sup>4</sup> base pairs. For the same population parameters, but <italic>s</italic> = 0.01 and 2<italic>Nλ</italic> = 5E−06, the expected waiting time between sweeps is 532,000 generations. Considering that most signatures of selection are dissipated by 400,000 generations for these parameters ##REF##11901132##[9]##,##REF##11861577##[23]##, this demonstrates that if selection is strong and rare on average, there will likely be a large variance across the genome, from strongly swept to essentially neutral looking regions (##FIG##0##Figure 1##). Capturing this variance is dependent upon the size of the sampled region as, while many values of <italic>s</italic> may reduce a 500 bp region for instance, only large selection coefficients are capable of reducing a 100 kb region, suggesting that larger region sizes should afford greater discriminatory power.</p>", "<p>In order to more precisely determine this ‘region size’ effect, we examined 500 bp, 1 kb, 2 kb, 5 kb, 10 kb, 25 kb, 50 kb, and 100 kb regions using simulated data (##FIG##1##Figure 2A##). First examining <italic>L</italic> = 500 bp regions (matching existing empirical datasets, <italic>e.g.</italic>, ##REF##17989248##[11]##,##REF##15987874##[21]##), we observe that there is relatively little difference in the coefficient of variation (CV) of <italic>π</italic> between RHH models of strong and weak selection (##FIG##1##Figure 2##), consistent with previous observations that <italic>s</italic> and <italic>2Nλ</italic> are difficult to estimate separately with data of this kind ##REF##16361239##[13]##.</p>", "<p>Examining larger regions, the CV is essentially unchanged under weak selection models once regions larger than 25 kb have been sequenced. Conversely, the CV continues to grow rapidly under a strong selection model, producing a four-fold difference in the CV at 50 kb of sequence relative to weak selection models, and over a five-fold difference at 100 kb for these parameters, for Drosophila-like parameters (<italic>θ</italic> = 0.01/site; <italic>ρ/θ</italic> = 10). The difference between strong and weak selection models in ##FIG##1##Figure 2## does not appear to be attributable to the total amount of surveyed sequence between the 100 kb and 500 bp regions. By comparing the distribution observed when considering ten 100 kb regions vs. two thousand 500 bp regions (and thus the same number of segregating sites on average) we still observe a large difference in CV at the scale of 100 kb, and little difference between models at the scale of 500 bp (results not shown).</p>", "<p>We found that the relative point at which the region size benefit plateaus is a function of <italic>θ</italic>, <italic>ρ</italic>/<italic>θ</italic>, 2<italic>Nλ</italic> and <italic>s</italic>. We examined the effect of doubling the recombination rate (such that <italic>ρ/θ</italic> = 20), and find that the CV is reduced under all models relative to <italic>ρ/θ</italic> = 10, and that the models begin to differentiate at smaller region sizes (##FIG##1##Figure 2B##). These effects are a result of the fact that the expected size of the swept region will decrease as the recombination rate increases ##UREF##1##[6]##. Additionally, using human-like parameters (<italic>θ</italic> = 0.002/site, <italic>ρ/θ</italic> = 1), we find that the pattern of an increasing CV with region size is still observed to some extent. However, the CV is much larger on average even under neutrality when <italic>ρ/θ</italic> = 1, and the models are more similar to one another with human-like parameters (##FIG##1##Figure 2C##) than with Drosophila-like parameters (##FIG##1##Figures 2A and B##). This implies that weak and strong selection models will be more difficult to distinguish in humans.</p>", "<p>It is noteworthy that for large surveyed regions, more strongly negative values of Fay and Wu's <italic>H</italic>-statistic (<italic>i.e.</italic>, SFS skewed towards high-frequency derived alleles) and Tajima's <italic>D</italic>-statistic (<italic>i.e.</italic>, SFS skewed towards rare alleles) are observed under strong selection models (##FIG##2##Figure 3##), suggesting that differences in the polymorphism site frequency spectrum may also be used to distinguish between models if large enough regions are surveyed. Though this differs qualitatively from the conclusions of Przeworski (2002), simulations demonstrate that this is attributable to a modeling difference (results not shown), as we here allow sweeps within the sampled region (following ##REF##17565955##[24]##). This discrepancy between modeling approaches will thus only become greater as region sizes increase.</p>", "<title>Estimating Recurrent Selection Parameters: An Approximate Bayesian Approach</title>", "<p>The above results suggest that focusing on variability across loci may distinguish models of strong, rare sweeps from those of frequent, weak sweeps. Thus, we here implement an approximate Bayesian (ABC) approach to estimate the strength of selection (<italic>s</italic>), the rate of fixation of beneficial mutations (2<italic>Nλ</italic>) and the neutral population mutation rate (<italic>θ = 4N<sub>e</sub>u</italic>) under a recurrent hitchhiking model. We begin by employing the observed mean and standard deviation of heterozygosity (<italic>π</italic>), which is closely related to previously published estimation procedures ##REF##17989248##[e.g., 11]##–##REF##18073425##[12]##. In order to evaluate this approach, we tested the performance using simulated data. ##SUPPL##0##Figure S1## shows distributions of maximum <italic>a posteriori</italic> (MAP) estimates of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> under two different models (strong rare and weak frequent selection), for 50 kb and 500 bp regions. In these simulations, <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> have fixed values indicated with the vertical dotted line.</p>", "<p>We find that this <italic>π</italic>-based estimation performs reasonably well, particularly when the size of surveyed regions is large and selection is strong. For 500 bp regions, MAP estimates are accurate within an order of magnitude. However, distributions of MAP estimates are typically widely dispersed, particularly when selection is weak (##SUPPL##0##Figure S1##; ##SUPPL##4##Table S1##). Additionally, estimation of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> is generally upwardly biased. Under the best conditions - large region sizes and strong selection - the performance of the estimator is greatly improved (RMSE(<italic>ŝ</italic>) = 0.179, and the relative bias, RB(<italic>ŝ</italic>) = −0.281).</p>", "<p>Given the computational efficiency of ABC, it is straightforward to explore multiple combinations of test statistics, in order to determine whether incorporating additional information from the site frequency spectrum or spatial distribution of sites may significantly improve the accuracy of estimation. We found that the incorporation of the mean and variance of several common summary statistics did not significantly improve or alter estimation, owing to correlations with <italic>π</italic> (results not shown). However, other statistics such as <italic>θ<sub>H</sub></italic>\n##REF##8722804##[25]##, and <italic>ZnS</italic>\n##UREF##3##[26]## are only weakly correlated with <italic>π</italic> (results not shown). As such, it may be anticipated that the addition of these statistics may provide additional information, which would allow for further discrimination between models.</p>", "<p>This intuition appears to be accurate. The addition of the mean and SD of <italic>ZnS</italic> and <italic>θ<sub>H</sub></italic> particularly, and the number of segregating sites (<italic>S</italic>) to a lesser extent, appear to improve the performance of the method considerably. For strong selection, even at the 500 bp scale, the addition of multiple summary statistics reduces the bias and RMSE by half relative to <italic>π</italic>-based estimation (##SUPPL##4##Table S1##), thereby improving the accuracy of estimation (##FIG##3##Figure 4##). This result suggests a distinct advantage to utilizing these additional summary statistics, particularly when surveying larger regions.</p>", "<title>The Effect of Variation in Model Parameters</title>", "<p>Though the parameters <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>ρ</italic> are fixed in the above simulations, these parameters likely vary among genomic regions in real data. While it is attractive to assume a fixed parameter model given its simplicity, if the true model is in fact one in which parameters are drawn from distributions, this may lead to a bias in estimation owing to misspecification of the model. We consider a variety of examples – those in which <italic>s</italic> and 2<italic>Nλ</italic> are drawn from exponentials, and <italic>ρ</italic> is drawn from an exponential or normal. When comparing between fixed and distributed models – the mean of the distribution is equal to the fixed value used previously (<italic>i.e.</italic>, if in the fixed model <italic>s</italic> = 0.01, the distribution model to which it would be compared would have <italic>s</italic> exponentially distributed with mean 0.01). ##SUPPL##1##Figure S2## documents the effect of modeling parameters drawn from distributions on the relative CV of π (compare to ##FIG##1##Figure 2##). As expected the relative CV is inflated compared to the fixed parameter model, which may lead to biases in estimation if unaccounted for.</p>", "<p>In order to consider the effect of model misspecification on parameter estimation, datasets are simulated under a model where parameters were drawn from distributions, yet priors are constructed assuming that these parameters have fixed values. Misspecification of the model in this way leads to an upward bias in the estimate of selection coefficients, and a downward bias in the estimated rate of selection (##FIG##4##Figure 5##). To account for this misspecification, the priors must be appropriately constructed, by allowing each locus within a given replicate dataset to also be drawn from distributions (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). As shown in ##FIG##4##Figure 5##, while the distribution of MAP estimates are more greatly dispersed when compared with ##FIG##3##Figure 4## (<italic>e.g.</italic>, under a fixed model the RMSE(<italic>ŝ</italic>) = 7.9E−06 for strong selection and large regions, and under a distributed model the RMSE(<italic>ŝ</italic>) = 1.11), the mean of the distribution nonetheless accurately reflects the means of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic> (for the above two models, the RB(<italic>ŝ</italic>) are 0.12 and 0.57, respectively; ##SUPPL##4##Table S1##). Additionally, for all estimated parameters, the relative bias is reduced for 50 kb relative to 500 bp regions.</p>", "<p>For comparison, an alternate distributed parameter model was considered. As opposed to <italic>s</italic> being drawn from an exponential distribution for each locus, we model <italic>s</italic> being drawn from an exponential distribution for each selective event. Results between the two models are similar, though this case results in consistently smaller RMSEs (results under this alternative model, mirroring ##FIG##4##Figure 5##, are given in ##SUPPL##4##Table S1##). This result suggests that this alternative distribution model is intermediate between the two extreme cases examined here - fixed models and distributed locus-by-locus models. Despite the overall improvement gained by modeling distributed parameters in general, an important limitation is the assumption that the shape of the underlying distribution of each parameter is known.</p>", "<p>The above simulations however, continue to assume a constant mutation rate among regions. In reality, the mutation rate may vary among loci, which may be a potential source of bias for the method ##REF##17989248##[11]##–##REF##18073425##[12]##. Thus, in order to consider the possible effects of mutation rate variation, the distribution of variation at synonymous sites among loci in the Andolfatto (2007) dataset (see below) was taken as a proxy for mutation rate variation. We estimated the parameters for a Γ-distribution using the distribution of synonymous site divergence estimates across loci. Modeling this observed distribution with simulated data (<italic>i.e.</italic>, Γ(200,2.5); ##SUPPL##2##Figure S3##), we found that the estimation was not affected and results resemble those of a fixed <italic>θ</italic> model (##SUPPL##0##Figure S1##, ##FIG##3##Figures 4##–##FIG##4##5##). This result suggests that the variation in mutation rate observed in <italic>D. melanogaster</italic> is not widely dispersed enough to impact estimation, and is thus not likely to be biasing our estimated parameter values.</p>", "<p>As there is relatively little variance at synonymous sites observed among regions in the Andolfatto (2007) dataset, data was simulated in which <italic>θ</italic> is much more widely dispersed (<italic>i.e.</italic>, Γ (10,50)), in order to determine the possible bias introduced by more extreme mutation rate variation. Importantly, under this model, estimation based upon and SD(<italic>π</italic>) becomes strongly biased in the direction of estimating larger selection coefficients, as heterogeneity in mutation rate is artificially inflating the variance among loci (##SUPPL##2##Figure S3##). However, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic>, and <italic>ZnS</italic>, results appear robust to mutation rate variation (for <italic>π</italic>-based estimation, the RB of <italic>ŝ</italic> = 8.95, for all statistics the RB = 0.51; ##SUPPL##4##Table S1##). This is owing to the fact that while <italic>π</italic> is greatly impacted by this heterogeneity, other statistics, such as <italic>ZnS</italic>, have standard deviations that vary greatly between RHH models, yet are largely unaffected by mutation rate variation within any given model. Importantly, we only here consider regional variation in mutation rates and not site-to-site variation within genes (<italic>e.g.</italic>, CpG in mammals).</p>", "<p>In summary, we propose that our estimator of recurrent hitchhiking model parameters that incorporates information from multiple summary statistics performs reasonably well. This method is preferable to a <italic>π</italic>-based approach both because it is more accurate and more robust to variation in mutation rate. The overall performance of the method will be greatly improved by the availability of genome-scale polymorphism data. An important point relevant to all of these models is that relatively simple adaptive models have been considered, and additional complexities such as recently increased or decreased rates of adaptation, variation in dominance of beneficial mutations, or selection from standing variation, have yet to be incorporated.</p>", "<title>An Application to Multi-Locus Data from <italic>D. melanogaster</italic>\n</title>", "<p>Here we apply our approach to the multi-locus data set of Andolfatto (2007), who surveyed 137 X-linked regions from an East African population of <italic>D. melanogaster</italic>\n##REF##17989248##[11]##. Though our performance evaluation of the method suggests that regions of this size are not ideal for estimation (the average region length in this dataset is 680 bps), they indicate at least the possibility of distinguishing weak from strong selection models, though such small regions cannot assure accurate parameter estimation. We estimated selection parameters both from 1) priors where these parameters are drawn from distributions (exp(<italic>s</italic>), exp(2<italic>Nλ</italic>) and N(<italic>ρ</italic>, <italic>ρ</italic>/2), and 2) in order to compare to previous estimation methods, priors that assume fixed values of <italic>s</italic>, <italic>2Nλ</italic> and <italic>ρ</italic>. The strength of selection for each sweep, <italic>s</italic>, is drawn from an exponential distribution (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). We ignore variation in <italic>θ</italic> among loci as we have shown that this is not expected to significantly impact estimation (see above).</p>", "<p>Shown in ##FIG##5##Figure 6## are marginal posterior distributions for selection parameters (assuming distributed parameters, <italic>ŝ</italic> = 2E−03, , and per site). Consistent with simulated data, parameter estimations assuming fixed values leads to considerably larger estimates of <italic>ŝ</italic>, and reduced estimates of (##FIG##5##Figure 6##, <italic>ŝ</italic> = 0.01, , and per site). It is thus important to emphasize that estimation will be sensitive to the underlying models chosen for the priors. Given that we expect these parameters to vary among loci, we consider the former estimate to perhaps be better, with the caveat that we lack precise knowledge of how these parameters are actually distributed (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref> for more details). Interestingly, the large estimate of compared to previous studies ##REF##17989248##[11]##–##REF##18073425##[12]## suggests a stronger mean reduction in genome variation due to hitchhiking (∼50%). Finally, it is additionally noteworthy that estimation does not necessarily need to be performed using the marginal posteriors as we have implemented here. For example, ##SUPPL##3##Figure S4## compares estimation between joint and marginal posteriors for our empirical dataset, and finds that while the estimates are similar, they are not identical. Understanding these differences, and better determining if estimation based upon joint posteriors may have any advantages, is a topic of future investigation.</p>", "<title>The Effect of Demography on the Estimator</title>", "<p>An important consideration we have not addressed thus far is the impact of non-equilibrium demography, which may closely resemble sweep-like patterns of variation and may be expected to bias the estimator ##REF##15911584##[e.g., 27]##–##REF##17473869##[28]##. For instance, a strong population bottleneck exhibits many characteristics of a selection model – greatly increasing the variance of summary statistics, and specifically producing very negative values of the <italic>H</italic>-statistic ##REF##12399406##[29]##–##REF##16299396##[32]##. In order to assess the potential bias induced by demography on the proposed estimator, we model two simple bottleneck models (BN1 and BN2) and a growth model (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). BN1 and the growth model were fit to match the observed mean <italic>π</italic> and Tajima's <italic>D</italic>. BN2 was chosen specifically to match the observed CV(π). Under all three models, the posterior distributions are localized around weaker selection coefficients, and larger rates, than we estimate from the observed data, with estimation based upon distributed priors (MAP estimates given in ##TAB##1##Table 2##).</p>", "<p>This result suggests both that, while the estimator is obviously sensitive to non-equilibrium demography, our empirical data is not easily explained by any of the demographic models considered (with the empirical estimates falling outside of the 95% CIs for the demographic models considered). This is particularly encouraging given that one of the bottleneck models, BN2, was chosen specifically to match the CV(π) that was observed for this dataset. Clearly, to minimize demographic effects, populations should be carefully chosen when possible. The dataset we have analyzed is from a putatively ancestral East African population that is believed to have been relatively demographically stable compared to non-African populations, which show signatures of a recent and severe bottleneck ##REF##17040129##[18]##, ##REF##15930491##[31]##–##REF##16299396##[32]##. Characterizing biases induced from a wider range of demographic models is a topic of future study, and will be important before performing estimation in other populations and species. One promising direction will likely take advantage of the observed correlation between <italic>π<sub>s</sub></italic> and <italic>K<sub>a</sub></italic>\n##REF##17989248##[11]##–##REF##18073425##[12]##, which is difficult to explain under neutral demographic models. The incorporation of divergence data of this sort may increase the robustness of the estimator to non-equilibrium perturbations ##REF##18073425##[12]##.</p>", "<title>Comparison with Existing Estimates of Recurrent Hitchhiking Parameters</title>", "<p>Several other studies have attempted to estimate parameters under a recurrent hitchhiking model, and a discussion of how our estimates compare with those studies is of considerable interest. As previous studies assumed fixed values of <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic>, it is most appropriate to first compare these estimates with our “fixed value” estimation. Li and Stephan (2006) employed a sliding window likelihood ratio test using multi-locus polymorphism data and estimate that <italic>ŝ</italic>∼0.002 and \n##REF##17040129##[18]##, which is similar to our estimates (##TAB##2##Table 3##). Their approach has a number of notable differences with ours: they co-estimate a growth model within their estimation procedure, use non-coding DNA rather than synonymous sites, and assume that all detectable sweeps have fixed immediately prior to sampling (<italic>i.e.</italic>, <italic>τ</italic> = 0). Given that our values of 2<italic>Nλs </italic>are quite similar, so is the expected level of reduction in genome variability (##TAB##2##Table 3##). Macpherson <italic>et al.</italic> (2007) used large-scale polymorphism data from six lines of <italic>D. simulans</italic> and estimate a strong average selection coefficient (<italic>ŝ</italic>∼0.01) ##REF##18073425##[12]##, which is identical to our fixed value estimate. The bigger difference is in our estimates of 2<italic>Nλ</italic>, with our estimate being ∼4× larger. However, given that the dataset examined here is from <italic>D. melanogaster</italic>, there is no reason to necessarily anticipate that these estimates should match.</p>", "<p>It is noteworthy that our estimated selection coefficient is an order of magnitude smaller (and our estimate of the rate an order of magnitude larger) when we assume that <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> are drawn from distributions rather than taking fixed values. Despite this, our estimated selection coefficient under the distributed model is still almost two orders of magnitude larger than Andolfatto's (2007) estimate ##REF##17989248##[11]##. Andolfatto's estimates of <italic>s</italic> and 2<italic>Nλ</italic> are particularly relevant, as we here examine the same dataset and arrive at quite different conclusions. The discord between estimates may arise from the fact that Andolfatto's estimate of <italic>s</italic> relies on estimating 2<italic>Nλ</italic> using the McDonald-Kreitman statistical framework ##REF##1904993##[33]##–##REF##15044594##[34]##. However, we note that with short surveyed fragments, our estimator of <italic>s</italic> is somewhat upwardly biased (##FIG##4##Figure 5##) so it will be interesting to apply our method to larger genomic regions when that data becomes available.</p>", "<p>Additionally, while Andolfatto (2007) and Macpherson <italic>et al.</italic> (2007) estimate a 20% average reduction in genome-wide variability, we estimate a considerably larger reduction (50%), which is more consistent with the estimate of 2<italic>Nλs</italic> of Li and Stephan (2006). This may to some extent explain Andolfatto's observation that the observed Tajima's <italic>D</italic> at synonymous sites is more negative than predicted by his estimates of <italic>s</italic> and 2<italic>Nλ</italic>. When we model a recurrent hitchhiking model with our estimated parameters, the average Tajima's <italic>D</italic> is −0.3, which is close to the observed average (−0.28). While a negative mean Tajima's <italic>D</italic> is usually interpreted in the context of demographic models (such as population growth, see for example ##REF##17040129##[18]##), it may instead imply that recurrent hitchhiking may be having a larger genome wide impact than previously appreciated.</p>", "<title>Conclusions</title>", "<p>While common/weak and rare/strong recurrent positive selection result in similar average levels of genome variation on average (for 2<italic>Nλs</italic> = constant), rare/strong selection greatly increases the variance of common summary statistics relative to common weak selection. We demonstrate, using an ABC approach based upon this observation, that the rate and the strength of selection may accurately be estimated jointly. Though there is some power to differentiate parameters using existing data, our results strongly suggest that genome scale data will afford much better discriminatory power. Our study also highlights that learning more about how parameters such as <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> are distributed among loci will be crucial for accurate parameter estimation.</p>" ]
[ "<title>Conclusions</title>", "<p>While common/weak and rare/strong recurrent positive selection result in similar average levels of genome variation on average (for 2<italic>Nλs</italic> = constant), rare/strong selection greatly increases the variance of common summary statistics relative to common weak selection. We demonstrate, using an ABC approach based upon this observation, that the rate and the strength of selection may accurately be estimated jointly. Though there is some power to differentiate parameters using existing data, our results strongly suggest that genome scale data will afford much better discriminatory power. Our study also highlights that learning more about how parameters such as <italic>s</italic>, 2<italic>Nλ</italic> and <italic>ρ</italic> are distributed among loci will be crucial for accurate parameter estimation.</p>" ]
[ "<p>Conceived and designed the experiments: JDJ KRT PA. Performed the experiments: JDJ KRT PA. Analyzed the data: JDJ KRT PA. Wrote the paper: JDJ KRT PA.</p>", "<p>The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to distinguish between these models, and apply it to multi-locus data from <italic>Drosophila melanogaster</italic>. We investigate the extent to which inference about the strength of selection is sensitive to assumptions about the underlying distributions of the rates of substitution and recombination, the strength of selection, heterogeneity in mutation rate, as well as the population's demographic history. We show that assuming fixed values of selection parameters in estimation leads to overestimates of the strength of selection and underestimates of the rate. We estimate parameters for an African population of <italic>D. melanogaster</italic> (<italic>ŝ</italic>∼2E−03, ) and compare these to previous estimates. Finally, we show that surveying larger genomic regions is expected to lend much more discriminatory power to the approach. It will thus be of great interest to apply this method to emerging whole-genome polymorphism data sets in many taxa.</p>", "<title>Author Summary</title>", "<p>Understanding the process of adaptive evolution requires quantifying the extent to which beneficial mutations contribute to differences between species. However, fundamental parameters of adaptation, such as the rate and strength of beneficial mutations, are poorly understood and have historically been difficult to estimate from data. In particular, distinguishing a high rate of weakly selected substitutions from a low rate of strongly selected substitutions has been problematic. Here, we introduce a new method to estimate the parameters of adaptive evolution from multi-locus population genetic data. We conduct simulations to show that this method is able to discriminate the rare/strong model from the frequent/weak model. Applying this method to an African population sample of <italic>Drosophila melanogaster</italic>, we estimate selection parameters and find that recurrent adaptive evolution has reduced genome variability by ∼50% on average. The availability of genome-scale population genetic data will lend considerable discriminatory power to the approach. Thus, this new approach represents an important step towards characterizing the nature of adaptive evolution in natural populations.</p>" ]
[ "<title>Supporting Information</title>" ]
[ "<p>The authors acknowledge Doris Bachtrog, Yuseob Kim, Molly Przeworski and members of the Aquadro lab for helpful comment and discussion.</p>" ]
[ "<fig id=\"pgen-1000198-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g001</object-id><label>Figure 1</label><caption><title>A cartoon representation of the difference between models of common weak and rare strong selection.</title><p>On the X-axis is distance along a chromosome in kilobases (kb), and the on the Y-axis is variability. The dotted-line represents the average heterozygosity, and the solid bars represent loci sequenced for polymorphism data. As shown, under the weak selection model each individual selective fixation impacts a small genomic region, though sweeps are occurring frequently. The combination results in a homogenizing effect across the chromosome. Alternatively, under the strong selection model each fixation impacts a large genomic region. However, because selection is rare, other regions will appear at equilibrium. Thus, sampling loci under these models, the mean level of variation among loci may be identical, but the variance between loci will be far greater under the strong selection case – with some loci falling in severely reduced regions of variation, and others in neutral regions.</p></caption></fig>", "<fig id=\"pgen-1000198-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g002</object-id><label>Figure 2</label><caption><title>The ratio of the coefficient of variation (CV) of <italic>π</italic> under four recurrent selection models to the CV of <italic>π</italic> under equilibrium neutrality, for four selection coefficients (<italic>s</italic> = 1E−02, 1E−03, 1E−04, and 1E−05).</title><p>\n<italic>n</italic> = 25. A) Drosophila-like parameters, <italic>ρ</italic>/<italic>θ</italic> = 10, <italic>ρ</italic> = 0.1/site, <italic>θ</italic> = 0.01/site. (B) Drosophila-like parameters, <italic>ρ</italic>/<italic>θ</italic> = 20, <italic>ρ</italic> = 0.2/site, <italic>θ</italic> = 0.01/site. (C) Human-like parameters, <italic>ρ</italic>/<italic>θ</italic> = 1, <italic>ρ</italic> = 0.002/site, <italic>θ</italic> = 0.002/site. The selection coefficient, <italic>s</italic>, and rate of advantageous substitution, 2<italic>Nλ</italic>, differ among selection models, though their product remains the same for each given value of <italic>ρ/θ</italic> (sλ = 2.5E−13 for <italic>ρ/θ</italic> = 10, 20; sλ = 5E−11 for <italic>ρ/θ</italic> = 1 and <italic>N</italic> = 10<sup>6</sup>). 1000 replicates were generated under each model for each data point. As seen, the models begin to differentiate from one another as the size of the sampled region gets larger, suggesting greater power to distinguish weak and strong selection models at larger physical scales.</p></caption></fig>", "<fig id=\"pgen-1000198-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g003</object-id><label>Figure 3</label><caption><title>Distributions of Fay and Wu's <italic>H</italic>-statistic ##REF##10880498##[5]## and Tajima's <italic>D</italic>-statistic ##UREF##5##[45]## under common weak and rare strong selection models.</title><p>(A) The distribution of Fay and Wu's <italic>H</italic> for 500 bp regions. (B) The distribution of Fay and Wu's <italic>H</italic> for 100 kb regions. (C) The distribution of Tajima's <italic>D</italic> for 500 bp regions. (D) The distribution of Tajima's <italic>D</italic> for 100 kb regions. 1000 replicates were generated under each model and the following parameters were fixed: <italic>ρ</italic> = 0.1/site, <italic>θ</italic> = 0.01/site (thus, <italic>ρ</italic>/<italic>θ</italic> = 10), and <italic>n</italic> = 25. The selection coefficient, <italic>s</italic>, and rate, 2<italic>Nλ</italic>, differ among models, though their product is the same (<italic>2Nλs</italic> = 5.0E−07). As shown in ##REF##11901132##[9]##, the mean <italic>H</italic> is positive under a recurrent sweep model. However, while we confirm that the means are positive and nearly identical for 2<italic>Nλs</italic> = constant, we find that previous attempts to differentiate these models have likely been hampered by the scale of the regions considered. Specifically, while the distributions for both statistics appear similar for 500 bp regions, they are quite distinct at larger physical scales (<italic>i.e.</italic>, 100 kb).</p></caption></fig>", "<fig id=\"pgen-1000198-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g004</object-id><label>Figure 4</label><caption><title>Approximate Bayesian estimation of the strength and rate of selection as well as the neutral <italic>θ</italic>, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>.</title><p>The model is one in which <italic>s</italic> and 2<italic>Nλ</italic> are fixed. For the strong selection case <italic>s</italic> = 1.0E−02, and 2<italic>Nλ</italic> = 2.0E−05, for weak selection <italic>s</italic> = 1.0E−04, and 2<italic>Nλ</italic> = 2.0E−03. <italic>ρ</italic> = 0.1/site and <italic>θ</italic> = 0.01/site. Shown are the distributions of 1000 MAP estimates. The dotted lines indicate the true values. The distributions for 10 50 kb region datasets are given in black, and for 1000 500 bp datasets in gray. As shown, the use of these multiple summary statistics improves estimation relative to <italic>π</italic> alone (##SUPPL##0##Figure S1##), reducing the RMSEs (##SUPPL##4##Table S1##).</p></caption></fig>", "<fig id=\"pgen-1000198-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g005</object-id><label>Figure 5</label><caption><title>Approximate Bayesian estimation of the strength and rate of selection as well as the neutral <italic>θ</italic>, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>.</title><p>The true model is one in which <italic>s</italic> and 2<italic>Nλ</italic> for each locus is drawn from exponential distributions. The mean <italic>s</italic> = 1.0E−02, and the mean 2<italic>Nλ</italic> = 2.0E−05 (given by dotted lines). Shown are the distributions of 1000 MAP estimates. <italic>ρ</italic> is given by a Normal(0.1, 0.05), and <italic>θ</italic> is fixed at 0.01/site. Results are given for estimation when priors are constructed under a distributed parameter model, as well as a fixed parameter model (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>), for 10×50 kb and 1000×500 bp regions. As shown, falsely assuming fixed selection parameters leads to consistent biases in estimation, whereas appropriately constructing the priors reduces the bias (see also ##SUPPL##4##Table S1##).</p></caption></fig>", "<fig id=\"pgen-1000198-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.g006</object-id><label>Figure 6</label><caption><title>Marginal posterior distributions of <italic>s</italic>, 2<italic>Nλ</italic>, and <italic>θ</italic>, for the 137-locus dataset of ##REF##17989248##[11]##, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>.</title><p>Results are given when the priors are constructed assuming fixed selection parameters, as well as when parameters for each locus are drawn from distributions (see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>). In order to model the dataset under consideration, priors are constructed such that each replicate consists of 137 loci each of the observed length. <italic>n</italic> = 12, <italic>ρ</italic> = 0.121, and <italic>N<sub>e</sub></italic> = 1.87<sup>6</sup> (in accord with the estimates of ##REF##17989248##[11]##). Consistent with the simulation results, assuming a model in which selection coefficients are fixed leads to larger estimates of <italic>ŝ</italic>, and reduced estimates of .</p></caption></fig>" ]
[ "<table-wrap id=\"pgen-1000198-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.t001</object-id><label>Table 1</label><caption><title>Definitions of commonly used symbols.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Symbol</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Definition</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>τ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Time since sweep in units of 4<italic>N</italic> generations</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>L</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">The length of the sequenced region</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>n</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sample size</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>θ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4<italic>Nμ</italic>; the population mutation rate</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>ρ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4<italic>Nr</italic>; the population recombination rate</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>s</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">The selection coefficient of beneficial mutations</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">2<italic>Nλ</italic> = <italic>Λ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">The rate of fixation of beneficial mutations</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000198-t002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.t002</object-id><label>Table 2</label><caption><title>Comparing empirical estimates with estimated demographic models<xref ref-type=\"table-fn\" rid=\"nt101\">a</xref>.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>ŝ</italic>\n<xref ref-type=\"table-fn\" rid=\"nt102\">b</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n<xref ref-type=\"table-fn\" rid=\"nt102\">b</xref>\n</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Empirical data</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2E−3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2E−4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Growth<xref ref-type=\"table-fn\" rid=\"nt103\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7E−6 (6E−6 – 9E−6)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1E−2 (1E−2 – 2E−2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">BN1<xref ref-type=\"table-fn\" rid=\"nt103\">c</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3E−5 (6E−6 – 7E−5)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7E−3 (1E−3 – 5E−2)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">BN2<xref ref-type=\"table-fn\" rid=\"nt104\">d</xref>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5E−5 (7E−6 – 1E−4)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4E−3 (8E−4 – 1E−2)</td></tr></tbody></table></alternatives></table-wrap>", "<table-wrap id=\"pgen-1000198-t003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000198.t003</object-id><label>Table 3</label><caption><title>Comparing estimates of recurrent hitchhiking model parameters in Drosophila.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>ŝ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Nˆ</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2Nλ<italic>ŝ</italic>\n</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Li &amp; Stephan 2006 ##REF##17040129##[18]##\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1E−11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.6E+06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.9E−04</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.9E−07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Andolfatto 2007 ##REF##17989248##[11]##\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.2E−05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.9E−10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.9E+06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.6E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.6E−08</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Macpherson <italic>et al.</italic> 2007 ##REF##18073425##[12]##\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.6E−12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.5E+06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1E−05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.1E−07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">this study (fixed <italic>s</italic>,<italic>λ</italic>,<italic>ρ</italic>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.011</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.9E−12</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.5E+06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.9E−05</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.3E−07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">this study (distrib. <italic>s</italic>,<italic>λ</italic>,<italic>ρ</italic>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.002</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.2E−11</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.4E+06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.0E−04</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.0E−07</td></tr></tbody></table></alternatives></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"pgen.1000198.s001\"><label>Figure S1</label><caption><p>Approximate Bayesian estimation of the strength and rate of selection as well as the neutral <italic>θ</italic>, when estimation is based upon the mean and SD of <italic>π</italic>. The model is one in which <italic>s</italic> and 2<italic>Nλ</italic> are fixed. For the strong selection case <italic>s</italic> = 1.0E−02 and 2<italic>Nλ</italic> = 2.0E−05, for weak selection <italic>s</italic> = 1.0E−04, and 2<italic>Nλ</italic> = 2.0E−03. <italic>ρ</italic> = 0.1/site and <italic>θ</italic> = 0.01/site. Shown are the distributions of 1000 MAP estimates. The dotted lines indicate the true values. The distributions for 10 50 kb region datasets are given in black, and for 1000 500 bp datasets in gray. As shown, the former affords more accurate estimation, and estimation is improved in general as <italic>s</italic> becomes large (see also ##SUPPL##4##Table S1##).</p><p>(0.2 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000198.s002\"><label>Figure S2</label><caption><p>The ratio CV to CV(equilibrium neutrality) for four values of <italic>s</italic>. The product <italic>2Nλs</italic> = 5E−07 for all panels. (A–D) Drosophila-like parameters: <italic>ρ</italic>/<italic>θ</italic> = 10 (<italic>ρ</italic> = 0.1/site, <italic>θ</italic> = 0.01/site), <italic>ρ</italic> = constant or Normal(0.1, 0.05). (E–H) Human-like parameters: <italic>ρ</italic>/<italic>θ</italic> = 1 (<italic>ρ</italic> = 0.002/site, <italic>θ</italic> = 0.002/site), <italic>ρ</italic> = constant or Exponential(0.1). (A,E) Exponential(<italic>s</italic>), Exponential(2<italic>Nλ</italic>), and <italic>ρ</italic> = Normal(0.1, 0.05). (B, F) Exponential(2<italic>Nλ</italic>), <italic>s</italic> = constant. (C, G) Exponential(<italic>s</italic>), 2<italic>Nλ</italic> = constant. (D, H) <italic>ρ</italic> = distributed, <italic>s</italic> = constant, 2<italic>Nλ</italic> = constant. The choice of exponentially distributed <italic>ρ</italic> for human-like parameters is motivated by evidence for greater heterogeneity in <italic>ρ</italic> relative to Drosophila ##REF##17146469##[39]##. Importantly, these models only represent one possible way of modeling distributions of <italic>s</italic> and 2<italic>Nλ</italic>, and alternative models may result in differing conclusions.</p><p>(0.2 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000198.s003\"><label>Figure S3</label><caption><p>Approximate Bayesian estimation of the strength and rate of selection as well as the neutral <italic>θ</italic>, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>, as well as with the mean and SD of <italic>π</italic> alone. The model is one in which <italic>s</italic> and 2<italic>Nλ</italic> are fixed, <italic>s</italic> = 1.0E−02, and 2<italic>Nλ</italic> = 2.0E−05, and <italic>θ</italic> is drawn from a Γ-distribution with mean 0.01 (given by dotted lines). <italic>ρ</italic> = 0.1. Shown are the distributions of 1000 MAP estimates. Results are given for <italic>θ</italic> drawn from two Γ-distributions, one meant to match the variance observed in the empirical dataset of Andolfatto (2007) (<italic>i.e.</italic>, Γ (200,2.5)), and the other simply for representing a very large variance (<italic>i.e.</italic>, Γ (10,50)). As shown, estimation based upon these multiple summary statistics appears to be robust to mutation rate variation, with <italic>π</italic>-based estimation being greatly biased (see also ##SUPPL##4##Table S1##).</p><p>(0.2 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000198.s004\"><label>Figure S4</label><caption><p>Joint posterior distributions of <italic>s</italic> and 2<italic>Nλ</italic>, for the 137-locus dataset of ##REF##17989248##[11]##, when estimation is based upon the means and SDs of <italic>π</italic>, <italic>S</italic>, <italic>θ<sub>H</sub></italic> and <italic>ZnS</italic>. Results are given when the priors are constructed assuming a distributed parameter model. In order to model the dataset under consideration, priors are constructed such that each replicate consists of 137 loci each of the observed length. <italic>n</italic> = 12, <italic>ρ</italic> = 0.121, and <italic>N<sub>e</sub></italic> = 1.87<sup>6</sup> (in accord with the estimates of ##REF##17989248##[11]##). The joint MAP is marked by the X, and the marginal MAPs (##FIG##5##Figure 6##) are given as dashed lines. As shown, estimation based upon joint posteriors is similar, though not identical, to the marginal posteriors.</p><p>(0.6 MB TIF)</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"pgen.1000198.s005\"><label>Table S1</label><caption><p>RMSE (RB).</p><p>(0.08 MB DOC)</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><fn id=\"nt101\"><label>a</label><p>estimation is performed using distributed priors (exp(2<italic>Nλ</italic>) per locus, exp(<italic>s</italic>) per sweep – see <xref ref-type=\"sec\" rid=\"s3\">Methods</xref>).</p></fn><fn id=\"nt102\"><label>b</label><p>MAP estimates (95% CI).</p></fn><fn id=\"nt103\"><label>c</label><p>model estimated to match the empirically observed <italic>π</italic> and Tajima's <italic>D</italic>.</p></fn><fn id=\"nt104\"><label>d</label><p>model estimated to match the empirically observed CV(<italic>π</italic>).</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>JDJ was supported by a National Science Foundation Biological Informatics postdoctoral fellowship. KRT was supported in part by setup funds.</p></fn></fn-group>" ]
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[ "<media xlink:href=\"pgen.1000198.s001.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000198.s002.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000198.s003.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000198.s004.tif\"><caption><p>Click here for additional data file.</p></caption></media>", "<media xlink:href=\"pgen.1000198.s005.doc\"><caption><p>Click here for additional data file.</p></caption></media>" ]
[{"label": ["3"], "element-citation": ["\n"], "surname": ["Stephan", "Wiehe", "Lenz"], "given-names": ["W", "THE", "MW"], "year": ["1992"], "article-title": ["The effect of strongly selected substitutions on neutral polymorphisms: analytical results based on diffusion theory."], "source": ["Theor Popul Biol"], "volume": ["41"], "fpage": ["137"], "lpage": ["154"]}, {"label": ["6"], "element-citation": ["\n"], "surname": ["Kaplan", "Hudson", "Langley"], "given-names": ["NL", "RR", "CH"], "year": ["1989"], "article-title": ["The \u201chitchhiking effect\u201d revisited."], "source": ["Genetics"], "volume": ["120"], "fpage": ["819"], "lpage": ["829"]}, {"label": ["22"], "element-citation": ["\n"], "surname": ["Wright", "Bi", "Schroeder", "Yamasaki", "Doebley"], "given-names": ["SI", "IV", "SG", "M", "JF"], "year": ["2005"], "article-title": ["The effects of artificial selection on the maize genome."], "source": ["Science"], "volume": ["308"], "fpage": ["13130"], "lpage": ["1314"]}, {"label": ["26"], "element-citation": ["\n"], "surname": ["Kelly"], "given-names": ["JL"], "year": ["1997"], "article-title": ["A test on neutrality based on interlocus associations."], "source": ["Genetics"], "volume": ["146"], "fpage": ["1179"], "lpage": ["1206"]}, {"label": ["36"], "element-citation": ["\n"], "surname": ["Przeworski", "Coop", "Wall"], "given-names": ["M", "G", "JD"], "year": ["2005"], "article-title": ["The signature of positive selection on standing genetics variation."], "source": ["Evolution Int J Org Evolution"], "volume": ["59"], "fpage": ["2312"], "lpage": ["23"]}, {"label": ["45"], "element-citation": ["\n"], "surname": ["Tajima"], "given-names": ["F"], "year": ["1989"], "article-title": ["Statistical methods for testing the neutral mutation hypothesis."], "source": ["Genetics"], "volume": ["123"], "fpage": ["437"], "lpage": ["460"]}]
{ "acronym": [], "definition": [] }
45
CC BY
no
2022-01-12 23:38:08
PLoS Genet. 2008 Sep 19; 4(9):e1000198
oa_package/2c/0b/PMC2529407.tar.gz
PMC2529408
18818729
[ "<title>Introduction</title>", "<p>Genome sequencing technology has moved into a new era with the introduction of extremely fast sequencing technologies that can produce over one billion base pairs (bp) of DNA in a single run. Some of the fastest methods today, based on strategies such as cyclic reversible termination ##REF##16339375##[1]## and ligation-based sequencing ##REF##18262675##[2]##, produce the shortest read lengths, ranging from 15–50 bp. These lengths are sufficient for resequencing projects, including efforts to sample the human population, but they have yet to prove as useful for sequencing of novel species. The difficulty is that no existing assembly algorithms can accurately reconstruct a genome from such short reads ##REF##18083777##[3]##.</p>", "<p>The first published report of a bacterial genome sequence from “short” reads used pyrosequencing technology, which was able to generate reads averaging 110 bp. That study ##REF##16056220##[4]## demonstrated the feasibility of assembling the small bacterial genome of <italic>Mycoplasma genitalium</italic> (580,069 bp) from reads that covered the genome 40-fold. This combination of coverage and read length allowed Margulies et al. to generate contiguous stretchs of DNA (contigs) averaging 22.4 kilobases (kb). Results using pyrosequencing have improved steadily as read lengths have increased to 250 bp and longer, but the difficulty of <italic>de novo</italic> assembly has raised questions about the utility of alternative sequencing technologies—those that produce reads shorter than 50 bp—for genome sequencing projects.</p>", "<p>Assembly of novel strains and species—where the genome has not previously been sequenced—from very short reads has proven more difficult, although simulation studies have indicated that it should be possible ##REF##16275781##[5]##. A recent study showed that a combination of pyrosequencing reads (average length 102 bp) and paired-end sequencing could be used to assemble a 4 million base pair (Mbp) genome into just 139 contigs, linked together in 22 scaffolds ##REF##17344419##[6]##. Another recent effort used a hybrid strategy that mixed pyrosequencing (110 bp reads) and traditional Sanger sequencing to produce draft assemblies of marine microbes ##REF##16840556##[7]##. In contrast, the very short reads generated by the Solexa Sequence Analyzer have thus far been useful primarily for polymorphism discovery in the human genome, for resequencing and polymorphism discovery in <italic>Caernohabditis elegans</italic>\n##REF##18204455##[8]##, and for other applications such as ChIP-seq ##REF##17558387##[9]##, which identifies genomic regions bound by transcription factors.</p>", "<p>The very short reads—currently 30–35 bp—produced by CRT technologies such as Solexa present a far more difficult assembly problem. Standard assembly algorithms such as Arachne ##REF##11779843##[10]##,##REF##12529310##[11]## and Celera Assembler ##REF##10731133##[12]## cannot process such short reads at all, spurring the development of several new algorithms designed for short reads, including SSAKE ##REF##17158514##[13]##, Velvet ##REF##18349386##[14]##, Edena ##REF##18332092##[15]##, and ALLPATHS ##REF##18340039##[16]##. These latter methods can handle Solexa data (though ALLPATHS has the additional requirement that the sequences must be paired-end reads), but they produce highly fragmented assemblies when provided with whole-genome data from a bacterial genome. The inherent problem with very short reads is that every repetitive sequence longer than the read length causes breaks in the assembly.</p>", "<p>To demonstrate the feasibility of assembling a bacterial genome from 33 bp reads, using related genomes to assist the process, we chose <italic>Pseudomonas aeruginosa</italic> strain PAb1, a highly virulent strain isolated from a frostbite patient. <italic>P. aeruginosa</italic> is a ubiquitous environmental bacteria of clinical importance as the leading cause of gram-negative nosocomial infections ##UREF##0##[17]##,##REF##15573054##[18]##. Several <italic>P. aeruginosa</italic> genomes have been sequenced previously, including two laboratory strains: PAO1 (6,264,404 bp), originally isolated from a wound, and PA14 (6,537,648 bp) isolated from a burn ##REF##10984043##[19]##,##REF##17038190##[20]##. PA14 and PAO1 are ∼99% identical across the 6.05 Mbp shared by both genomes, and their similarity to PAb1 allowed us to improve the assembly and provided a means to check its accuracy. One of our goals in sequencing PAb1 was to identify genomic differences that contribute to its altered pathogenicity.</p>", "<p>Here we report the assembly of <italic>P. aeruginosa</italic> PAb1 entirely from 33 bp reads, using a novel assembly strategy that takes advantage of related genomes and homologous protein sequences. The assembly is of very high quality, comparable to or better than draft assemblies produced using earlier sequencing technologies. This study shows that a novel bacterial genome can be sequenced entirely with very short read technology, without the use of paired-end sequences (which are not available from some short-read sequencers), and assembled into a high-quality genome. Even at 40-fold coverage, the amount of sequence represents just one-quarter of a single sequencing run on a Solexa instrument, which brings the sequencing cost easily within the reach of most scientists. By making all of our assembly software free and open source, we hope to further bring down the barriers to desktop whole-genome sequencing.</p>", "<title>Algorithm for Assembly of Very Short Reads</title>", "<p>We generated 8,627,900 random shotgun reads from <italic>P. aeruginosa</italic> PAb1 using Solexa technology. All reads were exactly 33 bp in length.</p>", "<p>We used four distinct computational steps to assemble the genome of PAb1. For the initial step, we used the comparative assembly algorithm AMOScmp ##REF##15383210##[21]##, which aligns all reads to a reference genome, and then builds contigs based on these alignments. The algorithm gains efficiency by avoiding the costly all-versus-all overlapping step, which is particularly difficult with very short reads due to the high incidence of false overlaps ##REF##17158514##[13]##. We modified AMOSCmp by tuning the MUMmer software ##REF##14759262##[22]##, which is run within AMOScmp, to look for exact matches to the reference genome of at least 17 bp, allowing at most two mismatches in each read. We found that careful trimming of the reads based on their matches to the reference produced better assemblies than un-trimmed reads. The initial assembly used 7,500,501 reads, leaving 1,127,399 as singletons (##TAB##0##Table 1##). The PAb1 genome is closer to PA14 (99.4% identical for 92% of the PAb1 genome) than to PAO1 (99.0% identical for 90% of the PAb1 genome), and we therefore used PA14 as the primary reference for orienting the contigs.</p>", "<p>Our second step was a novel enhancement to the comparative assembly strategy, in which we used multiple reference genomes (##FIG##0##Figure 1##). We used the complete genomes of both PAO1 ##REF##10984043##[19]## and PA14 ##REF##17038190##[20]## separately to build multiple comparative assemblies, and found that PA14 produced the better assembly, comprising 2,053 contigs containing 6,206,284 bp. (We also used the PA7 strain, but its greater evolutionary distance made it less useful.) The bulk of the sequence was contained in 157 contigs longer than 10 Kbp, which collectively covered 5,568,616 bp. There were 331,364 bp in the PA14 genome that were not covered by the initial assembly, due to divergence between the two strains. However, the gaps in the comparative assembly based on PAO1 occurred in different locations due to differences between the strains. The best assembly based on PAO1 comprised 2797 contigs covering 6,043,652 bp.</p>", "<p>We aligned the two assemblies to one another to identify locations where a contig in the PAO1-based assembly might span two or more contigs in the PA14-based assembly (##FIG##0##Figure 1##). For each such case, we filled the gap with the sequence from the PAO1 assembly using the Minimus assembler ##REF##17324286##[23]## to stitch together the contigs. This algorithm closed 203 gaps, reducing the number of contigs to 1850, of which all but 305 were &lt;200 bp. The bulk of the genome, 5,949,162 bp, was contained in just 113 contigs of 10,000 bp or longer. Note that the overlapping contigs between the two assemblies did not agree perfectly. In order to produce a clean merged assembly, we re-mapped the reads to the contigs using AMOScmp to create consistent multi-alignments.</p>", "<p>The third step used a novel algorithm, <italic>gene-boosted assembly</italic>. For this step, we took the contigs from the previous step and identified protein-coding genes using our annotation pipeline, which is based on Glimmer ##REF##17237039##[24]## and Blast ##REF##9254694##[25]##. Because amino acid sequences are much more conserved than nucleotide sequences, we were able to use the predicted protein sequences (primarily but not exclusively from other <italic>Pseudomonas</italic> species) to fill gaps even where the DNA sequences diverged. The annotation pipeline identified 5,769 proteins in the 305 longest contigs.</p>", "<p>From the initial annotation, we identified those genes that extended beyond the ends of contigs or that spanned the gaps between contigs. We extracted the amino acid sequences corresponding to these gap positions, with a small buffer sequence included on each side of each gap. Next we used tblastn ##REF##9254694##[25]## to align each protein sequence to all the unused reads translated in all 6 frames (##FIG##1##Figure 2##). This step identified, for each gap, a small set of reads that would fill in the missing protein sequence, and the tblastn results provided initial locations for a multiple alignment. We then used a new program, ABBA (Assembly Boosted By Amino acids), to assemble the reads together with the flanking contigs and close the gaps. This gene-boosted assembly protocol extended many contigs and closed 185 gaps, ranging in length from 14–1095 bp, reducing the number of long contigs to 120.</p>", "<p>As a separate test, we conducted a gene-boosted assembly of PAb1 using only the annotated proteins from PA14—without any reference genomic sequence. For this experiment, we aligned all the translated reads to each protein and used ABBA to assemble each one. For 4,572 of the proteins, ABBA produced a single contig that covered the entire reference protein, and another 831 proteins assembled into a few contigs. Thus 5,403 out of 5,602 (96%) of the PAb1 proteins can be assembled using a pure gene-boosting approach, and additional proteins would likely be assembled if we used a large set of proteins for boosting. This demonstrates that in the absence of a closely related genome sequence, gene-boosted assembly can use protein sequences—which diverge much more slowly than genomic DNA—to assemble most of the genes of a new bacterial strain, although the results will lack global genome structure information.</p>", "<p>The fourth step of our method identified any remaining DNA sequences that were (a) unique to PAb1 and (b) outside predicted gene regions. We separately constructed pure <italic>de novo</italic> assemblies of the 8.6 million Solexa reads using SSAKE, Edena, and Velvet. The Velvet assembly was the best of the three, creating 10,684 contigs, the longest being 16,239 bp (##TAB##0##Table 1##). We used MUMmer to align these contigs to the 120 long contigs in our scaffold from the previous step, and identified cases where <italic>de novo</italic> contigs spanned gaps or extended contigs. This step allowed us to close 46 gaps, reducing the number of contigs in our main scaffold to 74. After removing Velvet contigs that were already contained in our scaffold, we had 436 unplaced <italic>de novo</italic> contigs spanning 416,897 bp. The longest unplaced contig was 10,493 bp.</p>" ]
[ "<title>Methods</title>", "<p>Genomic DNA was extracted by SDS lysis, proteinase K digest, and phenol/chloroform extraction. Sequencing was performed by Illumina using the 1G Genome Analyzer, also known as the Solexa sequencer. The 8.6 million reads represent 1/4 of the current capacity of a flow cell. For sequencing trimming in step 1, we mapped all reads to the initial assembly and then trimmed up to three bases from the 3′ end when those bases failed to match a contig. The AMOScmp pipeline for trimming and short read assembly is described at <ext-link ext-link-type=\"uri\" xlink:href=\"http://cbcb.umd.edu/research/SR-assembly.shtml\">http://cbcb.umd.edu/research/SR-assembly.shtml</ext-link>. Contig merging in step 2 of our algorithm used the merger program from the EMBOSS package ##REF##10827456##[34]##. The Edena, Velvet, and ssake assemblers were run with a wide range of parameters in order to optimize them for the data used in this study, with the best results coming from Velvet with a minimum overlap requirement of 24 bases. (The other methods created more numerous, shorter contigs.) The ABBA assembler has been added to the free, open-source AMOS assembler package, which also includes the AMOScmp assembler. ABBA can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"http://amos.sourceforge.net/docs/pipeline/abba.html\">http://amos.sourceforge.net/docs/pipeline/abba.html</ext-link>. AMOS and additional modules developed in this study are freely available from <ext-link ext-link-type=\"uri\" xlink:href=\"http://cbcb.umd.edu/software\">http://cbcb.umd.edu/software</ext-link>, and the MUMmer system is freely available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://mummer.sourceforge.net\">http://mummer.sourceforge.net</ext-link>.</p>" ]
[ "<title>Results/Discussion</title>", "<p>Our final assembly contains one large scaffold with 76 contigs whose total length is 6,290,005 bp, with the longest contig at 512,638 bp. The 436 unplaced contigs, which should fit into the remaining gaps, represent sequence that is unique to PAb1. Our annotation shows that most of these contigs contain genes that are homologous to other <italic>Pseudomonas</italic> species. Several contigs contain bacteriophage genes, pointing to recent phage insertion events in PAb1. The final assembly thus consists of 512 contigs covering 6,706,902 bp, with 94% of the bases in a single large scaffold. Approximately 9% of the reads were not used in the assembly (##TAB##0##Table 1##); many of these can be mapped to contigs if we use relaxed matching criteria, indicating that they represent low-quality data. Our annotation of the PAb1 genome identified 5,602 protein-coding genes, as compared to 5,568 for PAO1 and 5,892 for PA14.</p>", "<p>All Solexa reads have been deposited in the Short Read Archive at NCBI, and the final genome sequence and annotation have been deposited in GenBank as sccession ABKZ01000000.</p>", "<p>We have demonstrated that it is possible to sequence and assemble a bacterial genome from deep sequencing using 33 bp reads. The final assembly has 40.3× coverage, with very high agreement among the individual reads at the vast majority of positions in the genome. To measure the accuracy of individual reads, we examined all positions in the assembly with &gt;20× coverage, which yielded 5.9 million positions. If we count as errors any bases that disagree with the consensus at those positions, we get an estimate based on internal consistency that the error rate per read is 1.04%. Based on this estimate, the expected number of errors for regions of the genome with coverage of &gt;20× is close to zero, except for systematic errors such as difficult-to-sequence regions. This illustrates how the great depth of sequencing possible with short-read technology produces higher quality assemblies—in regions with deep coverage—than would conventional Sanger sequencing at a typical 8× coverage depth.</p>", "<p>We evaluated the coverage to determine if the Solexa sequences were biased towards any portion of the genome, and found a small bias towards high-GC regions, which comprise most of the genome. In particular, regions with 60–70% GC, which comprised 79% of the genome, had 40× coverage. In contrast, regions with 50–55% GC (1.5% of the genome) had 14× coverage, and regions with &lt;50% GC (1.1% of the genome) had just 5× coverage.</p>", "<p>The alignment of <italic>P. aeruginosa</italic> PAb1 to strain PA14, which matches at 99.4% identity for &gt;90% of the genome, can be used to provide an estimate of the sequencing accuracy. To assess the question of whether differences between our assembly and the PA14 genome represented true differences or sequencing errors, we aligned the two genomes and identified all single nucleotide polymorphisms (SNPs). Out of 5,568,550 aligned bases from the longer PAb1 contigs, 5,537,508 agreed with PA14 and can be presumed correct. For each of the remaining 31,042 SNPs, we examined all reads that were assembled at that point and assessed whether (a) the depth of coverage was adequate, and (b) the PAb1 reads agreed on the consensus base. The coverage was 10-fold or greater for 95% of these SNPs. Using the conservative assumption that a SNP might be in error if the inter-read agreement was less than 80%, we found 1157 positions (out of 5,568,550) that might be sequencing errors. We also found 1104 insertions and deletions (indels) in the aligned regions, and our assembled reads were in perfect agreement for 917 of these. If we assume conservatively that the other 187 indels are errors, then considering both SNPs and indels, the accuracy of the assembled genome is greater than 99.97%.</p>", "<p>The assembly is sufficiently complete that we can confidently infer that genes are missing if their expected positions fall in the midst of contigs. Although deeper analysis will be presented in a followup paper, we note that the PAb1 strain is known for its hypermotility on low percentage agar media. Our sequence contains most of the genes required for swimming motility in <italic>P. aeruginosa</italic>\n##REF##14617143##[26]##, but is missing part of the pathway used by cyclic-di-GMP, a secondary signaling molecule, that has been implicated in repressing swimming motility ##REF##16530465##[27]##,##REF##16497924##[28]##. By searching all of the known <italic>P. aeruginosa</italic> genes in this pathway ##REF##16477007##[29]##,##REF##17645452##[30]##,##REF##17824927##[31]##, we found that three genes encoding diguanylate cylase and phosphodiesterase are missing: PA2771 and PA2818 (<italic>arr</italic>) from the PAO1 strain, and PA14_59790 (<italic>pvrR</italic>) from the PA14 strain ##REF##16121184##[32]##,##REF##11961556##[33]##. All three of these genes are located in chromosomal regions previously indicated as hyper-variable based on genomic hybridizations ##REF##16477007##[29]##. The altered gene content of PAb1 in the regulatory pathways repressing flagella may contribute to its observed hypermotility.</p>", "<p>The new algorithm described here make it possible for any scientist to acquire the entire genome of a bacterium at high speed and very low cost. One limitation of our method is that it depends on the existence of related genomes (for the comparative assembly step) and protein sequences (for the gene boosting step). However, GenBank already contains the complete genome sequences for &gt;650 microbial genomes, and draft sequences for nearly 1000 more. For many of these species, much larger numbers of related strains and species have yet to be sequenced. Our method opens the door to the use of whole-genome sequencing to study entire collections of bacteria, to rapidly identify genotypes from mutagenized genetic screens, and for other analyses that were previously too costly or technically infeasible. The gene-boosted assembly technique applies equally well to both short and long-read sequencing methods, and should also work for assembling the gene-containing regions of much larger genomes.</p>" ]
[ "<title>Results/Discussion</title>", "<p>Our final assembly contains one large scaffold with 76 contigs whose total length is 6,290,005 bp, with the longest contig at 512,638 bp. The 436 unplaced contigs, which should fit into the remaining gaps, represent sequence that is unique to PAb1. Our annotation shows that most of these contigs contain genes that are homologous to other <italic>Pseudomonas</italic> species. Several contigs contain bacteriophage genes, pointing to recent phage insertion events in PAb1. The final assembly thus consists of 512 contigs covering 6,706,902 bp, with 94% of the bases in a single large scaffold. Approximately 9% of the reads were not used in the assembly (##TAB##0##Table 1##); many of these can be mapped to contigs if we use relaxed matching criteria, indicating that they represent low-quality data. Our annotation of the PAb1 genome identified 5,602 protein-coding genes, as compared to 5,568 for PAO1 and 5,892 for PA14.</p>", "<p>All Solexa reads have been deposited in the Short Read Archive at NCBI, and the final genome sequence and annotation have been deposited in GenBank as sccession ABKZ01000000.</p>", "<p>We have demonstrated that it is possible to sequence and assemble a bacterial genome from deep sequencing using 33 bp reads. The final assembly has 40.3× coverage, with very high agreement among the individual reads at the vast majority of positions in the genome. To measure the accuracy of individual reads, we examined all positions in the assembly with &gt;20× coverage, which yielded 5.9 million positions. If we count as errors any bases that disagree with the consensus at those positions, we get an estimate based on internal consistency that the error rate per read is 1.04%. Based on this estimate, the expected number of errors for regions of the genome with coverage of &gt;20× is close to zero, except for systematic errors such as difficult-to-sequence regions. This illustrates how the great depth of sequencing possible with short-read technology produces higher quality assemblies—in regions with deep coverage—than would conventional Sanger sequencing at a typical 8× coverage depth.</p>", "<p>We evaluated the coverage to determine if the Solexa sequences were biased towards any portion of the genome, and found a small bias towards high-GC regions, which comprise most of the genome. In particular, regions with 60–70% GC, which comprised 79% of the genome, had 40× coverage. In contrast, regions with 50–55% GC (1.5% of the genome) had 14× coverage, and regions with &lt;50% GC (1.1% of the genome) had just 5× coverage.</p>", "<p>The alignment of <italic>P. aeruginosa</italic> PAb1 to strain PA14, which matches at 99.4% identity for &gt;90% of the genome, can be used to provide an estimate of the sequencing accuracy. To assess the question of whether differences between our assembly and the PA14 genome represented true differences or sequencing errors, we aligned the two genomes and identified all single nucleotide polymorphisms (SNPs). Out of 5,568,550 aligned bases from the longer PAb1 contigs, 5,537,508 agreed with PA14 and can be presumed correct. For each of the remaining 31,042 SNPs, we examined all reads that were assembled at that point and assessed whether (a) the depth of coverage was adequate, and (b) the PAb1 reads agreed on the consensus base. The coverage was 10-fold or greater for 95% of these SNPs. Using the conservative assumption that a SNP might be in error if the inter-read agreement was less than 80%, we found 1157 positions (out of 5,568,550) that might be sequencing errors. We also found 1104 insertions and deletions (indels) in the aligned regions, and our assembled reads were in perfect agreement for 917 of these. If we assume conservatively that the other 187 indels are errors, then considering both SNPs and indels, the accuracy of the assembled genome is greater than 99.97%.</p>", "<p>The assembly is sufficiently complete that we can confidently infer that genes are missing if their expected positions fall in the midst of contigs. Although deeper analysis will be presented in a followup paper, we note that the PAb1 strain is known for its hypermotility on low percentage agar media. Our sequence contains most of the genes required for swimming motility in <italic>P. aeruginosa</italic>\n##REF##14617143##[26]##, but is missing part of the pathway used by cyclic-di-GMP, a secondary signaling molecule, that has been implicated in repressing swimming motility ##REF##16530465##[27]##,##REF##16497924##[28]##. By searching all of the known <italic>P. aeruginosa</italic> genes in this pathway ##REF##16477007##[29]##,##REF##17645452##[30]##,##REF##17824927##[31]##, we found that three genes encoding diguanylate cylase and phosphodiesterase are missing: PA2771 and PA2818 (<italic>arr</italic>) from the PAO1 strain, and PA14_59790 (<italic>pvrR</italic>) from the PA14 strain ##REF##16121184##[32]##,##REF##11961556##[33]##. All three of these genes are located in chromosomal regions previously indicated as hyper-variable based on genomic hybridizations ##REF##16477007##[29]##. The altered gene content of PAb1 in the regulatory pathways repressing flagella may contribute to its observed hypermotility.</p>", "<p>The new algorithm described here make it possible for any scientist to acquire the entire genome of a bacterium at high speed and very low cost. One limitation of our method is that it depends on the existence of related genomes (for the comparative assembly step) and protein sequences (for the gene boosting step). However, GenBank already contains the complete genome sequences for &gt;650 microbial genomes, and draft sequences for nearly 1000 more. For many of these species, much larger numbers of related strains and species have yet to be sequenced. Our method opens the door to the use of whole-genome sequencing to study entire collections of bacteria, to rapidly identify genotypes from mutagenized genetic screens, and for other analyses that were previously too costly or technically infeasible. The gene-boosted assembly technique applies equally well to both short and long-read sequencing methods, and should also work for assembling the gene-containing regions of much larger genomes.</p>" ]
[]
[ "<p>Conceived and designed the experiments: SLS. Performed the experiments: DDS DP. Analyzed the data: SLS DDS DP VTL. Contributed reagents/materials/analysis tools: VTL. Wrote the paper: SLS VTL.</p>", "<p>Recent improvements in technology have made DNA sequencing dramatically faster and more efficient than ever before. The new technologies produce highly accurate sequences, but one drawback is that the most efficient technology produces the shortest read lengths. Short-read sequencing has been applied successfully to resequence the human genome and those of other species but not to whole-genome sequencing of novel organisms. Here we describe the sequencing and assembly of a novel clinical isolate of <italic>Pseudomonas aeruginosa</italic>, strain PAb1, using very short read technology. From 8,627,900 reads, each 33 nucleotides in length, we assembled the genome into one scaffold of 76 ordered contiguous sequences containing 6,290,005 nucleotides, including one contig spanning 512,638 nucleotides, plus an additional 436 unordered contigs containing 416,897 nucleotides. Our method includes a novel gene-boosting algorithm that uses amino acid sequences from predicted proteins to build a better assembly. This study demonstrates the feasibility of very short read sequencing for the sequencing of bacterial genomes, particularly those for which a related species has been sequenced previously, and expands the potential application of this new technology to most known prokaryotic species.</p>", "<title>Author Summary</title>", "<p>In this paper we demonstrate that a bacterial genome, <italic>Pseudomonas aeruginosa</italic>, can be decoded using very short DNA sequences, namely, those produced by the newest generation of DNA sequencers such as the Solexa sequencer from Illumina. Our method includes a novel algorithm that uses the protein sequences from other species to assist the assembly of the new genome. This algorithm breaks up the genome into gene-sized chunks that can be put back together relatively easily, even from sequence fragments as short as 30 bases of DNA. We also take advantage of the genomes of related species, using them as reference strains to assist the assembly. By combining these and other techniques, we were able to assemble 94% of the 6.7 million bases of <italic>P. aeruginosa</italic> into just 76 large pieces. The remaining 6% is contained in 436 smaller fragments. We have made all of our software available for free under open-source licenses, and we have deposited the newly assembled genome in the public GenBank database.</p>" ]
[]
[ "<p>Thanks to Julie Croft and R. Elizabeth Sockett (University of Washington) for providing the PAb1 strain and to Arthur Delcher for technical comments on the assembly methods.</p>" ]
[ "<fig id=\"pcbi-1000186-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pcbi.1000186.g001</object-id><label>Figure 1</label><caption><title>Comparative assembly using multiple genomes.</title><p>The target genome is shown in the center, aligned to two related genomes, A and B. The DNA sequence of the target diverges from the reference genomes in distinct loci, labeled X, Y, and Z. The comparative assembly based on genome A contains a gap corresponding to region Y, while the assembly based on genome B contains two gaps, corresponding to X and Z. The merged assembly will cover all of the target genome with no gaps.</p></caption></fig>", "<fig id=\"pcbi-1000186-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pcbi.1000186.g002</object-id><label>Figure 2</label><caption><title>Gene-boosted assembly.</title><p>All contigs are aligned with predicted gene sequences to identify genes that span 2 or more contigs. The DNA sequences of these spanning genes are cut out with a small buffer on each end. The amino acid translation of each gene fragment is then searched against a translated database of all singleton reads that have not yet been placed in the assembly. Finally, the reads identified by this process are assembled together with the two contigs to fill in the gap.</p></caption></fig>" ]
[ "<table-wrap id=\"pcbi-1000186-t001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pcbi.1000186.t001</object-id><label>Table 1</label><caption><title>Major steps in the assembly of <italic>P. aeruginosa</italic> from 33 bp Solexa reads.</title></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/><col align=\"center\" span=\"1\"/></colgroup><thead><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Assembly Step</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Input</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Number of Contigs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Contigs &gt;200 bp</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Largest Contig</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Singletons</td></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AMOScmp with PA14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8,627,900 reads</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2,053</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">428</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">170,485</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,127,399</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AMOScmp with PAO1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8,627,900 reads</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2,797</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">865</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">75,626</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,592,525</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Merged comparative assemblies</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4,850 contigs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,850</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">306</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">236,472</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,066,226</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Gene-boosted assembly</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">306 contigs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">120</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">512,638</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">NA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">De novo assembly by Velvet</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8,627,900 reads</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10,684</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7382</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">16,239</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,241,079</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Merged gene-boosted and Velvet assemblies</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">120 contigs, 7382 contigs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">76</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">512,638</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">822,210</td></tr></tbody></table></alternatives></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><fn id=\"nt101\"><p>The first column indicates the assembly strategies described in the text. Singletons refers to the number of reads that were not used to produce the contigs generated by each method.</p></fn></table-wrap-foot>", "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This work was supported in part by National Institutes of Health grants R01-LM006845 and R01-GM083873 to SLS.</p></fn></fn-group>" ]
[ "<graphic id=\"pcbi-1000186-t001-1\" xlink:href=\"pcbi.1000186.t001\"/>", "<graphic xlink:href=\"pcbi.1000186.g001\"/>", "<graphic xlink:href=\"pcbi.1000186.g002\"/>" ]
[]
[{"label": ["17"], "element-citation": ["\n"], "surname": ["Kasper", "Harrison"], "given-names": ["DL", "TR"], "year": ["2005"], "source": ["Harrison's Principles of Internal Medicine"], "publisher-loc": ["New York"], "publisher-name": ["McGraw-Hill"]}]
{ "acronym": [], "definition": [] }
34
CC BY
no
2022-01-13 00:55:15
PLoS Comput Biol. 2008 Sep 26; 4(9):e1000186
oa_package/36/30/PMC2529408.tar.gz
PMC2529451
18802464
[ "<title>Introduction</title>", "<p>In 1980, the World Health Organization declared that smallpox had been eliminated as a human disease ##REF##6252467##[1]##. Nevertheless, potential bioterrorist release of <italic>Variola major</italic>, the causative agent for smallpox, and human infection with monkeypox or other zoonotic orthopoxviruses has heightened interest in this family of viruses ##REF##18252104##[2]##. <italic>Variola major</italic> is particularly feared as a bioterrorism agent because of the high rate of transmission and up to 30% mortality caused by smallpox ##REF##14999635##[3]##. Fatal cases of smallpox were characterized by clinical findings similar to septic shock, likely mediated by the host inflammatory response to infection. However, molecules and signaling pathways that initiate and control protective and detrimental immune responses to <italic>Variola major</italic> remain poorly defined. Identifying molecular determinants of the innate immune response to poxviruses is critical to understanding pathogenesis of poxvirus infections and developing better therapies to prevent or ameliorate the sepsis-like disease manifestations. This knowledge also may lead to development of a safer smallpox vaccine that eliminates the high risk of severe, life-threatening complications associated with the current live, attenuated vaccinia virus vaccine. Improved understanding of the innate immune response to poxviruses will have benefits beyond advancing new vaccines and therapies to prevent and treat infection. Vaccinia virus is being investigated as a gene delivery, oncolytic, or immunizing vector for a wide variety of diseases, including cancer, HIV and malaria ##REF##18162040##[4]##–##REF##16621181##[8]##. Greater knowledge of normal host-pathogen interactions will enable more efficient targeting and efficacy of these vectors in patients. Finally, insights gained from studying pulmonary infection with poxviruses are expected to inform research on protective and harmful aspects of host immunity to other respiratory pathogens.</p>", "<p>Toll-like receptors (TLRs) have emerged as key molecules in initiating innate immune responses to a variety of different pathogens, and these receptors also regulate subsequent adaptive immune responses to infection. TLRs recognize defined molecular patterns associated with various pathogens, including bacteria, fungi, and viruses. In vitro studies have identified canonical ligands for different TLR family members, such as double-stranded RNA for TLR3 and bacterial lipopolysaccharide (LPS) for TLR4. However, recent studies suggest that TLRs may respond to a broader range of molecular patterns. For example, while TLR4-dependent recognition of LPS is well-established as a central regulator of effective host immunity to bacterial pathogens, TLR4 also may signal in response to a wide variety of endogenous ligands, such as heat shock proteins ##REF##17947709##[9]##,##REF##17082636##[10]##. TLR4 also may respond to some viral proteins, and TLR4-dependent signaling may be necessary to limit viral replication and disease morbidity in vivo ##REF##17292937##[11]##,##REF##12402188##[12]##. These studies emphasize that functions of TLRs in host immunity may extend to pathogens that do not carry known ligands for specific receptors, particularly as TLRs respond to infections in living animals.</p>", "<p>We recently established that TLR3 controls a detrimental innate immune response to pulmonary infection with vaccinia virus, a model virus for studies of orthopoxviruses ##REF##18097050##[13]##. Compared with wild-type mice, mice lacking TLR3 (TLR3<sup>−/−</sup>) had reduced viral replication and were protected against disease morbidity and mortality. Adverse effects of TLR3 signaling were caused in part by an excessive inflammatory response to infection. To further investigate TLR3 in poxvirus infection, we initially focused on functions of TIR domain-containing adapter inducing interferon-β (TRIF), the only known downstream adapter molecule for TLR3. Unexpectedly, mice lacking TRIF (TRIF<sup>−/−</sup>) did not reproduce protective effects of deleting TLR3, but TRIF<sup>−/−</sup> was more susceptible to vaccinia infection. These data prompted us to analyze functions of TLR4, the only other TLR known to signal through TRIF, in response to respiratory infection with vaccinia virus. We determined that TLR4 signaling protects mice against vaccinia infection, limiting viral replication and local inflammation.</p>" ]
[ "<title>Materials and Methods</title>", "<title>Mice</title>", "<p>TRIF<sup>−/−</sup> mice backcrossed to a C57BL/6 background were originally developed by the S. Akira laboratory and were bred at the University of Michigan. Wild-type (WT) C57BL/6J control mice were obtained from The Jackson Laboratory. Adult male and female mice ages 7 to 9 wk old were used for experiments. C3HeB/FeJ and C3H/HeJ mice were obtained from The Jackson Laboratory. Adult male mice ages 6 to 10 wk old were used for experiments. 17-wk-old mice were used as uninfected controls for histological studies.</p>", "<title>Vaccinia virus</title>", "<p>We prepared stocks of Vac-GFL, a recombinant Western Reserve (WR) vaccinia virus that expresses firefly luciferase and GFP, and determined viral titers as described previously ##REF##18097050##[13]##. Viral titers in excised organs were analyzed by serial dilution on Vero cells ##REF##16095645##[15]##.</p>", "<title>Cells</title>", "<p>Vero cells were maintained as we previously have described ##REF##16095645##[15]##. Primary bone marrow macrophages were obtained by flushing the femurs and tibiae of mice with cold PBS. This suspension was filtered through a 100 µm filter and a 40 µm filter. Macrophages were cultured in Dulbecco's modified Eagle medium supplemented with 20% L929-cell-conditioned media, 10% heat-inactivated fetal bovine serum, 1% L-glutamine, and 0.1% penicillin-streptomycin. Macrophages were cultured 1 wk in this media before performing experiments with them.</p>", "<title>Animal procedures</title>", "<p>All animal procedures were approved by the University of Michigan Committee on the Use and Care of Animals. Mice were infected i.n. with vaccinia virus as described previously ##REF##16095645##[15]##. Weights and rectal temperatures (Physitemp Instruments) were recorded on conscious mice immediately before infection and on each day throughout experiments.</p>", "<title>Bioluminescence imaging</title>", "<p>Bioluminescence imaging was performed on each day after infection using an IVIS 200 system (Caliper). Imaging and data analysis were performed as described previously ##REF##16095645##[15]##.</p>", "<title>Histology</title>", "<p>To prepare lungs for histology, mice were euthanized on days 3 and 5 post-infection, and lungs were inflated with 1 mL of 10% formalin in PBS. Lungs were excised, preserved in 10% formalin overnight or longer, and then transferred to 70% ethanol solution. Fixed tissues were paraffin embedded and sectioned by the Morphology Core Facility at the University of Michigan. Tissue sections were stained with Gill's hematoxylin and counterstained with eosin. Sites of viral replication in the lungs were identified by immunohistochemistry, based on detection of GFP from Vac-GFL. Paraffin-embedded lung sections were stained using the Vector Laboratories ABC staining kit. Tissue sections were stained with rabbit polyclonal anti-GFP antibody (1/500 dilution) (Invitrogen) and goat anti-rabbit secondary antibody (1/200 dilution; Vector Laboratories). Blocking solution consisted a 1/67 dilution of goat serum in PBS with 250 mM total NaCl.</p>", "<p>To quantify foci of inflammation in lung sections, we analyzed transverse lung sections through comparable portions of the upper and lower lobes of each lung. Sections were viewed under a 4× objective, and numbers of inflammatory foci were counted. Mean values for numbers of foci and SEM were calculated.</p>", "<title>Serum and tissue cytokines</title>", "<p>Blood was obtained from the abdominal aorta of euthanized mice and collected into heparinized tubes. Plasma was separated from cells by centrifugation. Bronchoalveolar lavage of TRIF<sup>−/−</sup> mice was performed by intratracheal instillation and withdrawal of 1 mL PBS in lungs of euthanized mice. Plasma and bronchoalveolar lavage fluid concentrations of TNF-α, IL-6, and MCP-1 were determined by ELISA performed by the University of Michigan Cancer Center Cellular Immunology Core Facility. Concentrations of IFN-β were measured by ELISA (PBL Biomedical Laboratories) according to the manufacturer's instructions.</p>", "<p>Lungs were harvested on day 3 or 5 post-infection and homogenized in 5 mL PBS with a Polytron tissue homogenizer (Brinkmann). Lung homogenates were centrifuged at 2111×g for 10 minutes at 4°C. Supernatants were removed and concentrations of TNF-α, IL-6, and MCP-1 measured by ELISA as described above.</p>", "<title>Flow cytometry</title>", "<p>Lungs were excised on day 3 or 5 post-infection and disaggregated by mechanical disruption in a blender (VWR). Cells were counted and analyzed by flow cytometry as described previously ##REF##16002685##[31]##. The following mAbs obtained from BD Pharmingen were used: RM4-4 (anti-murine CD4, rat IgG2b), 53-6.72 (anti-murine CD8, rat IgG2b), 1D3 (anti-murine CD19, rat IgG2a),M1/70 (anti-murine CD11b, rat IgG2b), HL3 (antimurine CD11c, hamster IgG1), 2.4G2 (anti-murine CD16/CD32 Fc block, rat IgG2b), 30-F11 (anti-murine CD45, rat IgG2b), and RB6-8C5 (anti-murine Ly6G Gr-1, rat IgG2b). Monoclonal Abs were primarily conjugated with FITC, PE, APC and APC-Cy7; biotinylated Abs were visualized using streptavidin-PerCP-Cy5.5 (BD Pharmingen). Isotype matched control mAbs (BD Pharmingen or eBioscience) were tested simultaneously in all experiments. All samples were analyzed on the BD LSR II flow cytometer with 3 lasers (488 nm blue, 405 nm violet and 633 nm HeNe red). CD45 APC-Cy7 and Invitrogen LIVE/DEAD Fixable Violet Dead Cell Stain were added to all lung mince samples. Subset analysis was performed on gated CD45-positive live cells. A minimum of 10,000 cells were analyzed for each sample. For all analyses, percentages for matched isotype control Abs were subtracted from values obtained for staining with specific Abs for individual markers.</p>", "<title>UV inactivation of virus</title>", "<p>Vac-GFL was UV-inactivated by irradiation for 90 s on “sterilize” setting in a GS Genelinker UV-chamber (BioRad) following incubation in a Hank's Balanced Salt Solution (HBSS) solution containing 1.0 µg Psoralen according to the protocol of Puhlmann and colleagues ##REF##10678358##[32]##.</p>", "<title>Statistics</title>", "<p>Data were analyzed by <italic>t</italic> test for pairwise comparisons, using Microsoft Excel or GraphPad Prism software. Differences with <italic>p</italic>&lt;0.05 were regarded as statistically significant.</p>" ]
[ "<title>Results</title>", "<title>TRIF<sup>−/−</sup> mice have a distinct phenotype from TLR3<sup>−/−</sup> mice</title>", "<p>We recently reported that TLR3<sup>−/−</sup> mice are protected from pulmonary vaccinia infection compared to wild type C57BL/6 controls ##REF##18097050##[13]##. The only known adaptor molecule for TLR3 is TRIF, so TLR3 is thought to signal exclusively through TRIF to control secretion of type I interferons and pro-inflammatory cytokines ##REF##17540179##[14]##. Because of this direct TLR3 to TRIF signaling pathway, we hypothesized that TRIF<bold><sup>−/−</sup></bold> mice would be protected against vaccinia infection, similar to TLR3<sup>−/−</sup> mice.</p>", "<p>We infected TRIF<sup>−/−</sup> and wild-type C57BL/6 mice with 1×10<sup>4</sup> pfu Vac-GFL intransally (i.n.) to reproduce the natural respiratory route of infection with <italic>Variola major</italic>. Vac-GFL is a recombinant vaccinia virus that expresses a reporter protein comprised of GFP fused to firefly luciferase ##REF##18097050##[13]##, which allows viral replication and dissemination to be quantified in living mice using bioluminescence imaging. We previously established that in vivo measurements of bioluminescence from Vac-GFL correlate directly with viral titers in a defined organ or tissue ##REF##16095645##[15]##. Bioluminescence imaging was performed daily to monitor replication of vaccinia virus, and weight loss was used as marker for systemic severity of disease.</p>", "<p>Unexpectedly, susceptibility of TRIF<sup>−/−</sup> mice to vaccinia infection was distinct from that of the TLR3<sup>−/−</sup> mice. TRIF<sup>−/−</sup> mice had less weight loss than wild-type mice on days 1–4 post-infection (<italic>p</italic>&lt;0.05), which is similar to our published results for TLR3<sup>−/−</sup> versus wild-type mice, (##FIG##0##Figure 1A##). However, TRIF<sup>−/−</sup> mice differed from TLR3<sup>−/−</sup> animals in that replication of Vac-GFL was greater in mice lacking TRIF, as quantified by region of interest analysis of head, chest, and abdomen sites on bioluminescence images. By area under the curve (AUC) analysis, TRIF<sup>−/−</sup> mice had significantly greater luminescence in their chests (from lung infection) than wild-type mice (##FIG##0##Figure 1B##; <italic>p</italic>&lt;0.01). These data indicate that a different and/or additional host molecule(s) controls responses to vaccinia in TRIF<sup>−/−</sup> mice relative to those mediated solely by TLR3. This experiment continued until day 7 post-infection, when the animals were euthanized to obtain plasma and bronchoalveolar (BAL) fluid for quantification of cytokines. Levels of IL-6, IL-4, IFN-γ, MCP-1, TNF-α, and TGF-β were measured in these samples, but no significant differences were seen between the TRIF<sup>−/−</sup> and WT mice (data not shown).</p>", "<title>TLR4 confers protection in pulmonary vaccinia infection</title>", "<p>We hypothesized that TLR4, the only other Toll-like receptor known to signal through TRIF, may control differing host responses to vaccinia in TLR3<sup>−/−</sup> versus TRIF<sup>−/−</sup> mice. TLR4 is reported to limit replication of a limited number of viruses ##REF##17292937##[11]##,##REF##11062499##[16]##, although functions of this receptor in vaccinia infection have not been established. To investigate TLR4 in host defense against vaccinia virus, we used C3H/HeJ mice, which have a point mutation in the cytoplasmic region of TLR4 that renders them unresponsive to LPS ##REF##10201887##[17]##. As controls, we used C3HeB/FeJ mice, which have normal, functional TLR4. C3HeB/FeJ mice are genetically similar to C3H/HeJ mice and are well-established as a control strain for experiments using C3H/HeJ animals ##REF##16920968##[18]##–##REF##9531309##[20]##.</p>", "<p>We infected C3H/HeJ and C3HeB/FeJ mice with 1×10<sup>4</sup> pfu Vac-GFL. Systemic effects of disease were monitored by weight loss and rectal temperature, while viral replication and spread were assessed with bioluminescence imaging. While both strains of mice lost body temperature in response to vaccinia infection ##REF##8855303##[21]##, temperatures decreased to a greater extent over the course of the infection in C3H/HeJ (TLR4 mutant) mice relative to C3HeB/FeJ (##FIG##1##Figure 2A##). C3H/HeJ mice had a slightly lower temperature than the controls prior to infection, but there were no differences between strains on days 1, 2, and 3 post-infection. Beginning on day 4, however, rectal temperatures were significantly lower in C3H/HeJ mice, reaching a mean temperature of 33°C on day 6 post-infection (<italic>p</italic>&lt;0.05). In contrast, the lowest mean temperature recorded in C3HeB/FeJ mice was 34.7°C on day 7. C3H/HeJ mice had significantly lower temperatures than control C3HeB/FeJ mice on days 4–7 (<italic>p</italic>&lt;0.05). As a second marker of disease severity, weight loss was monitored over the course of the disease (##FIG##1##Figure 2B##). Surprisingly, the TLR4 mutant mice lost slightly less weight than the controls with significant differences on days 1–3 and 7–8 post-infection (<italic>p</italic>&lt;0.05). Although the pattern of the weight loss difference is opposite that of the body temperature, it is consistent with the reduced weight loss observed in TLR3<sup>−/−</sup> and TRIF<sup>−/−</sup> mice compared with wild-type controls.</p>", "<p>Bioluminescence imaging showed that C3H/HeJ (TLR4 mutant) mice had significantly greater viral replication in the chest region on days 1, 4, and 5 post-infection (<italic>p</italic>&lt;0.05; ##FIG##1##Figure 2C##). Light measured in the chest region of interest predominantly represents viral replication in the lung. C3H/HeJ mice also had significantly more Vac-GFL bioluminescence in the chest over the full course of the experiment as determined by AUC analysis. AUC values for bioluminescence were 1.61×10<sup>8</sup> vs. 3.18×10<sup>7</sup> for C3H/HeJ and C3HeB/FeJ mice, respectively (<italic>p</italic>&lt;0.01). C3H/HeJ mice also had increased abdominal luminescence compared to control animals on day 4 (<italic>p</italic>&lt;0.05) and over the course of the experiment by AUC analysis (##FIG##1##Figure 2D##). AUC values for photons produced in abdominal regions were 1.68×10<sup>7</sup> and 5.21×10<sup>6</sup> for C3H/HeJ and control C3HeB/FeJ mice, respectively (<italic>p</italic>&lt;0.05). Bioluminescence in the head region did not differ between groups, and all mice recovered from infection (data not shown). Collectively, these data suggest that TLR4 limits respiratory infection and systemic spread of vaccinia virus.</p>", "<p>To establish effects of TLR4 on survival, we infected C3H/HeJ and control C3HeB/FeJ mice with 5×10<sup>5</sup> pfu Vac-GFL, a dose 1.5 logs higher than used previously. Using this inoculum, TLR4 mutant mice were clearly more susceptible to vaccinia infection. By day 10 post-infection, 70% of C3H/HeJ mice had died, while all control mice recovered from infection (##FIG##2##Figure 3A##). As in the previous experiment, loss of body temperature was measured as a sign of morbidity. C3H/HeJ mice were significantly more hypothermic than control C3HeB/FeJ mice on days 2 and 5–9 post-infection (<italic>p</italic>&lt;0.05; ##FIG##2##Figure 3B##). The rapid recovery of mean temperature in C3H/HeJ mice between days 9 and 10 is caused by death of the most hypothermic mice, while the surviving animals recovered temperature comparable to control C3HeB/FeJ mice. These data were consistent over 5 independent experiments. Weight loss also was monitored over the course of the disease. C3H/HeJ mice exhibited less weight loss than control C3HeB/FeJ animals over the first 7 d post-infection, and these differences were significant on days 2–4 and 6 (<italic>p</italic>&lt;0.05; ##FIG##2##Figure 3C##). The same trend was observed in two subsequent experiments. However, C3H/HeJ mice recovered weight more quickly than control C3HeB/FeJ animals on days 8–13, with significant differences observed on days 12 and 13 (<italic>p</italic>&lt;0.05). Both body temperature and weight loss are reported to be regulated by cytokines, including IL-1, IL-6, and TNF-α, as part of the “sickness response” ##REF##15970487##[22]##. The discrepancy between these parameters during vaccinia infection suggests underlying differences in mechanisms and pathways that regulate these two global measures of disease. These data highlight limitations of using weight loss alone as a measure of disease severity in vaccinia infection.</p>", "<p>With an inoculum of 5×10<sup>5</sup> pfu Vac-GFL, differences in viral replication between genotypes were even more pronounced than in the earlier experiment. Bioluminescence from Vac-GFL was greater in the head region of C3H/HeJ mice compared with controls. Differences between strains were statistically significant over the latter part of infection on days 5 and 7–10 (<italic>p</italic>&lt;0.05; ##FIG##3##Figure 4A##). Over the course of the experiment, there was a trend for higher head bioluminescence in C3H/HeJ mice as determined by AUC analysis, although this difference was not statistically significant. Similarly, bioluminescence in the chests of TLR4 mutant mice was significantly increased over the controls (<italic>p</italic>&lt;0.05) on days 3–7 and 10 post-infection (##FIG##3##Figure 4B,C##). At the peak of infection on day 6, the chest luminescence of the TLR4 mutant mice was 8-fold higher than that of the control mice. Moreover, the AUC for bioluminescence in C3H/HeJ TLR4 mutant mice was significantly greater than that for controls (7.22×10<sup>8</sup> vs. 8.20×10<sup>7</sup>, respectively; <italic>p</italic>&lt;0.05). Increased viral replication in lungs of C3H/HeJ was also confirmed by plaque assay (##FIG##4##Figure 5##). Finally, C3H/HeJ mice had greater systemic spread of the virus to the abdomen (##FIG##5##Figure 6A and 6B##). At the peak on day 5, the TLR4 mutant mice had 4.7-fold higher luminescence in the abdomen than wild-type controls. Differences between the two genotypes were significant on days 3–8 post-infection. The AUC of the abdominal luminescence in the C3H/HeJ mice was 4.39×10<sup>7</sup> compared with 9.39×10<sup>6</sup> in the control C3HeB/FeJ mice, respectively (<italic>p</italic>&lt;0.05). These data extend our initial observations of increased viral replication and dissemination in mice lacking functional TLR4. Taken together, loss of functional TLR4 renders C3H mice more susceptible to pulmonary vaccinia infection, as measured by multiple parameters. Therefore, TLR4 must recognize some exogenous or endogenous ligand present in vaccinia infection.</p>", "<p>To exclude the possibility of our results being affected by endotoxin contamination of our viral preparation, we infected RAW cells with Vac-GFL in the presence or absence of 10 µg/mL polymyxin B ##REF##17201926##[23]##. Levels of IL-6, TNF-α, and MCP-1 in the cell culture supernatants were assayed by ELISA. Adding polymyxin B did not affect levels of IL-6, MCP-1, or TNF-α in the supernatants of infected cells (data not shown), establishing that contaminating endotoxin did not affect our in vivo studies.</p>", "<title>Loss of TLR4 signaling does not abolish IFN-β production</title>", "<p>Type I interferons are essential to effective host defense against vaccinia infection ##REF##7609046##[24]##. TLR4 signaling results in production of Type I interferons through activation of transcription factors interferon regulatory factor 3 (IRF3) and NF-κB. To determine to what extent TLR4 regulates secretion of type I interferons during vaccinia infection, we measured concentrations of interferon-β (IFN-β) in lung tissue of C3H/HeJ and control mice. Mice were infected i.n. with 5×10<sup>5</sup> pfu Vac-GFL, and lungs were harvested on days 3 and 5 post-infection. The day 3 time point is early in the course of infection, at the beginning, or just before, differences in luminescence and body temperature appear. Day 5 is near the peak of the infection where differences between TLR4 mutant and control mice are most pronounced. Lungs were homogenized and concentrations of IFN-β in supernatants were measured by ELISA. Levels of IFN-β were below the limit of reliable detection on day 3 in both groups of mice. On day 5 post-infection, IFN-β levels in 8 of 9 control mice remained below the limit of detection. On the other hand, six of 9 C3H/HeJ mice had IFN-β above the limit of reliable detection on day 5 (##FIG##6##Figure 7##). Therefore, C3H/HeJ mice are capable of producing IFN-β despite the lack of TLR4 signaling, showing redundancy in signaling pathways that activate a type I interferon response to vaccinia.</p>", "<title>Loss of TLR4 does not eliminate the inflammatory response to vaccinia in the lung</title>", "<p>We hypothesized that protective effects of TLR4 may be mediated by pro-inflammatory cytokine responses, limiting viral replication and spread directly, or indirectly through recruitment of immune cells. To test this hypothesis, we infected C3H/HeJ and control mice with 5×10<sup>5</sup> pfu Vac-GFL i.n. and harvested lungs on days 3 and 5 post-infection. Supernatants from homogenized lungs were analyzed by ELISA for IL-6, TNF-α, and MCP-1. There were no significant differences in levels of any of these cytokines between groups of mice on day 3. On day 5, TNF-α and MCP-1 levels were the same in TLR4 mutant and control mice, but IL-6 levels were significantly higher in C3H/HeJ lungs (##FIG##7##Figure 8##). No significant differences were detected in the plasma at either time (data not shown) (p&gt;0.4). As with type I interferon, redundant signaling pathways are able to elicit NF-κB-dependent cytokine production in response to vaccinia infection.</p>", "<p>To analyze the degree and composition of leukocyte infiltrates in the lung, immune cells were isolated from uninfected mice and mice on days 3 and 5 post-infection. Numbers and types of cells were analyzed by flow cytometry. At day 3, there was a trend towards higher total CD45+ cells in the C3H/HeJ cell, but these differences were not significant. We also measured subsets of immune cells in the lung, including B lymphocytes, CD4 and CD8 lymphocytes, macrophages, dendritic cells, and neutrophils. However, there were no consistent differences in cell types recruited to lungs of infected C3H/HeJ and control C3HeB/FeJ mice (data not shown).</p>", "<p>To further assess the pattern of inflammation and tissue damage in TLR4 mutant lungs, we examined the lungs of vaccinia-infected mice by histology. Hematoxylin and eosin–stained sections showed foci of mixed and lymphocytic peribronchial and perivascular infiltrate (##FIG##8##Figure 9A and 9B##). Occasionally, infiltrating cells could be seen in alveoli separate from any peribronchial or perivascular focus. In foci of severe inflammation, epithelial cell necrosis was observed, and some inflammatory cells had apoptotic morphology. As a quantitative measure of inflammation, numbers of foci in each section were counted. On day 3 post-infection, the beginning of the interval when increased levels of virus could be discerned in lungs of TLR4 mutant mice, C3H/HeJ TLR4 mutant mice had significantly more foci of inflammation than controls (<italic>p</italic>&lt;0.05; ##FIG##9##Figure 10##). No consistent differences were detected on day 5. These data indicate that TLR4 is not required for producing an early local inflammatory response to vaccinia infection.</p>", "<title>Vaccinia predominantly infects epithelial cells in the lung</title>", "<p>To investigate the cell type(s) involved in the propagation of infection in the lungs, we performed immunohistochemical staining on paraffin-embedded lung sections with anti-GFP. Mice were infected with 5×10<sup>5</sup> pfu Vac-GFL, and lungs were harvested on days 3 and 5 post-infection. In all samples, intense anti-GFP staining was localized to bronchial epithelial cells with less extensive infection detected in alveolar epithelial cells (##FIG##10##Figure 11A and 11B##). Samples of both genotypes also showed some staining of cells among the inflammatory infiltrate, possibly macrophages, although firm identification could not be made. In all samples, the regions of positive anti-GFP antibody staining were associated with foci of inflammation, but many foci of inflammation had no regions of anti-GFP antibody staining. The distribution and types of infected cells did not differ between strains of mice. These findings suggest that in the lungs, vaccinia primarily replicates and spreads through epithelial cells.</p>", "<title>TLR4 recognizes a viral particle ligand</title>", "<p>In order to determine whether TLR4 was signaling in response to an endogenous or a viral ligand, we treated bone marrow macrophages isolated from C3H/HeJ and control mice with live or UV-inactivated virus (MOI = 5), and measured levels of TNF-α and IL-6 in the supernatant. The undiluted stock of UV-inactivated Vac-GFL (9.0×10<sup>7</sup> pfu/mL) produced no plaques or cytopathic effect in cultured Vero cells (data not shown). TLR4 mutant and control macrophages were equally resistant to viral replication, even when challenged with live virus at a high MOI (##FIG##11##Figure 12A##). TLR4 mutant and control macrophages treated with UV-inactivated virus produced significantly (<italic>p</italic>&lt;0.05) higher levels of IL-6 and TNF-α than cells of the same genotype treated with live virus (##FIG##11##Figure 12B and 12C##). This is likely due to the absence vaccinia-encoded inhibitors of TLR and other signaling pathways, such as N1L, A46R, and A52R ##REF##15215253##[25]##–##REF##12566418##[27]##, produced by replicating vaccinia virus. TLR4 mutant macrophages produced significantly higher levels of both IL-6 and TNF-α than control cells (<italic>p</italic>&lt;0.05; both live and UV-inactivated virus). This shows that TLR4 not only is unnecessary for the cytokine response of bone marrow macrophages to vaccinia virus, but it actually dampens that response. C3H/HeJ (TLR4 mutant) cells treated with UV-inactivated virus produced by far the highest levels of any condition, rising above IL-6 levels in C3H/HeJ-with-live virus cultures by 6- to 7-fold and 2- to 3-fold for TNF-α in the same cells. Cytokine levels in UV-inactivated C3H/HeJ cultures were approximately 6- to 10-fold higher than those in UV-inactivated C3HeB/FeJ cultures. The fact that macrophages were able to produce TNF-α and IL-6 in response to UV-inactivated virus, and that TLR4-intact macrophages produce significantly less of these cytokines, indicates that neither viral replication nor cell death is necessary for TLR4 recognition of vaccinia virus. This suggests that TLR4 recognizes a component of the viral particle rather than an endogenous ligand released from infected host cells.</p>" ]
[ "<title>Discussion</title>", "<p>The innate immune system is vital for host defense against poxviruses, but molecular mechanisms of virus recognition and host defense are incompletely understood. While a robust Th1 immune response is necessary to eliminate vaccinia and other poxviruses, an exaggerated innate immune response also may threaten the life of the host. In septic shock, a systemic “cytokine storm” causes blood vessel dilation and activation of the clotting cascade, leading to hypotension, hemolysis, and multi-organ failure. Severe and fatal cases of smallpox are characterized by fever, hypotension, coagulopathy, blood vessel dilatation, and leukocyte extravasation, all of which are resemble the pathophysiology of septic shock ##REF##14999635##[3]##. These observations, coupled with the absence of lesions from any location except the skin, suggest that an uncontrolled systemic immune response is the most dangerous aspect of poxvirus infection.</p>", "<p>As one mechanism through which immunity contributes to disease manifestations of poxviruses, we recently reported that TLR3 has a detrimental effect in vaccinia infection ##REF##18097050##[13]##. Specifically, mice lacking TLR3 had decreased viral replication, morbidity, and mortality following infection with vaccinia virus. These data established TLR3 as a key determinant of poxvirus pathogenesis and highlight the critical balance between effective and excessive innate immune responses during poxvirus infection.</p>", "<p>To further investigate signaling pathways by which TLR3 exacerbates poxvirus disease, we first analyzed vaccinia infection in mice lacking TRIF, the only known adapter protein for TLR3. Unlike TLR3<sup>−/−</sup> mice, viral replication was significantly greater in TRIF<sup>−/−</sup> mice relative to wild-type animals. These data suggested the possibility that protective effects of TRIF against vaccinia infection were mediated through TLR4. Besides TLR3, TLR4 is the only other TLR known to use TRIF as a signaling adaptor. Although TLR4 canonically recognizes bacterial LPS, this receptor also has been implicated in host defense against some viruses. For example, TLR4 is reported to recognize respiratory syncytial virus (RSV) protein F or vesicular stomatitis virus (VSV) glycoprotein G, thereby initiating protective innate immune responses ##REF##17292937##[11]##,##REF##11062499##[16]##.</p>", "<p>Unlike TLR3, TLR4 does not rely on TRIF exclusively, but also can signal through the adaptor myeloid differentiation factor 88 (MyD88). We hypothesized that in TLR3<sup>−/−</sup> mice, the normal inflammatory response was attenuated sufficiently to minimize injury to the host while still eliminating vaccinia virus. In TRIF<sup>−/−</sup> mice, we reasoned that signaling inputs from TLR3 and TLR4 were both blocked, thus decreasing the inflammatory response to such a degree that the host was not able to make an effective defense against the virus. Consistent with this hypothesis, we demonstrated a protective effect for TLR4 in pulmonary vaccinia infection. Mice with an inactivating mutation in TLR4 suffered increased mortality, more severe hypothermia, and increased viral replication in the head, chest, and abdomen. Further investigation into the mechanism of this protection, however, revealed a more complicated picture.</p>", "<p>The TLR4 signaling pathway results in activation of NF-κB and interferon regulatory factor 3, suggesting that TLR4-deficient mice would have increased viral replication because of impaired cytokine production and recruitment of immune cells to the lung and other sites of infection. However, levels of TNF-α, MCP-1, IL-6, and IFN-β in TLR4 mutant mice were equal to or even greater than those of the controls. Moreover, histological examination of infected lungs showed that TLR4 mutant mice had significantly more foci of inflammation in their lungs than did controls as early as day 3 post-infection. The results suggest either that TLR4 does not function in these aspects of host immunity to vaccinia virus or that other pattern recognition receptors compensate for loss of TLR4. For example, recent studies suggest protective functions of TLR2 and TLR9 in poxvirus infection ##REF##16973959##[28]##,##UREF##0##[29]##. The fact that the TLR4 mutant mice are still more susceptible to disease indicates that other pattern recognition receptors are not fully redundant to TLR4 in poxvirus infection.</p>", "<p>Immunohistochemical staining revealed vaccinia infection predominantly in bronchiolar epithelium with lesser amounts of viral GFP in alveolar epithelial cells. These data are consistent with previous studies showing that respiratory infection with poxviruses causes a necrotizing bronchopneumonia ##REF##11742030##[30]##. We also identified viral GFP antigen in immune cells in the lung, likely macrophages. Previous studies suggest that monocyte/ macrophage cell types are responsible for systemic spread of poxviruses ##REF##11742030##[30]##. While we cannot exclude the possibility that GFP is present in these cells because of phagocytosis rather than infection, our data are compatible with a model in which cells in the monocyte lineage are responsible for systemic dissemination of virus. The observation that both genotypes exhibited a similar repertoire of infected cells suggests that a difference in susceptibility of specific cell types does not account for increased susceptibility of the TLR4 mutant mice.</p>", "<p>Increased IL-6 and TNF-α levels in TLR4 mutant macrophages treated with UV-inactivated virus show that viral replication and cell damage are dispensable for TLR4 recognition of vaccinia. This suggests that TLR4 recognizes a component of the viral particle rather than a host ligand. In our model, the ligand recognized by TLR4 likely would be located in/on the intracellular mature virion (IMV) particle, the predominant form of virus isolated by standard purification procedures such as those used in this research. TLR4 predominantly localizes to the cell membrane, so candidate TLR4 ligands likely would be on the surface of the intracellular mature virion. However, crosslinking DNA in the viral genome with UV/psoralen treatment does not prevent vaccinia from entering the cell and uncoating, so the TLR4 ligand also could be a capsid protein or another protein present in the viral particle.</p>", "<p>Increased inflammation in TLR4 mutant mice may be secondary to increased viral burden or a primary effect of the loss of TLR4. Because TLR4 mutant macrophages secrete increased levels of IL-6 and TNF-α even when challenged with UV-inactivated virus, we propose that lack of TLR4 signaling causes increased inflammation. This interpretation also is supported by our data showing equal viral titers in TLR4 mutant and control macrophage cultures infected with live virus despite the higher cytokine levels in TLR4 mutant cell cultures. Consistent with our observations in TLR3<sup>−/−</sup> mice, TLR4 may provide its protection by dampening the inflammatory response elicited in response to vaccinia infection.</p>", "<p>In conclusion, this study demonstrates that TLR4 mediates a protective immune response to vaccinia virus. To our knowledge, it is the first to demonstrate such a role in the context of vaccinia infection, adding to a growing body of literature showing that TLR4 may respond to non-bacterial ligands and mediate protective effects against viruses. These data also highlight the complexity of TLR signaling in vivo in determining overall outcomes of infection. As the TLR4 mutant mice had equal or greater levels of interferon and proinflammatory cytokines in their lungs, we cannot attribute their increased susceptibility to impairment of TLR4-dependent interferon or cytokine production. Protective effects of TLR4 also cannot be attributed to altered or impaired effector cell recruitment or to increased susceptibility of a specific lung cell population to vaccinia infection. However, TLR4 differentially activates an aspect(s) of antiviral defense that is essential for early control of vaccinia replication and spread. Understanding details of this differential regulation will reveal strategies to enhance beneficial immunity to poxviruses and suppress detrimental host responses.</p>" ]
[]
[ "<p>Conceived and designed the experiments: MAH KEL JS JLC GDL. Performed the experiments: MAH JS GDL. Analyzed the data: MAH KEL JS GN JLC GDL. Contributed reagents/materials/analysis tools: MAH GDL. Wrote the paper: MAH JLC GDL.</p>", "<p>Innate immune responses are essential for controlling poxvirus infection. The threat of a bioterrorist attack using <italic>Variola major</italic>, the smallpox virus, or zoonotic transmission of other poxviruses has renewed interest in understanding interactions between these viruses and their hosts. We recently determined that TLR3 regulates a detrimental innate immune response that enhances replication, morbidity, and mortality in mice in response to vaccinia virus, a model pathogen for studies of poxviruses. To further investigate Toll-like receptor signaling in vaccinia infection, we first focused on TRIF, the only known adapter protein for TLR3. Unexpectedly, bioluminescence imaging showed that mice lacking TRIF are more susceptible to vaccinia infection than wild-type mice. We then focused on TLR4, the other Toll-like receptor that signals through TRIF. Following respiratory infection with vaccinia, mice lacking TLR4 signaling had greater viral replication, hypothermia, and mortality than control animals. The mechanism of TLR4-mediated protection was not due to increased release of proinflammatory cytokines or changes in total numbers of immune cells recruited to the lung. Challenge of primary bone marrow macrophages isolated from TLR4 mutant and control mice suggested that TLR4 recognizes a viral ligand rather than an endogenous ligand. These data establish that TLR4 mediates a protective innate immune response against vaccinia virus, which informs development of new vaccines and therapeutic agents targeted against poxviruses.</p>", "<title>Author Summary</title>", "<p>Toll-like receptors are a class of transmembrane proteins that detect the presence of infectious organisms and activate host innate and adaptive immune responses. Vaccinia virus is the prototypic poxvirus, and it is used as both a model and a vaccine for the virus that causes smallpox. We recently reported that Toll-like receptor 3 (TLR3), which recognizes double-stranded RNA, acts in vaccinia infection in a way that is detrimental to the host. TLR3 relays its signal to the nucleus using the adaptor protein TRIF. In this paper, we report that mice lacking TRIF are more susceptible to vaccinia infection than wild-type controls. TLR4 also uses TRIF to relay its signals. We report our findings that TLR4 has a protective effect in vaccinia infection. Mice with a nonfunctional mutant version of TLR4 are more susceptible to vaccinia infection than wild-type controls. The protection that TLR4 affords is not due to effects on secretion of proinflammatory cytokines or type I interferon, and the receptor also does not uniquely regulate recruitment of white blood cells to the site of infection. Rather, TLR4 recognizes a molecule in or on vaccinia virus to bring about a protective response that may be due to an ability to diminish the degree of inflammation caused by vaccinia infection.</p>" ]
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[ "<fig id=\"ppat-1000153-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g001</object-id><label>Figure 1</label><caption><title>TRIF<sup>−/−</sup> mice are more susceptible to vaccinia infection than WT.</title><p>TRIF<sup>−/−</sup> and WT BL/6 mice were infected with 1×10<sup>4</sup> pfu Vac-FL. (A) Weight loss, expressed as percent of initial weight. *<italic>p</italic>&lt;0.05. (B) Chest luminescence, expressed as photon flux. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g002</object-id><label>Figure 2</label><caption><title>TLR4 mutant mice are more susceptible to vaccinia than controls.</title><p>C3HeB/FeJ and C3H/HeJ mice were infected with 1×10<sup>4</sup> pfu Vac-GFL. (A) Body temperature. (B) Weight loss, expressed as percent of initial weight; (C) Chest luminescence. (D) Abdominal luminescence, expressed as photon flux. *<italic>p</italic>&lt;0.05. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g003</object-id><label>Figure 3</label><caption><title>Increased susceptibility of TLR4 mutant mice is more pronounced at higher viral dose.</title><p>C3HeB/FeJ and C3H/HeJ mice were infected with 5×10<sup>5</sup> pfu Vac-GFL intranasally. (A) Survival curve, expressed as percentage of mice surviving. (B) Body temperature. (C) Weight loss, expressed as percentage of initial weight. *<italic>p</italic>&lt;0.05. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g004</object-id><label>Figure 4</label><caption><title>Increased viral replication in TLR4 mutant mice is more pronounced at higher viral dose.</title><p>C3HeB/FeJ and C3H/HeJ mice were infected with 5×10<sup>5</sup> pfu Vac-GFL intranasally. (A) Head luminescence. (B) Chest luminescence. (C) Representative chest images. C3HeB/FeJ (left) and C3H/HeJ (right), 30 s exposure, f-stop 1. Purple denotes lower luminescence intensity; red, higher luminescence intensity.*<italic>p</italic>&lt;0.05. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g005</object-id><label>Figure 5</label><caption><title>Increased viral titers in TLR4 mutant lungs.</title><p>C3HeB/FeJ and C3H/HeJ mice were infected with 5×10<sup>5</sup> pfu Vac-GFL intranasally. Lung viral titer expressed as pfu/mL; lungs harvested day 6 post-infection. *<italic>p</italic>&lt;0.05. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g006</object-id><label>Figure 6</label><caption><title>Increased viral replication in TLR4 mutant abdomens is more pronounced at higher viral dose.</title><p>C3HeB/FeJ and C3H/HeJ mice were infected with 5×10<sup>5</sup> pfu Vac-GFL intranasally. (A) Abdominal luminescence. (B) Representative images of splenic luminescence C3HeB/FeJ (left) and C3H/HeJ (right) mice, 30 s exposure, f-stop 1. Purple denotes lower luminescence intensity; red, higher luminescence intensity.*<italic>p</italic>&lt;0.05. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g007\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g007</object-id><label>Figure 7</label><caption><title>Lack of TLR4 does not impair IFN-β production.</title><p>IFN-β concentrations in lung homogenate supernatants were measured by ELISA. Points represent individual mice. Dashed line represents lower limit of reliable detection on standard curve. Solid lines represent mean IFN-β concentration.</p></caption></fig>", "<fig id=\"ppat-1000153-g008\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g008</object-id><label>Figure 8</label><caption><title>Lack of TLR4 does not impair proinflammatory cytokine production.</title><p>IL-6 levels in the lung homogenate supernatants measured by ELISA. Error bars denote SEM. *<italic>p</italic>&lt;0.05.</p></caption></fig>", "<fig id=\"ppat-1000153-g009\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g009</object-id><label>Figure 9</label><caption><title>TLR4 alters the inflammatory response to vaccinia infection: representative photomicrographs.</title><p>Mice were infected intranasally with 5×10<sup>5</sup> pfu Vac-GFL. Lungs were harvested on days 3 and 5 post-infection, preserved in 10% formalin, paraffin-embedded, and stained with H&amp;E. (A, B) representative sections of C3H/HeJ (A) and C3HeB/FeJ (B) lung tissue obtained on day 3 post-infection. B = bronchiole; V = blood vessel; arrows denote inflammatory foci.</p></caption></fig>", "<fig id=\"ppat-1000153-g010\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g010</object-id><label>Figure 10</label><caption><title>TLR4 alters the inflammatory response to vaccinia infection: quantification of foci of inflammation.</title><p>Data are expressed as number of foci per section. *<italic>p</italic>&lt;0.01. Error bars denote SEM.</p></caption></fig>", "<fig id=\"ppat-1000153-g011\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g011</object-id><label>Figure 11</label><caption><title>Vaccinia localizes to the bronchial and alveolar epithelium.</title><p>Lungs harvested from Vac-GFL-infected mice (5×10<sup>5</sup> pfu) on day 5 post-infection were stained with anti-GFP and counterstained with hematoxylin. (A) Representative section of C3H/HeJ lung, 150×. (B) Representative section of C3HeB/FeJ lung, 150×.</p></caption></fig>", "<fig id=\"ppat-1000153-g012\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.ppat.1000153.g012</object-id><label>Figure 12</label><caption><title>TLR4 recognizes an endogenous ligand and downregulates cytokine secretion.</title><p>Control C3HeB/FeJ and TLR4 mutant C3H/HeJ bone marrow macrophages were treated with live or UV-inactivated Vac-GFL at MOI = 5 (5×10<sup>5</sup> pfu). (A) Viral titers in macrophage cultures treated with live virus. IL-6 (B) and TNF-α (C) concentrations in supernatants measured by ELISA. For all combinations of pairs of points, <italic>p</italic>&lt;0.05 except the pair denoting TNF-α in control cells treated with live vs. UV-inactivated virus at 6 hours post-infection.</p></caption></fig>" ]
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[ "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>This research was supported by R21AI066192 and RO1 HL082480 from the National Institutes of Health (NIH) and Merit Review funds from the Department of Veterans Affairs. Support for imaging experiments was provided by NIH grant R24CA083099 for the University of Michigan Small Animal Imaging Resource.</p></fn></fn-group>" ]
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[]
[{"label": ["29"], "element-citation": ["\n"], "surname": ["Samuelsson", "Hausmann", "Lauterbach", "Schmidt", "Akira"], "given-names": ["C", "J", "H", "M", "S"], "year": ["2008"], "article-title": ["Survival of lethal poxvirus infection in mice depends on TLR9, and therapeutic vaccination provides protection."], "source": ["J Clin Invest [Epub ahead of print]"]}]
{ "acronym": [], "definition": [] }
32
CC BY
no
2022-01-13 03:40:34
PLoS Pathog. 2008 Sep 19; 4(9):e1000153
oa_package/8e/8f/PMC2529451.tar.gz
PMC2529452
18802465
[ "<title>Introduction</title>", "<p>Harlequin ichthyosis (HI, OMIM 242500) is a rare and devastating congenital disorder characterised by premature delivery and thick, hyperkeratotic, ‘armour’-like skin plaques. This immobile skin or ‘collodion membrane’ constricts the embryo causing odema, limb contractures and eversion of the eyelids and lips. Despite the provision of neonatal intensive care to ameliorate dehydration and the application of high-dose retinoid therapy ##REF##11679007##[1]##, many infants die from respiratory distress, bacterial infections and feeding difficulties ##REF##15996518##[2]##. In surviving patients, the skin barrier dysfunction remains, leading to excessive transepidermal water loss, impairment of thermal regulation and an increased risk of cutaneous infection. The gross phenotypic and barrier defects in HI are thought to primarily result from abnormal lipid metabolism in the epidermis.</p>", "<p>In mammalian skin the outer layer, or stratum corneum, maintains barrier function. Within this layer, corneocytes are embedded in a lamellar intercellular lipid complex of cholesterol, phospholipids and ceramides. Small, specialised vesicular structures known as lamellar bodies (LBs) are thought to traffic many of these components to the surface of differentiating keratinocytes ##UREF##0##[3]##. Ceramides contribute to both lamellar extracellular lipids and to a covalently attached lipid layer known as the corneocyte lipid envelope (CLE) ##REF##3790600##[4]##. They are derived primarily from the conversion of glucosylceramides through the action of β-glucocerebrosidase ##REF##1594045##[5]## and to a lesser extend by the conversion of sphingomyelin by sphingomyelinase ##REF##10951284##[6]##. Most ceramide processing in the stratum corneum is thought to occur extracellularly after docking of the LBs with the cell surface, however significant levels of glucosylceramides and ceramides are found within the cell and in other layers of the epidermis ##REF##11710923##[7]##.</p>", "<p>Two independent studies have established that mutations in the <italic><underline>A</underline>TP <underline>b</underline>inding <underline>c</underline>assette A12</italic> (<italic>ABCA12</italic>) gene cause HI ##REF##16007253##[8]##,##REF##15756637##[9]##. The ABC proteins are thought to act primarily as transporters of molecules across cellular membranes and like other family members <italic>ABCA12</italic> encodes a polytopic transmembrane (TM) protein comprising at least 12 TM domains and 2 ATP binding cassettes. Mutations in <italic>ABCA12</italic> are also associated with a less severe disease known as lamellar ichthyosis-2 (LI2, OMIM 601277)##REF##12915478##[10]##. Initial studies of these conditions indicate that LI2 is caused by missense, potentially hypomorphic, mutations in or near the first ATP binding domain (NBD1) whereas HI is associated with mutations that either abolish ABCA12 protein production or produce a protein with severely impaired function ##REF##16007253##[8]##–##REF##12915478##[10]##.</p>", "<p>The co-localisation of ABCA12 with LBs ##REF##17927575##[11]##, the common malformation of these organelles in HI ##REF##16847209##[12]##, the mis-localisation of glucosylceramide in HI keratinocytes and the correction of this abnormality by ABCA12 expression ##REF##16007253##[8]## present prima facie evidence that the protein plays an active role in trafficking lipids into LBs. More specifically, the abnormal LBs in HI granular layer keratinocytes and lack of extra-cellular lipid lamellae in patients imply that lipid transport to the intercellular lamella is disrupted. Despite these observations the nature and scope of Abca12's involvement in lipid homeostasis remain unclear. Several of the 48 member ABC protein family are known to play critical roles in controlling lipid levels, primarily by mediating their efflux from the cell. Cholesterol metabolism is perhaps the best studied of these pathways, as defects in clearance of cholesterol from vascular cells constitute a key element in the development of atherosclerosis. ABCA1, in particular, is considered the primary mediator of cholesterol efflux and mutations in the gene are associated with reduced cholesterol efflux and absent reverse cholesterol transport in both humans (Tangier disease) and in animal models ##REF##10431237##[13]##–##REF##10431238##[15]##.</p>", "<p>Genetic studies in the mouse have proven to be a very powerful approach to understanding human diseases that affect embryonic development. We have undertaken a genotype driven ENU screen which identifies pedigrees in which mice die embryonically or neonatally, irrespective of the cause or timing of death, and simultaneously maps the causative mutations within the genome. Using this strategy we have identified a pedigree carrying a mutation in one of the transmembrane domains of Abca12. Pups homozygous for the mutation die shortly after birth and show hallmarks of HI including hyperkeratosis, abnormal extracellular lipid lamellae and defects in cornified envelope processing. We have used this model to follow disease progression in utero and we report profound defects in lipid homeostasis demonstrating the extent to which Abca12 plays a pivotal role in maintaining the skin's lipid balance. Our study identifies Abca12 as a key regulator of lipid transport and homeostasis, and describes specific lipid efflux functions, including that of cholesterol, with broader implications for other lipid-related metabolic disorders.</p>" ]
[ "<title>Materials and Methods</title>", "<title>ENU Mutagenesis Screen</title>", "<p>Male 129/Sv mice were injected with a total dose of 200–400 mg/kg of N-Ethyl-N-Nitrosourea (ENU) in 3 weekly doses. ENU-treated males were mated with a C57BL/6 female and male G<sub>1</sub> mice were crossed to C57BL/6 females. A G<sub>2</sub> daughter was then backcrossed to her G<sub>1</sub> father to produce G<sub>3</sub> progeny for typing using 139 polymorphic markers spaced evenly throughout the genome ##REF##1353738##[46]##. Genotyping the G<sub>2</sub> female allowed us to identify those markers that were heterozygous and hence informative in the final screen of G<sub>3</sub> mice. Embryonic lethal mutations therefore manifest as a reduction in the expected frequency of 129/Sv homozygosity in adult animals. Those pedigrees in which no 129/Sv homozygosity of an informative SSLP was observed in G<sub>3</sub> mice at weaning were recovered by performing IVF using cryopreserved G<sub>1</sub> male sperm and eggs from C57BL/6 females. G<sub>2</sub> mice heterozygous for the region of interest were then inter-crossed and their progeny analysed to determine whether 129/Sv homozygosity of linked markers was also absent in the second cohort. In these cases, the location of the embryonic lethal mutation was refined by genotyping key recombinants with additional polymorphic markers. Genomic DNA was extracted from tail biopsies and subjected to PCR amplification with oligonucleotide primers designed using the GABOS/GAFEP program (<ext-link ext-link-type=\"uri\" xlink:href=\"http://bioinf.wehi.edu.au/gabos/index.php\">http://bioinf.wehi.edu.au/gabos/index.php</ext-link>). To remove primers and unincorporated nucleotides, post-PCR reactions were treated with ExoSAP-IT (USB) according to the manufacturer's instructions and filtered through Sephadex columns. Amplicons were then sequenced directly using BigDye Terminator v3.0 (Applied Biosystems).</p>", "<title>TEWL and Skin Permeability Assays</title>", "<p>Assays of epidermal barrier function were performed essentially as previously described ##REF##9502735##[17]##. Gravimetric TEWL assays were performed using skin samples excised from the lateral thoracolumbar region of E18.5 embryos. Embryos and skin were photographed with a Zeiss Axiocam camera mounted on a Zeiss Stemi microscope. Comparison of TEWL was made using logistic regression models. All other statistical analyses were performed using the statistical software package STATA Version 7 (Stata Corporation USA).</p>", "<title>Cornified Envelope Preparations</title>", "<p>Cornified envelopes and epidermal protein samples were prepared as previously described ##REF##11564887##[47]##,##REF##2579164##[48]##. Size calculations were performed using the Image J software package (NIH).</p>", "<title>Immunohistochemistry</title>", "<p>Nile Red staining was performed as previously described ##REF##11741264##[49]##. IHC and IF were performed on citrate antigen retrieved paraffin embedded tissues or on frozen sections. Antibodies used were: rabbit anti-cytokeratin 14, -cytotkeratin 10, -cytokeratin 6, -filaggrin and -loricrin (Covance, 1∶1000); mouse anti-keratin 14 (1∶200, LL002, gift from Fiona Watt); goat anti-Abca12 (Santa Cruz Biotechnology, 1∶50). Secondary antibodies were from Molecular Probes. Samples were imaged by epifluorescence on an Olympus Provis AX70 or by confocal, using a Lecia SPE microscope. Cell proliferation and differentiation were assayed by counting phospho-histone H3<sup>+</sup> and K14<sup>+</sup>/K10<sup>+</sup> positive basal interfollicular keratinocytes in multiple fields under 20× magnification (n = 15 and 7 respectively).</p>", "<title>Transmission Electron Microscopy</title>", "<p>Mid-dorsum skin was processed for EM as described ##REF##1991982##[20]## with minor modifications. Following fixation and cryoprotection, samples were OCT embedded, frozen on dry ice and 40 µm sections cut using a Leica CM 3050 S cryostat. Washed samples were post-fixed with 0.2% ruthenium tetroxide (Polysciences, USA), 0.5% potassium ferrocyanide in 0.1 M sodium cacodylate, pH 7.4 in the dark for 60 min. Following rinsing in water, samples were dehydrated in an alcohol series and embedded in Spurrs resin. Sections were cut using a Leica Ultracut S ultra-microtome, mounted on copper grids and contrasted with methanolic uranyl acetate and aqueous lead citrate before imaging in a JEOL 1011 TEM with a MegaView III CCD cooled digital camera (Soft Imaging Systems, Germany).</p>", "<title>Whole Epidermis Lipid Analysis</title>", "<p>All solvents were of HPLC grade and were used without further purification. N-Palmitoyl-<italic>d3</italic>-glucosylceramide (GC 16:0(<italic>d3</italic>)) and N-palmitoyl-<italic>d3</italic>-lactosylceramide (LC 16:0(<italic>d3</italic>)) were from Matreya Inc. (Pleasant Gap, USA). Sphingosine (Sph, 17:1 base), ceramide (Cer) 17:0, sphingomyelin (SM)16:0<italic>(d31)</italic> and phosphatidylcholine (PC) 17:0/17:0 were from Avanti Polar Lipids (Alabaster, USA), Cholesteryl ester (CE) 17:0 was from Mp Biomedicals (Seven Hills, NSW, Australia). Lipid analysis was performed independently on 7 <italic>Abca12<sup>+/+</sup></italic>, 8 <italic>Abca12<sup>el12/+</sup></italic> and 6 <italic>Abca12<sup>el12/el12</sup></italic> embryos. E18.5 fetus skins were incubated in phosphate buffered saline containing 5 mM EDTA for 1 h at 37°C. The epidermal layer was then peeled from the skin with tweezers, weighed and homogenized in 1.0 ml of PBS using a dounce homogenizer. Protein determination was performed using the Micro BCA Protein Assay Kit (Pierce, Rockford, Il, USA). Total cholesterol was determined using the Amplex Red Cholesterol Assay Kit (Invitrogen, Mount Waverly, Vic, Australia). Total lipids were extracted from tissue homogenates (100 µL containing approximately 100 µg protein) according to established methods ##REF##13428781##[50]##, incorporating 400 pmol of each of the following internal standards: GC 16:0(<italic>d3</italic>), LC 16:0(<italic>d3</italic>) Sph (17:1 base), Cer 17:0, SM16:0<italic>(d31)</italic>, PC 14:0/14:0 and CE 17:0. Lipid extracts were reconstituted in 200 µL 10 mM, NH<sub>4</sub>COOH in methanol. Lipid analysis was performed by liquid chromatography, electrospray ionisation-tandem mass spectrometry (LC ESI-MS/MS) using a HP 1100 liquid chromatography system combined with a PE Sciex API 2000 Q/TRAP mass spectrometer with a turbo-ionspray source (250°C) and Analyst 1.4.2 data system. LC separation of lipids was performed on an Alltima C18, 3 um, 50×2.1 mm column using the following gradient conditions; 70% A reducing to 0% A over three minutes followed by 5 minutes at 0% A, a return to 70% A over 0.1 minute then 1.9 minutes at 70% A prior to the next injection. Solvent A and B consisted of tetrahydrofuran∶methanol∶water in the ratios (30∶20∶50) and (70∶20∶10) respectively, both containing 10 mM NH<sub>4</sub>COOH. Quantification of individual species of Sph, Cer, GC, LC, SM, PC and CE was performed using multiple-reaction monitoring (MRM) in positive ion mode. MRM product ions used were <italic>m/z</italic> 264 [sphingosine–H<sub>2</sub>O]<sup>+</sup> for sphingosine, Cer, GC and DHC, <italic>m/z</italic> 184 [phosphocholine]<sup>+</sup> for SM, PC and <italic>m/z</italic> 369 [cholesterol-H<sub>2</sub>O]<sup>+</sup> for CE. Each ion pair was monitored for 50 ms with a resolution of 0.7 amu at half-peak height and averaged from continuous scans over the elution period. Lipid concentrations were calculated by relating the peak area of each species to the peak area of the corresponding internal standard.</p>", "<title>Isolation of Embryonic Skin Fibroblasts</title>", "<p>The skin was separated from the mouse embryos (last week of gestation). Skin tissue was finely minced, resuspended in 0.05% Trypsin/EDTA solution, incubated for 30 min at 37°C, vigorously shaken, incubated for another 10 min at 37°C and neutralized with medium containing 10% FBS. Cells were seeded and incubated overnight in CO<sub>2</sub> incubator before unattached cells and debris were washed out.</p>", "<title>qPCR Analysis of Abca1 Expression</title>", "<p>Quantitative expression of <italic>Abca1</italic> was determined by qPCR from cDNA transcribed from Trizol prepared sample total RNA and amplification using SYBR GreenER PCR mix (Invitrogen) by primer sequences previously optimized for this approach ##REF##11279093##[51]##. Assays were performed in triplicate and standardized to an internal 18S rRNA control.</p>", "<title>Lipid Efflux Assays</title>", "<p>Human HDL (1.085&lt;d&lt;1.21) and apoA-I were isolated from pooled normolipidemic human plasma supplied by Red Cross as described previously ##REF##8969186##[52]##. LDL was purified from human plasma by sequential centrifugation and acetylated as described by Basu et al. ##REF##184464##[53]##. Cholesterol and phospholipid efflux were assessed as described previously ##REF##11060357##[54]##. Briefly, fibroblasts were incubated in labeling medium containing [<sup>3</sup>H]cholesterol (75 kBq/ml) or [methyl- <sup>14</sup>C] choline (0.2 MBq/ml) for 48 hours. Cells were then incubated for 18 hr in serum-free medium in the presence or absence of the LXR agonist TO-901317 (final concentration 4 µM) to stimulate expression of ABC transporters and cholesterol efflux. Cells were then washed with PBS and incubated for 2 h in either serum-free medium alone (blank) or in serum-free medium supplemented with 30 µg/ml of lipid-free apoA-I. For cholesterol efflux analysis, aliquots of medium and cells were counted. For phospholipid efflux lipids were extracted from cells and medium ##REF##13428781##[50]## and counted. The efflux was calculated as radioactivity in the medium/(radioactivity in the medium+radioactivity remaining in the cells)×100%. Non-specific efflux (i.e. the efflux in the absence of acceptor) was subtracted.</p>", "<title>Oil Red O Staining</title>", "<p>Cells were incubated in the presence of TO-901317 (final concentration 4 µM) and in the presence or absence of AcLDL (10 µg/ml) in serum-containing medium for 18 hrs. After washing with PBS, cells were fixed in 3.7% formaldehyde for 2 min, washed with water, and incubated at room temperature for 1 h with Oil Red O working solution (Fisher Biotech).</p>" ]
[ "<title>Results</title>", "<title>A Novel Recessive ENU Mutagenesis Screen Identifies an Animal Model of HI</title>", "<p>Mutations that cause recessive lethality in embryos or neonates (and markers to which they are closely linked) are homozygous at reduced frequency among adults. This banality formed the basis of a genetic screen to identify genes required for mouse development (##FIG##0##Figure 1A##). Briefly, 129/Sv male mice were injected with ENU and mated to C57BL/6 females. Their first-generation (G<sub>1</sub>) male progeny were again crossed to C57BL/6 females, and then backcrossed to one of their second-generation (G<sub>2</sub>) daughters to yield a third-generation (G<sub>3</sub>). For those pedigrees in which 20 or more G<sub>3</sub> mice were generated, the sperm of the founding G<sub>1</sub> mouse was frozen. Adult G<sub>3</sub> mice were genotyped with a panel of simple sequence length polymorphic markers and regions in which no animals showed homozygosity of the 129/SV alleles, despite both parents being heterozygous, were highlighted as being linked to a potential recessive lethal ENU-induced mutation. The presence of recessive lethal mutation was then confirmed by generating and genotyping a second cohort of G<sub>3</sub> animals from the frozen sperm of the founding G<sub>1</sub> male.</p>", "<p>In our initial screen, we set up 40 G<sub>1</sub> male mice to breed and generated 18 pedigrees that contained more than 20 G<sub>3</sub> mice. To prove the principle of the approach, we have proceeded with one pedigree, <italic><underline>E</underline>mbryonic <underline>L</underline>ethal 12</italic> (EL12). In this pedigree, we found 129/SV alleles that were absent in all of the adult G<sub>3</sub> mice. Notably, among 34 G<sub>3</sub> EL12 mice, we observed none that were homozygous for the129/Sv allele of <italic>D1Mit156</italic>, even though both parents were heterozygous for the 129/Sv allele of this marker. This was confirmed in a second cohort of 31 G<sub>3</sub> mice. Using a total of 463 mice and 13 polymorphic markers, we refined the interval harboring the lethal mutation to 4.7 Mb between D1Mit178 and D1Mit482 (##FIG##0##Figure 1B##). We sequenced the exons and intron/exon boundaries of the 13 genes in the candidate interval and found a single G to A transition of exon 41 of <italic>Abca12</italic> (##FIG##0##Figure 1C##). Abca12 is a member of the ABC transporter family of proteins, and the mutant allele (<italic>Abca12<sup>el12</sup></italic>) results in a point mutation (G1997D) in the first helix of the protein's second transmembrane array (##FIG##0##Figure 1D##) which is highly conserved in a diverse range of organisms (##FIG##0##Figure 1E##).</p>", "<title>\n<italic>Abca12<sup>el12//el12</sup></italic> Mice Die Neonatally</title>", "<p>Consistent with the results of the genetic screen, at weaning no <italic>Abca12<sup>el12/el12</sup></italic> mice were detected from heterozygous crosses however examination of litters at E18.5 found normal mendelian ratios of viable but phenotypically abnormal <italic>Abca12<sup>el12/el12</sup></italic> embryos (n = 17/57 embryos). <italic>Abca12<sup>el12/el12</sup></italic> pups were occasionally found in the first few hours after birth but were often dead or severely dehydrated and had failed to suckle normally. Recent studies by Yanagi et al., indicate a role for Abca12 in lung development and defects in this organ may contribute to neonatal death ##UREF##1##[16]##. To follow the development of the phenotype we examined cohorts of embryos from various developmental stages. At E14.5 and E15.5 homozygous embryos appeared normal; however from E16.5 onwards they were characterised by an absence of normal skin folds around the trunk and limbs. As development progressed, <italic>Abca12<sup>el12/el12</sup></italic> embryos developed a taut, thick epidermis and multiple contractures affecting the limbs (##FIG##1##Figure 2A##,##FIG##2##3A##). Late stage <italic>Abca12<sup>el12/el12</sup></italic> embryos were also found to be smaller than their wild type or heterozygous littermates (##FIG##1##Figure 2A##, ##FIG##2##3A##), a phenotype we assayed in newborn mice (p = 0.0023, data not shown). Skin sections from affected embryos revealed a hyperkeratotic phenotype from E16, and confirmed the absence of normal folding (##FIG##1##Figure 2B##).</p>", "<p>Histologically all epidermal cell layers were apparent in <italic>Abca12<sup>el12/e1l2</sup></italic> embryos, although the size of the granular layer progressively increased at the expense of the spinous layer (##FIG##1##Figure 2B##, ##FIG##4##5A##). By parturition the cornified layers had coalesced into thick sheets of 20–30 enucleate corneocytes. The basal layer in <italic>Abca12<sup>el12/el12</sup></italic> mice also lost the dense palisaded nuclear organisation apparent in wild type and <italic>Abca12<sup>el12/+</sup></italic> mice (##FIG##1##Figures 2B##, ##FIG##3##4A##). Consistent with the apparently restrictive nature of the cornified layer, the epidermis as a whole was 30% thinner at E17.5 and P1 in <italic>Abca12<sup>el12/el12</sup></italic> animals (data not shown). Despite this constriction, hair follicles formed and differentiated relatively normally (##FIG##1##Figure 2B##, ##FIG##2##3F##) and complete histological examination of E18.5 embryos did not identify overt anomalies in other organs. Adult and embryonic <italic>Abca12<sup>el12/+</sup></italic> mice had no overt phenotype, no obvious histological abnormalities and were healthy and fertile.</p>", "<title>\n<italic>Abca12<sup>el12/el12</sup></italic> Mice Have a Defect in Skin Barrier Function</title>", "<p>As the <italic>Abca12<sup>el12/el12</sup></italic> mice apparently died from dehydration, we tested skin barrier function which normally initiates in the mouse from E16, and acquires almost full adult function by E18.5 ##REF##9502735##[17]##. We measured permeability of E18.5 embryos against the dye toluidine blue and found that <italic>Abca12<sup>el12/el12</sup></italic> embryos had uniform absence of barrier function (##FIG##2##Figure 3A##). To determine if this defect contributed to the dehydration observed in homozygous animals we harvested the dorsal epidermis from E18.5 embryos and measured the ability of the skin to retain water using a trans-epidermal water loss (TEWL) assay over a 5 hour time course. A significant difference in TEWL from <italic>Abca12<sup>el12/el12</sup></italic> embryos was observed as early as 60 minutes (##FIG##2##Figure 3B##), confirming that mutations in <italic>Abca12</italic> in mice also lead to the defects in barrier formation that are observed in HI patients.</p>", "<title>Defects in the Cornified Envelope of <italic>Abca12<sup>el12/el12</sup></italic> Mice</title>", "<p>HI patients develop a thick armour like stratum corneum and a suite of defects in the biochemistry of this layer. To examine the stratum corneum we harvested cornified envelopes (CE) from E18.5 embryonic skin. In wild type mice, large squames were present in expected numbers whereas <italic>Abca12<sup>el12/el12</sup></italic> mice were found to have sparse CEs which were both small and unable to structurally withstand the purification procedure (##FIG##2##Figure 3C,D##). While the levels of filaggrin in the epidermis of E18.5 <italic>Abca12<sup>el12/el12</sup></italic> mice were slightly increased, its processing into a functional 27 kDa monomer was ablated (##FIG##2##Figure 3E##) as has previously been observed in HI patients ##REF##1688598##[18]##, indicating that normal LB and Abca12 function is required for this process. The level of other barrier proteins such as loricrin was unaffected (##FIG##2##Figure 3E##, data not shown). While keratin VI expression in interfollicular keratinocytes has been noted in some studies of HI skin ##REF##1688598##[18]##, no aberrant expression of this hyperproliferative marker was detected in <italic>Abca12<sup>el12/el12</sup></italic> mice (##FIG##2##Figure 3F##). These observations suggest that the hyperkeratotic phenotype in these animals is not a result of increased cell proliferation in the basal epidermal layer. To confirm these findings we surveyed cell proliferation and apoptosis from E17.5 to P1 (using Ki67, PCNA, phospho-histone H3 and TUNEL staining) and found no significant differences (##FIG##2##Figure 3G##, data not shown).</p>", "<title>\n<italic>Abca12<sup>el12/el12</sup></italic> Epidermis Undergoes Premature Differentiation</title>", "<p>Many defects of barrier function have profound impacts on the epidermis as a whole. We were able to show by histology that alterations in both nuclear organisation and cellular architecture of the basal cell layer characterises <italic>Abca12<sup>el12/el12</sup></italic> embryos and postpartum epidermis. We investigated expression of markers of basal and differentiating keratinocytes during this period and observed normal levels of Abca12 staining in epidermal cells in the uppermost granular and cornified layers of the epidermis (##FIG##3##Figure 4A##), in a pattern similar to that observed in developing human skin ##REF##17591952##[19]##. Expression of filaggrin, which usually marks the granular layer of the epidermis, was detected in keratinocytes juxtaposed to the basal layer itself and in some cells expressing keratin 14 (##FIG##3##Figure 4B##). Additionally, we demonstrated a significant increase in basal (and spinous) layer keratinocytes dually expressing keratins 10 and 14 (##FIG##3##Figure 4C,D##). These observations indicate that keratinocytes in affected epidermis undergo premature differentiation, either as a result of defects in the cornifying layer which overlies them or as a consequence of defects in the balance of intracellular lipids in these cells.</p>", "<title>\n<italic>Abca12<sup>el12/el12</sup></italic> Keratinocytes Have LB Defects</title>", "<p>Thin sections of affected epidermis highlighted the striking hyperkeratosis in <italic>Abca12<sup>el12/el12</sup></italic> epidermis (##FIG##4##Figure 5A##). To investigate Abca12 mediated alterations in epidermal lipid composition we stained the epidermis with the lipophilic dye Nile Red. <italic>Abca12<sup>el12/el12</sup></italic> mice displayed very little of the normal lipid deposition in intercellular spaces of the cornified envelope (##FIG##4##Figure 5B##). To confirm these effects at an ultrastructural level we performed transmission electron microscopy on epidermal tissue at E18.5, utilising ruthenium tetroxide postfixation of thin epidermal sections ##REF##1991982##[20]## to investigate the lamellar lipids which normally surround cells of the stratum corneum. We demonstrated an absence of these elements in the spaces between the corneocytes and cornified/granular layer (##FIG##4##Figures 5C–F##), although the CLE was still apparent in <italic>Abca12<sup>el12/el12</sup></italic> tissues, an observation previously observed in HI biopsies ##REF##10998161##[21]## (data not shown). The absence, relative scarcity or malformation of LBs is particularly characteristic of HI ##REF##1688598##[18]##, ##REF##426527##[22]##–##REF##1281866##[25]##. While relatively scarce structures resembling LBs were apparent within the granular layers in <italic>Abca12<sup>el12/el12</sup></italic> animals (##FIG##4##Figure 5E,F##), most lacked the multilayered lamellar cargos present in control skin (##FIG##4##Figure 5D##). Fusion of LBs with the surface of the normal granular cells was commonly observed in wild type skin (##FIG##4##Figure 5C##) and occurred occasionally in affected epidermis (##FIG##4##Figure 5F##). Normally, corneocytes are filled with uniformly opaque keratin-filaggrin protein, however in affected epidermis they contained numerous vesicular and lamellar structures (##FIG##4##Figure 5H,I##), defects found in both <italic>in situ</italic> and reconstituted HI epidermis ##REF##17591952##[19]##,##REF##10998161##[21]##,##REF##16675967##[26]##. Whilst our cellular and biochemical analysis suggested that some aspects of cornified cell envelope formation were disrupted in homozygous mice we did not observe overt differences in this structure during our EM studies. Indeed EM studies show that as well as a normally formed cornified envelope (##FIG##4##Figure 5G–I##) there is increased retention of corneodesmosomes in the distal layers of the stratum corneum (##FIG##4##Figure 5G,H##). The persistence of these structures provide a mechanistic basis for the hyperkeratotic phenotype in our animals and serves to explain the relative decrease in extraction of CE's as noted above. The epidermis of the <italic>Abca12</italic> homozygous mice bear many if not all of the features of HI, establishing them as an excellent model in which to study the biochemical basis of this disease.</p>", "<title>Defects in Lipid Homeostasis in <italic>Abca12<sup>el12/el12</sup></italic> Epidermis</title>", "<p>While previous studies of cultured human HI keratinocytes have identified defects in the traffic of glucosylceramides, global analysis of defects in lipid homeostasis, either <italic>in vitro</italic> or <italic>in vivo</italic>, have not been performed. We therefore utilised the <italic>Abca12<sup>el12</sup></italic> model to examine whether defects in lipid homeostasis were apparent in our mice. We harvested whole epidermis from the mid-dorsum of E18.5 homozygous, heterozygous and wild type littermates and empirically assayed for levels of a panel of thirteen different lipid species. Consistent with reports that Abca12 regulates the trafficking of glucosylceramide we detected greater than 2-fold increases in this lipid species in <italic>Abca12<sup>el12/el12</sup></italic> epidermis (##FIG##5##Figure 6A##). We also detected striking increases in the relative levels of all ceramide species in affected versus wild type skin with highest proportional differences in C18, C20 and C22 species, indicating that their transport is also reliant on Abca12 function (##FIG##5##Figure 6A##). Sphingosine, a breakdown product of ceramide, was also markedly increased. This was in spite of the fact that we were unable to resolve intercellular lipid lamellae in the cornified envelope indicating an intracellular build-up of these species. Furthermore, significantly increased levels of cholesterol were observed in the epidermis of <italic>Abca12<sup>el12/el12</sup></italic> mice (##FIG##5##Figure 6A##). No differences were observed in total levels of phosphatidylinositols, phosphatidylethanolamines, phosphatidylcholines, acyl- and lyso-phosphatidylcholines and sphingomyelin, suggesting that the defects apparent in our mice, while more broad and widespread than previously appreciated, were not a consequence of universal dysregulation of lipid homeostasis. Notably, we failed to identify any significant differences in lipid levels in <italic>Abca12<sup>el12/+</sup></italic> skin, confirming the absence of a haplo-insufficient phenotype highlighted by our histological survey.</p>", "<title>Defects in Lipid Efflux in <italic>Abca12<sup>el12/el12</sup></italic> Fibroblasts</title>", "<p>Having established a role for Abca12 in lipid metabolism in keratinocytes we wondered whether the protein might be more widely involved in this process. To assess the generality of the involvement of Abca12 in lipid metabolism, we investigated lipid efflux from <italic>Abca12<sup>el12/el12</sup></italic>, <italic>Abca12<sup>+/el12</sup></italic> and <italic>Abca12<sup>+/+</sup></italic> mouse skin fibroblasts ##REF##17095732##[27]##. To establish the specific involvement of ABC transporters, cholesterol efflux was compared with or without activation of LXR, which greatly increases expression of most ABC transporters including Abca12 ##REF##17611579##[28]##. As expected, in wild type cells activation of LXR resulted in a more than doubling of cholesterol efflux to apolipoprotein A-I (apoA-I) (##FIG##5##Figure 6B##). In <italic>Abca12<sup>+/el12</sup></italic> cells the effect was less pronounced, but there was still a statistically significant increase of the efflux from activated versus non-activated cells. In contrast, in <italic>Abca12<sup>el12/el12</sup></italic> cells activation of LXR did not result in elevation of cholesterol efflux. Specific, ABC-dependent cholesterol efflux (i.e. the difference in the efflux with and without activation) was virtually zero (##FIG##5##Figure 6B##). Current models suggest that the cholesterol efflux to apoA-I is fully controlled by ABCA1 ##REF##17353664##[29]##; however, in <italic>Abca12<sup>el12/el12</sup></italic> cells there was no ABC-dependent efflux to apoA-I despite the animals having functional Abca1. Further, when phospholipid efflux was compared in <italic>Abca12<sup>el12/el12</sup></italic> and <italic>Abca12<sup>+/+</sup></italic> fibroblasts, activation of cells with LXR agonist resulted in a 25% increase in phospholipid efflux in <italic>Abca12<sup>+/+</sup></italic> cells (p&lt;0.05), but no increase in <italic>Abca12<sup>el12/el12</sup></italic> (not shown). To determine whether the loss of Abca12 was affecting the production and abundance of the Abca1 protein in cells from <italic>Abca12<sup>el12/el12</sup></italic> mice we performed western blotting for Abca1. Strikingly, loss of Abca12, even in a heterozygous state, led to concomitant decreases in Abca1 protein, providing a functional link between loss of Abca12 and impairment of cholesterol efflux (##FIG##5##Figure 6C, upper panel##). Analysis of transcription of <italic>Abca1</italic> in these cells highlighted 5 fold less expression in mutant versus wild type fibroblasts but no significant difference between wild type and heterozygotes (##FIG##5##Figure 6C, lower panel##).</p>", "<p>Impairment of cholesterol efflux is a frequent cause of excessive accumulation of neutral lipids in cells, especially when exposed to acetylated low density lipoprotein (AcLDL), a cholesterol donor for poorly regulated cholesterol uptake pathways. We compared accumulation of neutral lipids in <italic>Abca12<sup>el12/el12</sup></italic>, <italic>Abca12<sup>+/el12</sup></italic> and <italic>Abca12<sup>+/+</sup></italic> fibroblasts treated or not treated with AcLDL by staining lipids with Oil Red O. Wild type fibroblasts did not accumulate lipids independently of the presence of AcLDL indicating that lipid homeostasis pathways successfully cope with excessive lipid delivery (##FIG##5##Figure 6D##). <italic>Abca12<sup>+/el12</sup></italic> fibroblasts also did not accumulate lipids in the absence of AcLDL, but there was visible lipid accumulation in the presence of AcLDL. <italic>Abca12<sup>el12/el12</sup></italic>cells accumulated lipids both in the absence and presence of AcLDL, the accumulation being more severe in the presence of AcLDL. Thus, lipid homeostasis is severely impaired in <italic>Abca12<sup>el12/el12</sup></italic> fibroblasts.</p>" ]
[ "<title>Discussion</title>", "<p>Forward genetic screens in mice remain an important source of models of genetic disorders in humans. In this report we have used a forward genetic approach to identify a model of harlequin ichthyosis which has allowed us to characterise Abca12's function as a key regulator of lipid homeostasis and cholesterol transport. Current recessive ENU mutagenesis approaches to identify embryonic lethal mutations in the mouse either require the analysis of large numbers of embryos to identify defects, or the use of mice carrying engineered balancer chromosomal rearrangements tagged with visible phenotypic markers ##REF##15951745##[30]##. While the latter approach can very efficiently identify all the mutations that cause lethality between conception and weaning, and has the advantage of simultaneously isolating and mapping mutations, the genomic region screened is restricted to that delimited by the balancer chromosome. We have developed a simple genome-wide approach which obviates the requirement to dissect embryos and which simultaneously isolates and maps mutations. We inter-crossed two inbred mouse strains, one of which was mutagenised with ENU, established pedigrees from the resultant offspring, and screened these for regions of the genome under-represented for the mutagenised genetic background. As a consequence we simultaneously identified and mapped lethal mutations in an unbiased genome wide manner.</p>", "<p>Using this approach we have isolated a mouse model of Harlequin Ichthyosis, a hyperkeratotic and often lethal disease of the epidermis. We observed many HI features in our <italic>Abca12<sup>el12/el12</sup></italic> mice including severe hyperkeratosis, LB defects, absence of intercellular lamellae, aberrant filaggrin processing, neonatal death, defects in lipid metabolism, congenital contractures and the absence of skin barrier function. Studies of HI pathology suggest that the disease may be grouped into 3 subtypes ##REF##1688598##[18]##. The altered LB structure, absence of keratin VI expression and defects in filaggrin processing indicate that our mutant is equivalent to Type 1 HI proposed by this scheme although recent genotype/phenotype analysis suggests no correlation between mutation and phenotype ##REF##16902423##[31]##. Defects in the CE are characteristic of LI ##REF##8097623##[32]##,##REF##9517915##[33]##, but our EM investigation showed no obvious deficiencies in this structure. A missense mutation similar to that of our mouse (glycine to charged amino acid in a highly conserved TM domain residue) has been shown to cause severe HI ##REF##16902423##[31]##. It is highly unlikely that our model is of LI2, in which missense mutations have only been found within or near the first nucleotide binding domain of the protein ##REF##12915478##[10]##. This, coupled with the severity of disease in our mouse, suggests the G1997D mutation severely affects Abca12 activity although it remains to be determined whether it mis-localises or has altered transport function, as both have been observed in TM mutations in ABC family members causing severe disease ##REF##15158913##[34]##–##REF##11927667##[36]##. The <italic>Abca12<sup>el12/el12</sup></italic> phenotype closely matches a targeted deletion of exon 10 generated by Lexicon Genetics, an allele in which postnatal lethality and absence of heterozygous effects were noted (<italic>Abca12<sup>tm1Lex</sup></italic>, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.informatics.jax.org\">www.informatics.jax.org</ext-link>). However, the Lexicon study undertook no characterisation of homozygous animals beyond noting lethality. A recent similar study by Yanagi et al., demonstrated barrier defects in mice lacking Abca12 and suggest that postnatal death is a result of defects in lung function in newborn animals ##UREF##1##[16]##. Our mouse parallels HI in almost every respect and has allowed us to investigate several aspects of disease which have been impossible in the limited patient samples available.</p>", "<p>We first examined the temporal progress of disease. <italic>Abca12<sup>el12/el12</sup></italic> mice displayed severe hyperkeratosis around the time of stratification of the cornified epidermal layer (E16.5). This phenotype increased in severity as development progressed to the point where the epidermis restricts the normal growth of the embryo. <italic>Abca12<sup>el12/el12</sup></italic> skin progressively enters a state of premature differentiation characterised by loss of normal basal cell architecture, mis-expression of differentiated keratins in basal keratinocytes, reduction in the size of the spinous cell layer and expansion of granular layers. We show that the retention of cornified squames in the upper layers of the epidermis which contribute to this restrictive epidermis is not due to hyper-proliferation or alteration in apoptosis. Instead we observe defects in the deposition of extra-cellular lipid lamellae and in proteolytic activity in the epidermis, indicating that the hyperkeratosis in our mice is due to failure to form and shed cornified envelopes from the skin surface. Our EM studies indicated that this retention was in part due to persistence of corneodesmosomes into the distal layers of the epidermis. This retention defect may explain why the CE's isolated directly from the skin surface by detergent extraction were relatively sparse and also exhibit fragility. Our results are consistent with previous studies indicating that the defects in LB loading can result in decreases in co-transport of proteases required for normal desquamation ##REF##10612259##[37]## and which has been suggested as a mechanism by which HI hyperkeratosis might occur ##REF##18245815##[38]##. Our results lend weight to this hypothesis. These defects also contribute to the loss of barrier function of mutant epidermis. As with human HI patients ##REF##1688598##[18]##, defects in proteolytic cleavage of filaggrin characterise the mice. These defects in the proteolytic processing of components are almost certainly reflected in the unusual presence of inclusions and vesicles within the normally uniform cells of the stratum corneum when examined by EM. In addition to these defects in the cornified layer, our observation of differentiation defects in <italic>Abca12<sup>el12/el12</sup></italic> mice indicates that defects in the HI epidermis affect all layers of the skin.</p>", "<p>Insights into the mechanisms by which loss of Abca12 function might affect the skin was revealed by our analysis of lipid species present in the epidermis. Previous studies have shown that Abca12 is important in controlling glucosylceramide trafficking in keratinocytes ##REF##16007253##[8]##, where it localises to the golgi and lamellar bodies ##REF##17927575##[11]##, an observation which correlates well with the striking increase in levels of glucosylceramide in the <italic>Abca12<sup>el12/el2</sup></italic> epidermis. However, our investigations of the <italic>Abca12<sup>el12/el12</sup></italic> mice revealed that defects in lipid homeostasis in the skin extend well beyond glucosylceramide. Despite the absence of intercellular lipid lamellae we detected significant increases in both ceramide and free cholesterol in the epidermis. We propose that increases in ceramide (and indeed sphingosine, a ceramide breakdown product) might reflect continuing unchecked de-novo synthesis and accumulation of this species, because of the absence of an Abca12 mediated trafficking mechanism to remove glucosylceramide from the cell. Cholesterol is also a known cargo of lamellar bodies ##UREF##0##[3]## and its increased concentration in the epidermis probably reflects defects in trafficking of LBs or of a failure of loading this component as a consequence of loss of Abca12. The ratio of ceramide, cholesterol and fatty acids in the epidermis is also a key determinant of barrier function in the skin, and normal LB formation ##REF##8507075##[39]## and induction of the synthesis machinery for these compounds is an early response to compromises in barrier function ##REF##9406821##[40]##,##REF##1940639##[41]##. Consequently the defects in lipid levels in Abca12 mutant skin might actually be exacerbated by these positive feedback loops beyond primary defects related to Abca12 transport dysfunction.</p>", "<p>We find that the effects of Abca12 mutation are not limited to keratinocytes. Skin fibroblasts isolated from mutant mice showed an impairment of their ability to maintain cholesterol efflux to apoA-I proportional to gene dose. Cholesterol efflux to apoA-I is a key pathway responsible for maintaining cellular cholesterol homeostasis and is believed to be fully controlled by another ABC transporter, ABCA1 ##REF##11483617##[42]##. Here we demonstrate that this is not the case, and that Abca12 is also essential for cholesterol efflux to apoA-I. Phospholipid efflux was also impaired, consistent with the currently adopted view of the mechanism of ABCA1-dependent cholesterol efflux ##REF##16798073##[43]##. We demonstrate that in primary cells from <italic>Abca12<sup>el12/el12</sup></italic> mice, loss of Abca12 function results in decreased transcription of <italic>Abca1</italic>. The basis of this association remains unclear but the alteration in transcription may not be the only explanation of significant ablation of ABCA1-dependent cholesterol efflux observed in these cells. This is particularly notable in fibroblasts heterozygous for the el12 mutation, which have normal levels of <italic>Abca1</italic> transcription but which display significant decreases in Abca1 abundance and in efflux of cholesterol to apoA-1. Given that many ABC transporters form homo- or hetero-oligomers and that oligomerisation of Abca1 is important for its function ##REF##15280376##[44]## we speculate that a direct association between Abca12 and Abca1 might additionally be essential for functional stabilization of Abca1 and normal cholesterol efflux to ApoA1. The exact mechanism by which this occurs remains to be determined. Significantly, fibroblasts lack the classic LB organelles observed in keratinocytes. Our results therefore indicate that the Abca12 protein is capable of regulating the accumulation and efflux of lipids without this highly specialized organelle.</p>", "<p>Impairment of cholesterol efflux led to intracellular accumulation of neutral lipids, most likely cholesteryl esters; in <italic>Abca12<sup>el12/el12</sup></italic> cells. These lipids accumulated even in the absence of a challenge with extra-cellular lipid delivery through AcLDL. This finding has implications for another important pathology, atherosclerosis. Accumulation of cholesterol is a crucial element of the pathogenesis of atherosclerosis and impairment of cholesterol efflux, especially against a background of hypercholesterolemia, is a key contributor to the risk of atherosclerosis and coronary heart disease. Polymorphisms in <italic>ABCA1</italic> are one of the strongest factors affecting plasma levels of high density lipoprotein ##REF##17353664##[29]## and risk of cardiovascular disease ##REF##12763760##[45]##. Our findings suggest that Abca12 is also required for the cholesterol efflux pathway to function and therefore should be taken into account when investigating mechanisms of atherosclerosis or considering targets for its treatment. <italic>Abca12</italic> is expressed in primary macrophages at levels approximately 10 fold greater than the fibroblasts in which we have demonstrated cholesterol efflux defects in this study (unpublished observations). The severity and rarity of HI have precluded studies addressing associations between HI and heart disease, but the availability of this mouse model will allow us to investigate this relationship.</p>", "<p>We have detailed a genetic screening protocol which concurrently identifies and maps postnatal or embryonic lethal mutations in an unbiased genome wide manner. This approach has allowed us to characterise an animal model of HI, providing a unique avenue by which to pursue therapeutic interventions for this and other ichthyoses. Our results demonstrate that HI should be viewed as a disease in which defects in Abca12 function lead to profound dysregulation of lipid metabolism in the epidermis. Furthermore we show in fibroblasts that the protein is a key regulator of cholesterol efflux, an observation with direct relevance to other defects of lipid homeostasis, including atherosclerosis.</p>" ]
[]
[ "<p>Conceived and designed the experiments: IS DFH AAH NM PJM DS BTK DJH. Performed the experiments: IS DFH AAH NM PJM SE JEC CAdG BTK. Analyzed the data: IS DFH AAH NM PJM SE KS CAdG MB DS BTK DJH. Contributed reagents/materials/analysis tools: IS DS BTK DJH. Wrote the paper: IS DFH DS BTK DJH.</p>", "<p><bold>¶:</bold> These authors are joint senior authors on this work.</p>", "<p>Harlequin Ichthyosis (HI) is a severe and often lethal hyperkeratotic skin disease caused by mutations in the ABCA12 transport protein. In keratinocytes, ABCA12 is thought to regulate the transfer of lipids into small intracellular trafficking vesicles known as lamellar bodies. However, the nature and scope of this regulation remains unclear. As part of an original recessive mouse ENU mutagenesis screen, we have identified and characterised an animal model of HI and showed that it displays many of the hallmarks of the disease including hyperkeratosis, loss of barrier function, and defects in lipid homeostasis. We have used this model to follow disease progression in utero and present evidence that loss of Abca12 function leads to premature differentiation of basal keratinocytes. A comprehensive analysis of lipid levels in mutant epidermis demonstrated profound defects in lipid homeostasis, illustrating for the first time the extent to which Abca12 plays a pivotal role in maintaining lipid balance in the skin. To further investigate the scope of Abca12's activity, we have utilised cells from the mutant mouse to ascribe direct transport functions to the protein and, in doing so, we demonstrate activities independent of its role in lamellar body function. These cells have severely impaired lipid efflux leading to intracellular accumulation of neutral lipids. Furthermore, we identify Abca12 as a mediator of Abca1-regulated cellular cholesterol efflux, a finding that may have significant implications for other diseases of lipid metabolism and homeostasis, including atherosclerosis.</p>", "<title>Author Summary</title>", "<p>Harlequin Ichthyosis is a severe inherited disease in which the skin develops as thick armour-like plates. While many HI patients die at birth, those who survive are subject to dehydration and infection. The disease is caused by defects in a protein called ABCA12, which is thought to function by transporting lipids within the cells of the skin. Here, we describe a new genetic screen that we have used to identify a mouse model that develops the hallmarks of HI and carries a mutation in Abca12. We have used this model to elucidate Abca12's significant role in the transport of lipids within the skin, and we demonstrate that the loss of these lipids contributes to the dehydration in affected embryos and newborns. We attribute specific transport functions to the protein and show that it can mediate the efflux of a number of different lipids from the cell including, importantly, cholesterol. Cholesterol transport by proteins related to Abca12 plays a critical role in the development of a number of diseases, including heart and peripheral vascular disease, and the description of these functions for Abca12 suggest that it may play a wider role in controlling lipid metabolism.</p>" ]
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[ "<p>We thank Steven Mihajlovic and Ian Boundy for histology, Kelly Trueman, Melanie Howell, Maggie Wilk, Mathew Salzone and Shauna Ross for excellent animal husbandry, Stephen Wong for assistance with qPCR analysis and Monash MicroImaging and the Peter MacCallum Cancer Centre Microscopy Core for assistance.</p>" ]
[ "<fig id=\"pgen-1000192-g001\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g001</object-id><label>Figure 1</label><caption><title>An ENU recessive mutagenesis screen identifies a lethal mutation <italic>Abca12</italic>.</title><p>Mutagenised 129/Sv males were crossed with C57BL/6 females and the resultant G<sub>1</sub> males crossed again to C57BL/6. Pedigrees were established by crossing G<sub>1</sub> males with their G<sub>2</sub> daughters and the G<sub>3</sub> offspring were then subjected to genome wide screening for absence of homozygosity of the mutagenised (129/Sv) strain (A). The recessive <italic>embryo lethal 12</italic> (<italic>el12</italic>) mutation was identified using this approach and mapped by recombination to Chromosome 1 (B). Open rectangles indicate haplotypes homozygous for the 129/Sv mutagenised background and filled rectangles are C57BL6/J homozygotes or heterozygotes. Recombination frequency and markers position are indicated. A missense G1997D mutation was identified in <italic>Abca12</italic> (C, D). The <italic>el12</italic> mutation alters a residue in the second TM region of the protein which is conserved in all species examined (human, mouse, dog, chicken, platypus, microbat and shrew) (E).</p></caption></fig>", "<fig id=\"pgen-1000192-g002\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g002</object-id><label>Figure 2</label><caption><title>The <italic>Abca12<sup>el12/el12</sup></italic> phenotype.</title><p>\n<italic>Abca12<sup>el12/el12</sup></italic> mice display an epidermal phenotype visible from E16.5 and by E18.5, the thickening of the cornified envelope produces a constrictive, taut and shiny epidermis resulting in limb contractures (A). Sections of epidermis at E17.5 and birth demonstrate severe hyperkeratosis characterised by the formation of a 20–30 cell layer thick stratum corneum. Cell architecture in other layers of the epidermis is also affected, with a reduction in the size of the spinous cell layer and lack of dense palisaded basal cell nuclear architecture (B).</p></caption></fig>", "<fig id=\"pgen-1000192-g003\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g003</object-id><label>Figure 3</label><caption><title>Barrier defects in <italic>Abca12<sup>el12/el12</sup></italic> mice.</title><p>\n<italic>Abca12<sup>el12/el12</sup></italic> mice have defects in barrier formation as evidenced by dye exclusion (A) and trans-epidermal water loss (B) assays at E18.5. Cornified envelopes prepared from <italic>Abca12<sup>el12/el12</sup></italic> mice are fragile and reduced in size compared with wild type littermate controls (<italic>Abca12<sup>el12/el12</sup></italic> CEs concentrated 15 times, C, D: p = 6×10<sup>−23</sup>). Western blotting indicates defects in filaggrin processing in <italic>Abca12<sup>el12/el12</sup></italic> epidermis (E, arrow) while expression of other CE proteins such as loricrin is unaffected. Expression of “proliferative” keratin VI is present only in the differentiating hair follicle of both mutant and wild type epidermis (F) and cell proliferation at E17.5 is normal as assayed by phospho-histone H3 staining (G; p = 0.27).</p></caption></fig>", "<fig id=\"pgen-1000192-g004\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g004</object-id><label>Figure 4</label><caption><title>Pathology of the Abca12 mutant epidermis.</title><p>Abca12 protein is detected in suprabasal keratinocytes in the granular and cornified cell layers of mutant and wild type epidermis (A). Keratinocytes in Abca12 mutant skin undergo premature differentiation highlighted by strong filaggrin expression in cells juxtaposed to K14<sup>+</sup> basal keratinocytes (B) and increased co-expression of keratins 10 and 14 (white arrows), especially in basal cells (yellow arrows) (C). Increases in co-expression of K10 and K14 are significant at both E18.5 (p = 0.016) and at P1 (p = 4.44×10<sup>−6</sup>) (D). Samples are of E18.5 (A) or P1 (B, C) epidermis counterstained with DAPI. Scale bars = 30 µm.</p></caption></fig>", "<fig id=\"pgen-1000192-g005\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g005</object-id><label>Figure 5</label><caption><title>Ultrastructural defects in <italic>Abca12<sup>el12/el12</sup></italic> mice.</title><p>Thin sections of <italic>Abca12<sup>el12/el12</sup></italic> epidermis illustrated hyperkeratosis and expansion of the stratum granulosum (A). Nile red staining shows reduced intercellular lamellae lipids at E18.5 (B). In wild type epidermis intercellular lipid lamellae (white arrow and inset) were noted as well as LBs fusing with the surface of granular cells (red arrow) (C). Lamellar bodies in wild type and heterozygous embryonic epidermis were normally loaded with lipid (D). In mutant skin, LBs lacked lamellar cargo (E, F arrowheads) but fused with the granular cell membrane (F; arrows). Mutant epidermis had a normal cornified envelope (G,H) with persistent corneodesmosomes in distal layers of the stratum corneum (G, H, red arrowheads) and the cornified layer had multiple lipid inclusions (G, black arrowheads). Unlike the uniform contents of wild type cornified cells mutant cell layers contained vesicular fibrillar structures (I, red arrows) and frequent inclusion bodies (I, black arrows). EM scale bars in C–F, H, I equal 200 nm and 2 µm in G. C–F and I were stained with ruthenium tetroxide, G and H with osmium tetroxide.</p></caption></fig>", "<fig id=\"pgen-1000192-g006\" position=\"float\"><object-id pub-id-type=\"doi\">10.1371/journal.pgen.1000192.g006</object-id><label>Figure 6</label><caption><title>Defects in skin lipid composition and cellular lipid efflux mediated by Abca12.</title><p>Analysis <italic>Abca12<sup>el12/el12</sup></italic> epidermal lipids indicate significant increases in levels of ceramide (Cer), glucoslyceramide (GC), sphingosine (Sph) and free cholesterol in <italic>Abca12<sup>el12/el12</sup></italic> skin at E18.5. Total phosophatidylcholine (PC) and cholesterol ester (CE) levels were unchanged (A, ***p&lt;0.0001; **p&lt;0.001; *p&lt;0.01 versus wilt type epidermis) (A). Defects in fibroblast cholesterol efflux were assayed using [<sup>3</sup>H]cholesterol and incubation in the presence (+) or absence (−)) of the LXR agonist TO-901317. Means plus or minus standard deviation of quadruplicate determinations are shown. (**p&lt;0.001 versus non activated cells; <sup>*#</sup> p&lt;0.001 versus ABCA12<sup>+/+</sup> cells; <sup>*</sup>p&lt;0.01 versus ABCA12<sup>−/+</sup> cells) (B). Expression of Abca1 was determined at the protein level by Western blotting (C, upper panel) and decreases in <italic>Abca12</italic> homozygous mutant cells were shown to be in part due to decreases in transcription of <italic>Abca1</italic> (C, lower panel, fold change in transcription relative to el12/el12, ***p&lt;0.0005). Lipid accumulation in fibroblasts the presence or absence of the lipid donor acetylated LDL (AcLDL) was assayed by Oil Red O staining (magnification ×100) (D).</p></caption></fig>" ]
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[ "<fn-group><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn><fn fn-type=\"financial-disclosure\"><p>National Health and Medical Research Council (Program Grant No. 461219, Australia Fellowship to DJH, R. Douglas Wright Fellowship to IS, Health Professional Research Fellowship to DFH, Research Fellowship to DS), the Australian Research Council (Queen Elizabeth II Fellowship to BTK), the Monash Fellowship program to IS and a National Collaborative Research Infrastructure Strategy grant to the Australian Phenomics Network.</p></fn></fn-group>" ]
[ "<graphic xlink:href=\"pgen.1000192.g001\"/>", "<graphic xlink:href=\"pgen.1000192.g002\"/>", "<graphic xlink:href=\"pgen.1000192.g003\"/>", "<graphic xlink:href=\"pgen.1000192.g004\"/>", "<graphic xlink:href=\"pgen.1000192.g005\"/>", "<graphic xlink:href=\"pgen.1000192.g006\"/>" ]
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[{"label": ["3"], "element-citation": ["\n"], "surname": ["Elias", "Feingold", "F", "Elias", "Feingold"], "given-names": ["P", "KR", "M", "P", "KR"], "year": ["2006"], "source": ["Epidermal lamellar body as a multifunctional secretory organelle;"], "publisher-loc": ["New York"], "publisher-name": ["Taylor and Francis"], "fpage": ["261"], "lpage": ["272"]}, {"label": ["16"], "element-citation": ["\n"], "surname": ["Yanagi", "Akiyama", "Nishihara", "Sakai", "Nishie"], "given-names": ["T", "M", "H", "K", "W"], "year": ["2008"], "article-title": ["Harlequin ichthyosis model mouse reveals alveolar collapse and severe fetal skin barrier defects."], "source": ["Hum Mol Genet"]}]
{ "acronym": [], "definition": [] }
54
CC BY
no
2022-01-12 23:38:08
PLoS Genet. 2008 Sep 19; 4(9):e1000192
oa_package/3a/ff/PMC2529452.tar.gz
PMC2529468
18776948
[ "<title>Introduction</title>", "<p>Herpes simplex virus type 1 (HSV-1) is a large, enveloped, double-stranded DNA virus with a genome of approximately 150 kbp. HSV-1 is widespread in the human population and commonly causes infections of the skin or mucosal surfaces. Occasionally, it can cause serious diseases such as sporadic encephalitis and ocular infections [##UREF##0##1##,##REF##15862167##2##]. In the eye, HSV-1 infection usually results in blepharitis, conjunctivitis, corneal epithelial keratitis, and ulcerative and/or stromal keratitis [##REF##16807055##3##]. The pathologies of these diseases are associated with several events such as the infiltration of neutrophils and mononuclear lymphocytes and the expression of growth factors, proinflammatory factors, and cytokines, which include transforming growth factor-β (TGF-β), IL-2, IL-6, IL-8, TNF-α, and interferon-β (IFN-β) [##REF##10624423##4##, ####REF##16423052##5##, ##REF##9013970##6####9013970##6##]. These studies suggest that growth factors and cytokines are extremely important in regulating the body’s reaction to viral infection.</p>", "<p>TGF-β is a superfamily of cytokines, which affect a range of biological processes such as cell growth, differentiation, matrix production, migration, and apoptosis [##REF##9393997##7##]. Furthermore, the TGF-β pathway is an important target for several viral proteins that interfere with signal transduction and transcription control in infected cells [##REF##12393612##8##, ####REF##7689810##9##, ##REF##12145312##10##, ##REF##15753369##11####15753369##11##]. Upon activation of the TGF-β signaling pathway, TGF-β family members bind to the TGF-β type II receptor (TβR-II). TβR-II then recruits and phosphorylates TGF-β type I receptors (TβR-I), which in turn phosphorylates the intracellular effectors (i.e., SMAD2 and SMAD3). Subsequently, SMAD2 and SMAD3 complexes, which are associated with SMAD4, are translocated into the nucleus and regulate the transcription of target genes [##REF##9393997##7##,##REF##14534577##12##,##REF##15130563##13##]. A previous study demonstrated that TGF-β isoforms are expressed in the human cornea [##REF##7796607##14##,##REF##7530663##15##], and TGF-β is believed to be one of the major factors involved in cell migration in the cornea and corneal wound healing [##REF##15980223##16##, ####REF##16966143##17##, ##REF##15448476##18####15448476##18##]. Furthermore, TGF-β signaling through the SMAD pathway is activated in response to corneal wounds in which the basement membrane is removed [##REF##15980223##16##]. Earlier studies suggested that TGF-β might be important in the pathology of various disease processes involved with viral infection. This has been demonstrated for a variety of viruses including cytomegalovirus (CMV), human immunodeficiency virus (HIV), canine distemper virus, rhinovirus, HSV-1, and human T-cell leukemia virus (HTLV) [##REF##12393612##8##,##REF##7689810##9##,##REF##10762074##19##, ####REF##17151785##20##, ##REF##12479398##21##, ##REF##8057454##22####8057454##22##]. Corneal epithelial cells are the first line of defense against microbial infection and against further damage to the underlying stroma. Therefore, we must understand the role of TGF-β in the pathology of viral infection in the corneal epithelium. It is reasonable to suppose that TGF-β and SMADs play a critical role in the pathology of HSV-1 infection in the cornea. The present study was undertaken to examine whether TGF-β isoforms and SMADs (SMAD2 and SMAD3) are involved in HSV-1 corneal epithelial infection in vitro.</p>" ]
[ "<title>Methods</title>", "<title>Cell culture</title>", "<p>The human corneal epithelial cell line (HCEC) that we used was described previously [##REF##17223104##23##]. Cells were cultured in DMEM/high glucose supplemented with 10% fetal bovine serum (FBS; Hyclone, Logan, UT), 10 ng/ml human epidermal growth factor (EGF; Sigma, St Louis, MO), 5 μg/ml insulin, 5 μg/ml human transferrin (Sigma), and 0.4 μg/ml hydrocortisone (Gibco BRL, Grand Island, NY). The cells were incubated at 37 °C in a 5% CO<sub>2</sub>-95% air incubator. Experiments were performed when cells were at 80%-90% confluence.</p>", "<title>Virus infection</title>", "<p>Stocks of the HSV-1 (F strain) used in this study were propagated on HEp-2 cells grown in DMEM/F12, which contained 10% newborn bovine serum. The titer of virus stocks was determined according to a previously described method [##REF##7228400##24##]. After cells were grown to 80%-90% confluence, cells were infected at a multiplicity of infection (MOI) of 5. After 1 h of adsorption at 37 °C with intermittent rocking, the inoculum was removed, and the medium was replaced with serum-free DMEM/high glucose. At the indicated times, cells were harvested for further experiments. To confirm virus infection, two virus genes (i.e., DNA polymerase and <italic>VP16</italic>) of HSV-1 were examined by reverse transcription polymerase chain reaction (RT–PCR) using the primers listed in ##TAB##0##Table 1##. Two genes were detected in HSV-1 infected cells, which implied that HCE cells were successfully infected by HSV-1.</p>", "<title>RNA isolation and reverse transcription polymerase chain reaction analysis</title>", "<p>Cells were harvested and washed with phosphate buffered saline (PBS). Total RNA was isolated with TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. The quantity and quality of total RNA were estimated by spectrophotometry and agarose electrophoresis. Subsequently, RNA was reverse-transcribed into cDNA using a RevertAid<sup>TM</sup> First Strand cDNA synthesis kit (Fermentas, Glen Burnie, MD). cDNA was then amplified by GoTaq® Green Master mix (Promega, Madison, WI) using the specific primers listed in ##TAB##0##Table 1##. The PCR products were electrophoresed in GoldView<sup>TM</sup> stained 2% agarose gels (SBS Genetech, Beijing, China). Quantification of the bands was performed using a BioImaging System (UVP, Upland, CA) and Gel-pro software (Media Cybernetics, Bethesda, MD), and the level of mRNA was expressed as the ratio of integrated optical density (IOD) of specific PCR products over <italic>GAPDH</italic> IOD.</p>", "<title>Indirect immunofluorescence</title>", "<p>HCE cells were cultured on a glass coverslip in 12 well chamber dishes and infected with HSV-1 as described above. At the indicated times, changes in cellular morphology were photographed using a phase-contrast microscope. Slide-mounted cells were used for indirect immunofluorescence analysis according to the method described previously [##REF##18385786##25##]. The cells were blocked by endogenous peroxidase-blocking solution and followed by goat serum (each for 10 min at 37 °C). After blocking nonspecific binding, cells were incubated with rabbit anti-human monoclonal/polyclonal antibodies that recognize TGF-β1 (Santa Cruz, Delaware Avenue, CA), TGF-β2 (Santa Cruz), SMAD3, and phospho-SMAD3 (both from Cell Signaling, Danvers, MA) at 4 °C overnight. Cells were then incubated with FITC-conjugated secondary goat anti-rabbit IgG (Zhongshan Goldenbridge, Beijing, China) at 37 °C for 1 h. Prior to mounting, cells were stained with propidium iodide (PI) for 10 min. Cells were then observed using a confocal laser scanning microscope (Carl Zeiss, Jena, Germany). Cells incubated with PBS (instead of the first antibody) were used as negative controls.</p>", "<title>Measurement of TGF-β1 protein in human corneal epithelial cells by ELISA</title>", "<p>At 0 h, 12 h, and 24 h p.i., HSV-1 infected HCE cells were lysed with lysate buffer (Pierce, Rockford, IL). The samples were sonicated and centrifuged at 12,000 rpm for 30 min at 4 °C to remove cellular debris. Protein content in the supernatant was determined by the bicinchoninic acid method using BSA as the standard. The TGF-β1 levels of cell homogenate were assayed using a specific TGF-β1 enzyme-liked immunosorbent assay kit (Boster, Wuhan, China), and human TGF-β1 was used to construct a standard curve. The amount of TGF-β1 protein in the cell was normalized to the total amount of cellular protein. Absorbance values were read at 450 nm by an ELISA enzyme-labeled device.</p>", "<title>Statistical analysis</title>", "<p>Statistical analysis of data was performed by one-way ANOVA and a Student–Newman–Keuls test to determine statistically significant differences (p&lt;0.05) between uninfected and HSV-1 infected cells.</p>" ]
[ "<title>Results</title>", "<title>Morphological changes of HSV-1 infected human corneal epithelial cells</title>", "<p>Cell morphological changes were observed under phase-contrast microscopy. Normal HCE cells exhibited a typical cobblestone appearance (##FIG##0##Figure 1A##). Following HSV-1 infection and up to 8 h p.i., the cell morphology of infected groups was similar to the uninfected group. Compared with control cells, a cytopathic effect (CPE) in HCE cells could be observed at 8 h and 12 h p.i. (##FIG##0##Figure 1B,C##). The space between infected cells increased, and the cobblestone appearance disappeared. At 24 h p.i., most of the infected cells exhibited a CPE (dead cells were observed floating in the medium), and many giant multinucleated cells could be seen (##FIG##0##Figure 1D##).</p>", "<title>Expression of TGF-β isoforms in HSV-1 infected human corneal epithelial cells in vitro</title>", "<p>First, the mRNA level of TGF-β isoforms (i.e., <italic>TGF-β1</italic>, <italic>TGF-β2</italic>, and <italic>TGF-β3</italic>) in HCE cells infected with HSV-1 was estimated using RT–PCR (##FIG##1##Figure 2##). The mRNA level of <italic>TGF-β1</italic> decreased significantly by 40.3%, 57.3%, and 70.4% at 8 h, 12 h, and 24 h p.i., respectively, when compared with uninfected cells (p&lt;0.01). However, mRNA profiles of <italic>TGF-β2</italic> and <italic>TGF-β3</italic> in infected cells at 8 h, 12 h, and 24 h p.i were similar to that of uninfected cells (p&gt;0.05).</p>", "<p>To further verify the results of PCR, indirect immunofluorescence was used to observe the changes of TGF-β1and TGF-β2 protein expression in HCE cells infected with HSV-1 (##FIG##2##Figure 3##). The intensity of immunostaining for TGF-β1 decreased at 12 h and 24 h p.i. compared with the control (##FIG##2##Figure 3A##). The decrease of TGF-β1 protein in HSV-1 infected HCE cells was also found by ELISA measurement (##FIG##2##Figure 3C##). Significant decreases in the levels of TGF-β1 protein were observed using two immunomethods. However, compared with normal cells, TGF-β2 protein remained present in infected cells at both 12 h and 24 h p.i. when we examined the cells by immunocytochemical staining (##FIG##2##Figure 3B##).</p>", "<title>Expression of <italic>SMAD2</italic> and <italic>SMAD3</italic> in HSV-1 infected HCE cells</title>", "<p>The expression of <italic>SMAD2</italic> and <italic>SMAD3</italic> in HCE cells infected with HSV-1 was detected by RT–PCR (##FIG##3##Figure 4##). This study found a clear reduction in mRNA level coding for <italic>SMAD3</italic> in HSV-1 infected cells. Compared with normal cells, <italic>SMAD3</italic> mRNA levels decreased significantly by 37.5% (12 h p.i.) and 53.1% (24 h p.i.; p&lt;0.01) in infected cells. However, the mRNA levels of <italic>SMAD2</italic> remained unchanged during the course of infection (p&gt;0.05).</p>", "<p>To examine whether the down-regulation of <italic>SMAD3</italic> mRNA also results in a reduction in protein level, SMAD3 and phospho-SMAD3 protein expressions during HSV-1 infection were analyzed by immunocytochemistry. Compared with normal cells, protein expression of SMAD3 and phospho-SMAD3 in infected cells was weaker at 12 h and 24 h p.i. (##FIG##4##Figure 5##).</p>" ]
[ "<title>Discussion</title>", "<p>The cornea contains three principal cell types, epithelial cells, keratocytes, and endothelial cells. Previous studies have shown that corneal epithelial cells are capable of supporting efficient HSV-1 replication [##REF##1657554##26##,##REF##3019382##27##]. Balliet et al. [##REF##17207829##28##] reported that a recombinant HSV-1 virus, KOS-CMVGFP, expressing enhanced green fluorescent protein (EGFP) could infect mice as efficiently as a wild-type virus. They found that fluorescence was observed in eyes as distinct small foci on the cornea at day 1 p.i., and that the fluorescence spread throughout the eye between days 1 and 3 p.i. Finally, the foci grew larger and coalesced, resulting in large, dendritic corneal lesions. Consistent with the studies described above, our work also demonstrated that the HCE cell is highly permissive to HSV-1 infection in vitro. When HCE cells were infected with HSV-1 at a MOI of 5, a cytopathic effect was observed at 8 h p.i. HSV-1 infection caused an increase in the number of dead cells, which may be the reason for the dendritic keratitis observed in vivo. Furthermore, we also observed expression of two viral genes (DNA polymerase and <italic>VP16</italic>) in infected cells by RT–PCR, which implies that HSV-1 caused a productive infection of HCE cells. Therefore, HCE cells are susceptible to HSV-1 infection, and it can provide a useful in vitro model for research of HSV-1 infection in the cornea.</p>", "<p>TGF-β isoforms and SMAD family members have been identified in mammalian cells. In the eye, TGF-β isoforms are expressed in different ocular tissues [##REF##7796607##14##,##REF##7530663##15##,##REF##8045718##29##]. In the cornea, SMAD2 and SMAD4 were expressed and translocated into the nuclei, and SMAD7 was overexpressed during corneal epithelial wound repair [##REF##15980223##16##,##REF##15855641##30##]. In the cultured retinal pigment epithelial cell line (D407), TGF-β can stimulate the translocation of SMAD2 (but not SMAD1) from the cytoplasm into the nuclei [##REF##14641267##31##]. Therefore, TGF-β isoforms and SMADs may play important roles in the pathogenesis of ocular diseases. However, there is limited research on the effect of TGF-β isoforms and SMADs in cells infected by HSV-1. Accordingly, the objective of the present study was to investigate whether the expression of TGF-β isoforms and SMADs in HCE cells is affected by HSV-1 infection in vitro.</p>", "<p>The effect of viral infection on TGF-β expression has been described for several viruses including HIV, CMV, and HSV-1 in other tissues [##REF##10762074##19##,##REF##1700428##32##,##REF##12599069##33##]. In CMV infection, TGF-β1 was detected in increasing amounts in infected human foreskin fibroblast and astrocyte supernatants, and TGF-β1 transcription was significantly increased when compared to that of uninfected cells [##REF##8057454##22##,##REF##12599069##33##]. In vitro HSV-1 infection of human mononuclear cells resulted in a significant time-dependent increase in the release of TGF-β1 protein into supernatants [##REF##10762074##19##]. These studies showed that virus infection could induce the overexpression of TGF-β1 with respect to protein expression and mRNA levels. However, in a study on mouse cornea infected with HSV-1, Hu et al. [##REF##10624423##4##] showed that levels of <italic>TGF-β</italic> mRNA decreased in inflamed corneas. Our study demonstrated that the expression of <italic>TGF-β1</italic> at both the mRNA and protein level was down-regulated in HCE cells infected by HSV-1 at 8 h p.i. and beyond. However, during the course of HSV-1 infection, the transcription of <italic>TGF-β2</italic> and <italic>TGF-β3</italic> remained unchanged compared to uninfected cells. These results suggested that <italic>TGF-β</italic> expression in response to HSV-1 infection is specific to cell type.</p>", "<p>The current study also showed that HSV-1 infection caused a decline in the transcription of <italic>SMAD3</italic> in HCE cells but had no effect on the expression of <italic>SMAD2</italic>. Similarly, by confocal laser scanning microscopy, HSV-1 infected HCE cells displayed weak immunostaining for SMAD3 and phospho-SMAD3. Although measuring protein levels with a quantitative method such as western blot would provide more convincing evidence of protein expression change, the immunostaining result was consistent with the data of RT–PCR analysis for <italic>SMAD3</italic>. These results suggested that <italic>SMAD3</italic> decreased in both mRNA and protein levels in HSV-1 infected HCE cells.</p>", "<p>It has been demonstrated that in virus infections, <italic>TGF-β</italic> could be regulated by the SMAD subfamily. In HPV infected cells, viral E7 oncoprotein blocks through its constitutive interactions with SMAD2, SMAD3, and SMAD4, both SMAD transcriptional activity and the ability of TGF-β to inhibit DNA synthesis [##REF##12145312##10##]. E6 oncoprotein of HPV-5 inhibits SMAD3 transactivation by interacting with SMAD3, destabilizing the SMAD3/SMAD4 complex, and inducing the degradation of both proteins [##REF##17020941##34##]. Virus proteins also interfere with TGF-β signaling via SMAD proteins as observed in HTLV-1 infected ATL cells [##REF##12393612##8##] and in Kaposi's sarcoma-associated herpes virus infection [##REF##15753369##11##]. These results show that suppression of SMAD-mediated TGF-β signaling in virus infected cells might contribute to the carcinogenesis. The present study focuses on HSV-1 infected corneal epithelial cells, which characterizes recurrent inflammation of the cornea in vivo. The fundamental physiologic roles of SMAD3 are involved in the processes of tissue repair and fibrosis [##REF##16298342##35##]. Decreased <italic>SMAD3</italic> expression could reduce formation and nuclear import of transcriptionally active SMAD heterocomplexes and decrease transcription of TGF-β1 regulated target genes, which result in reduced inflammatory cell infiltrates, reduced auto-induction of <italic>TGF-β</italic>, and reduced elaboration of collagen [##REF##11451911##36##]. This may be the cause of the observed decreases of TGF-β1 and SMAD3 in HSV-1 infected HCE cells in this study, which occurred as an in vivo inflammatory process.</p>", "<p>The interplay between HSV-1 and its host involves numerous factors, and the virus employs several mechanisms to combat many antiviral responses enacted by the infected cell [##REF##12805425##37##]. Expression of <italic>TGF-β1</italic> and <italic>SMAD3</italic> in HSV-1 infected HCE cells decreased in this study, which suggested that they may be involved in corneal diseases that are associated with HSV-1 infection. The specific function of TGF-β1 and SMAD3 in HSV-1 corneal infection requires further investigation.</p>" ]
[]
[ "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>The present study was undertaken to investigate whether transforming growth factor-β (<italic>TGF-β</italic>) isoforms (TGF-β1, TGF-β2, and TGF-β3) and SMADs (<italic>SMAD2</italic> and <italic>SMAD3</italic>) are involved in herpes simplex virus type 1 (HSV-1) corneal infection.</p>", "<title>Methods</title>", "<p>Human corneal epithelial cells (HCE) were infected with HSV-1 at a multiplicity of infection of 5. Cell morphological changes were observed under phase-contrast microscopy. Levels of mRNA for <italic>TGF-β</italic> isoforms 1, 2, and 3 as well as for <italic>SMAD2</italic> and <italic>SMAD3</italic> were measured by reverse transcription polymerase chain reaction (RT–PCR) at 0 h, 4 h, 8 h, 12 h, and 24 h after infection. Protein expression of TGF-β1, TGF-β2, SMAD3, and phospho-SMAD3 were analyzed by indirect immunofluorescence at 0 h, 12 h, and 24 h post-infection (p.i.) in HCE cells. Protein expression of TGF-β1 was also evaluated by ELISA.</p>", "<title>Results</title>", "<p>Following HSV-1 infection, a cytopathic effect in HCE cells was observed at 8 h p.i. and became significant at 24 h p.i. Compared with normal cells, the mRNA levels of <italic>TGF-β1</italic> in HSV-1 infected HCE cells decreased significantly at 8 h, 12 h, and 24 h p.i. (p&lt;0.01), and the expression of <italic>SMAD3</italic> was also dramatically decreased 12 h and 24 h p.i. (p&lt;0.01). No noticeable changes were found as a result of infection with respect to levels of <italic>TGF-β2</italic>, <italic>TGF-β3</italic>, and <italic>SMAD2</italic> in HCE cells. Protein expression of TGF-β1, SMAD3, and phospho-SMAD3 decreased in infected cells at 12 h and 24 h p.i. compared with normal cells, but TGF-β2 had no change.</p>", "<title>Conclusions</title>", "<p><italic>TGF-β1</italic> and <italic>SMAD3</italic> may be involved in the pathology of corneal diseases associated with HSV-1 infection.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>The authors are grateful to Professor Zhichong Wang (Zhongshan Ophthalmic Center) for providing us with the human corneal epithelial cell line. This study was supported in part by an NSFC grant, No. 30572002, and by Funds from the Department of Science and Technology of Guangdong Province, China, No. 2004A30801001.</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Morphological changes of human corneal epithelial cells infected with HSV-1. <bold>A</bold>: Normal human corneal epithelial cells exhibited a cobblestone appearance. <bold>B</bold>: The cytopathic effect could be seen at 8 h p.i. The space between infected cells increased. After cells were infected with HSV-1 for 12 h (<bold>C</bold>) and 24 h (<bold>D</bold>), the cobblestone appearance disappeared and many giant multinucleated cells could be seen. Magnification: 200X.</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Reverse transcription polymerase chain reaction analysis of the <italic>TGF-β</italic> isoforms in human corneal epithelial cells infected with HSV-1. <bold>A</bold>: Products of RT-PCR that were run on 2% agarose gel electrophoresis. The intensities of <italic>TGF-β1</italic> bands decreased significantly at 8 h, 12 h, and 24 h p.i., while that of <italic>TGF-β2</italic> and <italic>TGF-β3</italic> bands unchanged. <italic>GAPDH</italic> was used as an internal control. <bold>B</bold>: The level of mRNA was expressed as the ratio of integrated optical density (IOD) of specific PCR products over <italic>GAPDH</italic> IOD. Each data was the mean value of three independent experiments. Single asterisks indicate significant differences (p&lt;0.05).</p></caption></fig>", "<fig id=\"f3\" fig-type=\"figure\" position=\"float\"><label>Figure 3</label><caption><p>Protein expression of TGF-β1 and TGF-β2 in human corneal epithelial cells infected with HSV-1. In <bold>A</bold> and <bold>B</bold>, indirect immunofluorescence analysis was used to find the protein expression in cells. FITC marked the secondary antibody (green; left), and PI dyed the nucleus (red; middle). Merged images were showed at the right of <bold>A</bold> and <bold>B</bold>. Scale bar: 10 μm. <bold>C</bold>: The expression of TGF-β1 by ELISA in HCE cells infected with HSV-1 was measured at 0 h, 12 h, and 24 h p.i. Significant decreases of the TGF-β1 protein in cell lysates were seen in both 12 h and 24 h post-infected cells (p&lt;0.05). Each data was the mean value of four independent assays.</p></caption></fig>", "<fig id=\"f4\" fig-type=\"figure\" position=\"float\"><label>Figure 4</label><caption><p>Reverse transcription polymerase chain reaction analysis of <italic>SMAD2</italic> and <italic>SMAD3</italic> in human corneal epithelial cells infected with HSV-1. <bold>A</bold>: Agarose gel pattern of RT–PCR products. The band intensities of <italic>SMAD3</italic>, not <italic>SMAD2</italic>, decreased during the period of post-infection. <italic>GAPDH</italic> was used as an internal control. <bold>B</bold>: The level of mRNA was expressed as the ratio of IOD of specific PCR products over the <italic>GAPDH</italic> gene IOD. The mean values of three independent experiments are shown. Single asterisks indicate significant differences (p&lt;0.05).</p></caption></fig>", "<fig id=\"f5\" fig-type=\"figure\" position=\"float\"><label>Figure 5</label><caption><p>Colocalization of SMAD3 and phospho-SMAD3 protein in human corneal epithelial cells. FITC marked the secondary antibody (green; left), and PI dyed the nucleus (red; middle). Merged images were showed at the right of <bold>A</bold> and <bold>B</bold>. Both SMAD3 (<bold>A</bold>) and phospho-SMAD3 (<bold>B</bold>) were more weakly expressed at 12 h and 24 h p.i. compared to the uninfected cells. Scale bar: 10 μm.</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Primer sequences and length of amplicons.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"95\" span=\"1\"/><col width=\"278\" span=\"1\"/><col width=\"72\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gene</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Primer sequences</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Product size (bp)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>TGF-β1</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-GGGACTATCCACCTGCAAGA-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">239<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-CCTCCTTGGCGTAGTAGTCG-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>TGF-β2</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-GTGGAGGTGCCATCAATA-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">499<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-GAGGAGCGACGAAGAGTA-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>TGF-β3</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-CAA AGGGCTCTGGTGGTC-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">216<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-CGGGTGCTGTTGTAAAGTG-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>SMAD3</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-AGGAGAAATGGTGCGAGA A-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">197<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-CCACAGGCGGCAGTAGAT-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>SMAD2</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-TCACAGTCATCATGAACTCAAGG-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">471<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-TGTGACGCATGGAAGGTCTCTC-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>DNA polymerase</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-ATCAACTTCGACTGGCCCTT-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">179<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-CCGTACATGTCGATGTTCAC-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>VP16</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-GGTCGCAACAGAGGCAGTCA-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">418<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′-CCCGAACGCACCCAAATC-3′<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>GAPDH</italic><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">forward: 5′-GCACCGTCAAGGCTGAGAAC-3′<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">138<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> </td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reverse: 5′- TGGTGAAGACGCCAGTGGA-3′</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]
[]
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[ "<graphic xlink:href=\"mv-v14-1631-f1\"/>", "<graphic xlink:href=\"mv-v14-1631-f2\"/>", "<graphic xlink:href=\"mv-v14-1631-f3\"/>", "<graphic xlink:href=\"mv-v14-1631-f4\"/>", "<graphic xlink:href=\"mv-v14-1631-f5\"/>" ]
[]
[{"label": ["1"], "citation": ["Roizman B, Knipe DM. Herpes simplex viruses and their replication. In: Knipe DM, Howley PM, editors. Fields Virology. Philadelphia: Lippincott Raven Press; 2001. p. 2399\u2013460."]}]
{ "acronym": [], "definition": [] }
37
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 3; 14:1631-1638
oa_package/54/55/PMC2529468.tar.gz
PMC2529469
18776949
[ "<title>Introduction</title>", "<p>The cornea is characterized by an absence of blood vessels under physiologic conditions [##REF##17051153##1##]. Corneal avascularity is maintained by a balance between angiogenic and anti-angiogenic molecules [##REF##15037577##2##, ####REF##11815230##3##, ##REF##17074526##4##, ##REF##10235544##5##, ##REF##12470964##6####12470964##6##] and is required for optical clarity and the maintenance of vision. Thus, corneal neovascularization (CNV) can lead to impaired vision when it arises from any cause including corneal infections, misuse of contact lenses, chemical burns, and inflammation [##REF##11507336##7##, ####REF##2446823##8##, ##REF##6186626##9####6186626##9##]. In most of these conditions, a large number of neutrophils infiltrate into the cornea before the onset of CNV followed by an infiltration of monocytes/macrophages. Although neutrophils are presumed to be involved in CNV, we have previously shown that alkali-induced CNV developed independently of granulocyte infiltration [##REF##17251813##10##].</p>", "<p>Leukocyte infiltration is regulated by coordinative actions of adhesion molecules and chemokines with the chemokine receptor expression pattern on leukocytes determining their responsiveness to a given chemokine [##REF##9023058##11##]. Monocytes/macrophages express a distinct set of chemokine receptors including CCR1, CCR2, CCR5, and CX3CR1 on their cell surface [##REF##16212895##12##, ####REF##16908772##13##, ##REF##15067194##14####15067194##14##]. We have previously found a potent angiogenic factor, vascular endothelial growth factor (VEGF), which was detected in intraocularly infiltrating monocytes/macrophages before CNV development [##REF##17251813##10##]. CNV could be consistently attenuated by genetic ablation of either the <italic>CCR2</italic> or <italic>CCR5</italic> gene [##REF##12827053##15##,##REF##12556387##16##], which also reduced intraocular VEGF production. In contrast, several other groups have provided evidence to indicate that infiltrating macrophages have anti-angiogenic activities in choroidal neovascularization [##REF##16903779##17##]. In line with this notion, we also observed that intraocularly infiltrated CX3CR1-positive macrophages expressed anti-angiogenic molecules such as thrombospondins and were protective against alkali-induced CNV [##REF##18322241##18##]. Thus, the monocyte/macrophage population may be heterogeneous in terms of angiogenic activities, which depends on their chemokine receptor expression pattern.</p>", "<p>We previously observed that CCR1 was expressed in endothelial cells in human hepatoma tissue [##REF##12651617##19##]. Furthermore, both CCR1-knockout (KO) and CCL3-KO mice exhibited impairment in carcinogen-induced hepatocarcinogenesis with reduced macrophage infiltration and intra-tumor neovascularization [##REF##16284949##20##]. These observations would imply that involvement of the CCL3-CCR1 axis in neovascularization is essential. Because CCL3 can also bind to CCR5 as well as CCR1 [##REF##10714678##21##], we compared the molecular pathological changes between WT mice and mice that were deficient in CCL3, CCR1, or CCR5 in a frequently used ocular neovascularization model, alkali-induced CNV [##REF##17251813##10##,##REF##12827053##15##,##REF##12556387##16##,##REF##18322241##18##], to address the roles of CCL3 and its receptors in CNV. We provided definitive evidence to indicate involvement of the CCL3/CCR5 axis but not the CCL3/CCR1 axis in alkali-induced CNV.</p>" ]
[ "<title>Methods</title>", "<title>Reagents and antibodies</title>", "<p>Recombinant CCL3/MIP-1α (270-LD) and goat anti-mouse CCL3 antibodies were obtained from R&amp;D Systems (Minneapolis, MN). Rat anti-mouse F4/80 (clone A3–1) monoclonal antibody (mAb) was from Serotec (Oxford, UK). Polyclonal rabbit antibody to CD31 (ab28364) was purchased from Abcam (Cambridge, UK). Rat anti-mouse CD31 (MEC13.3), purified rat anti-mouse-Ly-6G and Ly-6C (Gr-1) mAbs (clone RB6–8C5), and purified rat anti-mouse CCR5 mAb (clone C34–3448) were purchased from BD PharMingen (San Diego, CA). Goat anti-CCR1 pAb (C-20) was obtained from Santa Cruz Biotechnology (Santa Cruz, CA). Alexa Fluor (AF) 488 donkey anti-rat IgG (H<sup>+</sup>L), AF594 donkey anti-rabbit IgG (H<sup>+</sup>L), and AF594 donkey anti-goat IgG (H<sup>+</sup>L) were purchased from Invitrogen (Shanghai, China).</p>", "<title>Mice</title>", "<p>CCL3-deficient (CCL3-KO) mice were obtained from Jackson Laboratories (Bar Harbor, ME). CCR1-deficient (CCR1-KO) and CCR5-deficient (CCR5-KO) mice were generous gifts from Dr. P. M. Murphy and Dr. J. L. Gao (NIADID, NIH, Bethesda, MD) [##REF##9166425##22##] and from Dr. Kouji Matsushima (University of Tokyo, Tokyo, Japan), respectively [##REF##12524535##23##]. These deficient mice were backcrossed with BALB/c for more than eight generations. Pathogen-free BALB/c mice were obtained from Clea Japan (Yokohama, Japan) and were designated as WT mice. All animal experiments were performed under specific pathogen-free conditions in the Institute for Experimental Animals (Kanazawa University, Kanazawa, Japan) in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and complied with the standards set out in the Guidelines for the Care and Use of Laboratory Animals of Kanazawa University.</p>", "<title>Alkali-induced corneal injury model</title>", "<p>Corneal injury was induced by placing a 2 mm filter disc saturated with 1 N NaOH onto the left eye of the mouse as previously described [##REF##17251813##10##,##REF##18322241##18##]. In some experiments, the alkali-treated eyes received 5 μl of CCL3 preparation dissolved in 0.2% sodium hyaluronate (Sigma-Aldrich, St. Louis, MO) at a concentration of 3 μg/ml or 5 μl of 0.2% sodium hyaluronate as the vehicle twice a day for seven days immediately after the alkali injury. At the indicated time intervals, mice were killed, and whole eyes were removed. The eyes were fixed in 10% neutrally buffered formalin or were snap frozen in optimal cutting temperature (OCT) compound (Sakura Finetek, Torrance, CA). In some mice, the corneas were removed from both eyes, placed immediately into RNALate (Qiagen, Tokyo, Japan), and kept at −86 °C until total RNA extraction was performed. Each experiment was repeated at least three times.</p>", "<title>Biomicroscopic examination</title>", "<p>Eyes were examined under a surgical microsystem (Lecia MZ16; Leica Microsystems GmbH, Wetzlar, Germany) 14 days after alkali injury by two independent observers with no prior knowledge of the experimental procedures as described previously [##REF##17251813##10##,##REF##18322241##18##].</p>", "<title>Histological and immunohistochemical analysis</title>", "<p>The paraffin-embedded tissues were cut into 5 µm thick slices and were then subjected to hematoxylin and eosin staining. Other sections were deparaffinized with xylene and rehydrated through graded concentrations of ethanol for immunohistochemical detection of F4/80 positive, CCL3 positive, CCR1 positive, or CCR5 positive cells as described previously [##REF##16284949##20##]. The numbers of F4/80 positive cells were counted at 200 fold magnification in five randomly chosen fields of corneal sections from each animal [##REF##18322241##18##,##REF##16284949##20##] by an examiner with no prior knowledge of the experimental procedures. The numbers of positive cells/mm<sup>2</sup> were calculated.</p>", "<title>A double-color immunofluorescence analysis</title>", "<p>A double-color immunofluorescence analysis was performed to determine the cells expressing CCR5 and CCR2. Briefly, the fixed cryosections (8 µm thick) were incubated with PBS containing 1% normal donkey serum and 1% BSA to reduce nonspecific reactions. Thereafter, the sections were incubated with a combination of rat anti-CCR5 and rabbit anti-CCR2 overnight at 4 °C. After being rinsed with PBS, the sections were then incubated with a combination of Alexa Fluor 488 donkey anti-rat IgG and Alexa Fluor 594 donkey anti-rabbit IgG (1/100) for 40 min at room temperature in the dark. Finally, the sections were washed with PBS, and immunofluorescence was visualized in dual-channel mode with a fluorescence microscope (Olympus, Tokyo, Japan). Images were processed using Adobe Photoshop software version 7.0 (Adobe Systems, Tokyo, Japan).</p>", "<title>Enumeration of corneal neovascularization</title>", "<p>Deparaffinized sections (5 µm thick) and fixed cryosections (8 µm thick) were stained using rabbit anti-CD31 antibody (ab28364) [##REF##17251813##10##] and rat anti-CD31 antibody (MEC13.3) [##REF##18322241##18##], respectively. The numbers and sizes of the CNV were determined as previously described [##REF##17251813##10##] by an examiner with no prior knowledge of the experimental procedures. Most sections were taken from the central region of the cornea. The numbers and areas of corneal neovascularization were evaluated on at least two sections from each eye.</p>", "<title>Semi-quantitative reverse transcription polymerase chain reaction</title>", "<p>Total RNAs were extracted from the corneas or cultured macrophages with the use of RNeasy Mini Kit (Qiagen, Tokyo, Japan), and the resultant RNA preparations were further treated with RNase-free DNase I (Life Technologies Inc., Gaithersburg, MD) to remove genomic DNA. Total RNA (2 μg) were reverse-transcribed at 42 °C for 1 h in 20 μl of reaction mixture containing mouse Moloney leukemia virus reverse transcriptase and hexanucleotide random primers (Qiagen). cDNA (twofold serially diluted) was amplified for <italic>β-actin</italic> (##TAB##0##Table 1##) to estimate the amount of transcribed cDNA. Then, equal amounts of cDNA products were amplified for the target genes using the primers under the following conditions: denaturation at 94 °C for 2 min followed by the optimal cycles of 30 s at 94 °C, 45 s at 53–57 °C, 1 min at 72 °C, and a final 10 min extension step at 72 °C (##TAB##0##Table 1##). The amplified polymerase chain reaction (PCR) products were fractionated on a 1.0% agarose gel and visualized by ethidium bromide staining. The band intensities were measured, and their ratios to β-actin were determined with the aid of <ext-link ext-link-type=\"uri\" xlink:href=\"http://rsbweb.nih.gov/ij/download.html\">NIH Image Analysis</ext-link> software.</p>", "<title>Murine peritoneal macrophages isolation and culture</title>", "<p>Peritoneal macrophages were obtained as described previously [##REF##18322241##18##]. The cells were suspended in antibiotic-free RPMI medium containing 10% fetal bovine serum (FBS) and incubated in a humidified incubator at 37 °C in 5% CO<sub>2</sub> in 24 well cell culture plates (Nalge Nunc International Corp., Naperville, IL). Two hours later, non-adherent cells were removed, and the medium was replaced. The cells were then stimulated with the indicated concentrations of murine CCL3 for 12 h. Total RNAs were extracted from the cultured cells and subjected to reverse transcription polymerase chain reaction (RT–PCR) as described above. In another series of experiments, murine macrophages were seeded onto 12 well plates at 5×10<sup>5</sup> cells/well. After adhesion, the cells were stimulated with the indicated concentrations of murine CCL3 for 24 h in a 37 °C incubator with 5% CO<sub>2</sub>. Supernatants were collected to determine VEGF concentrations using a mouse VEGF ELISA Kit (R&amp;D Systems) according to the manufacturer’s instructions.</p>", "<title>Statistical analysis</title>", "<p>The means and the standard error of the mean (SEM) were calculated for all parameters determined in the study. Data were analyzed statistically using one-way ANOVA or two-tailed Student’s <italic>t</italic>-test. A value of p&lt;0.05 was accepted as statistically significant.</p>" ]
[ "<title>Results</title>", "<title>Intracorneal expression of CCL3 and its receptor, CCR1 and CCR5, after alkali-induced corneal injury</title>", "<p>We first examined the expression of CCL3, a ligand for CCR5 and CCR1, in corneas after alkali-induced injury. <italic>CCL3</italic> mRNA was barely detectable in untreated eyes but was markedly increased after alkali injury (##FIG##0##Figure 1A##). Concomitantly, CCL3 protein was immunohistochemically detected in epithelial cells and infiltrating cells after alkali injury but not in untreated eyes (##FIG##0##Figure 1B##). Moreover, alkali injury markedly augmented the mRNA expression of specific receptors for <italic>CCL3</italic>, <italic>CCR1</italic>, and <italic>CCR5</italic> (##FIG##0##Figure 1A##). Furthermore, immunohistochemical analysis demonstrated the infiltration of CCR1 expressing leukocytes, which started two days after the injury and increased thereafter (##FIG##0##Figure 1B##). These observations suggest that alkali injury induced intracorneal production of CCL3, which in turn attracted CCR1 expressing or CCR5 expressing leukocytes into the cornea.</p>", "<title>Impaired alkali-induced corneal neovascularization in CCL3-KO and CCR5-KO but not CCR1-KO, mice</title>", "<p>We next explored the effects of genetic ablation of <italic>CCL3</italic>, <italic>CCR1</italic>, and <italic>CCR5</italic> on alkali-induced CNV. CNV was macroscopically evident in WT mice two weeks after the injury as we have previously reported [##REF##17251813##10##]. In line with the previous report [##REF##12556387##16##], macroscopic CNV was markedly attenuated in CCR5-KO mice (##FIG##1##Figure 2A##). Moreover, macroscopic CNV was markedly reduced in CCL3-KO mice but in not CCR1-KO mice (##FIG##1##Figure 2A,B,C##). Although corneas are physiologically avascular, alkali injury markedly increased the vascular areas in corneas of WT and CCR1-KO mice to similar extents, but the increment was significantly reduced in CCL3-KO mice (##FIG##1##Figure 2B,C##). These observations would indicate that the CCL3-CCR5 axis was indispensable for alkali-induced CNV, but the CCL3-CCR1 axis was not.</p>", "<title>Reduced intraocular macrophage infiltration in CCL3-KO but not CCR1-KO mice</title>", "<p>We previously observed that Gr-1 positive granulocytes and F4/80 positive macrophages infiltrated injured corneas, reaching their peak levels two to four days after the injury in WT mice [##REF##17251813##10##,##REF##18322241##18##]. Leukocytes, particularly monocytes/macrophages, can be a rich source of angiogenic factors [##REF##12882810##24##, ####REF##11979237##25##, ##REF##12832439##26##, ##REF##12882811##27##, ##REF##10440240##28####10440240##28##]. Given the fact that CCL3 recruit macrophages, which express CCR1 and CCR5, we examined the effects of CCL3 deficiency on leukocyte infiltration into the wounded cornea. Neither F4/80 positive macrophages nor Gr-1 positive granulocytes were present in untreated corneas of WT and CCL3-KO mice. Gr-1 positive granulocytes infiltrated to similar extents into the corneas of both WT and CCL3-KO mice after the injury (data not shown). In contrast, F4/80 positive macrophage infiltration was markedly reduced in CCL3-KO mice but not in CCR1-KO mice when compared with WT mice (##FIG##1##Figure 2D##). Thus, CCL3 may regulate intraocular infiltration of F4/80 positive macrophages but not Gr-1 positive granulocytes.</p>", "<title>Reduced vascular endothelial growth factor expression in CCL3-KO mice after alkali injury</title>", "<p>The balance between angiogenic and anti-angiogenic factors can determine the outcome of angiogenetic processes in various situations. Hence, we examined the mRNA expression of angiogenic and anti-angiogenic factors in corneas after the injury. Alkali injury increased intraocular mRNA expression of an angiogenic factor, basic fibroblast growth factor (<italic>bFGF</italic>), and an anti-angiogenic molecule, thrombospondin (<italic>TSP-1</italic>), in WT and CCL3-KO mice to similar extents (##FIG##2##Figure 3A##). In contrast, mRNA expression of another potent angiogenic factor, <italic>VEGF</italic>, was markedly augmented in WT mice, and the increase was markedly attenuated in CCL3-KO mice (##FIG##2##Figure 3A##).</p>", "<title>Enhanced vascular endothelial growth factor expression by murine peritoneal macrophages with CCL3 stimulation</title>", "<p>We next examined the effects of exogenous CCL3 on <italic>VEGF</italic> expression by mouse peritoneal macrophages at the mRNA and protein levels. CCL3 markedly enhanced the mRNA expression of <italic>VEGF</italic> by peritoneal macrophages (##FIG##2##Figure 3B##). Concomitantly, CCL3 increased VEGF protein production by macrophages in a dose-dependent manner (##FIG##2##Figure 3C##). These observations would indicate that CCL3 can activate macrophages to produce an angiogenic factor, VEGF.</p>", "<title>Simultaneous CCR2 expression by intracorneally infiltrating CCR5 expressing cells</title>", "<p>We previously revealed that the CCL2/CCR2 interactions could induce VEGF expression [##REF##18322241##18##]. Hence, we next examined whether intraocularly infiltrating CCR5 expressing cells also expressed CCR2. A double-color immunofluorescence analysis demonstrated that CCR5 expressing cells also expressed CCR2 (##FIG##3##Figure 4A##). Moreover, alkali injury-induced increases in intracorneal CCR5 positive cell numbers were attenuated in CCL3-KO mice (##FIG##3##Figures 4B,C##). Thus, it is likely that CCL3 can regulate intraocular infiltration of CCR5 expressing macrophages, which can express VEGF by the CCL2/CCR interactions.</p>", "<title>Restoration of alkali-induced corneal neovascularization in CCL3-KO mice by topical CCL3 application</title>", "<p>Finally, we examined the effects of topical CCL3 application on alkali-induced CNV of CCL3-KO mice. CCL3-KO mice exhibited reduced alkali-induced CNV at both macroscopic and microscopic levels compared with WT mice (##FIG##4##Figure 5A-C##). Topical CCL3 application restored CNV to an extent similar to that seen in WT mice (##FIG##4##Figure 5A-C##). Concomitantly, CCL3 treatment reversed the macrophage infiltration in CCL3-KO mice to similar levels as WT mice (##FIG##4##Figure 5D##). Thus, CCL3 may induce the infiltration of macrophages, which in turn may produce a potent angiogenic factor, VEGF, and eventually promote alkali-induced CNV.</p>" ]
[ "<title>Discussion</title>", "<p>Tissue injury induced the expression of various growth factors, cytokines, and chemokines, which all contribute to tissue repair in a coordinated manner [##REF##11310836##29##]. In cooperation with adhesion molecules, chemokines can regulate the trafficking of various types of leukocytes, which in turn regulate two processes of tissue repair, granulation tissue formation, and neovascularization by producing various growth factors and cytokines [##REF##11310836##29##, ####REF##12843410##30##, ##REF##16202600##31##, ##REF##12087057##32####12087057##32##]. Moreover, several chemokines can directly regulate neovascularization [##REF##16397233##33##]. Alkali injury induced a transient macrophage infiltration into eyes with enhanced intraocular CCL3 expression. Several independent groups reported that CCL3 has direct effects on endothelial cells [##REF##16397233##33##] and that its receptor, CCR1, was expressed in certain types of endothelial cells [##REF##12651617##19##,##REF##16284949##20##]. However, CCL3 can restore CNV in CCL3-KO mice to similar levels shown in WT mice. This restoration of CNV is seen even if CCL3 was administered only in the early phase after the injury at the time when the endothelial cells are absent. Thus, CCL3 may not directly target endothelial cells in this model.</p>", "<p>The cornea lacks vasculature under normal physiologic conditions. Corneal avascularity is maintained by the balance between angiogenic factors including VEGF and bFGF and anti-angiogenic factors including TSP-1 and soluble VEGF receptor I [##REF##17051153##1##,##REF##15037577##2##]. Alkali injury augmented intraocular mRNA expression of <italic>bFGF</italic> and <italic>TSP-1</italic> in CCL3-KO mice to an extent similar to that in WT mice. On the contrary, alkali-induced, enhanced VEGF expression was markedly attenuated in CCL3-KO mice. Because soluble VEGF receptor I, a decoy receptor for VEGF, is constitutively present and acts as a major anti-angiogenic factor in the cornea [##REF##17051153##1##], the reduced VEGF expression in CCL3-KO mice may account for attenuated CNV after alkali injury in these mice. Moreover, CCL3 can augment VEGF production by macrophages. Thus, it is likely that CCL3 induced CNV indirectly by inducing the infiltration and activation of macrophages, a major source of VEGF.</p>", "<p>Macrophages are proposed to play crucial roles in tissue repair based on the observations that these cells can abundantly produce various growth and angiogenic factors. As seen with other types of leukocytes, macrophage infiltration is regulated mainly by coordinated actions of adhesion molecules and chemokines. Chemokines bind to their cognate receptors on leukocytes to exert their actions. Macrophages express a limited set of chemokine receptors including CCR1, CCR2, CCR5, and CX3CR1 and exhibit chemotaxis to their ligands [##REF##16212895##12##, ####REF##16908772##13##, ##REF##15067194##14####15067194##14##]. CCL3 utilizes two distinct receptors, CCR1 and CCR5 [##REF##16212895##12##], with slight differences in their expression patterns [##REF##16212895##12##,##REF##10714678##21##]. Reduced CNV in CCR5-KO mice prompted us to evaluate the roles of CCL3 and CCR1 in this model. CCL3 deficiency but not CCR1 deficiency reduced alkali-induced CNV. We recently observed that bleomycin-induced intrapulmonary macrophage accumulation and subsequent pulmonary fibrosis was attenuated in CCL3-KO and CCR5-KO mice but not in CCR1 KO mice [##REF##17322370##34##]. This suggests that CCR5 expressing cells are distinct from CCR1 expressing cells. Indeed, a double-color immunofluorescence analysis demonstrated that CCR5 expressing cells did not express CCR1 simultaneously (unpublished data). Thus, CCL3 may generally regulate macrophage functions by binding CCR5 expressed on their surface but not CCR1.</p>", "<p>A partial reduction of macrophage infiltration by CCL3 deficiency suggests a contribution of other chemokines such as CCL2 and CX3CL1 to macrophage infiltration. This may further indicate heterogeneity of monocytes/macrophages in terms of chemokine receptor expression patterns as previously suggested by Geissmann and colleagues [##REF##12871640##35##] who proposed the presence of two blood monocytes consisting of inflammatory CX3CR1<sup>low</sup>CCR2<sup>+</sup> and resting CX3CR1<sup>high</sup>CCR2<sup>-</sup> populations. Macrophages are presumed to exert pro-angiogenic actions under various situations [##REF##12882810##24##, ####REF##11979237##25##, ##REF##12832439##26##, ##REF##12882811##27##, ##REF##10440240##28####10440240##28##], but Apte and colleagues [##REF##16903779##17##] demonstrated anti-angiogenic activities of macrophages in CNV. These observations suggest a functional heterogeneity among macrophages during the angiogenetic process. Indeed, we demonstrated that CX3CR1 positive macrophages could dampen alkali-induced CNV by producing anti-angiogenic molecules [##REF##18322241##18##], which is in contrast to the observations on CCR2-deficient mice [##REF##12827053##15##]. We previously revealed that the CCL2/CCR2 interactions were involved in VEGF production [##REF##18322241##18##] and observed that CCR5 expressing cells simultaneously expressed CCR2. Thus, CCR5 deletion reduced the number of CCR2 expressing macrophages, the cells that can express VEGF, and as a result, this reduction eventually prevented alkali-induced CNV.</p>", "<p>Several independent groups have reported the presence of resident macrophages, dendritic cells, langerhans cells, and T cells in normal corneas [##REF##12091426##36##, ####REF##12556386##37##, ##REF##11867578##38##, ##REF##17960132##39####17960132##39##]. The number of resident macrophages in the normal corneal stroma is around 100 per mm<sup>2</sup> [##REF##12091426##36##], and in the current experimental conditions, we detected few, if any, F4/80 positive or CD68 positive macrophages in the normal corneas [##REF##18322241##18##]. Thus, it is not likely that corneal resident macrophages contribute directly to CNV.</p>", "<p>However, the simple dichotomy of monocytes/macrophages proposed by Geissmann was complicated by the observation that CCR2<sup>-</sup> and CCR2<sup>+</sup> monocytes depended on CCR5 and CX3CR1, respectively, when they entered into atherosclerotic plaques [##REF##17200718##40##]. Ambati and colleagues [##REF##12827053##15##] reported that CCR2 deficiency inhibited CNV, but they did not examine the roles of macrophages in this process. Thus, the effects of the CCR2 axis on macrophage infiltration in CNV or the interaction between the CCR2 and CCR5 axis remain unclear. More detailed analysis on this point will clarify the molecular and cellular mechanisms underlying macrophage infiltration and subsequent CNV development.</p>" ]
[]
[ "<title>Purpose</title>", "<p>To evaluate the roles of CCL3 and its specific chemokine receptors, CCR1 and CCR5, in alkali-induced corneal neovascularization (CNV).</p>", "<title>Methods</title>", "<p>Chemical denudation of corneal and limbal epithelium was performed on wild-type (WT) BALB/c mice and CCL3-, CCR1-, and CCR5-deficienct (knockout [KO]) counterparts. Two weeks after injury CNV was quantified by immunostaining with anti-CD31. Angiogenic factor expression and leukocyte accumulation in the early phase after injury were quantified by reverse transcription polymerase chain reaction (RT–PCR) and immunohistochemical analysis, respectively.</p>", "<title>Results</title>", "<p>Alkali injury augmented the intraocular mRNA expression of CCL3 and its receptors, CCR1 and CCR5, together with a transient infiltration of F4/80 positive macrophages and Gr-1 positive neutrophils. Compared with WT mice, CCL3-KO and CCR5-KO mice but not CCR1-KO mice exhibited reduced CNV two weeks after injury both macroscopically and microscopically as evidenced by CD31 positive areas. Concomitantly, the infiltration of F4/80 positive macrophages but not Gr-1 positive neutrophils was significantly attenuated in CCL3-KO mice compared with WT mice. Intracorneal infiltration of CCR5 expressing cells was significantly impaired in CCL3-KO mice compared with WT mice. Alkali injury induced a massive increase in the intraocular mRNA expression of a potent angiogenic factor, vascular endothelial growth factor (VEGF), in WT mice whereas these increments were severely retarded in CCL3-KO mice. Moreover, CCL3 enhanced VEGF expression by murine peritoneal macrophages at both the mRNA and the protein level. Furthermore, topical CCL3 application restored CNV, which was macroscopically and microscopically reduced in CCL3-KO mice after two weeks to levels similar to those found in WT mice.</p>", "<title>Conclusions</title>", "<p>In alkali-induced CNV, CCL3 induced macrophages to infiltrate and produce VEGF by binding to CCR5 but not to CCR1 and eventually promoted angiogenesis.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>This work was supported by the International Cooperative Program of Kanazawa University (N.M.), National Natural Science Foundation in China (NSFC No 30572120, NSFC No 30771978), Jiangsu Natural Science Foundation (BK2006528), China Postdoctoral Science Foundation (2005038587), grants from Soochow University (No90134602; P.L.), and National Natural Science Key Program Foundation in China (NSFC No 30330540; X.Z.). We express our sincere gratitude to Dr. Che John Connon, Ph.D. (Division of Pharmacology, School of Pharmacy, University of Reading, Berkshire, UK), for creative reading. Drs Mukaida and Zhang contributed equally to the research.</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>The expression of CCL3 and its receptors in cornea after alkali injury. <bold>A</bold>: Semi-quantitative RT–PCR was performed to assess mRNA expression of <italic>CCL3</italic> and its receptors, <italic>CCR1</italic> and <italic>CCR5</italic>, and the ratios of target gene expression to <italic>β-actin</italic> were determined. All values represent the mean±SEM of three to five independent measurements. <bold>B</bold>: Whole eyes were obtained at 0, 2, 4, and 7 days after alkali injury and processed for immunohistochemical analysis using anti-CCL3 (upper panels) or anti-CCR1 antibodies (lower panels). Representative results from five individual animals are shown. Original magnifications, 400X. Scale bar, 50 μm.</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Alkali-induced corneal neovascularization and macrophage infiltration. <bold>A</bold>: The macroscopic appearances of WT, CCL3-KO, CCR1-KO, and CCR5-KO mouse eyes two weeks after alkali injury are illustrated. Representative results from at least 10 animals in each group are shown here. <bold>B</bold>: Corneal tissues were obtained from WT, CCR1-KO, and CCL3-KO mice two weeks after the injury. Tissues were stained with hematoxylin and eosin (left panels) or immunostained with anti-CD31 antibodies (right panels), and representative results from five individual animals are shown. Original magnifications, 400X. Scale bar, 50 μm. <bold>C</bold>: CNV numbers per mm<sup>2</sup> in hot spots (upper panel) and % CNV areas in hot spots (lower panel) were determined on corneas obtained from WT or KO mice two weeks after the injury. Each value represents the mean and SEM (n=5 animals). An asterisk represents a p&lt;0.05 and that the value was obtained comparing WT and CCL3-KO mice. <bold>D</bold>: The numbers of infiltrated F4/80 positive macrophages were determined two and four days after the injury. Each value represents the mean and SEM (n=5). The double asterisk indicates a p&lt;0.01 and that the value was obtained comparing WT and CCL3-KO mice.</p></caption></fig>", "<fig id=\"f3\" fig-type=\"figure\" position=\"float\"><label>Figure 3</label><caption><p>Angiogenic factor expression. <bold>A</bold>: RT–PCR analysis of pro-angiogenic and anti-angiogenic gene expressions in the injured corneas of WT and CCL3-KO mice. RT–PCR analysis was performed on total RNAs extracted from eyes 0, 2, 4, and 7 days after alkali injury, and then the ratios of VEGF to β-actin, bFGF to β-actin, and TSP-1 to β-actin of WT (black bars) and CCL3-KO mice (open bars) were determined. All values represent the mean and SEM (n=3-5 animals). The asterisk denotes a p&lt;0.05; the hash mark denotes a p&lt;0.01 and that the value was obtained comparing WT and KO mice. The effects of CCL3 on VEGF expression by murine peritoneal macrophages is shown in <bold>B</bold> and <bold>C</bold>. <bold>B</bold>: RT–PCR was performed on macrophages incubated with the indicated concentrations of CCL3 for 12 h, and the ratio of VEGF to β-actin was calculated. Each value represents the mean and SEM (n=3). <bold>C</bold>: Murine macrophages were stimulated with either 0, 10, or 100 ng/ml of CCL3 for 24 h. VEGF concentrations in the supernatants were determined with ELISA as described in Methods. The representative results from three independent experiments are shown. The asterisk denotes a p&lt;0.05 and the double asterisk denotes a p&lt;0.01 when compared to untreated.</p></caption></fig>", "<fig id=\"f4\" fig-type=\"figure\" position=\"float\"><label>Figure 4</label><caption><p>Intracorneal CCR5 positive cell infiltration. <bold>A</bold>: A double-color immunofluorescence analysis of CCR5-expressing cells is illustrated. Corneas were obtained from WT mice 0 and 4 days after the injury. The samples were immunostained with a combination of anti-CCR5 and anti-CCR2 antibodies as described in Methods and observed with fluorescence microscopy (original magnification, 400X). Signals were digitally merged in the right panels. Arrows indicate the double, positively stained cells. Representative results from three independent experiments are shown. <bold>B</bold>: Corneal tissues from WT mice (left panel) or CCL3-KO mice (right panel) obtained four days after the injury were stained with anti-CCR5 Ab. Scale bar, 100 μm. <bold>C</bold>: The numbers of intracorneal CCR5 positive cells four days after the injury were determined as described in Methods, and the mean and SEM are shown here (n=5).</p></caption></fig>", "<fig id=\"f5\" fig-type=\"figure\" position=\"float\"><label>Figure 5</label><caption><p>The effects of topical CCL3 application on corneal neovascularization. <bold>A</bold>: Macroscopic appearances of WT, CCL3-KO mice, and CCL3-KO mice topically applied with CCL3 two weeks after alkali injury are shown. Representative results from five animals from each group are shown here. <bold>B</bold>: Corneal tissues were obtained two weeks after the injury from WT, CCL3-KO, and CCL3-KO mice topically applied with CCL3 and were immunostained with anti-CD31 antibodies. Representative results from five individual mice from each group are shown. Original magnification, 400X. Scale bar, 50 μm. <bold>C</bold>: The CNV numbers per mm<sup>2</sup> in hot spots (left panel) and % CNV areas in hot spots (right panel) were determined. Each value represents the mean and SEM (n=5 animals). <bold>D</bold>: The number of infiltrated F4/80 positive macrophages was determined on WT, CCL3-KO, and CCL3-KO, which were all treated with CCL3, two days after the injury. Each value mean represents both the mean and SEM (n=5). The asterisk denotes a p&lt;0.05, and the double asterisk means a p&lt;0.01 when compared with CCL3-KO (this applies to both <bold>C</bold> and <bold>D</bold>).</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Sequences of the primers used for reverse transcription polymerase chain reaction.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"86\" span=\"1\"/><col width=\"243\" span=\"1\"/><col width=\"54\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"45\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gene name</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sequence</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Product size (bp)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Annealing temperature (°C)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>PCR cycles</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>CCR1</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TTTTAAGGCCCAGTGGGAGTT<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">475<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: TGGTATAGCCACATGCCTTT<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>CCR5</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F:GTCCTCCTCCTGACCACCTT<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">122<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: GGGTTTAGGCAGCAGTGTGT<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>CCL3/MIP-α</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: ATCATGAAGGTCTCCACCAC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">284<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: TCTCAGGCATTCAGTTCCAG<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>VEGF</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: CTGCTGTACCTCCACCATGCCAAGT<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">509<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CTGCAAGTACGTTCGTTTAACTCA<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>bFGF</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: CTTCCCACCAGGCCACTT<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">370<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CTGTCCAGGTCCCGTTTT<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>TSP-1</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: ACCAAAGCCTGCAAGAAAGA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">311<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: ATGCCATTTCCACTGTAGCC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>β-actin</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TGTGATGGTGGGAATGGGTCAG<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">514<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: TTTGATGTCACGCACGATTTCC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>All primers used were purchased from Genset Oligos (Kyoto, Japan). The amplification was performed using a GeneAmp<sup>®</sup> PCR System 9700 (Perkin-Elmer, Foster City, CA). In the table, \"F\" indicates forward primer and \"R\" indicates reverse primer.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"mv-v14-1614-f1\"/>", "<graphic xlink:href=\"mv-v14-1614-f2\"/>", "<graphic xlink:href=\"mv-v14-1614-f3\"/>", "<graphic xlink:href=\"mv-v14-1614-f4\"/>", "<graphic xlink:href=\"mv-v14-1614-f5\"/>" ]
[]
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{ "acronym": [], "definition": [] }
40
CC BY
no
2022-01-12 14:57:34
Mol Vis. 2008 Sep 5; 14:1614-1622
oa_package/78/e9/PMC2529469.tar.gz
PMC2529470
18776950
[ "<title>Introduction</title>", "<p>The mature neural retina is organized into a three-layered structure consisting of Müller glia, astrocytes, and six types of neurons. These cells are assumed to differentiate in a precise histogenic order from a single population of multipotent retinal precursors [##REF##11801336##1##]. Various molecules, such as transcription factors and neurotrophic factors, have been reported to play important roles in retinal cell differentiation [##REF##9767078##2##]. However, the intrinsic properties of retinal cells at different developmental stages are still vague. This is in part due to the lack of markers that can identify distinct stages of retinal progenitor cells. In our previous studies, we have tried to identify markers of retinal progenitor cells by employing flow cytometry and cell sorting. Using a panel of antibodies against cell-surface antigens, we screened mouse retinal cells at various developmental stages for reactivity. This technique obtained unique expression patterns for more than 30 antigens in the developing retina. Among them, some CD antigens, such as SSEA-1 (CD15) and c-kit (CD117) were identified as retinal progenitor cell markers in early and late immature stages, respectively [##REF##17069792##3##,##REF##16499901##4##].</p>", "<p>Since this approach only identifies known molecules, we used proteomics to examine the comprehensive expression profile of total membrane proteins from embryonic and adult mouse retina. Information about membrane proteins, which are expressed in a specific manner in the developing retina, may not only serve as a tool for purification of retinal subfractions by cell sorting, but may also be useful for analyzing the regulation of retinal development by receptor-signaling and cell surface molecules. To establish such a database, we used shotgun analysis and a nanoflow liquid chromatography-mass spectrometry/ mass spectrometry (LC-MS/MS) system to examine total protein expression in purified membrane fractions [##REF##17688421##5##]. We identified several membrane-associated proteins which are expressed in embryonic retina [##REF##17688421##5##], and among the proteins, we focused on glycoprotein m6a (M6a) in this work.</p>", "<p>M6a is a transmembrane protein that belongs to the myelin proteolipid protein (PLP) family. The M6a gene encodes a 278 amino acid protein that contains four putative transmembrane domains with both the N- and C-termini facing the cytosol. PLP constitutes the most abundant protein (approximately 50%) in the central nervous system (CNS) myelin sheath and is involved in signaling through integrins in oligodendrocytes [##REF##7512350##6##]. Although PLP and its splice variant DM20 are glial proteins, M6a is found exclusively in neurons [##REF##8398137##7##]. M6a is present on the postmitotic neurons of the developing neural tube at embryonic day 9 (E9) and is continuously expressed in multiple regions of the CNS in the mouse. Furthermore, the M6a protein is located at the leading edge of the growth cone in cultured cerebellar neurons [##REF##1286213##8##]. Recent studies have suggested the importance of M6a in the process of neural development, such as neurite extension, survival [##REF##1564456##9##], and differentiation [##REF##12359212##10##]. Furthermore, M6a has been found to play an important role in neurite outgrowth and filopodium and spine formation, and may also be involved in synapse formation in cultured hippocampal cells [##REF##16286650##11##]. These findings indicate the possible involvement of M6a in neuronal survival and differentiation. However, the expression pattern and function of M6a in the mouse retina have not been investigated to date.</p>", "<p>We identify M6a as a gene that is expressed in the embryonic retina and reveal the expression patterns of M6a in the neural processes, including the nerve fiber layer (NFL), inner plexiform layer (IPL), and outer plexiform layer (OPL), of the immature mouse retina. We also show that the expression of M6a parallels that of synaptophysin. Forced expression of M6a in mouse retinal explant cultures resulted in enhancement of neurite extension, which suggests that M6a plays important roles in the regulation of neurites in the embryonic retina.</p>" ]
[ "<title>Methods</title>", "<title>Isolation of retina from mice</title>", "<p>ICR mice were obtained from Japan SLC, Inc. (Hamamatsu, Japan) and Clea Japan, Inc. (Tokyo, Japan). Mice are housed under 12/12 light/dark cycles in standard shoebox cages with water and food at 23°C. The day that a vaginal plug was observed was considered to be embryonic day 0 (E0), and the day of birth was marked as postnatal day 0 (P0). All animal experiments were approved by the Animal Care Committee of the Institute of Medical Science, University of Tokyo. Mice were euthanized by decapitation or cervical dislocation under anesthesia.</p>", "<title>Plasmid construction and production of retrovirus</title>", "<p>The mouse M6a cDNA was cloned by RT–PCR from pooled mouse cDNA from P1 retina. The primers were designed based on the sequences available in the database. A full-length fragment of M6a was cloned into the <italic>Not</italic> I site of the pMXc-IRES-EGFP retrovirus vector (kindly provided by Dr. T. Kitamura, University of Tokyo, Japan), which directs expression of the cloned genes together with enhanced green fluorescent protein (EGFP) from upstream long terminal repeat (LTR) promoter. Production of the retrovirus was performed using the PLAT-E packaging cell line [##REF##10871756##12##] as previously described [##REF##15121868##13##]. Briefly, PLAT-E was transfected with retrovirus vectors containing various genes by using Fugene6 transfection reagent (Roche, Indianapolis, IN) according to the manufacturer’s instructions. Two days after transfection, cell supernatants containing retrovirus were harvested and concentrated by centrifugation in a centrifugal filter device (Millipore, Billerica, MA).</p>", "<title>RT–PCR</title>", "<p>Total RNA was purified from mouse retinas by use of Trizol reagent (Gibco BRL, Carlsbad, CA), and cDNA was synthesized with Superscript II (Gibco BRL). The primer sets were tested for several different cycling numbers by using rTaq (Takara, Otsu, Japan), and the semiquantitative cycle number was determined for each primer set. Bands were visualized with ethidium bromide.</p>", "<title>Retinal explant culture, retrovirus infection, and monolayer culture</title>", "<p>Retinal explants were prepared as previously described [##REF##15121868##13##]. Briefly, the neural retina was isolated on a Millicell chamber filter and placed with the ganglion cell layer facing upwards. The filters were inserted into six-well plates and cultured in 1 ml of explant culture medium (50% MEM with Hepes, 25% Hank’s solution, 25% heat-inactivated horse serum, 200 mM L-glutamine and 5,75 mg/ml glucose) [##REF##15121868##13##]. Infection of retrovirus was done by exposing the concentrated virus solution to the explant for initial two days, as described previously [##REF##16179606##14##]. Cells were then fixed with 4% paraformaldehyde (PFA) and frozen sectioned. For neurite extension assay, monolayer culture of retina was conducted. Retinal explants were prepared from E17 retinas and infected with retroviruses. After three days in culture, the cells were dissociated by treatment with 0.25% trypsin, and replated on eight-well chamber slides (BD Falcon, Bedford, MA) that were coated with ornithine (Sigma, St. Louis, MO) and fibronrctin (Sigma). The cells were cultured for an additional 11 days in Dulbecco’s modified Eagle’s medium/F-12 medium (Gibco BRL) that was supplemented with 1% fetal bovine serum (JRH Biosciences, Lenexa, KS) and 1% N2 (Gibco). Cells were fixed with 4% PFA and immunostained anti-green fluorescent protein (GFP; Clontech Laboratories, Palo Alto, CA) and anti-glutamine synthetase (GS; Chemicon, Temecula, CA) antibodies. The neurite lengths of GFP-positive and GS-negative cells were examined using Axioplan2 fluorescent microscopy (Carl Zeiss, Oberkochen, Germany). Then, neurite lengths of the cells were measured from randomly taken images using AxioVision 4.6 software (Carl Zeiss, Oberkochen, Germany) and Adobe Photoshop Elements 2 (Adobe Systems, San. Jose, CA). For reaggregation culture, retroviruses were infected into retinal explant cultures prepared from E17.5 retina. After overnight culture, cell were dissociated by treatment with 0.25% trypsin and used as donor cells. They were then mixed with three times number of dissociated retinal cells that had been isolated from the same brood and cultured overnight without virus infection. Then, the aggregates were cultured for eight days, and neurite extension was evaluated after immunostaining with anti-GFP antibody.</p>", "<title>Immunohistochemistry and antibodies</title>", "<p>Immunohistochemistry of retinal explants was performed as previously described [##REF##16499901##4##,##REF##15121868##13##]. Briefly, frozen-sections of retinal explant were pre-incubated in a blocking solution containing 2% bovine serum albumin and incubated with the appropriate primary antibody solutions. The primary antibodies and their concentration in reaction solution used were as follows: 1:5,000 dilution anti-GFP polyclonal antibody (Clontech Laboratories), 1:1,000 anti-M6a (clone 321; MBL, Nagoya, Japan), 1:100 anti-Rho4D2 (kindly provided by Dr. R. S. Molday, The University of British Columbia, Vancouver, Canada), 1:1,000 GS (Chemicon), 1:500 anti-Hu C/D (Molecular Probes, Inc., Eugene, OR), 1:100 anti-protein kinase C (PKC; Oncogene Research Product, Boston, MA), and 1:100 anti-Ki67 (BD Biosciences) monoclonal antibodies. The primary antibodies were visualized by using appropriate second antibodies conjugated with 1:1,000 Alexa Fluor 488 or 546. All samples were sealed using VectaShield mounting media (Vector Laboratories, Burlingame, CA) containing DAPI for nuclear staining.</p>", "<title>BrdU labeling and detection</title>", "<p>Three days after retrovirus infection, retinal explants were incubated with 5 μM bromodeoxyuridine (BrdU) for 24 h before they were harvested and fixed with 4% PFA. The samples were embedded in optimal cutting temperature (OCT) compound and frozen-sectioned. The sections were treated with 1 U/μl of DNase (Takara) in PBS for 1 h at 37 °C, and the incorporated BrdU was visualized immunohistochemically using an anti-BrdU monoclonal antibody (Roche, Indianapolis, IN) and the appropriate secondary antibodies.</p>" ]
[ "<title>Results</title>", "<title>M6a is expressed in the neuronal processes of the mouse retina</title>", "<p>To obtain comprehensive expression profiles of the membrane proteins of embryonic and adult mouse retinas, we analyzed the membrane fractions for total proteins using shotgun analysis on a nanoflow LC-MS/MS system [##REF##17688421##5##]. With this approach, we detected M6a in samples prepared from embryonic retinas, but not in samples from adult retinas [##REF##17688421##5##]. M6a is known to be widely expressed in brain [##REF##1286213##8##], whereas its detailed expression in the neural retinas of mammals has not been reported. We examined the expression of M6a mRNA over time, using semiquantitative RT–PCR (##FIG##0##Figure 1A##). M6a was expressed in E14 mouse retinas, and expression continued after birth with a slight decrease in the intensities of the bands between P12 and P15. Finally, a weak band was observed in the adult retina samples. We used immunohistochemistry to investigate the spatial and temporal expression patterns of M6a in mouse retina sections from various developmental stages.</p>", "<p>In the E14 retina, M6a was mainly and strongly expressed in the NFL, which consists of the axons of ganglion cells, but was not observed in the neuroblastic layer (NBL), which consists of proliferating retinal progenitor cells (##FIG##0##Figure 1B##). However, in P1 (##FIG##0##Figure 1B,C##) and P5 (data not shown) retinas, strong expression of M6a was confined to the IPL and also in NFL, which consists of innermost region, but not in ganglion cell layer (GCL). At P10 (data not shown) and in the adult retina, strong expression of M6a was detected in the IPL, and weak signals were observed in NFL, OPL, and inner nuclear layer (INL; ##FIG##0##Figure 1D##).</p>", "<title>M6a protein colocalizes with synaptic markers of postmitotic cells</title>", "<p>The βIII tubulin protein is expressed at early stages by differentiated neurons, including ganglion and amacrine cells and by most retinal neurons up to P7 [##REF##14519497##15##]. Double staining of retinal cells with anti-βIII tubulin and anti-M6a antibodies revealed that most cells were double-positive at these stages, which indicates that M6a is expressed on postmitotic mature neurons (##FIG##0##Figure 1C##). These results are comparable with a previous report that immunolabeling with anti-M6a antibodies was evident throughout the CNS of the embryonic mouse, but was absent from the zones of cell proliferation adjacent to the ventricles [##REF##1286213##8##]. Not only for neurons, but M6a was also weakly expressed in processes of Müller glia cells, which are evident from the co-expression of M6a with Müller glia marker, glutamine synthetase (##FIG##0##Figure 1D##).</p>", "<p>Since previous reports have located M6a immunoreactivity in the synapses of the rat cerebellum and in the axon terminals of the rat cerebellar molecular layer [##REF##10447243##16##], we examined the coexpression of M6a and the presynaptic marker, synaptophysin, by immunostaining with both anti-M6a and antisynaptophysin antibodies (##FIG##0##Figure 1E##). We found that M6a expression colocalized with that of synaptophysin from the embryonic to adult stages (##FIG##0##Figure 1E##).</p>", "<title>M6a overexpression does not affect cell fate and subretinal localization of retinal precursors</title>", "<p>To examine the biologic significance of M6a for retinal development, we investigated the effects of ectopically expressed M6a in a mouse retinal explant culture prepared from E17, which provides an excellent model to monitor retinal differentiation in vitro [##REF##15121868##13##]. By E17, ganglion cells and a few other cell types have begun to differentiate, and after two weeks in culture, all of the retinal subpopulations have differentiated normally. The mouse retinal explant prepared from E17 was infected with retroviruses that encode either M6a-IRES-EGFP or IRES-EGFP (control) and cultured for two weeks. Since the retrovirus infects only mitotic cells, retinal precursor cells were assumed to be the major targets of gene transfer. We examined the subretinal localization of virus-infected cells. The number of M6a-EGFP-expressing cells was lower in the ONL and slightly higher in the INL than that of control EGFP-expressing retinal cells, but both differences were not statistically significant (##FIG##1##Figure 2A##). We then examined the differentiation of virus-infected cells by immunostaining frozen sections with antibodies against various marker proteins for retinal cell subpopulations (##FIG##1##Figure 2B##). The antibodies used were antirhodopsin for rod photoreceptors, anti-HuC/D for retinal ganglion cells, and amacrine cells, anti-PKC for bipolar cells, and anti-GS for Müller glia. The percentage of rhodopsin positive M6a-overexpressing cells was not significantly, but few times higher than that of control cells (##FIG##1##Figure 2B##). The other retinal cell populations were not affected by M6a overexpression, which suggests that M6a does not play a role in retinal differentiation.</p>", "<title>M6a does not affect retinal precursor cell proliferation</title>", "<p>We next examined whether M6a affects the proliferation of retinal cells by measuring BrdU incorporation. Retrovirus-mediated gene transfer into retinal explants prepared from E15.5 or E17.5 was conducted, and proliferating cells were labeled with 5 μM BrdU for the final 24 h of four days of culture. In all cases BrdU incorporation was slightly higher in the M6a-expressing cells than in control cells. Despite this general trend, statistical analysis revealed that the differences were not statistically significant (##FIG##1##Figure 2C##). Immunostaining the sections with the antiproliferation antigen, Ki67 antibody [##REF##6339421##17##], produced slightly more Ki67-positive cells among the M6a-expressing cells than the control cells. These differences were not considered statistically significant, thus confirming the BrdU results (##FIG##1##Figure 2D##). Taken together, these results suggested a minor trend of increased proliferation associated with M6a but demonstrated that M6a does not regulate retinal cell proliferation significantly.</p>", "<p>To confirm the proliferation results, we performed a clonal assay [##REF##16499901##4##] to test the proliferation capabilities of the virus-transfected M6a cells (data not shown). The colony sizes showed no significant differences between the control and M6a-transfected retinal cells.</p>", "<title>M6a overexpression promotes neurite outgrowth in retina</title>", "<p>Since M6a has been implicated in neurite extension in brain, we investigated whether M6a was also involved in neurite extension in the retina, using monolayer cultures of retinas infected with retroviruses that encode control EGFP or M6a-IRES-EGFP. Neurite extension in monolayer cultures was examined by measuring the length of neurites from photographs taken under a fluorescence microscope. To distinguish neural cells from glial cells, we stained the retinal cells with antibody against GS, which is a glial cell marker, and evaluated the neurite lengths of the GS-negative and EGFP-positive cells. In both the control and M6a-expressing samples, approximately 60% of the cells extended neurites by 0–10 μm of neurite. However, when we compared the cell population distribution categorized by neurite length, we discovered that M6a-expressing cells extended longer neurites than did control virus-infected cells (##FIG##2##Figure 3A##). The average neurite lengths were greater than 10 μm for the M6a-overexpressing cells (39.2 μm) and control cells (25.5 μm; ##FIG##2##Figure 3B##). We next confirmed these results with a different method of culture. We prepared reaggregation cultures by mixing dissociated virus-infected retinal explants prepared from E17.5 and dissociated retinal cells isolated from the same brood. Eight days later, percentage of M6a-overexpressing retinal cells bearing neurite over 30 μm length was about twice-times higher than that of control EGFP expressing cells (##FIG##2##Figure 3C,D##). This indicates that M6a plays a role in promoting the neurite extension during retinal development.</p>" ]
[ "<title>Discussion</title>", "<title>M6a protein is targeted to the neuron processes of the murine retina</title>", "<p>In the present study, strong expression of M6a was detected in the ganglion cell axons and processes of cells located in the INL of the immature murine retina. A previous report on in situ hybridization of M6a in the <italic>Xenopus</italic> eye demonstrated expression of M6a mRNA in the INL and GCL, and weak expression in the ONL [##REF##10397631##18##], which suggests that the expression of M6a in the retina is conserved in vertebrates.</p>", "<p>In mouse hippocampal tissues, M6a mRNA has been found to be expressed in granule cells of the dentate gyrus and pyramidal neurons in CA1 and CA3; immunoreactivity for M6a was concentrated in the regions of relatively dense synaptic contact [##REF##16286650##11##]. Similar results have been obtained for the rat [##REF##10447243##16##]. This implies that the translated M6a protein is targeted to processes distal to the somata. The expression of M6a paralleled that of the presynaptic protein, synaptophysin, in the mouse retina, suggesting that the subcellular distribution of M6a is the same in the brain and retina. It has been established that synapse formation by dissociated neurons in culture strongly correlates with focal accumulations of structures that can be labeled with antibodies against synaptic vesicle proteins, such as synaptophysin [##REF##1904480##19##]. Therefore, our results indicate a possible role for M6a in the formation of synapses in neural retinas.</p>", "<title>M6a does not affect retinal progenitor cell differentiation and proliferation</title>", "<p>We found that cell fate and subretinal localization of retinal progenitor cells were not affected by M6a overexpression. To date, there has been no report on the promotion of proliferation by M6a. On the other hand, it has been reported that the addition of anti-M6a antibody decreases the survival of dissociated neurons in culture and inhibits the extension of neurites in cultured cerebellar explants [##REF##1564456##9##]. M6a belongs to the proteolipid protein (PLP)/DM20 family of myelin proteins. PLPs play a pivotal role in early oligodendrocyte differentiation and survival [##REF##8807448##20##]. PLP members have been observed to form a complex with integrins and may participate in integrin receptor signaling in oligodendrocytes [##REF##12196561##21##]. Given the high degree of homology between M6a and PLP, it is possible that this transmembrane protein is also related to integrin signaling. Our preliminary results from examining integrin family proteins in the developing retina reveal the expression of integrin αv in the embryonic mouse retina (unpublished results) which suggests that M6a plays role in retinal development through interaction with integrins to mediate signals for retinal development.</p>", "<title>M6a promotes neurite outgrowth in the retina</title>", "<p>In the present study, we observed promotion of retinal neurite extension by M6a. This protein has been reported to enhance neurite extension in rat pheochromocytoma PC12 cells and to induce an increase in the intracellular Ca<sup>2+</sup> concentration of PC12 cells [##REF##12359212##10##]. The anti-M6 antibody efficiently interfered with Ca<sup>2+</sup> influx, which suggests that M6a acts as a Ca<sup>2+</sup> channel blocker in PC12 cells. As a second messenger, Ca<sup>2+</sup> has been shown to participate in neurite extension, filopodium and spine activity, and neuronal differentiation [##REF##10638760##22##]. M6a possesses two PKC phosphorylation sites. Treatment of PC12 cells with a PKC inhibitor eliminates the ability of M6a to promote nerve growth factor-primed neurite extension [##REF##12359212##10##]. Thus the putative phosphorylation sites in the cytosolic domains may facilitate M6a regulation and may be relevant for intracellular signaling. Therefore, the promotion of neurite extension by M6a in retinal cells seen in the present study is possibly associated with Ca<sup>2+</sup> influx, and phosphorylation of PKC may remove this function. Future studies will focus on this hypothesis.</p>" ]
[]
[ "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>Glycoprotein m6a (M6a) is a cell-surface glycoprotein that belongs to the myelin proteolipid protein family. M6a is expressed mainly in the nervous system, and its expression and function in mammalian retina have not been described. Using proteomics analysis of mouse retinal membrane fractions, we identified M6a as a retinal membrane protein that is strongly expressed at embryonic stages. Our aim was to reveal the function of M6a in development of mouse retina in this work.</p>", "<title>Methods</title>", "<p>Detailed expression pattern of M6a was examined by immunostaining using frozen sections of mouse retina obtained at various developmental stages. For functional analysis of M6a in mouse retinal development, we performed retorovirus-mediated overexpression of M6a in mouse retinal explant culture. Then, cell differentiation, proliferation and structural maturation of the cells were examined.</p>", "<title>Results</title>", "<p>M6a transcripts were strongly expressed in embryonic retina. After completion of retinal differentiation, the level of expression decreased as mouse development progressed. Immunohistochemistry showed that in the immature mouse retina, M6a was strongly expressed in the axons of retinal ganglion cells. After birth, M6a expression was confined to the inner plexiform layer, and finally, to the inner and outer plexiform layers of adult mouse retina. M6a expression was completely paralleled by that of the synaptic marker, synaptophysin. Mouse retinal progenitor cells that overexpressed M6a following retrovirus-mediated gene transfer were subjected to in vitro explant or monolayer cultures. The neurite outgrowth of M6a-overexpressing retinal cells was strikingly enhanced, although M6a did not affect differentiation and proliferation.</p>", "<title>Conclusions</title>", "<p>These results suggest that M6a plays a role in retinal development by regulating neurites, and it may also function to modulate synaptic activities in the adult retina.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>This work was supported by a grant-in-aid from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Expression of M6a in mouse retinas from various developmental stages. <bold>A:</bold> Semiquantitative RT–PCR for M6a using total RNA extracted from mouse retinas at various developmental stages. Glyceraldehyde-3-phosphate dehydrogenase (G3PDH) was used as the control. <bold>B-E:</bold> Immunostaining of M6a in frozen sections of mouse retina from various developmental stages. Coimmunostaining was performed with anti-M6a and anti-βIII-tubulin (<bold>C</bold>), anti-glutamine synthetase (<bold>D</bold>), or antisynaptophysin (<bold>E</bold>) antibodies. The right two columns are magnified figure of the square area indicated by broken lines in left two columns <bold>B</bold>. The scale bar represents 100 μm. The following abbreviations are used in this figure: inner plexiform layer (IPL); outer plexiform layer (OPL); ganglion cell layer (GCL); neuroblastic layer (NBL); inner nuclear layer (INL); outer nuclear layer (ONL); retinal pigment epithelium (RPE).</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Effects of forced expression of M6a on retinal differentiation and proliferation. <bold>A:</bold> Sublayer distributions of virus-transduced enhanced green fluorescent protein (EGFP)-positive cells in retinal explants. Retinal explants were infected with retorovirus particles that encode M6a and EGFP. After 14 days, the explants were harvested and frozen-sections prepared. Immunostaining was performed using an anti-EGFP antibody. The percentages of cells in each sublayer are shown. More than 200 cells were counted for each sample, and the standard deviation (SD) was calculated from three independent experiments. <bold>B:</bold> Differentiation of virus-infected cells examined by immunostaining to identify subpopulations within the retina. The percentages of marker-positive cells in the EGFP-positive population are shown. Rhodopsin for rod, HuC/HuD for amacrine, glutamine synthetase for Müller glia, and protein kinase C for bipolar were used as markers. More than 100 cells were examined for each sample, and the average value from three independent experiments is shown with the SD. <bold>C</bold>,<bold>D</bold>: Proliferation of M6a-expressing retinal cells was examined by measuring incorporation of bromodeoxyuridine (BrdU; <bold>C</bold>) or expression of the Ki67 antigen (<bold>D</bold>). BrdU was present for the final 24 h of four days of culture of retinal explants, and frozen sections were immunostained using antibodies against BrdU. The same samples were immunostained with the anti-Ki67antibody. The percentage of positive cells with SD are shown. Listed below each panel is the stage when each retinal explant was prepared and its culture period. The following abbreviations are in effect: outer nuclear layer (ONL); inner nuclear layer (INL); ganglion cell layer (GCL).</p></caption></fig>", "<fig id=\"f3\" fig-type=\"figure\" position=\"float\"><label>Figure 3</label><caption><p>Effects of forced expression of M6a on retinal neurite extension. <bold>A</bold>,<bold>B</bold>: Neurite extension of virus-transduced enhanced green fluorescent protein (EGFP)-positive cells in retinal monolayer cultures. Retinal explants were infected with retorovirus particles that encode M6a and EGFP, and the cells were dissociated and subjected to monolayer culturing. After 11 days of culture, the cultures were harvested and stained with anti-GFP antibody. Neurite length was examined by measuring the EGFP-positive neurites on photographs taken under a fluorescence microscope. The length distribution percentages for the neurites are shown in <bold>A</bold>. The average lengths of neurites longer than 10 μm are shown in <bold>B</bold>. More than 100 cells were counted for each sample, and essentially the same results were obtained in three independent experiments. <bold>C</bold>,<bold>D:</bold> Neurite extension of virus-transduced EGFP-positive cells in retinal reaggregation cultures. Morphology of the M6a-EGFP virus-transfected retinal cells in reaggregation culture (<bold>C</bold>). The percentage of cells with neurite extensions of 30-100 μm or 100-200 μm in the M6a and control EGFP expressed retinal cells (<bold>D</bold>). Asterisk is p&lt;0.05 by the Student's <italic>t</italic>-test.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"mv-v14-1623-f1\"/>", "<graphic xlink:href=\"mv-v14-1623-f2\"/>", "<graphic xlink:href=\"mv-v14-1623-f3\"/>" ]
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{ "acronym": [], "definition": [] }
22
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 3; 14:1623-1630
oa_package/06/75/PMC2529470.tar.gz
PMC2529471
18776951
[ "<title>Introduction</title>", "<p>The identification of genes and loci causing inherited retinal diseases such as retinitis pigmentosa (RP), macular degeneration (MD), and Usher (USH) syndrome are crucial for disease management [##REF##17651254##1##]. Inherited retinal degeneration (RD) is the major cause of blindness in the developed world. <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.sph.uth.tmc.edu/retnet/\">Retnet</ext-link> lists 28 different categories, and two complex forms of retinal disease, including 191 loci that have been mapped; the disease gene has been identified for 140 of these loci. While great strides have been made to identify genes and mutations causing these diseases, progress has been hampered by their enormous complexity, due to genetic, allelic, phenotypic, and clinical heterogeneity of patient populations [##REF##14750594##2##]. For example, autosomal dominant RP has been associated with mutations in 16 genes and another locus with the gene yet to be determined; autosomal dominant macular degeneration has been associated with mutations in 14 genes and eight other loci; and autosomal recessive RP has been associated with mutations in 21 genes with five other loci. Thus, it is clear that there are a good number of existing RD loci for which the mutant genes are yet to be determined and, in addition, yet to be discovered new loci for various monogenic and complex RDs such as age-related macular degeneration (AMD) and diabetic retinopathy.</p>", "<p>We propose a technique to identify possible gene candidates for these human disease loci: gene expression analysis in mouse models of photoreceptor dystrophy. These analyses could identify genes that are misregulated during photoreceptor degeneration, correlating the human orthologs with chromosomal locations associated with inherited human retinal degeneration. Here we have followed this approach by using gene expression analysis from three unrelated mouse models of photoreceptor dystrophy. Two models match the human condition, as the same gene functions are affected (the <italic>rd1</italic> and the <italic>rd2</italic> mouse [##REF##7843898##3##,##REF##2918924##4##]) as well as a popular oxidative stress model thought to be relevant for diseases such as AMD (light-damage (LD) in albino mice [##REF##12589782##5##]). The results are based on the premise that information collected about the orthologs of genes between species that exhibit the same trait or disease may be useful. Orthologs, by definition, evolved from the same gene, and usually share the same function. Correlating this information may provide evidence to determine a regulatory pattern of orthologs associated with congruent traits [##REF##15514669##6##]. Genes that matched to the human RD loci and that were commonly up- or downregulated in all three models of degeneration are thought to be good candidates, especially if a literature search suggests that the known biologic information might be relevant to RD. Finally, as human retinal degenerations are usually caused by missense or nonsense mutations resulting in altered gene expression, the approach using expression differences to identify candidates is acceptable.</p>", "<p>One of the genes, <italic>PLA2G7</italic> (PAF-AH, Lp-PLA2), a candidate for a dominant form of macular dystrophy, benign concentric annular macular dystrophy (BCMAD), was selected for further study. The main function of platelet-activating factor (PAF) acetylhydrolase is to convert PAF into the biologically inactive lyso-PAF [##REF##3558407##7##]. However, PLA2G7 also hydrolyzes oxidized phospholipids. Oxidized phospholipids are known to initiate cell death, triggering the intrinsic apoptotic caspase cascade [##REF##18460921##8##]. Oxidative stress is activated in the photoreceptors of the three models of retinal degeneration studied herein [##REF##16626700##9##] and is a contributing factor in AMD [##REF##15078679##10##]. In addition, products generated by PLA2G7, lysophosphatidylcholine and oxidized nonesterified fatty acids, are thought to contribute to inflammation in atherosclerosis, coronary artery disease, and stroke [##REF##17877917##11##]. Plasma PLA2G7 activity levels can be used in these diseases as a biomarker, while also functioning as an independent risk predictor for cardiovascular disease [##REF##15713711##12##]. Finally, inflammation has been proposed as a possible driving force of AMD pathology [##REF##11587915##13##, ####REF##15761122##14##, ##REF##16099945##15##, ##REF##15761121##16##, ##REF##16518403##17##, ##REF##15761120##18####15761120##18##].</p>", "<p>The reverse-mapping approach identified possible novel disease candidates for RD, which are discussed in the context of their known gene function and possible involvement in disease pathology. Of the identified candidate genes, two of them were previously confirmed to be the disease genes in loci associated with photoreceptor degeneration, supporting the validity of our approach while four additional genes were novel candidates for three mapped RD chromosomal loci. One of the candidate gene products, Pla2g7, was localized to the mouse photoreceptor inner and outer segments, and retinal tissue activity levels were significantly reduced before photoreceptor cell death. Hence, this tactic has resulted in the identification of novel candidates for three RD loci and demonstrated this as a feasible approach to identifying gene candidates for other human diseases as well.</p>" ]
[ "<title>Methods</title>", "<title>Animals</title>", "<p>C57BL/6 <italic>rd1</italic> [##REF##4369896##19##] and <italic>rd2</italic> [##REF##2918924##4##] mice were gifts from Drs. Debora Farber and Gabriel Travis (both at University of California, Los Angeles, CA). Both strains were maintained as homozygotes. C57BL/6 and BALB/c mice were generated from breeding pairs obtained from Harlan Laboratories (Indianapolis, IN). Animals were housed in the Medical University of South Carolina (MUSC) Animal Care Facility under a 12 h:12 h light–dark cycle with access to food and water ad libitum. The ambient light intensity at the eye level of the animals was 85±18 lux. Light damage was produced by exposing the BALB/c animals to constant fluorescent light for 24 or 48 h at an illuminance of approximately 1500 lux. This intensity reduces the number of photoreceptors to 50% within 10 days in 3-month-old (young adult) albino mice [##REF##12589782##5##]. All experiments were performed in accordance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the University Animal Care and Use Committee.</p>", "<title>Microarray analyses</title>", "<title>Samples</title>", "<p>Affymetrix oligonucleotide (MGU74AV2) arrays (Affymetrix Inc, Santa Clara, CA), containing 12489 genes and ESTs, were used for this analysis as described previously [##REF##15218024##20##]. The Affymetrix CEL files, containing the raw intensity values, were used for expression data analysis. To determine genes that could be potential candidates for retina-specific chromosomal locations, we compared gene expression data from the three unrelated mouse models of photoreceptor dystrophy. To analyze genes involved in neurodegeneration, we argued that genes altered early in the progression would be involved in initiating degeneration. For the <italic>rd1</italic> mouse, we collected retinas from days P6 and P10, which represent early time points during which cGMP continues to rise [##REF##7843898##3##] and apoptosis is initiated [##REF##8302876##21##]; for the <italic>rd2</italic> mouse, we collected retinas from P14 and P21, representing early time points during the first phase of apoptosis [##REF##8302876##21##]; and finally for the light-damaged paradigm, we collected retinas 24 and 48 h after the onset of constant light at 1500 lux, a point at which a few TUNEL-positive photoreceptors can be observed, but no cell loss can yet be documented [##REF##16626700##9##].</p>", "<title>RNA isolation</title>", "<p>All chemicals used in this study were at least molecular biology grade material and purchased from Fisher Scientific (Pittsburgh, PA), unless otherwise noted. Animals (see Samples for ages of animals) were sacrificed by decapitation and retinas isolated and stored in RNA-later (Ambion, Austin, TX) at −20 °C. Retinas from four animals per genotype per time point were pooled, and each data point was obtained in duplicate. Pooling is recommended as the method of choice to reduce the number of arrays needed to generate reliable data [##REF##15218024##20##,##REF##12925512##22##]. Total RNA was isolated using Trizol (Ambion), followed by a clean-up using RNAeasy minicolumns (Qiagen, Valencia, CA). The quality of the RNA was examined by gel electrophoresis, and spectrophotometry [##REF##15218024##20##].</p>", "<title>Microarray procedures</title>", "<p>Sample preparation and hybridization was performed as described in the Affymetrix Expression Analysis Technical Manual and published previously [##REF##15218024##20##]. In short, double-stranded cDNA was generated (SuperScript™ II Reverse Transcriptase; Invitrogen, Carlsbad, CA) using 5 μg total RNA as starting material, and purified using phase-lock gel columns (Eppendorf, Westbury, NY) followed by ethanol precipitation. The purified cDNA served as a template for the generation of biotinylated cRNA, using the BioArray™ HighYield™ RNA transcript labeling kit (Enzo Diagnostics, New York, NY). Labeled probes were purified using the RNEasy mini kit (Qiagen, Valencia, CA), fragmented by metal-induced hydrolysis at 94 °C for 35 min (100 mM potassium acetate, 30 mM magnesium acetate, and 40 mM tris-acetate) and stored at -80 ºC. The length of the cRNA and fragmentation was confirmed by agarose gel electrophoresis. Hybridization with equal amounts of labeled cRNA (15 µg/array) and readout was performed by the DNA Microarray Core Facility at MUSC, using the Affymetrix Fluidics Station.</p>", "<title>Data analysis</title>", "<title>Normalization and filtering</title>", "<p>Genechips were scanned using the Affymetrix scanner (Microarray Suite 5.0 software) to obtain probe level data. Outputs were scaled to the same target intensity. The raw Affymetrix data (absolute expression level and perfect match (PM)-values) was used for normalization. Each of the three model sets were normalized using quantile normalization on the probe and probe set level. This procedure was done using <ext-link ext-link-type=\"uri\" xlink:href=\"http://biosun1.harvard.edu/complab/dchip/).\">Dchip</ext-link> software [##UREF##0##23##]. Gene filtering was performed individually on the three retinal degeneration sets. Normalized data was filtered on significant p-values (≤0.05) in fold change and difference of the means between experimental and age-matched control samples (value of ≥100). With an estimated median expression level of 90 this automatically excludes low-expressing genes. <ext-link ext-link-type=\"uri\" xlink:href=\"http://biopuce.insa-toulouse.fr/ExperimentExplorer/venn/\">Venn analysis</ext-link> was used to identify genes that localized to RD loci.</p>", "<title>Analysis of retinal degeneration chromosomal loci</title>", "<p>For us to be able to match the differentially expressed mouse genes to known chromosomal locations involved in retinal degeneration, we needed human orthologs to these mouse genes. The <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.affymetrix.com/index.affx\">Affymetrix</ext-link> NetAffx Analysis Center was used to obtain the human orthologs, as well as accession numbers and chromosome locations for all genes. The list of human ortholog locations was correlated with the 191 human retina-specific locations currently listed in <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.retnet.org\">RetNet</ext-link>, to determine which locations were unknown and unsolved.</p>", "<title>Gene ranking and probability</title>", "<p>To determine the probability of one of the genes in our analysis falling into one of the retina-related loci, we implemented an algorithm using the gene lengths, locus lengths, and chromosome lengths. Probability was determined by calculating the ratio of gene length to locus length over the ratio of the gene length to chromosome length. Genes were ranked based on a combined score of probability: 5 (0%–4.9%), 4 (5%–9.9%), 3 (10%–14.9%), 2 (15%–19.9%), 1 (20%-above). This score was multiplied by the number of models in which the genes were differentially expressed (3, 2, or 1), resulting in a maximum score of 15.</p>", "<title>Gene ontology analysis</title>", "<p>Gene Ontology (GO) analysis on the identified genes was done using <ext-link ext-link-type=\"uri\" xlink:href=\"http://gostat.wehi.edu.au/\">GoStat</ext-link> by Tim Beissbarth. GO p-values were computed, and the GO terms with significant p-values identified to compile the final list of overrepresented GO terms. All ontologies (Molecular Function, Biologic Process, and Cellular Component) were analyzed as a group.</p>", "<title>Pla2g7 analysis</title>", "<title>Immunohistochemistry</title>", "<p>For immunohistochemical analysis, eyes were fixed in 4% paraformaldehyde, rinsed, cryoprotected in 30% sucrose overnight, frozen in TissueTek O.C.T. (Fisher Scientific) and cut into 14 μm cryostat sections. Immunohistochemistry was performed as described previously [##REF##10516311##24##] using an anti-PAF-AH antibody (Lis-1; Abcam, Cambridge, MA) at 1:100. For visualization, a fluorescent-labeled secondary antibody (Alexa 488; Invitrogen, Carlsbad, CA) was used. Each staining was performed on slides from at least three animals per condition. Sections were examined by fluorescence microscopy (Zeiss) and images were false-colored using Adobe<sup>®</sup> Photoshop (Adobe Systems, San Jose, CA).</p>", "<title>Activity assay</title>", "<p>PLA2G7 is known to catalyze the hydrolysis of the substrate platelet-activating factor (PAF) into the biologically inactive lyso-PAF. The assay (Cayman Chemical, Ann Arbor, MI) uses 2-thio PAF as a substrate for PAF-AH. Hydrolysis produces free thiols, reacting it with an excess of 5,5‘-dithio-bis-2-nitrobenzoic acid (DTNB); which is measured spectrometrically. Neither the substrate nor the lyso-PAF react with DTNB. As a negative control the enzyme source (plasma or retina) is heat-inactivated for 15 min and used with the substrates; human PLA2G7 provided in the kit was taken as positive control for all the measurements. The commercial kit was used according to the manufacturer’s recommendations.</p>", "<p>For tissue levels, retinas were dissected out from eyes of <italic>rd1</italic> (P10), <italic>rd2</italic> (P21), and 48 h light-damaged BALB/c mice and corresponding control animals. Retinas were homogenized in 100 μl of cold Tris-Cl buffer (0.1 M, pH 7.2) and centrifuged at 10,000x g for 15 min at 4 °C. Supernatants were collected and total protein content in each sample assayed by the Bradford method. To determine plasma levels, blood was collected from the submandibular vein in isoflurane anesthetized mice. The vein was punctured with a 22 gauge needle, which initiates blood flow and sample collected with a pipette using citrate as an anticoagulant (0.38% final concentration). Plasma samples were collected after centrifugation (800x g for 10 min at 4 °C).</p>", "<p>The assay-mixtures each contained 10 μl of sample, to which 5 μl of assay buffer was added to each well of a 96 well flat-bottom plate. Reaction in each well was initiated by adding 200 μl of substrate solution (2-thio PAF). Following incubation at room temperature (30 min for retina, 1 min for plasma), 10 μl of DTNB was added to each well. Color development was measured in a spectrophotometer (Softmax; Molecular Devices, Sunnyvale, CA) at 405 nm, 1 min after the addition of DTNB. Specific activity of PLA2G7 was calculated from the absorbance values (extinction coefficient for DTNB at 405 nm, 12.8/mM/cm). Data are expressed as mean±SE of at least three independent Pla2g7 activity measurements in units of specific activity for tissue [μmol/minute/mg of protein] or plasma [μmol/minute/ml of plasma].</p>" ]
[ "<title>Results</title>", "<title>Identification of candidate genes</title>", "<p>The <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.retnet.org\">RetNet</ext-link> database currently lists 191 retina-specific human loci: 140 of the human disease loci are mapped and the disease gene identified, leaving 51 of the human loci uncharacterized (see <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.retnet.org\">RetNet</ext-link>). By correlating the nucleotide position data for each of these unknown locations with those of the 12489 genes and ESTs present on the MGU74Av2 array and their orthologs, we have the potential to identify candidate genes for 37 of these unsolved disease loci (approximately 73%).</p>", "<p>Mapped but unidentified chromosomal disease loci are typically large, some spanning many cM, and harbor upwards of hundreds of genes. For example, the 37 RD loci for which genes matched in the MGU74Av2 array range in size from 1.8 to 49.2 Mbp (median size: 16.71 Mbp). The average number of genes contained within a location of 16.71 Mbp is 393.18, based on an average gene density of 40–45 kb [##REF##7919921##25##]. Identifying potential candidates requires additional search criteria. Underlying an identification of a mapped locus are genetic differences influencing the susceptibility to a trait or disease. Thus, here we argued that these presumed genetic differences should be reflected in the difference in retinal gene expression of mice with RD.</p>", "<p>To analyze differences in gene expression related to photoreceptor degeneration, we selected three unrelated mouse models of photoreceptor dystrophy: the <italic>rd1</italic> mouse (calcium overload) [##REF##10672249##26##]; the <italic>rd2</italic> mouse (structural defect due to a mutation in the disc rim protein peripherin); and constant light-damage (LD; oxidative stress) [##REF##7843898##3##,##REF##12589782##5##,##REF##1986774##27##]. The <italic>rd1</italic> mouse is considered a model for RP, whereas the <italic>rd2</italic> mouse and the LD model are used as models for both RP and macular degeneration. For each mouse model, we determined changes in gene expression between the experimental animals and their age-matched controls at two consecutive time points early in the progression of degeneration. For a given gene to be considered as a possible candidate or a retina-specific location, it had to be significantly up- or downregulated (p&lt;0.05) with a predefined mean difference in expression level (≥100) in at least one of the three models. Of the 902 genes that met these criteria, 20 genes were found to have human orthologs that were localized to human retinal degeneration (##TAB##0##Table 1##). Experimental data regarding gene expression levels and fold differences in gene expression (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-1.pdf\">Appendix 1</ext-link>) are provided in the supplemental material section. These 20 genes were ranked on two criteria: (a) based on their probability of falling within a human disease locus by chance; (b) multiplied by the number of models in which the genes were differentially expressed. Due to the significant difference in locus size for the different diseases (i.e., the 20 loci range in size from 1.7 to 60 Mbp), the probability ranged from 1.9% to 46.9% (##TAB##1##Table 2##, column 5), with the median probability of 6.6%.</p>", "<title>Gene ontology analysis of identified genes</title>", "<p>To gain biologic understanding from the identified genes found in unsolved chromosomal locations (##TAB##0##Table 1##), we analyzed their functional annotations. GO identifications (GO IDs) and GO terms were retrieved for all significant ontologies (Biologic Process, Molecular Function, and Cellular Compartment). The GO terms associated with the 20 identified genes were compared to those of the reference group (all genes present on the array minus those listed in ##TAB##0##Table 1##), determining significantly over-represented terms and obtaining important GO terms that describe these differentially regulated genes. The significantly overrepresented GO terms that were retrieved for the upregulated genes included the terms “defense response,” “immune response,” and “complement activation,” whereas in the downregulated genes, the terms identified the keywords “positive gene regulation of rhodopsin,” “retinal rod cell development,” and “thioredoxin peroxidase activity” (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-2.pdf\">Appendix 2</ext-link>).</p>", "<title>Pla2g7 in retinal degeneration</title>", "<p><italic>Pla2g7</italic> (PAF-AH, Lp-PLA2), a possible candidate for a dominant form of macular dystrophy (BCMAD), was selected for further study. <italic>Pla2g7</italic> mRNA levels are significantly down-regulated in P10 <italic>rd1</italic> [fold difference (lower bound; upper bound)] [-1.39 (-1.26; -1.54)], P21 <italic>rd2</italic> [-4.5 (-3.5; -5.55)] and 48 h of light-damage in the BALB/c mouse retina [-2.37 (-1.57; -4.7)], which is before significant cell loss [##REF##16626700##9##] (see ##FIG##0##Figure 1A##). Pla2g7 localization in ocular tissues and PAF-AH activity levels in plasma and retina tissue were investigated.</p>", "<p><italic>Pla2g7</italic> is a gene highly enriched in the mouse retina according to the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.stjudebgem.org/web/mainPage/mainPage.php\">Brain Gene Expression Map</ext-link>; in the retina, <italic>Pla2g7</italic> mRNA is present in the outer nuclear layer (ONL) that contains only the cell bodies with the nuclei of rods and cones (retina <ext-link ext-link-type=\"uri\" xlink:href=\"http://cepko.med.harvard.edu/default.asp\">SAGE library</ext-link>) [##REF##15226823##28##]. Immunohistochemistry revealed labeling in the photoreceptors (##FIG##1##Figure 2##), in particular the inner and outer segments, with additional labeling in the outer and inner plexiform layer, as well as staining of cells in the inner retina.</p>", "<p>Pla2g7 activity was compared in soluble extracts of retina and in plasma (##FIG##0##Figure 1B,C##). Serum levels of Pla2g7 activity were not affected by RD triggered by either genetic (<italic>rd1</italic>, <italic>rd2</italic>) or environmental insults (LD; ##FIG##0##Figure 1C##). Plasma levels in the P10, C57BL/6 wild-type mouse were higher than that obtained at P21, but not different from <italic>rd1</italic> at P10 nor <italic>rd2</italic> at P21 ([in μmol/min/ml plasma] P10: <italic>wt</italic>, 0.093±0.013 versus <italic>rd1</italic>, 0.091±0.0058; P21: <italic>wt</italic>, 0.0367±0.00009 versus <italic>rd2</italic>, 0.0387±0.0012). Likewise, no difference was identified in light-damaged BALB/c retina (cyclic light, 0.0935±0.005 versus LD, 0.0913±0.006). When compared to their respective age-matched controls, Pla2g7 levels were reduced by ~30% in the <italic>rd1</italic> retinas, by ~70% in the <italic>rd2</italic> retinas, and by ~50% in the light-damaged retinas (##TAB##2##Table 3##). Relative changes in retina <italic>Pla2g7</italic> mRNA levels were a good predictor of retina cytosolic Pla2g7 activity levels.</p>" ]
[ "<title>Discussion</title>", "<title>Comparative genomics analysis to identify novel disease genes</title>", "<p>RD-causing mutations are found in genes whose proteins participate typically in one of four mechanisms: outer segment morphogenesis, cellular metabolism, function of the retinal pigment epithelium, and the photoreceptor signal transduction cascade [##REF##11462214##29##]. However, other genes that are not typical photoreceptor-specific genes have been identified to have mutations in inherited RD, which include mutations in components of the alternative complement pathway (part of the body’s innate immune system) that have been shown to be associated with AMD [##REF##15761122##14##,##REF##15761121##16##, ####REF##16518403##17##, ##REF##15761120##18####15761120##18##]. Hence, we used a comparative genomics approach to aid in the identification of potentially novel disease genes.</p>", "<p>Herein we have used gene expression analysis in three independent models of photoreceptor dystrophy, which showed key pathologies also seen in the human conditions, to identify novel candidates for gene loci known to be associated with inherited retinal diseases. While it would have been beneficial to obtain retina-specific arrays for our analysis, the U74Av2 arrays used had a present rate (i.e., genes that are expressed in the retina) of &gt;50%, representing &gt;6000 genes/ESTs. These 6,000 elements cover an estimated 50% of the 13k mammalian retinal transcriptome, as defined by Schulz and colleagues [##REF##15283859##30##] or approximately 62% of the mouse retinal transcriptome identified by Blackshaw and coworkers [##REF##15226823##28##]. Thus, our proposed “fishing expedition” still presents tremendous advantages when compared with a hypothesis-driven data analysis that investigates one gene at a time, but will likely miss roughly 50% of potential candidates.</p>", "<p>To identify novel genes, we carefully filtered the genes that matched human RD loci to eliminate false positives, resulting in 20 potential gene candidates. These genes were ranked to focus on genes that have a low probability of falling within a region of interest by chance. To corroborate the potential for these 20 identified genes to be part of the molecular signature of photoreceptors and potentially prime candidate genes for human disease, the genes were further characterized based on their known retinal expression patterns. First, the list of genes was entered into the eye database at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.Genenetwork.org\">Genework</ext-link> to determine which genes would be correlated based on gene expression in the eye, within the BXD strains of mice along with the mouse diversity panel. Three subnetworks were identified (##TAB##1##Table 2##, column 10); a photoreceptor-specific network (<italic>Pla2g7</italic>, <italic>Gnb1</italic>, <italic>Pde6β</italic>, <italic>Vtn</italic>, and <italic>Nrl</italic>), transcription factors (<italic>Fos</italic>, <italic>Egr1</italic>, and <italic>Cebpδ</italic>), and one specific for immune-response (<italic>H2-D2</italic>, <italic>C1qβ</italic>, <italic>C1qc</italic>, <italic>H2-K</italic>, <italic>C1qα</italic>, <italic>C4</italic>, <italic>H2-T23</italic>, <italic>Isgf3g,</italic> and <italic>Irf7</italic>), as well as three unassociated genes (<italic>Gbp1</italic>, <italic>Gbp3</italic>, and <italic>Prdx2</italic>). Second, to determine whether these genes were expressed in the normal photoreceptors, we examined whether they were expressed in the outer nuclear layer (rods and cones) as assessed by Blackshaw and colleagues [##REF##15226823##28##] using a mouse retina <ext-link ext-link-type=\"uri\" xlink:href=\"http://cepko.med.harvard.edu/default.asp\">SAGE library</ext-link> or our own quantitative RT–PCR data on mouse ONL [##REF##16626700##9##] (##TAB##1##Table 2##, column 7). All but one of the genes that were identified in at least two out of three models were found to be present in the photoreceptors, for a total of 13 out of 20. Three out of 20 genes were found to be absent, and no information was available for the remaining four out of 20 genes. Third, this set of 20 genes was compared with genes identified to be misregulated under unique retinal injury conditions such as diabetes [##REF##15623795##31##], ischemia-reperfusion injury [##REF##12714663##32##], retinal tears [##REF##15277499##33##], elevation of intraocular pressure [##REF##15037594##34##], laser-induced injury [##REF##12657576##35##], photoreceptor degeneration induced by a photoreceptor-specific cadherin knockout [##REF##15872101##36##], as well as a model of bright-light damage [##REF##15872101##36##] (##TAB##1##Table 2##, columns 8 and 9). As expected, more extensive overlap was observed with bright-light-damage-induced genes, as one of our models was the constant, low-light-induced photoreceptor cell death model (<italic>C1qα</italic>, <italic>C4</italic>, <italic>Gbp1</italic>, <italic>H2-K1</italic>, <italic>H2-T2B</italic>, <italic>Irf7</italic>, <italic>Isgf3g,</italic> and <italic>Nrl</italic>); however, few genes were found to overlap with the general retinal injury models (diabetes: none; ischemia-reperfusion injury: none; retinal tears: <italic>Egr1</italic>, <italic>Fos</italic>, <italic>C1qβ</italic>, and <italic>Cebpδ</italic>; elevation of intraocular pressure: <italic>Egr1</italic>, <italic>Cebpδ</italic>; laser-induced injury: none) or with the photoreceptor cadherin knockout (<italic>C4</italic> and <italic>Cebpδ</italic>). Thus, it appears that the transcription factors <italic>Fos</italic>, <italic>Egr1,</italic> and <italic>Cebpδ</italic> are induced during general retinal injury, as is the complement system (<italic>C4</italic> and <italic>C1qβ</italic>). In summary, the final list of genes should have a high potential of detecting photoreceptor-specific disease genes.</p>", "<title>Candidate genes for retinal disease</title>", "<p>Twenty genes passed our stringent selection criteria. Two out of the 20 genes confirmed monogenic loci associated with photoreceptor degeneration, which are typically named for the one gene carrying mutations responsible for disease (i.e., NRL and PDE6B), demonstrating that our method is able to identify previously characterized human retinal disease genes, and thus confirming the validity of our approach. Fourteen genes were identified that fell within the boundaries of the monogenic locus for which the responsible gene has already been identified, and are thus considered innocent bystanders: Locus (identified gene) TULP1 (<italic>H2-D1</italic>, <italic>H2-K</italic>, <italic>C4</italic>, <italic>H2-T23</italic>), RDS/RP7 (<italic>Pla2g7</italic>); GUCA1A (<italic>Pla2g7</italic>), GUCA1B (<italic>Pla2g7</italic>), NRL (<italic>C1qc</italic> and <italic>Isgf3g</italic>), UNC119 (<italic>Vtn</italic>), PDE6A (<italic>Egr1</italic>), MCDR2 (<italic>Pde6β</italic>), ABCA4 (<italic>Gbp1</italic> and <italic>Gbp3</italic>), R9AP (<italic>Prdx2</italic>), TEAD1 (<italic>Irf7</italic>). The eight remaining genes are potential candidates for mapped disease loci (##TAB##0##Table 1##). After subtracting those genes that were determined to be injury-related genes (<italic>Fos</italic>, <italic>C1qβ</italic>, <italic>Cebpδ</italic>, and <italic>Egr1</italic>), four potential genes remained. One of those genes was differentially expressed in three models (<italic>Pla2g7</italic>), two genes in two models (<italic>C1qc</italic> and <italic>Gnb1</italic>), and one additional gene (<italic>C1qα</italic>) was expressed in one of our models (<italic>rd2</italic>) and the bright-light-damage model [##REF##15872101##36##]. These four genes are further discussed immediately below.</p>", "<p><italic>PLA2G7</italic> <bold>(</bold>phospholipase A2, group VII), the top-ranked gene, is localized within the BCMAD locus, a dominant form of macular dystrophy. <italic>Pla2g7</italic>, which is expressed specifically in mouse photoreceptors, was downregulated in all three mouse models of RD. One activity of the enzyme PLA2G7 is to hydrolyze oxidized phospholipids, which are known to be generated in photoreceptors during normal light exposure. Deficiency of plasma PLA2G7 has been shown to increase the risk of vascular disease due to its antiinflammatory properties, and its ability to control levels of oxidative stress and lipid peroxidation [##REF##3558407##7##]. Variants in <italic>PLA2G7</italic> have also be found to be associated with the risk of asthma [##REF##10733466##37##]. Three nonsynonymous polymorphisms appear to be associated with disease, the R92H, A379V, and I198T variants [##REF##18204052##38##]. All three have decreased substrate affinity of PAF, which could prolong the half-life of this highly inflammatory protein [##REF##10733466##37##]. Herein, we found that tissue and plasma levels of Pla2g7 might be differentially regulated; retinal degeneration was only associated with tissue, but not plasma levels of this enzyme. In a parallel study, we have confirmed that plasma levels of PLA2G7 appear not to be associated with a higher risk of AMD, as assessed in a population of the Rotterdam study [##UREF##1##39##].</p>", "<p><italic>GNB1</italic> (guanine nucleotide binding protein, beta 1), the beta-subunit of rod-specific transducin, is localized to the LCA9 and RP32 loci. RP32, a locus for autosomal recessive retinitis pigmentosa, is located between 1p13.3 and 1p21.2, and marks a severe version of RP [##REF##16189710##40##]. The LCA9 locus involved in autosomal recessive Leber congenital amaurosis, has been mapped to 1p36 by linkage mapping [##REF##12734549##41##]. Gao and colleagues have recently reported an association of <italic>GNB1</italic> intronic variants with autosomal recessive RP, as well as autosomal recessive cone-rod dystrophy [##REF##17356515##42##]. On the other hand, Kitamura and colleagues have identified the <italic>Gnb1</italic> gene as the site of mutation responsible for autosomal dominant Rd4, and have demonstrated that haploinsufficiency is the cause of disease [##REF##16565360##43##]. This would tend to rule out <italic>GNB1</italic> as the gene responsible for autosomal recessive LAC9 and RP32.</p>", "<p>Complement component 1, q subcomponent, alpha and c polypeptides (<italic>C1qα</italic> and <italic>C1qc</italic>), which are upregulated in retinal degeneration, are also localized to the LCA9 and RP32 loci. C1qα and C1qc are part of the complement component C1q, which is an element of the classical complement pathway of innate immunity. The complement pathway is one of the major means by which the body recognizes foreign antigens and pathogens as well as tissue injury, ischemia, apoptosis, and necrosis (reviewed in [##REF##9799710##44##]). However, in addition to important roles in normal host responses to self and foreign antigens, the complement system is increasingly recognized to be causally involved in tissue injury during ischemic, inflammatory and autoimmune diseases (reviewed in [##REF##12804527##45##]). Recent genetic evidence has identified variations in the complement inhibitory protein factor H (also known as CFH) [##REF##15761122##14##,##REF##15761121##16##, ####REF##16518403##17##, ##REF##15761120##18####15761120##18##], as well as variations in the genes for complement factor B, C2, and C3 [##REF##16518403##17##,##REF##17634448##46##], as major risk factors for the disease. However, it is unclear how misregulation of the complement system leads to the observed pathology. In mouse models of retinal disease, eliminating <italic>C1qα</italic> neither alters the course of photoreceptor degeneration in the <italic>rd1</italic> mouse [##REF##17069800##47##], nor changes the development of choroidal neovascularization triggered by laser photocoagulation of Bruch’s membrane [##REF##16849499##48##].</p>", "<title>Conclusion</title>", "<p>We have shown that the comparative genomics approach verified existing RD genes as well as identified novel RD candidate genes. This approach may be useful for focusing the search for novel genes in both RD and other diseases for which there are appropriate mouse animal models. Further studies are now needed to provide more evidence of the functionality, role, and relevance of these genes. Those studies should include sequencing of the human genes in patients with the appropriate diagnosis as well as the generation of appropriate knockout mouse strains, or elimination/activation of the targeted gene or pathway by pharmacological or molecular means. We hope to test these and other hypotheses that were generated in an unbiased and rational strategy that we systematically developed in this report.</p>" ]
[ "<title>Conclusion</title>", "<p>We have shown that the comparative genomics approach verified existing RD genes as well as identified novel RD candidate genes. This approach may be useful for focusing the search for novel genes in both RD and other diseases for which there are appropriate mouse animal models. Further studies are now needed to provide more evidence of the functionality, role, and relevance of these genes. Those studies should include sequencing of the human genes in patients with the appropriate diagnosis as well as the generation of appropriate knockout mouse strains, or elimination/activation of the targeted gene or pathway by pharmacological or molecular means. We hope to test these and other hypotheses that were generated in an unbiased and rational strategy that we systematically developed in this report.</p>" ]
[ "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>Retinal degeneration (RD) is a complex mechanism that appears to involve many biologic processes including oxidative stress, apoptosis, and cellular remodeling. Currently there are 51 mapped, but not identified, RD human disease loci.</p>", "<title>Methods</title>", "<p>To assign possible disease genes to RD loci, we have used a comparative genomics procedure that incorporates microarray gene expression data of three independent mouse models for photoreceptor dystrophy (<italic>rd1</italic>, <italic>rd2,</italic> and constant light-damage in BALB/c mice), human ortholog data, and databases of known chromosomal locations involved in human RD. Immunohistochemistry and enzyme activity assays were used to further characterize a candidate gene product.</p>", "<title>Results</title>", "<p>Our analysis yielded candidate genes for four mapped, but unsolved, human chromosomal locations and confirmed two previously identified monogenic disease loci for human RD, thus validating our approach. <italic>PLA2G7</italic> (phospholipase A2, group VII; PAF-AH, Lp-PLA2), a candidate for a dominant form macular dystrophy (Benign Concentric Annular Macular Dystrophy [BCMAD]), was selected for further study. The PLA2G7 enzyme is known to mediate breakdown of oxidatively damaged phospholipids, a contributor to oxidative stress in the retina. PLA2G7 protein was enriched in mouse photoreceptor inner and outer segments. In the <italic>rd1</italic>, <italic>rd2</italic>, and BALB/c mice exposed to constant light, retinal tissue activity levels, but not plasma levels, were significantly reduced at the onset of photoreceptor cell death.</p>", "<title>Conclusions</title>", "<p>We have shown that this comparative genomics approach verified existing RD genes as well as identified novel RD candidate genes. The results on the characterization of the PLA2G7 protein, one of the novel RD genes, suggests that retinal tissue PLA2G7 levels may constitute an important risk factor for BCMAD. In summary, this reverse mapping approach, using accepted mouse models of human disease and known human RD loci, may prove useful in identifying possible novel disease candidates for RD and may be applicable to other human diseases.</p>" ]
[]
[ "<title><bold>Appendix 1:</bold> Experimental data on which the selection of genes listed in ##TAB##0##Table 1## of the manuscript is based.</title>", "<p><bold>A</bold>: Gene expression data for genes that are differentially regulated in the three mouse models of retinal dystrophy—<italic>rd1</italic> mouse, <italic>rd2</italic> mouse, and light-damage (LD) in the albino mouse—as identified by DChip analysis. Gene expression data for the experimental and control group at the two experimental time points are listed as follows: columns 2–5 <italic>rd1</italic> mouse at postnatal days 6 and 10 (P6, P10); columns 8–11 <italic>rd2</italic> mouse, P14, and P21; and columns 14–17 BALB/c control and BALB/c LD at 24 h and 48 h of LD. Each value represents the average of two replicates. <bold>B:</bold> Differences in gene expression levels (fold change) and respective difference of the mean (Δ mean) between experimental retinas and their age-matched controls. <italic>Rd1</italic> retinas were analyzed from postnatal day (P) P6, P10], <italic>rd2</italic> retinas from P14, P21, and light-damaged retinas (LD) after 24 and 48 h of light exposure. Gene expression analysis contains procedures for strong control of false discovery rate (FDR). As indicated in the legend to ##TAB##0##Table 1##, the Affymetrix Gene ID and the gene symbol represents the common denominator for table identification in ##TAB##0##Table 1## and ##TAB##1##Table 2## as well as <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-1.pdf\">Appendix 1</ext-link>, and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-2.pdf\">Appendix 2</ext-link>.</p>", "<title><bold>Appendix 2:</bold> Gene ontology analysis for differentially regulated genes found in unsolved locations.</title>", "<p>Gene ontology terms that are associated with the differentially regulated genes found in unsolved locations (tabulated in ##TAB##0##Table 1##-i.e., experimental list) were analyzed. Over-represented terms for the biologic processes describing these identified genes were determined by comparing them with the reference genes (i.e., all the genes present on the entire array minus the experimental list). The top GO identifications (GO ID; column 1) and GO terms (column 2) are listed to characterize as many genes possible with significant GO terms. The genes represented by those GO terms (column 3), as well as the corresponding p-values (column 4) are listed.</p>", "<title>Acknowledgments</title>", "<p>Funding was provided by National Institutes of Health (NIH) / National Eye Institute grants EY-13520 (B.R.) and vision core EY-14793; the Karl Kirchgessner Foundation (B.R.); and an unrestricted grant to Medical University of South Carolina from Research to Prevent Blindness (RPB), Inc.. B.R. is a RPB Olga Keith Weiss Scholar. The Medical University of South Carolina microarray facility is supported by NIH/National Center for Research Resources South Carolina Center of Biomedical Research Excellence for Cardiovascular Disease (RR-16434), and a National Cancer Institute Shared Resource grant (R24 CA95841). The MUSC animal facility was completed with the help of a NIH construction grant (C06 RR015455). We thank Michael Danciger for helpful discussions throughout the project, Katie Hulse, Yao Guo, and Heather Lohr for generating the microarray data, Adam Richards and Drs. Paul Nietert for assistance with statistical algorithms, and Luanna Bartholomew for critical review.</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Pla2g7 mRNA and activity levels, analyzing levels from P10 <italic>rd1</italic>, P21 <italic>rd2,</italic> and 48 h light-exposed BALB/c animals and their respective age-matched controls. <bold>A</bold>: <italic>Pla2g7</italic> mRNA levels were plotted from <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-1.pdf\">Appendix 1</ext-link>. Retina <italic>Pla2g7</italic> mRNA levels are significantly reduced in all three genotypes when compared to controls. Data are expressed as mean±SD of the two arrays analyzed per genotype. <bold>B</bold>: Tissue retina Pla2g7 levels as measured in a calorimetric assay using 2-thio platelet activating factor (PAF) as substrate, revealed that activity levels in retinas from the three genotypes correlated well with the respective reduced amount of mRNA found in the tissue. Data are expressed as mean±SEM of at least three, independent samples in unit of activity (μmol/min/mg of protein). <bold>C</bold>: Plasma Pla2g7 levels measured in mandibular blood samples revealed that the two genetic mutations (<italic>rd1</italic> and <italic>rd2</italic>) or the environmental stress (constant light) did not influence systemic, plasma-derived Pla2g7 activity. Data are expressed as mean±SEM of at least three independent samples in unit of activity (μmol/min/mL of plasma). In the graph, red indicates control and blue indicates experimental. The following abbreviations were used: light-damage (LD) and not significant (n.s.)</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Pla2g7 localization. Immunohistochemistry was performed in juvenile C57BL/6 (P17) frozen sections (<bold>A</bold>), using no primary antibody conditions as the negative control (<bold>B</bold>). Pla2g7 was found to be localized throughout the retina. Relatively elevated levels were found in the photoreceptor inner and outer segments, whereas moderate staining was found in the two plexiform layers, as well as the inner nuclear layer (INL) and the retinal ganglion cell (RGC) layer. For each image, the corresponding DIC image is provided. The following abbreviations were used: retinal pigment epithelium (RPE), outer segments (OS), inner segments (IS), outer nuclear layer (ONL), outer plexiform layer (OPL), inner nuclear layer (INL), inner plexiform layer (IPL), and RGC: retinal ganglion cells (RGC). Scale bar in (<bold>A</bold>) represents 20 μm.</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Genes corresponding to human disease loci identified by being either commonly up- or down-regulated in three, independent mouse models of photoreceptor dystrophy.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"63\" span=\"1\"/><col width=\"144\" span=\"1\"/><col width=\"45\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"72\" span=\"1\"/><col width=\"99\" span=\"1\"/><thead><tr><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Affymetrix</bold>
<bold>gene ID</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gene name</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gene</bold>
<bold>symbol</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Mouse</bold>
<bold>transcript</bold>
<bold>ID</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Human ortholog NM</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Ortholog</bold>
<bold>chromosomal</bold>
<bold>location</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Retinal degeneration locus</bold></th></tr></thead><tbody><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">160901_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">FBJ osteosarcoma oncogene<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Fos</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=31560587\">NM_010234</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=91199549\">NM_001040059</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">14q24.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>LCA3</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">97540_f_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">histocompatibility 2, D region locus 1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-D1</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=68534958\">NM_001025208</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=62912478\">NM_005516</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">6p21.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">TULP1/RP14<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">101923_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">phospholipase A2 group VII (platelet-activating factor acetylhydrolase, plasma)<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Pla2g7</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=158635996\">NM_013737</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=189095270\">NM_005084</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">6p21.2-p12<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">RDS/RP7; GUCA1A, GUCA1B; <bold>BCMAD</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98549_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">vitronectin<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Vtn</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=6755986\">NM_011707</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=88853068\">NM_000638</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">17q11<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">UNC119/HRG4<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98579_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">early growth response 1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Egr1</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=76559936\">NM_007913</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=31317226\">NM_001964</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">5q31.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BSMD</bold>, PDE6A<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">92223_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">complement component 1, q subcomponent, C chain<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>C1qc</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=113680119\">NM_007574</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=166235904\">NM_172369</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p36.11<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">NRL/RP27<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">96020_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">complement component 1, q subcomponent, beta polypeptide<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>C1qb</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=133893071\">NM_009777</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=87298827\">NM_000491</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p36.3-p34.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>LCA9, RP32</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103033_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">complement component 4 (within H-2S)<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>C4</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=157951697\">NM_009780</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=67782350\">NM_000592</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">6p21.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">TULP1/RP14<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98472_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">histocompatibility 2, T region locus 23<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-T23</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=149363639\">NM_010398</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=6552332\">NM_005252</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">6p21.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">TULP1/RP14<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">94701_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">phosphodiesterase 6B, cGMP, rod receptor, beta polypeptide<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Pde6b</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=113930734\">NM_008806</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=105990536\">NM_000283</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">4p16.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">PDE6B/CSNB3, MCDR2<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">102612_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">neural retina leucine zipper gene<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Nrl</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=7657466\">NM_015810</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=124494252\">NM_006177</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">14q11.1-q11.2<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">NRL/RP27<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">160894_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">CCAAT/enhancer binding protein (C/EBP), delta<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Cebpd</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=110665734\">NM_007679</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=125661056\">NM_005195</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">8p11.2-p11.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CORD9</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">94854_g_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">guanine nucleotide binding protein, beta 1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Gnb1</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=111186467\">NM_008142</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=20357526\">NM_002074</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p36.3-p34.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>LCA9, RP32, RD4</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">93120_f_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">histocompatibility 2, K region<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-K</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=133922587\">NM_001001892</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=163310739\">NM_002127</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">6p21.3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">TULP1, RP14<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98562_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">complement component 1, q subcomponent, alpha polypeptide<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>C1qa</italic></bold><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=124286804\">NM_007572</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=87298824\">NM_015991</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p36.3-p34.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>LCA9, RP32</bold><hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">95974_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">guanylate nucleotide binding protein 1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Gbp1</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=111186467\">NM_008142</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=20357526\">NM_002074</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p36.3-p34.1<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">ABCA4<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103202_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">guanylate nucleotide binding protein 3<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Gbp3</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=134053870\">NM_018734</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=31543391\">NM_133263</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1p22.2<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">ABCA4<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103634_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">interferon dependent positive acting transcription factor 3 gamma<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Isgf3g</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=31982244\">NM_008394</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=82734235\">NM_006084</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">14q11.2<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">NRL/RP27<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">104669_at<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">interferon regulatory factor 7<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Irf7</italic><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=118130788\">NM_016850</ext-link><hr/></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=4809285\">NM_004030</ext-link><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">11p15.5<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">TEAD1/AA/TCF13/ TEF1<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">99608_at</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">peroxiredoxin 2</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Prdx2</italic></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=166235200\">NM_011563</ext-link></td><td valign=\"middle\" align=\"left\" rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=33188450\">NM_005809</ext-link></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">19p13.2</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">R9AP</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2\" position=\"float\"><label>Table 2</label><caption><title>Characterization of genes identified as candidates for human disease loci.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"57\" span=\"1\"/><col width=\"44\" span=\"1\"/><col width=\"55\" span=\"1\"/><col width=\"57\" span=\"1\"/><col width=\"59\" span=\"1\"/><col width=\"36\" span=\"1\"/><col width=\"57\" span=\"1\"/><col width=\"35\" span=\"1\"/><col width=\"40\" span=\"1\"/><col width=\"45\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Affymetrix gene ID</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gene symbol</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Mis-regulation</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Animal models</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Probability</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Score</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>ONL: sage, qRT-PCR*</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>injury</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Bright LD #</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Retina network</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">160901_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Fos</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.044<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">97540_f_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-D1</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">101923_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Pla2g7</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98549_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Vtn</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.019<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98579_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Egr1</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.132<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">92223_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>C1qc</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.143<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">96020_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>C1qb</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.116<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">_*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103033_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>C4</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98472_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-T23</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">94701_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Pde6b</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.179<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">102612_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Nrl</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.066<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">160894_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Cebpd</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd1/rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.238<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">_<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">94854_g_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>Gnb1</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.135<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">93120_f_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>H2-K</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic>/LD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.046<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">98562_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>C1qa</italic></bold><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.135<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">_<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">95974_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Gbp1</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0338<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103202_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Gbp3</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0338<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">103634_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Isgf3g</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.066<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">x<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">104669_at<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Irf7</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0611<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">_<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">99608_at</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>Prdx2</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">down</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4691</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3\" position=\"float\"><label>Table 3</label><caption><title>Enzyme activity for Pla2g7 in retinal degeneration</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"63\" span=\"1\"/><col width=\"76\" span=\"1\"/><col width=\"79\" span=\"1\"/><col width=\"46\" span=\"1\"/><thead><tr><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Genotype treatment</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Control</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Experimental</bold></th><th valign=\"middle\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>p-value</bold></th></tr></thead><tbody><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>rd1</italic><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2753±0.01<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1953±0.006<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">&lt;0.01<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><italic>rd2</italic><hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0805±0.0035<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.027±0.0.0032<hr/></td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">&lt;0.001<hr/></td></tr><tr><td valign=\"middle\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">light damage</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.123±0.015</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.059±0.0012</td><td valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">&lt;0.001</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>The highlighted gene symbols (column 3) represent the mapped, but unsolved loci (column 7), the remaining genes localize to loci that have already been solved. The genes are documented with respect to the mouse gene name, symbol and mouse transcript ID (columns 2-4) and their human ortholog (column 5). Column 6 provides information about the chromosomal location of the human ortholog; column 7 lists the name(s) of the loci. Please note that in some entries in the locus column, there are multiple names given, meaning that more than one trait resides in that chromosomal location; however these may or may not be related. Column 1, the Affymetrix Gene ID; and column 3, the gene symbol; represent the common denominators for all Tables in the manuscript (##TAB##0##Table 1## and ##TAB##1##Table 2##) and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-1.pdf\">Appendix 1</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.molvis.org/molvis/v14/a194/app-2.pdf\">Appendix 2</ext-link>.</p></table-wrap-foot>", "<table-wrap-foot><p>Genes are identified by Affymetrix Gene ID (column 1) and gene symbol (column 2) for easy comparison with ##TAB##0##Table 1##. Column 3 identifies the type of misregulation (up- or down-regulated) and column 4 documents in which animal models the misregulation occurs. The probability of each gene to fall within the respective locus is listed in column 5; this probability multiplied by the number of models in which the genes are differentially expressed (3, 2, or 1) produced a gene ranking score (maximum column 6). The remaining columns document whether the respective gene is present in photoreceptors based on the retina SAGE library (column 7: x, present; -, absent; ?, no data available; *confirmed by qRT-PCR [##REF##16626700##9##]), whether the gene is misregulated in retina injury models (column 8; identified by +) or after bright light exposure (column 9; identified by +), or which genes were found to cluster together (eye database at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.Genenetwork.org\">Genenetwork</ext-link>; column 10; 3 clusters, 1-3 were identified, as well as three unclustered genes).</p></table-wrap-foot>", "<table-wrap-foot><p>Quantification of specific activity of Pla2g7 [μmol/minute/mg of protein] in retina extracts collected from P10 <italic>rd1</italic>, P21 <italic>rd2</italic> mice, and 3-month old BALB/c mice after 24 h of light damage (column 2), together with their age-matched controls (column 3). Cytosolic levels of Pla2g7 were significantly reduced (column 4) in all three models of photoreceptor degeneration. Data is expressed as mean±SEM for 3-5 samples per condition.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"mv-v14-1639-f1\"/>", "<graphic xlink:href=\"mv-v14-1639-f2\"/>" ]
[]
[{"label": ["23"], "citation": ["Cheng Li. Wing Hung Wong. DNA-Chip Analyzer (dChip). In: G Parmigiani, ES Garrett, R Irizarry and SL Zeger, editors. The analysis of gene expression data: methods and software. New York: Springer, 2003. p. 120-141."]}, {"label": ["39"], "citation": ["Vingerling JR, Ho L, Rohrer B, Witteman JCM, de Jong PVM. Lipoprotein-Associated phospholipase A2 and risk of aging macula disorder: the Rotterdam Study. ARVO Annual Meeting; 2008 April 27-May 1; Fort Lauderdale (FL)."]}]
{ "acronym": [], "definition": [] }
48
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 5; 14:1639-1649
oa_package/a0/f5/PMC2529471.tar.gz
PMC2530489
18776953
[ "<title>Introduction</title>", "<p>Aniridia is a congenital ocular disorder characterized by bilateral variable iris hypoplasia with an estimated occurrence of one in every 64,000–96,000 live births worldwide [##REF##17948455##1##]. The manifestations of the aniridia phenotype are variable, ranging from thinning of the stroma and absent pupillary sphincter to complete aniridia [##REF##7369316##2##,##REF##1463039##3##]. In addition to iris hypoplasia, other ocular congenital defects may be present such as cataracts, foveal hypoplasia, nystagmus, corneal opacity, lens dislocation, and glaucoma with significant loss of vision [##UREF##0##4##]. Because of the wide spectrum of clinical manifestations associated with this ocular pathology, Gronskov et al. [##REF##10234503##5##] proposed to categorize the phenotype into six different levels based on iris presentation. However, this classification is not widely used.</p>", "<p>Approximately two thirds of cases are familial with an autosomal dominant inheritance pattern, probably with complete penetrance [##REF##10234503##5##,##REF##13709149##6##]. Some sporadic aniridia cases have the WAGR syndrome (Wilms tumor, aniridia, genitourinary anomalies, and mental retardation; OMIM <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=194072\">194072</ext-link>). Several genes at 11p13 are deleted in the WAGR syndrome including <italic>WT1</italic> and the evolutionarily conserved paired box gene 6 (<italic>PAX6</italic>) [##REF##1334370##7##].</p>", "<p>The human <italic>PAX6</italic> spans 26 kilobases (kb), contains 14 exons [##REF##1684738##8##,##REF##1345175##9##], and encodes the PAX6 transcription factor. <italic>PAX6</italic> is considered the master control gene for ocular morphogenesis and contributes to central nervous system development [##REF##7482776##10##]. Like other transcriptional activators of the PAX family, PAX6 contains two DNA-binding domains (a paired domain at the NH<sub>2</sub>-terminus and a middle homeodomain) and a proline-serine-threonine (PST)-rich transactivator domain at the COOH-terminus [##REF##1684738##8##,##REF##1345175##9##].</p>", "<p>Homozygous loss of <italic>PAX6</italic> is thought to lead to early embryonic lethality [##REF##7951315##11##]. Heterozygous mutations are found in approximately 40%–80% of all non-syndromic aniridia cases [##REF##1345175##9##,##REF##9138149##12##, ####REF##9792406##13##, ##REF##11479730##14##, ##REF##12634864##15####12634864##15##], and most are searched by single strand conformation polymorphism (SSCP), which is considered one of the most useful molecular detection methods [##REF##9138149##12##,##REF##9482572##16##]. There are no clear gene hotspots, and the majority of mutations in <italic>PAX6</italic> are predicted to introduce premature termination codons, most of which are assumed to be functionally null because of haploinsufficiency [##REF##12634864##15##]. To date, more than 400 <italic>PAX6</italic> mutations have been reported (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Online Human PAX6 Allelic Database</ext-link>). The most frequent mutations are c.1080C&gt;T (c.718C&gt;T), c.969C&gt;T (c. 607C&gt;T), c.1311C&gt;T (c.949C&gt;T), and c.1629insT (c.1267dupT).</p>", "<p>The molecular basis of aniridia in Mexico is poorly characterized. In fact, there is only one report of three different intragenic deletions of <italic>PAX6</italic> found in five unrelated cases in the Mexican population. Interestingly, the authors of this study suggested a founder effect for a four-base intragenic deletion (c.732_735delAACA) in exon 7 in Mexican aniridia patients because this mutation was found in three nonrelated cases [##REF##16617299##17##]. In the present study, we further analyze <italic>PAX6</italic> variants in a group of Mexican aniridia patients and describe associated ocular findings.</p>" ]
[ "<title>Methods</title>", "<p>We evaluated 30 unrelated aniridia probands recruited from two referral hospitals in Mexico City, the National Institute of Pediatrics and the Dr. Luis Sanchez Bulnes Hospital. All individuals were of Mexican origin, showed no associated systemic abnormalities, and had normal psychomotor development. Patients were categorized according to Gronskov’s iris classification [##REF##10234503##5##].</p>", "<p>This study was conducted in accordance with the World Medical Association Declaration of Helsinki and was approved by the respective local research and ethics committees. Written informed consent was obtained from all participants.</p>", "<p>Genomic DNA was extracted from peripheral blood leukocytes using the PureGene DNA purification kit (Gentra Systems, Minneapolis, MN). <italic>PAX6</italic> mutation screening was performed by polymerase chain reaction (PCR) amplification of all 14 exons and immediate flanking sequences using the primers and conditions proposed by Love et al. [##REF##9671274##18##] followed by SSCP analysis in 1X Mutation Detection Enhancement gels (BioWhittaker Molecular Applications, Rockland, ME). Gels were run under constant power (6 W) for 12 h at room temperature and visualized by silver nitrate staining (Silver Stain Kit, Bio-Rad Laboratories, Hercules, CA). Fragments displaying abnormal electrophoretic patterns were purified by the silica column method (QIAquick, Gel Extraction Kit; QIAGEN Inc. Valencia CA) and directly sequenced using a Big Dye Terminator Kit with an automated ABI PRISM Model 377 sequencer (Applied Biosystems, Foster City, CA) according to the manufacturer’s recommendations. The mutations identified in the probands were sought in parents that were available. The nomenclature used for describing novel genetic changes follows the recommendations of the Human Genome Variation Society [##REF##10612815##19##], and nucleotides were numbered according to the consensus coding DNA sequence of <italic>PAX6</italic> isoform a (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/projects/CCDS/CcdsBrowse.cgi?REQUEST=ALLFIELDS&amp;DATA=CCDS31451.1+&amp;ORGANISM=0&amp;BUILDS=CURRENTBUILDS\">CCDS31451.1</ext-link>). In silico analyses of novel missense mutations and intronic changes were performed using the <ext-link ext-link-type=\"uri\" xlink:href=\"http://blocks.fhcrc.org/sift/SIFT.html\">SIFT</ext-link> program and the <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cbs.dtu.dk/services/NetGene2/\">NetGene2 Server</ext-link>, respectively. The intronic nucleotide variation, IVS2+9G&gt;A (c.-129+9G&gt;A), reported previously as pathogenic [##REF##17417613##20##], was sought in 103 nonrelated healthy Mexican newborns using the PCR restriction fragment length polymorphism (PCR-RFLP) method by amplifying the 3′ end of exon 2 according to Love et al. [##REF##9671274##18##] and restricting with the AciI enzyme where the presence of the G allele eliminates the restriction site. The Hardy–Weinberg equilibrium conformance was evaluated using the <ext-link ext-link-type=\"uri\" xlink:href=\"http://bioinfo.iconcologia.net/index.php?module=Snpstats\">SNPstats</ext-link> software.</p>" ]
[ "<title>Results</title>", "<p>Phenotypic information was available from 28 of the 30 probands, and a summary of findings is given in ##TAB##0##Table 1##. The median age of cases was 5.2 years, and 18 of the probands (62%) were female. Eighteen of the cases (62%) were sporadic cases, and 11 had at least one relative with aniridia. Absent or nearly absent irides were evident in 26 cases (93%), and these were categorized as Iris 5 or Iris 6 according to Gronskov’s classification [##REF##10234503##5##]. Of the remaining two cases, one was classified as Iris 3 and 4 (one eye each) and the other was classified as Iris 4. At least two ocular-associated alterations were present in 21 patients (75%), and the most common alterations were nystagmus (75%), macular hypoplasia (57%), and congenital cataracts (53%). Other less frequent features were optic nerve hypoplasia and keratopathy. Six individuals had glaucoma, which was congenital in two cases. The iris defect was not associated with any other ocular abnormality in only one patient (case 13).</p>", "<p>Molecular findings are summarized in ##TAB##1##Table 2##. We detected 11 SSCP mobility shifts in <italic>PAX6</italic> products, all of which were consistent with the presence of mutations or neutral polymorphisms after sequencing. Causal mutations of the aniridia phenotype were found in 9 of 30 cases, yielding a detection rate of 30%. All mutations were heterozygous and unique except for the recurrent mutation, c.969C&gt;T, which was observed in two sporadic unrelated cases. Four mutations were novel, c.184_188dupGAGAC, c.361T&gt;C, c.879dupC, and c.277G&gt;A. The remaining four mutations identified (c.969C&gt;T, IVS6+1G&gt;C, c.853delC, and IVS7–2A&gt;G) have been previously reported (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>) . Additionally, we found two intronic, nonpathogenic variations, IVS9–12C&gt;T and IVS2+9G&gt;A, both of which have also been previously described [##REF##17417613##20##,##REF##15086958##21##]. Of the nine probands in whom pathological mutations were identified, only nine parents were available for molecular analysis (##TAB##1##Table 2##).</p>", "<p>With respect to novel changes, case 4 showed an insertion of a GAGAC sequence at nucleotide position 184, causing a frameshift arising from tandem duplication of nucleotides 184–188 that is predicted to encode a protein truncated in the paired domain. At evaluation, the patient exhibited a phenotype characterized by nystagmus, macular hypoplasia, and subtotal aniridia defect (Iris 5 in Gronskov’s classification). His mother had a normal ocular phenotype and did not have the mutation. A DNA sample from his father was not available, but he was referred to as visually healthy.</p>", "<p>Case 6 was a female patient with a novel missense substitution. Her right eye exhibited an eccentric pupil, circumpupillary iris hypoplasia (Iris 3), and cortical cataract. In the left eye, she had an atypical sector nasal iris coloboma (Iris 4), stromal hypoplasia, and total cataract (##FIG##0##Figure 1## and ##FIG##1##Figure 2##). The missense mutation identified was c.361T&gt;C in exon 7 that changes serine 121 to proline (p.S121P) in the paired domain. Her mother exhibited foveal hypoplasia and nystagmus with whole irides, and her sister had congenital cataracts, nystagmus, and macular hypoplasia. Both affected relatives had the mutated allele.</p>", "<p>A base duplication at position 879 in exon 10 was found in case 21 and his mother. This previously unreported duplication (c.879dupC) causes a frameshift and introduces a premature stop codon 47 nucleotides downstream in the PST domain. The patient had Iris 5 with the associated ocular abnormalities of macular hypoplasia and nystagmus. The clinical manifestations of his mother were not available.</p>", "<p>Female case 22 showed the novel missense substitution, c.277G&gt;A, in exon 6, which encodes part of the extreme amino end of the paired domain. The mutation changes glutamate at position 93 to lysine. This case also had a previously reported intronic polymorphism (IVS9–12C&gt;T) [##REF##15086958##21##]. The patient presented with total aniridia (Iris 6), nystagmus, and congenital cataracts. Her mother was referred to as affected, but we could not accomplish family studies because the patient resided in an orphanage.</p>", "<p>With respect to previously reported mutations, we found the IVS6+1G&gt;C splice-site mutation [##REF##16712695##22##] in case 18 who had Iris 5, microcornea, nystagmus, ectopia lentis, and macular and optic nerve hypoplasia. Her unaffected parents did not show this splice site change. Additionally, the patient and her father showed the previously described intronic substitution, IVS2+9G&gt;A [##REF##17417613##20##]. We searched for this substitution in 103 Mexican healthy controls and observed 19 heterozygotes (G/A) and two newborns homozygous for the A allele.</p>", "<p>The only deletion that we observed was the previously reported loss of cytosine at position 853 (c.853delC) that introduces a premature stop codon 43 nucleotides downstream [##REF##10234503##5##]. This deletion was found in case 20, but phenotypic information was not available.</p>", "<p>We found the c.969C&gt;T nonsense substitution (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>), which changes arginine 203 to a UGA stop codon in the linker region, in two unrelated probands (case 10 and case 24); both were sporadic aniridia cases. Unfortunately, phenotypic information on case 24 and his parents were unavailable, but the molecular study was normal in both parents. Case 10 was a female dizygotic twin who showed subtotal aniridia (Iris 5), nystagmus, and macular hypoplasia. Her male twin and mother were genotypically normal and had a normal ocular phenotype, but the father was not studied.</p>", "<p>Finally, we also observed a mutation that produces a substitution in the splice acceptor site of intron 7 (IVS7–2A&gt;G). An in silico analysis of this mutation, which has been previously reported in another single study [##REF##16712695##22##], revealed the possible use of different cryptic splice sites. The individual with this mutation (case 26) had Iris 6 with nystagmus, cataract, and strabismus. Other members of her family were referred to as having a normal ocular phenotype, but they were unavailable for study.</p>" ]
[ "<title>Discussion</title>", "<p>To the best of our knowledge, this is the first work on aniridia, apart from the original report, that uses the Gronskov classification of iris hypoplasia. Gronskov originally reported that the proportion of patients with Iris grade 1 to 4 was approximately 40% [##REF##10234503##5##] whereas we found only two index cases (7%), one with Iris grade 3 and 4, another with Iris 4, and none with lesser severity. This discrepancy might be explained by ascertainment bias, reflecting the fact that first-contact ophthalmologists are more familiar with the classic or severe aniridia presentation than with milder phenotypes. Another reason might be that individuals with milder cases, which are generally asymptomatic, do not seek medical care. In our opinion, Gronskov’s classification [##REF##10234503##5##] should be widely used as a way to improve diagnosis, detect potential complications, and provide genetic counseling in aniridia cases with milder phenotypes.</p>", "<p>To our knowledge, this work represents the third largest aniridia series (only smaller than those published by Gronskov et al. [##REF##11479730##14##] and Vincent et al. [##REF##12634864##15##]) that included a molecular study of <italic>PAX6</italic>. Although we analyzed the entire coding region of the <italic>PAX6</italic> gene in this work, the mutation detection rate of 30% that we found was lower than the 80% and 55% rates reported by the groups of Gronskov et al. [##REF##11479730##14##] and Vincent et al. [##REF##12634864##15##], respectively, who used diverse techniques for detecting pathological mutations. In this work, we used the SSCP technique exclusively, which is a widely used and efficient method for detecting mutations in <italic>PAX6</italic> [##REF##9138149##12##,##REF##9482572##16##]. However, a low rate of <italic>PAX6</italic> mutation detection (40%) using the SSCP technique has also been reported in patients described by Glaser et al. who proposed the possibility of mutations in more distant <italic>cis</italic> regulatory sequences [##REF##1345175##9##]. Our low detection rate might be consistent with this interpretation because contiguous regulatory or non-coding sequences were not analyzed in our study. However, it also could be because of limitations of the SSCP technique itself as large genomic rearrangements would not be identified by this methodology. The inclusion of other mutation detection techniques in future studies would be expected to improve our mutation detection rate.</p>", "<p>We identified eight different causal <italic>PAX6</italic> mutations in nine unrelated cases with isolated aniridia. The nature of the mutations was very similar to that reported in other populations [##REF##10234503##5##,##REF##9792406##13##,##REF##12634864##15##,##REF##15086958##21##]. Interestingly, we did not find the intragenic deletions previously reported in five Mexican patients, suggesting that these deletions might not be as frequent in our population as thought by Ramirez-Miranda et al. [##REF##16617299##17##]. In this same context, our findings do not provide support for a founder effect of a specific mutation in the Mexican population [##REF##16617299##17##].</p>", "<p>The only intragenic deletion identified (c.853delC) produces a frameshift and introduces a premature stop signal 42 codons downstream in exon 8. If it were translated, the predicted truncated PAX6 product would retain the paired domain but lack the homeobox and PST transactivator domain. This mutation has been observed twice before, once in a male patient with aniridia (Iris 4), cataracts, and nystagmus [##REF##10234503##5##] and once in a female in which only aniridia was mentioned [##REF##16712695##22##]. Unfortunately, our case was unavailable for phenotype-genotype correlation.</p>", "<p>The duplications, c.184_188dupGAGAC and c.879dupC, are novel, and both give rise to frameshifts, introducing premature stop codons in the paired domain and PST region, respectively. Phenotypes observed in other cases with insertion mutations are severe [##REF##10234503##5##,##REF##15918896##23##]. Consistent with this, our cases with these mutations had Iris 5.</p>", "<p>The nonsense substitution, c.969C&gt;T, which changes an arginine codon (CGA) to a stop codon (UGA), was detected in two unrelated, sporadic cases (cases 10 and 24). This mutation has been previously found in at least 20 patients worldwide including familial and sporadic cases, making it one of the three more frequent changes in <italic>PAX6</italic> along with c.1080C&gt;T (27 cases) and c.1311C&gt;T (20 cases; <ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>). The differences in the ethnic origins of patients bearing the c.969C&gt;T change indicate that this mutation is recurrent in <italic>PAX6</italic>. The recurrence of these three mutations might be explained at least in part by the presence of CpG dinucleotides in <italic>PAX6</italic> that tend to become methylated and might thereby create conditions favorable for C&gt;T substitutions as a consequence of spontaneous deamination of cytosine residues [##REF##15918896##23##]. Our two patients positive for c.969C&gt;T might represent independent mutational events since they were unrelated.</p>", "<p>With respect to the phenotype of c.969C&gt;T heterozygotes, there are only five cases described in the <ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>. Interestingly, one had partial aniridia with foveal hypoplasia and nystagmus, and the other four had aniridia with the associated ocular manifestations of nystagmus, cataracts, glaucoma, or corneal erosion. Of our two patients positive for c.969C&gt;T, clinical information was available for only case 10. This patient had a severe phenotype and was classified as Iris 5 with nystagmus and macular hypoplasia.</p>", "<p>Literature reports based on the haploinsufficiency model have suggested that frameshift and nonsense mutations predicted to result in a truncated protein such as those described above are likely to exert their pathological effects through a “nonsense-mediated-decay” process where translation to protein might not occur because the mRNA is degraded [##REF##15086958##21##,##REF##15918896##23##]. However, it has also been noted that truncating mutations located downstream of DNA-binding domains especially those in exons 12 and 13 might have a dominant-negative effect [##REF##15918896##23##,##REF##9705283##24##]. In the present work, we did not identify nonsense mutations in this extreme 3′ region of the <italic>PAX6</italic> gene.</p>", "<p>On the other hand, both novel missense mutations observed in the present work–c.277G&gt;A (p.E93K) and c.361T&gt;C (p.S121P)–might affect the function of the paired-box domain of the PAX6 protein because the properties of the substituted amino acids are quite different. In one case (p.E93K), a negatively charged glutamate is replaced by a positively charged lysine. In the other (p.S121P), the polar serine residue is replaced by the non-polar amino acid, proline. Moreover, glutamate 93 and serine 121 are largely invariant among closely related PAX family members with glutamate 93 conserved in PAX3, PAX4, and PAX7 and serine 121 conserved in eight <italic>PAX</italic> family genes (<ext-link ext-link-type=\"uri\" xlink:href=\"http://blast.ncbi.nlm.nih.gov/Blast.cgi\">Protein BLAST</ext-link>). An in silico analysis using the <ext-link ext-link-type=\"uri\" xlink:href=\"http://blocks.fhcrc.org/sift/SIFT\">SIFT</ext-link> program predicted that protein function would be affected (p&lt;0.01), providing support for a possible pathogenic effect of these mutations, but further functional analyses are needed to confirm this.</p>", "<p>Missense mutations, which account for roughly 17% of changes in <italic>PAX6</italic> worldwide, potentially retain residual protein activity and have been associated with milder phenotypes [##REF##10234503##5##,##REF##9482572##16##,##REF##15918896##23##]. Consistent with this, case 6 who had a c.361T&gt;C mutation showed Iris 3 (circumpupillary iris hypoplasia) and Iris 4 (atypical sector coloboma), which were the mildest iris grades found in the probands of our series. In contrast, case 22 carrying a c.277G&gt;A substitution had complete aniridia (Iris 6) as well as nystagmus and cataracts. Although both of these mutations affect the paired domain, the c.277G&gt;A mutation is located in the NH<sub>2</sub>-region and would therefore be expected to have a more profound effect on paired domain structure and function than the COOH-terminally localized c.361T&gt;C mutation. This difference in location may account for the observed phenotypic differences, but additional studies will be required to support this idea.</p>", "<p>In some cases, missense mutations in <italic>PAX6</italic> have also been associated with neurodevelopmental abnormalities such as absence/hypoplasia of the anterior commissure, callosal area, or pineal gland; olfactory system anomalies; cerebellar coordination problems; mental retardation; and epilepsy [##REF##7951315##11##,##REF##9482572##16##,##REF##17417613##20##,##REF##11431688##25##, ####REF##14683729##26##, ##REF##12731001##27##, ##REF##15079031##28####15079031##28##]. In fact, Dansault et al. [##REF##17417613##20##] suggested that these abnormalities should be systematically investigated in every patient with aniridia. In cases 6 (age 8 years) and 22 (age 15 years) who had the missense mutation, clinical neurological anomalies were not observed, but cerebral CT scan or MRI imaging were not performed. Further descriptions of aniridia cases with missense mutations and neurodevelopmental anomalies will be needed to improve genotype-phenotype correlations. In addition to the novel missense substitution, c.277G&gt;A, female case 22 had the intronic polymorphism, IVS9–12C&gt;T, which is thought to represent a neutral variant [##REF##15086958##21##].</p>", "<p>With respect to the splice-site mutation, IVS7–2A&gt;G [##REF##16712695##22##], an in silico analysis performed with the <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cbs.dtu.dk/services/NetGene2/\">NetGene2 Server</ext-link> predicted that this change would eliminate the activity of the natural acceptor site in intron 7 and activate different cryptic acceptor sites within the exon or intron 8. It could, however, result in the use of the natural acceptor site in intron 8 and thereby lead to an in-frame, exon-skipping event that deletes exon 8. This mutation has been previously observed in a single case [##REF##16712695##22##] with aniridia, cataracts, nystagmus, and corneal dystrophy (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>). Similarly, our patient with this mutation (case 26) had a complete iris defect (Iris 6), nystagmus, cataract, and strabismus but without the corneal anomalies that might be present at an older age.</p>", "<p>The previously reported IVS6+1G&gt;C substitution [##REF##16712695##22##] disrupts the conserved dinucleotide GT in the intron 6 splice-donor site and might lead to the use of an alternative in-frame donor site inside exon 6. The predicted protein would lack the last 36 amino acid residues encoded by this exon, and the resulting deletion of a portion of the paired domain would be expected to lead to a severe phenotype (<ext-link ext-link-type=\"uri\" xlink:href=\"http://pax6.hgu.mrc.ac.uk/\">Human PAX6 allelic database</ext-link>). Consistent with this, the ocular phenotype of our patient was Iris 5 with nystagmus, microcornea, ectopia lentis, and macular and optic nerve hypoplasia. Both parents were considered healthy and were negative for IVS6+1G&gt;C. This mutation has been reported once before in an aniridia patient but without the description of other clinical data [##REF##16712695##22##]. Remarkably, there have been at least nine previous reports of a substitution at guanine by either adenine or thymine in the +1 position in GT donor sites in aniridia patients [##REF##9138149##12##,##REF##8364574##29##,##REF##12552561##30##].</p>", "<p>In addition, case 18 and her unaffected father showed the previously described IVS2+9G&gt;A substitution [##REF##17417613##20##]. Although this intronic change was assumed to be potentially pathogenic by Dansault et al. [##REF##17417613##20##] who observed it in a sporadic case with microphthalmia and other ocular abnormalities but not in 200 normal healthy individuals, an in silico analysis of this variant predicted that the binding capacity of the natural donor site would be unchanged. In our own search of 103 healthy Mexican newborns, we found this variant in a heterozygous state in 19 individuals and in a homozygous state in two. Hence, our data indicate that IVS2+9G&gt;A is a neutral polymorphism and is not responsible for a pathological phenotype. The allele frequencies obtained for this polymorphism were in Hardy–Weinberg equilibrium.</p>", "<p>In summary, most of the mutations detected in our analysis alter invariant amino acid residues in the paired domain or predict truncation of the PAX6 protein. Four of the <italic>PAX6</italic> mutations identified in this study are novel. In addition, our results lend support to the notion that c.969C&gt;T is one of the three more frequent causal mutations in isolated aniridia cases and provide evidence that the IVS2+9G&gt;A (c.-129+9G&gt;A) variant is a neutral polymorphism.</p>" ]
[]
[ "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>Paired box gene 6 (<italic>PAX6)</italic> heterozygous mutations are well known to cause congenital non-syndromic aniridia. These mutations produce primarily protein truncations and have been identified in approximately 40%–80% of all aniridia cases worldwide. In Mexico, there is only one previous report describing three intragenic deletions in five cases. In this study, we further analyze <italic>PAX6</italic> variants in a group of Mexican aniridia patients and describe associated ocular findings.</p>", "<title>Methods</title>", "<p>We evaluated 30 nonrelated probands from two referral hospitals. Mutations were detected by single-strand conformation polymorphism (SSCP) and direct sequencing, and novel missense mutations and intronic changes were analyzed by in silico analysis. One intronic variation (IVS2+9G&gt;A), which in silico analysis suggested had no pathological effects, was searched in 103 unaffected controls.</p>", "<title>Results</title>", "<p>Almost all cases exhibited phenotypes that were at the severe end of the aniridia spectrum with associated ocular alterations such as nystagmus, macular hypoplasia, and congenital cataracts. The mutation detection rate was 30%. Eight different mutations were identified: four (c.184_188dupGAGAC, c.361T&gt;C, c.879dupC, and c.277G&gt;A) were novel, and four (c.969C&gt;T, IVS6+1G&gt;C, c.853delC, and IVS7–2A&gt;G) have been previously reported. The substitution at position 969 was observed in two patients. None of the intragenic deletions previously reported in Mexican patients were found. Most of the mutations detected predict either truncation of the PAX6 protein or conservative amino acid changes in the paired domain. We also detected two intronic non-pathogenic variations, IVS9–12C&gt;T and IVS2+9G&gt;A, that had been previously reported. Because the latter variation was considered potentially pathogenic, it was analyzed in 103 healthy Mexican newborns where we found an allelic frequency of 0.1116 for the A allele.</p>", "<title>Conclusions</title>", "<p>This study adds four novel mutations to the worldwide <italic>PAX6</italic> mutational spectrum, and reaffirms the finding that c.969C&gt;T is one of the three more frequent causal mutations in aniridia cases. It also provides evidence that IVS2+9G&gt;A is an intronic change without pathogenic effect.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>This work was partially supported by the Hospital Dr. Luis Sánchez Bulnes, Asociación Para Evitar la Ceguera en México (Mexico City, Mexico).</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Right eye iris and pupil of aniridia case 6 who had a novel missense mutation (c.361T&gt;C) located in the NH<sub>2</sub>-region of the paired domain of <italic>PAX6</italic>. This eye exhibited eccentric pupil and circumpupillary iris hypoplasia (Iris 3).</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Left eye iris of aniridia case 6 who had a novel missense mutation (c.361T&gt;C) located in the NH<sub>2</sub>-region of the paired domain of <italic>PAX6</italic>. This eye exhibited partial absence of iris, an atypical sector nasal iris coloboma (Iris 4), stromal hypoplasia, and a total cataract.</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Iris grade and ocular associated findings in 30 Mexican nonrelated aniridia cases.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"35\" span=\"1\"/><col width=\"28\" span=\"1\"/><col width=\"46\" span=\"1\"/><col width=\"70\" span=\"1\"/><col width=\"40\" span=\"1\"/><col width=\"59\" span=\"1\"/><col width=\"67\" span=\"1\"/><col width=\"56\" span=\"1\"/><col width=\"66\" span=\"1\"/><col width=\"66\" span=\"1\"/><col width=\"79\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Case</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sex</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age (years)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Inheritance</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Iris grade</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Best corrected visual acuity</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Nystagmus</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Cataract</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Glaucoma/</bold>
<bold>treatment</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Macular hypoplasia</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Other</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/600<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/200<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ptosis<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/100<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ptosis<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/380<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ptosis, strabismus<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 3 and 4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">7<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FC 0.5 mt<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+, SG, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Kerathopathy<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/200<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ptosis<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/40<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FC 4 mt<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+, SG, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ectopia lentis, ONH<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">12<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FC 1 mt<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">congenital, SG, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Corneal leucoma<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">14<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/40<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FC 1.5 mt<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+, SG, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Kerathopathy, ONH<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ectopia lentis, microcornea<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ectopia lentis, microcornea, ONH<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/200<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ptosis, strabismus<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">21<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">22<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/130<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/160<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/200<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Ectopia lentis<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">26<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sporadic<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/200<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Strabismus<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">27<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.7<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">congenital, SG, MD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Corneal leucoma<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20/120<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Familial</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">FF</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">+</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2\" position=\"float\"><label>Table 2</label><caption><title><italic>PAX6</italic> gene mutations and polymorphisms identified in nine non-related Mexican aniridia cases.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"41\" span=\"1\"/><col width=\"48\" span=\"1\"/><col width=\"86\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"117\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"81\" span=\"1\"/><col width=\"90\" span=\"1\"/><col width=\"144\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Case</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Iris grade*</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Nucleotide change**</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Nucleotide change***</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>mRNA/</bold>
<bold>protein effect</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Exon/</bold>
<bold>Domain</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Mother´s Genotype</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Father´s Genotype</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Status/</bold>
<bold>Reference</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.184_188dupGAGAC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.T63fsX18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 6/
Paired box<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Novel<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 3 and 4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.361T&gt;C<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.S121P<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 7/
Paired box<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heterozygous for c.361T&gt;C<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Novel<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.607C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.969C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.R203X<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 8/
Linker region<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously reported (Human PAX6 allelic database)<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.357+1G&gt;C<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.IVS6+1G&gt;C<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Cryptic donor splice-site and in-frame deletion of 36 amino acids coded by exon 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intron 6/
Paired box<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously reported [22]<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heterozygous for
c.-129+9G&gt;A<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heterozygous for IVS2+9G&gt;A<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intron 2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Homozygous for G allele<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heterozygous for IVS2+9G&gt;A
(c.-129+9G&gt;A)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously described as polymorphism (Human PAX6 allelic database), but also as a possible pathogenic variant [20]. Present study confirmed that it is a polymorphism<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.491delC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.853delC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.P164fsX43<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 7/
Linker region<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously reported [5,22]<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">21<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.879dupC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.T293fsX47<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 10/
PST domain<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Heterozygous for c.879dupC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Novel<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">22<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.277G&gt;A<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.E93K<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 6/
Paired box<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Novel<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> <hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.766-12C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IVS9-12C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">None<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intron 9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Polymorphism previously reported [21]<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">?<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.607C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.969C&gt;T<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">p.R203X<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Exon 8/
Linker region<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously reported (Human PAX6 allelic database)<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Iris 6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">c.524-2A&gt;G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IVS7-2A&gt;G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">In silico prediction: 3 cryptic acceptor splice-sites (2 out-of-frame and 1 in-frame) inside exon 8 or in-frame exon 8 skipping.</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intron 7/
Linker region</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Not available</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Previously reported [22]</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>M: Male; F: Female; Iris 3: circumpupillary iris hypoplasia; Iris 4: atypical sector coloboma; Iris 5: subtotal aniridia; Iris 6: complete aniridia; FF: fix and follow; FC: finger count; +: present; -: absent; ?: information not available; SG: surgical; MD: medical; ONH: optic nerve hypoplasia.</p></table-wrap-foot>", "<table-wrap-foot><p>An asterisk indicates that the measurements were according to Gronskov’s classification [##REF##10234503##5##]. A question mark means that an ophthalmic evaluation was not available. A double asterisk symbol indicates that the gene mutation nomenclature was according to den Dunnen and Antonarakis [##REF##10612815##19##]. A triple asterisk symbol denotes that the gene mutation nomenclature was according to previously proposed nomenclature by Ton et al. [##REF##1684738##8##].</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"mv-v14-1650-f1\"/>", "<graphic xlink:href=\"mv-v14-1650-f2\"/>" ]
[]
[{"label": ["4"], "citation": ["Traboulsi E, Zhu D, Maumenee IH. Aniridia. In: Traboulsi E editor. Genetic diseases of the eye. New York, NY: Oxford University Press; 1998. p.99\u2013114."]}]
{ "acronym": [], "definition": [] }
30
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 8; 14:1650-1658
oa_package/7a/76/PMC2530489.tar.gz
PMC2530517
18776954
[ "<title>Introduction</title>", "<p>Glaucoma is the second leading cause of vision loss, and approximately 15% of blindness worldwide result from glaucoma [##REF##9300658##1##]. It is a group of poorly understood neurodegenerative disorders that are usually associated with elevated intraocular pressure [##REF##17914928##2##]. Glaucoma is clinically and genetically heterogeneous with several different forms, each with diverse causes and severities. Clinically, it is characterized by slow but progressive degeneration of retinal ganglion cells and their axons, leading to deterioration of the visual field and to optic nerve atrophy.</p>", "<p>Although rare, primary congenital glaucoma (PCG) is the most common form of glaucoma in infants with an overall occurrence of 1 in 10,000 births [##REF##15478740##3##]. It is prevalent in countries where consanguinity is common with incidence as high as 1 in 1,250 births in the Slovak population, 1 in 2,500 births in Saudi Arabia, and 1 in 3,300 births in the state of Andhra Pradesh in India [##REF##12912697##4##,##REF##14507861##5##]. PCG is an inherited ocular congenital anomaly of the trabecular meshwork and anterior chamber angle [##REF##14375435##6##, ####REF##13647611##7##, ##REF##484670##8##, ##REF##7342408##9####7342408##9##]. This leads to the obstruction of aqueous outflow and increased intraocular pressure (IOP) resulting in optic nerve damage leading to childhood blindness. The disease manifests in the neonatal or early infantile period with symptoms of photophobia, epiphora, signs of globe enlargement, edema, opacification of the cornea, and breaks in Descemet's membrane. The mode of inheritance is largely autosomal recessive with variable penetrance, but rare cases of pseudo dominance are also seen in families with multiple consanguinity [##REF##7219964##10##, ####REF##2676634##11##, ##REF##1428571##12##, ##REF##9497261##13####9497261##13##]. To date, three genetic loci have been reported for autosomal recessive PCG, <italic>GLC3A</italic> (2p21; OMIM <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=231300\">231300</ext-link>), <italic>GLC3B</italic> (1p36; OMIM <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=600975\">600975</ext-link>), and <italic>GLC3C</italic> (14q24.3), with pathogenic mutations only reported in the human cytochrome P450 gene (<italic>CYP1B1</italic>; OMIM <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id=601771\">601771</ext-link>) [##REF##9097971##14##,##REF##8842741##15##]. It is significant to note that <italic>CYP1B1</italic> mutations have also been reported in patients with early onset of primary open-angle glaucoma. Additionally, autosomal dominant forms of PCG have been reported, and <italic>MYOC</italic>, a gene associated with primary open-angle glaucoma, is reported to play a possible role in the pathogenesis [##REF##2620399##16##,##REF##15733270##17##].</p>", "<p>The current study is aimed to explore the genetic basis of PCG in the Pakistani population. A genome wide linkage analysis was performed, which showed segregation of PCG in two consanguineous Pakistani families. Microsatellite markers on chromosome 14q24.2–24.3 cosegregated with the disease phenotype and defined the disease locus as spanning a 6.56 cM (~4.2 Mb) genetic interval flanked by D14S289 proximally and D14S85 distally.</p>" ]
[ "<title>Methods</title>", "<p>Thirteen consanguineous Pakistani families with PCG were recruited to participate in this study to understand the genetic aspects of glaucoma at the Centre of Excellence in Molecular Biology (Lahore, Pakistan). Institutional Review Board approval was obtained for this study from the Centre of Excellence in Molecular Biology (CEMB). The participating subjects gave informed consent consistent with the tenets of the Declaration of Helsinki. Both families described in this study are from the Punjab province of Pakistan.</p>", "<p>A detailed medical history was obtained by interviewing family members. All of the ophthalmic examinations including slit lamp biomicroscopy and applanation tonometry were completed at the Layton Rahmatullah Benevolent Trust (LRBT) hospital (Lahore, Pakistan). Diagnosis of PCG was based on established criteria that include measurement of IOP, measurement of corneal diameters, and observation of optic nerve head where possible as well as symptoms of corneal edema including photophobia, buphthalmos, cloudy cornea, and excessive tearing. Patients with elevated IOP associated with other systemic or ocular abnormalities were excluded. Blood samples were collected from affected and unaffected family members. DNA was extracted by a non-organic method as described by Grimberg et al. [##REF##2813076##18##].</p>", "<title>Genotype analysis</title>", "<p>A genome wide scan was performed with 382 highly polymorphic fluorescent markers from the ABI PRISM Linkage Mapping Set MD-10 (Applied Biosystems, Foster City, CA) having an average spacing of 10 cM. Multiplex polymerase chain reactions (PCRs) were performed in a 5 μl mixture containing 40 ng genomic DNA, various combinations of 10 μM dye labeled primer pairs, 0.5 μl 10X GeneAmp PCR Buffer II, 0.5 μl 10mM Gene Amp dNTP mix, 2.5 mM MgCl<sub>2</sub>, and 0.2 U of Taq DNA polymerase (AmpliTaq Gold Enzyme; Applied Biosystems). Amplification was performed in a GeneAmp PCR System 9700 (Applied Biosystems). Initial denaturation was performed for 5 min at 95 °C followed by 10 cycles for 15 s at 94 °C, for 15 s at 55 °C, and for 30 s at 72 °C, and then 20 cycles for 15 s at 89 °C, for 15 s at 55 °C, and for 30 s at 72 °C. The final extension was performed for 10 min at 72 °C followed by a final hold at 4 °C. PCR products from each DNA sample were pooled and mixed with a loading cocktail containing HD-400 size standards (PE Applied Biosystems). The resulting PCR products were separated in an ABI 3100 DNA Analyzer and analyzed by using the GeneMapper software package (Applied Biosystems).</p>", "<title>Linkage analysis</title>", "<p>Two point linkage analysis were performed using the FASTLINK version of MLINK from the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/CBBresearch/Schaffer/fastlink.html\">LINKAGE</ext-link> Program Package (provided in the public domain by the Human Genome Mapping Project Resources Centre, Cambridge, UK) [##REF##6585139##19##,##REF##8056435##20##]. Maximum LOD scores were calculated using ILINK. Autosomal recessive PCG was analyzed as a fully penetrant trait with an affected allele frequency of 0.001. The marker order and distances between the markers were obtained from the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.marshfieldclinic.org\">Marshfield</ext-link> database. For the initial genome scan, equal allele frequencies were assumed while for fine mapping, allele frequencies were estimated from 100 unrelated and unaffected individuals from the Punjab province of Pakistan.</p>", "<title>Mutation screening</title>", "<p>Individual exons were amplified by PCR using primer pairs designed by using the <ext-link ext-link-type=\"uri\" xlink:href=\"http://primer3.sourceforge.net/\">primer3</ext-link> program (primer sequences and annealing temperatures are available upon request). Amplifications were performed in 25 μl reactions containing 50 ng of genomic DNA, 2.5 μl 10X GeneAmp PCR Buffer II, 8 pmoles of each primer, 2.5 mM dNTP, 2.5 mM MgCl<sub>2</sub>, and 0.2 U Taq DNA polymerase. Amplification was performed in a GeneAmp PCR System 9700 (Applied Biosystems). PCR amplification consisted of a denaturation step at 96 °C for 5 min followed by 40 cycles, each cycle starting at 96 °C for 45 s followed by 57 °C for 45 s and 72 °C for 1 min. PCR products were analyzed on 2% agarose gel and purified by ethanol precipitation. The PCR primers for each exon were used for bidirectional sequencing using Big Dye Terminator Ready reaction mix (Applied Biosystems) according to manufacturer instructions. Sequencing products were precipitated and resuspended in 10 μl of formamide and denatured at 95 °C for 5 min. Sequencing was performed on an ABI PRISM 3100 Automated sequencer (Applied Biosystems). Sequencing results were assembled by the ABI PRISM sequencing analysis software version 3.7 and analyzed using <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.technelysium.com.au/chromas.html\">Chromas</ext-link> software (version 1.45).</p>" ]
[ "<title>Results</title>", "<p>The two families reported here, PKGL005 and PKGL025, are from the Punjab province of Pakistan. Ophthalmic examinations and medical history for both families concluded that a total of 11 affected individuals in both families have primary congenital glaucoma (PCG). The symptoms of PCG in affected individuals of PKGL005 appeared in the first three years of life. Visual acuity was confined to light perception and/or counting fingers. The cup to disc ratios of affected individuals 11 and 41 were 0.8 (OD) and 1.0/0.3 (OD/OS), and the recorded IOPs for individuals 11 and 41 were 32/20 mm Hg (OD/OS) and 38/30 mm Hg (OD/OS), respectively (##TAB##0##Table 1##). On the other hand, symptoms of PCG in PKGL025 were either present at birth or appeared in the first six weeks of life. Visual acuity was reduced to counting figures and/or light perception with bilateral buphthalmos eyes. The IOPs for affected individuals in PKGL025 were either above the normal range or was controlled by medical treatment (##TAB##0##Table 1##).</p>", "<p>Initially, all reported loci for PCG were excluded for linkage using closely spaced microsatellite markers (data not shown). A genome wide scan was completed with the ABI MD10 panel, which consisted of 382 polymorphic microsatellite markers and spaced at an average of 10 cM across the whole genome. During the genome-wide scan, LOD scores above 1.5 were obtained for markers D6S308, D10S59, D10S1652, D11S1314, D14S74, D14S68, D16S404, and D18S53 in PKGL005 and for markers D2S112, D3S1279, D9S1776, D14S74, and D21S263 in PKGL025. Of these markers, D6S308, D10S59, D10S1652, D11S1314, D16S404, and D18S53 have closely flanking markers yielding large negative LOD scores in PKGL005. Similarly, in PKGL025, D2S112, D3S1279, D9S1776, and D21S263 have closely flanking markers yielding large negative LOD scores. Linkage to markers other than chromosome 14q markers that showed LOD scores greater than 1.5 during the genome-wide scan was further excluded by haplotype analysis of closely flanking markers.</p>", "<p>Two point linkage analysis provided the first evidence of linkage to markers at 14q24.2–24.3 with maximum LOD scores of 5.88 and 6.19 with markers D14S61 and D14S43 at θ=0 for families PKGL005 and PKGL025, respectively. Additional STR markers selected from the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov\">NCBI</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.marshfieldclinic.org\">Marshfield</ext-link> databases were genotyped to define the linkage interval in these families. Two point LOD scores of 4.96, 5.60, 4.01, 4.84, 4.76, 5.88, 3.50, and 3.69 with D14S77, D14S43, D14S284, D14S1036, D14S85, D14S61, D14S59, and D14S1008 at θ=0 were obtained for PKGL005 (##TAB##1##Table 2##). Similarly, two point LOD scores 4.66, 6.19, 4.44, 5.28, 3.19, and 5.38 with D14S77, D14S43, D14S284, D14S1036, D14S85, and D14S74 at θ=0 were obtained for PKGL025 (##TAB##2##Table 3##).</p>", "<p>Haplotype analysis supports the results of linkage analysis as shown in ##FIG##0##Figure 1##. There is a proximal recombination in affected individual 19 of PKGL025 at D14S63 and in affected individuals 28 and 41 of PKGL005 at D14S289. Similarly, there is distal recombination in affected individual 28 of PKGL025 at D14S606 and in affected individual 41 of PKGL005 and unaffected individual 23 of PKGL025 at D14S74 as well as in unaffected individual 14 of PKGL025 at D14S85. Taken together, these results suggest the disease locus lies in a 6.56 cM (~4.2 Mb) region flanked by markers D14S289 and D14S85. As marker D14S1036 is uninformative for individual 10 of PKGL025, it is possible that the distal boundary lies proximal to marker D14S1036. Alleles for D14S77, D14S43, D14S284, D14S76, and D14S1036 were homozygous for all affected individuals in families PKGL005 and PKGL025 whereas the normal individuals are either heterozygous carriers of the disease allele or are homozygous for the normal allele.</p>", "<p>The critical interval on chromosome 14q24.2–24.3 harbors coenzyme Q6 homolog (<italic>COQ6</italic>), which encodes a flavin-dependent monooxygenase in <italic>Saccharomyces cerevisiae</italic>. This suggests a functional similarity with <italic>CYP1B1</italic>. We investigated the <italic>COQ6</italic> gene to identify the mutation leading to the disease phenotype in these families by sequencing all coding exons, exon-intron boundaries, and the 5'-untranslated region, but we did not find any pathogenic mutations in this gene. Our sequencing results identified previously reported SNPs <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=3213692\">rs3213692</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=2074930\">rs2074930</ext-link> in PKGL005 and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=17552038\">rs17552038</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=3213692\">rs3213692</ext-link>, and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=7141392\">rs7141392</ext-link> in PKGL025.</p>" ]
[ "<title>Discussion</title>", "<p>Here, we report autosomal recessive primary congenital glaucoma (PCG) in two large consanguineous Pakistani families, mapped to chromosome 14q24.2–24.3. Maximum LOD scores of 5.88 and 6.19 with markers D14S61 and D14S43 at θ=0 for families PKGL005 and PKGL025, respectively, the lack of LOD scores above 2.0 for any markers other than chromosome 14q in the entire genome scan, and the disease haplotype segregating with the disease phenotype in both families strongly suggest that the PCG locus maps to chromosome 14q24.2–24.3 in these families. Haplotype analysis of these two families refines the disease interval to a 6.56 cM (~4.2 Mb) region flanked by markers D14S289 and D14S85. Localization of the disease interval to 14q24.2–24.3 in two consanguineous Pakistani families strongly suggests genetic heterogeneity of primary congenital glaucoma.</p>", "<p>To date, three PCG loci have been mapped to chromosomes 2p21 (<italic>GLC3A</italic>), 1p36 (<italic>GLC3B</italic>), and 14q24.3 (<italic>GLC3C</italic>) whereas mutations associated with PCG have only been reported in the <italic>CYP1B1</italic> gene [##REF##9497261##13##, ####REF##9097971##14##, ##REF##8842741##15####8842741##15##,##REF##8586416##21##]. Previously, <italic>GLC3C</italic> was localized to chromosome 14q24.3 flanked by markers D14S61 and D14S1000 as shown in ##FIG##1##Figure 2## [##UREF##0##22##]. In PKGL025, individual 14 delineates the distal boundary at marker D14S85, strongly suggesting that the disease locus in PKGL025 does not overlap with <italic>GLC3C</italic>. As both families in this study come from similar geographical and racial backgrounds, haplotype analysis of both families strongly suggests that the region flanked by markers D14S289 and D14S85 harbors the disease causing gene. However, we cannot rule out the possibility that the disease phenotype in these two families is caused by two different mutations, and the pathogenic mutation for PKGL005 may be present in a gene localized in a region overlapping with the <italic>GLC3C</italic> locus.</p>", "<p>The critical interval on chromosome 14q24.2–24.3 harbors 97 genes including coenzyme Q6 homolog (<italic>COQ6</italic>), WD repeat domain 21A (<italic>WDR21A</italic>), and ceh-10 homeo domain containing homolog (<italic>CHX10</italic>). COQ6 is a lipid soluble antioxidant and an obligatory component of the respiratory chain and uncoupling proteins [##REF##17482888##23##,##REF##15620378##24##]. <italic>COQ6</italic> in <italic>Saccharomyces cerevisiae</italic> encodes a flavin-dependent monooxygenase, suggesting a functional similarity with <italic>CYP1B1</italic>, a mixed-function monooxygenase that belongs to the cytochrome P450 1B subfamily [##REF##12721307##25##]. We sequenced all the coding exons and exon-intron boundaries as well as the 5’ and 3’ regions of affected individuals of families PKGL005 and PKGL025; however we did not identify any pathogenic mutation.</p>", "<p>WDR21A belongs to the WD repeat protein family. Members of this family are involved in a variety of cellular processes including cell cycle progression, signal transduction, apoptosis, and gene regulation. Mutations in <italic>WDR36,</italic> also a member of the WD repeat protein family, have been associated with adult-onset primary open-angle glaucoma (POAG) [##REF##15677485##26##]. In contrast, <italic>CHX10</italic> is homeobox transcription factor gene that is expressed in progenitor cells of the developing neuroretina and in the inner nuclear layer of the mature retina. In humans, <italic>CHX10</italic> mutations are associated with microphthalmia with cataracts and iris abnormalities, isolated microphthalmia with coloboma 3, isolated microphthalmia 2, and isolated microphthalmia with cloudy corneas [##REF##15257456##27##, ####REF##17661825##28##, ##REF##10932181##29####10932181##29##]. Similarly, mutations in <italic>CHX10</italic> cause microphthalmia, progressive degeneration of the retina, and an absence of the optic nerve in mice [##UREF##1##30##]. We are currently sequencing these two candidate genes to identify any pathogenic mutations.</p>", "<p>In summary, we have localized autosomal recessive PCG to chromosome 14q24.2–24.3 in two consanguineous Pakistani families. Identification of the PCG causing gene at this locus will help to unveil the underlying molecular complexity of primary congenital glaucoma and will be a valuable addition to the existing repertoire of glaucoma genetics, particularly of PCG. Finally, it will be helpful in screening for carrier status and genetic counseling of PCG families especially in the Pakistani population to prevent severe visual impairment and blindness.</p>" ]
[]
[ "<p>The first two authors contributed equally to this work.</p>", "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>Two consanguineous Pakistani families with autosomal recessive primary congenital glaucoma were recruited to identify the disease locus.</p>", "<title>Methods</title>", "<p>Ophthalmic examinations including slit lamp biomicroscopy and applanation tonometry were employed to classify the phenotype. Blood samples were collected and genomic DNA was extracted. A genome wide scan was performed on both families with 382 polymorphic microsatellite markers. Two point LOD scores were calculated, and haplotypes were constructed to define the disease interval.</p>", "<title>Results</title>", "<p>Clinical records and ophthalmic examinations suggest that affected individuals in families PKGL005 and PKGL025 have primary congenital glaucoma. Maximum two-point LOD scores of 5.88 with D14S61 at θ=0 and 6.19 with D14S43 at θ=0 were obtained for families PKGL005 and PKGL025, respectively. Haplotype analysis defined the disease locus as spanning a 6.56 cM (~4.2 Mb) genetic interval flanked by D14S289 proximally and D14S85 distally.</p>", "<title>Conclusions</title>", "<p>Linkage analysis localizes autosomal recessive primary congenital glaucoma to chromosome 14q24.2–24.3 in consanguineous Pakistani families.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>The authors are grateful to members of both families for their participation in this study. We sincerely thank the staff of the Layton Rehmatullah Benevolent Trust (LRBT) hospital for their help in the clinical evaluations of the affected individuals. This work was supported by the Higher Education Commission (Islamabad, Pakistan), the Ministry of Science and Technology (Islamabad, Pakistan), and the COMSTECH.EMRO project of the World Health Organization (Registration No: RAB and GH 06–07_24).</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Pedigree of families PKGL005 and PKGL025. Squares denote males, circles indicate females, filled symbols represent affected individuals, double lines between individuals indicate consanguinity, and a diagonal line through a symbol signify that the family member is deceased. The haplotypes of 15 adjacent chromosome 14q14.2–24.3 microsatellite markers for families PKGL005 (<bold>A</bold>) and family PKGL025 (<bold>B</bold>) are shown with alleles forming the risk haplotype shaded black, alleles cosegregating with primary congenital glaucoma (PCG) but not showing homozygosity shaded gray, and alleles not cosegregating with PCG shown in white.</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Schematic representation of linkage on chromosome 14q24.2–24.3 in families PKGL005 and PKGL025. Filled circles denote STR markers, and solid vertical lines represent the chromosomal intervals in which markers are homozygous for affected members of each of the two families.</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Clinical features of affected individuals of families PKGL005 and PKGL025.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"76\" span=\"1\"/><col width=\"70\" span=\"1\"/><col width=\"60\" span=\"1\"/><col width=\"61\" span=\"1\"/><col width=\"60\" span=\"1\"/><col width=\"80\" span=\"1\"/><col width=\"70\" span=\"1\"/><col width=\"66\" span=\"1\"/><col width=\"83\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Family number</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Individual ID</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Gender</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age of onset</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age at time of study</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Maximum IOP (OD/OS)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>C/D ratio (OD/OS)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Visual acuity (OD/OS)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Other changes</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL005<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32/20*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8/NV<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CF/CF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Megalocornea<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL005<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38/30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CF/CF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Megalocornea<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL025<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">By birth<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20*/24*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CF/CF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Buphthalmos<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL025<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">By birth<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 months<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25/26<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CF/CF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Buphthalmos<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL025<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">By birth<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 years<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12*/16*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9/0.9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CF/CF<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Megalocornea, cornea haze<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PKGL025</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">By birth</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NA/38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NV/1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NPL/NPL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Buphthalmos</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2\" position=\"float\"><label>Table 2</label><caption><title>Two point LOD scores of PKGL005 with chromosome 14q markers.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"63\" span=\"1\"/><col width=\"37\" span=\"1\"/><col width=\"37\" span=\"1\"/><col width=\"41\" span=\"1\"/><col width=\"45\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"43\" span=\"1\"/><col width=\"40\" span=\"1\"/><col width=\"42\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>Marker</bold></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>cM</bold></th><th rowspan=\"2\" valign=\"top\" align=\"center\" scope=\"col\" colspan=\"1\"><bold>Mb</bold></th><th colspan=\"9\" valign=\"top\" align=\"center\" scope=\"colgroup\" rowspan=\"1\"><bold>Two-point LOD score values at recombination fraction (θ=)</bold><hr/></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>Zmax</bold></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>θ max</bold></th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"center\" scope=\"colgroup\" rowspan=\"1\"><bold>0.00</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.01</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.03</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.05</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.07</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.09</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.10</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.20</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.30</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.71<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-5.50<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S258<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.65<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-5.49<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.83<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S289<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.06<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.14<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S77<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.82<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.84<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.61<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.91<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.80<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.51<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S43<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54.98<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.60<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.49<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.98<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.73<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.46<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.33<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.02<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.74<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.60<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S284<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.75<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.01<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.89<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.65<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.42<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.94<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.82<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.72<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.84<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.01<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S76<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.82<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.88<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.71<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.56<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.39<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S1036<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.83<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.84<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.73<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.48<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.98<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.73<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.61<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.84<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S85<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.76<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.76<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.65<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.40<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.65<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.52<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.76<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S61<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86.29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.37<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.88<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.75<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.50<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.98<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.71<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.58<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.88<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S59<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.40<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.99<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.79<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.59<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.49<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.53<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.75<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S74<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.70<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.06<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.95<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.93<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.70<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.96<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.06<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S1008<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89.19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.94<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.61<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.43<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.88<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.79<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.90<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S606<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-0.08<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.46<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.47<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.41<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.31<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.54<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.47<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S974</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">93.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-2.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.07</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3\" position=\"float\"><label>Table 3</label><caption><title>Two point LOD scores of PKGL025 with chromosome 14q markers.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"62\" span=\"1\"/><col width=\"40\" span=\"1\"/><col width=\"37\" span=\"1\"/><col width=\"39\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"31\" span=\"1\"/><col width=\"34\" span=\"1\"/><col width=\"54\" span=\"1\"/><col width=\"40\" span=\"1\"/><col width=\"49\" span=\"1\"/><thead><tr><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>Marker</bold></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>cM</bold></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>Mb</bold></th><th colspan=\"9\" valign=\"top\" align=\"left\" scope=\"colgroup\" rowspan=\"1\"><bold>Two-point LOD score values at recombination fraction (θ=)</bold><hr/></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>Zmax</bold></th><th rowspan=\"2\" valign=\"top\" align=\"left\" scope=\"col\" colspan=\"1\"><bold>θ max</bold></th></tr><tr><th valign=\"top\" colspan=\"1\" align=\"left\" scope=\"colgroup\" rowspan=\"1\"><bold>0.00</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.01</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.03</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.05</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.07</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.09</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.10</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.20</bold></th><th valign=\"top\" align=\"left\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>0.30</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S63<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">69.18<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44.71<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-2.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.03<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S258<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">76.28<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50.65<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.03<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S289<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">78.20<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.63<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.56<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.94<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.94<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.88<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.94<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.03<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S77<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80.82<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53.63<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.66<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.59<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.44<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.28<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.09<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.91<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.78<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.66<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S43<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.16<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.98<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.03<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.72<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.44<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.13<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.84<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.72<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S284<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.44<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.31<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.06<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.63<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.38<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.06<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.06<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.44<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S76<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55.82<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.32<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.27<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.17<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.07<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.97<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.86<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.29<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.79<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.32<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S1036<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55.83<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.28<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.16<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.91<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.63<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.38<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.13<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.99<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.69<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.44<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.28<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S85<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">84.69<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.13<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.06<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.94<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.68<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.62<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.81<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.06<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S61<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">86.29<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">56.37<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-4.34<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.91<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.22<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.22<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.16<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.09<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.47<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.78<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.05<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S59<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">87.36<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58.11<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-4.34<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.01<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S74<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">87.36<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58.70<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.38<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.25<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.13<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.38<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.00<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S1008<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">89.19<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59.94<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-4.34<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.50<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.75<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.05<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S606<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">91.62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-4.34<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.16<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.84<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.00<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.03<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.97<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.91<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.09<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.09<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3.03<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.07<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\">D14S974</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">93.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-4.34</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.38</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.38</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.94</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2.44</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">0.07</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>An asterisk indicates that IOP is controlled by medical or surgical treatment. IOP, intraocular pressure; OD, right eye; OS, left eye; PL, perception of light; NPL, no perception of light; HM, hand motion; NA, not available; NV, no view because of eyeball atrophy or corneal opacity; CF, counting fingers.</p></table-wrap-foot>", "<table-wrap-foot><p>LOD scores were calculated at different θ values for each marker with the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/CBBresearch/Schaffer/fastlink.html\">FASTLINK</ext-link> version of MLINK from the LINKAGE program package. Maximum LOD scores for each marker were calculated using ILINK.</p></table-wrap-foot>", "<table-wrap-foot><p>LOD scores were calculated at different θ values for each marker with the <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/CBBresearch/Schaffer/fastlink.html\">FASTLINK</ext-link> version of MLINK from the LINKAGE program package. Maximum LOD scores for each marker were calculated using ILINK.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"mv-v14-1659-f1\"/>", "<graphic xlink:href=\"mv-v14-1659-f2\"/>" ]
[]
[{"label": ["22"], "citation": ["Stoilov IR, Sarfarazi M. The third genetic locus (GLC3C) for primary congenital glaucoma (PCG) maps to chromosome 14q24.3. ARVO Annual Meeting; 2002 May 5-10; Fort Lauderdale (FL)"]}, {"label": ["30"], "surname": ["McInnes", "Basu", "Novak", "Ploder", "Liang", "Hawes", "Taylor", "Roderick", "Goldman", "Hankin", "Burmeister"], "given-names": ["RR", "S", "J", "L", "MY", "N", "B", "T", "D", "M", "M"], "article-title": ["The ocular retardation (oc"], "sup": ["J"], "source": ["Am J Hum Genet"], "year": ["1994"], "volume": ["55"], "issue": ["suppl."], "fpage": ["A3"]}]
{ "acronym": [], "definition": [] }
30
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 5; 14:1659-1665
oa_package/71/92/PMC2530517.tar.gz
PMC2530518
18776955
[ "<title>Introduction</title>", "<p>Glaucoma is the second leading cause of blindness worldwide, affecting more than 70 million people. Primary open-angle glaucoma (POAG) is the most common form of this ocular disease. A population-based, cross-sectional study showed that glaucoma is the major cause of blindness in China, and POAG is a key form of the disease [##REF##10922206##1##]. Noticeably, incidence rates of secondary glaucoma and congenital glaucoma are 0.52% and 0.02%, respectively. The prevalence of primary glaucoma is 1%-2% in the population over age 40.</p>", "<p>POAG is usually asymptomatic until the late stage of the disease. This make early diagnosis almost impossible. And when POAG reaches the late stage, irreversible damages such as chronic, progressive apoptosis of optic ganglion cells and visual field damage usually occur. The most important risk factor for POAG is family history [##REF##15161538##2##]. First-degree relatives of individuals affected with POAG are 10 times more likely to develop POAG [##REF##11026970##3##]. Since its first implication in the genetic linkage to POAG in 1997, numerous mutations in the myocilin (<italic>MYOC</italic>) gene have been identified and their specific phenotypes have been characterized. Although the mechanism underlying glaucoma is poorly understood, a growing body of evidence suggests that there is a genetic link between <italic>MYOC</italic> mutations and the pathogenesis of glaucoma. So far, more than 70 mutations of <italic>MYOC</italic> have been documented in POAG families or sporadic POAG patients. Some of them such as Gln48His [##REF##12447164##4##] in exon 1, Asp208Glu [##REF##10798654##5##] in exon 2, and Pro370Leu [##REF##9328473##6##] and Thr377Met [##REF##9792882##7##] in exon 3 of <italic>MYOC</italic> were confirmed to correlate with POAG. Interestingly, <italic>MYOC</italic> mutations have been found to vary with different ethnic groups and geographic locations [##REF##12789574##8##, ####REF##10196380##9##, ##REF##12189160##10##, ##REF##15338275##11##, ##REF##16431959##12##, ##REF##15851979##13##, ##REF##10916185##14####10916185##14##]. In the current study, we performed <italic>MYOC</italic> mutation screening in a large glaucoma family affected with POAG, and our results suggest that novel mutations of <italic>MYOC</italic>, Pro13Leu and Gln337Stop, may be associated with POAG. This study will also discuss the significance of our findings to genetic counseling.</p>" ]
[ "<title>Methods</title>", "<title>Clinical examination and diagnosis of primary open-angle glaucoma</title>", "<p>Clinical examinations were performed including visual acuity, slit lamp biomicroscopy, applanation tonometry, gonioscopy, funduscopy, and perimetry. Family members were divided into three groups: (1) affected individuals, (2) asymptomatic individuals, and (3) suspect individuals. POAG is defined by a normal appearing anterior chamber angle along with two of the following symptoms [##REF##9535666##15##]: elevation of intraocular pressure (IOP&gt;21 mmHg), characteristic visual field defects, and glaucomatous optic nerve head changes (cup-disc ratio&gt;0.6 or notches). Subjects meeting only one of these symptoms were defined as suspect. Individuals without any manifestations were defined as asymptomatic. Patients were diagnosed and treated at the Eye and ENT Hospital (Fudan University, Shanghai, China). All participants in this research had given informed consent after receiving detailed explanation of the nature and possible consequences of the study.</p>", "<title>Genetic analysis</title>", "<p>Genomic DNA was extracted from peripheral blood leukocytes using standard procedures. The coding sequences of <italic>MYOC</italic> (GenBank <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&amp;id=62899622\">AB006688</ext-link>) were amplified by polymerase chain reaction (PCR). Amplifications of three exons were performed in a 25 μl reaction containing 50 ng of genomic DNA mixed with 10X buffer, 50 pmol primers, 2.5 mM NTP, and 1 ul Taq polymerase. PCR conditions were as follows: initial denaturation at 94 °C for 5 min followed by 35 cycles of denaturation at 94 °C for 45 s, annealing at a temperature specific for each primer for 45 s (##TAB##0##Table 1##), and extension at 72 °C for 1 min. A final extension at 72 °C for 10 min completed the reaction. The reactions were performed with a GeneAmp PCR system 9600 (Applied Biosystems, Foster City, CA). Subsequently, 8 μl of the PCR product for each polymorphic site was digested completely with Bme1390 I, BselI, Eco721, MspI, Bpu11021, PagI, BsmaI, and BseD, according to instructions recommended by the manufacturer. Genotype analysis was determined by 12% polyacrylamide gel. Direct sequencing was performed on an Applied Biosystems 3730 DNA Analyzer (Applied Biosystems) according to the BigDye Terminator version 3.1 protocol. <italic>MYOC</italic> mutations were screened in each surviving individual. Presymptomatic genetic diagnoses were determined for family members who sought information and instruction about their disease status according to the phenotype of POAG, the pattern of inheritance, their clinical status, and genetic analysis results of their family. Follow-up plans were established after these diagnoses.</p>", "<title>Secondary structure prediction</title>", "<p>We also used <ext-link ext-link-type=\"uri\" xlink:href=\"http://antheprot-pbil.ibcp.fr\">Antheprot</ext-link> software to analyze the possible effects of these mutations on the secondary structure of the corresponding proteins.</p>" ]
[ "<title>Results</title>", "<title>Phenotypes of the patients</title>", "<p>The five-generation family exhibited an autosomal dominant pattern of inheritance (##FIG##0##Figure 1##). A total of 11 patients were identified with POAG (##TAB##1##Table 2##). The information for III:8, III:14, and III:15 were not complete. Their diagnoses were based on medical histories, which included elevation of intraocular pressure (IOP &gt;21 mmHg) and characteristic visual field defects.</p>", "<p>Onset ages of patients ranged from16 to 41 years. Most patients had typical glaucoma changes in the optic disc and the visual field (##FIG##1##Figure 2##). All patients showed more damage to the optic nerve head in the right eye than in the left. All affected family members had a noticeable increase in intraocular pressure (IOP) higher than 22 mmHg. The IOP of these patients could not be controlled with available antiglaucoma medication. Most of the patients underwent antiglaucoma surgeries, and subjects III:1 and III:8 had repeated operations due to the failure of the first procedure but still couldn’t control the development of the pathogenetic condition.</p>", "<title>Clinical examination of the consulters</title>", "<p>There were altogether 26 consulters with no visible optic head damage and visual field defects (##TAB##2##Table 3##), and two of whom (III:16 and IV:25) were marked as suspects since their maximum IOP was higher than 21 mmHg. No evidence for glaucoma was found in the other 24 consulters.</p>", "<title>Mutation screen of <italic>MYOC</italic></title>", "<p>With polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and gene sequencing technologies, we identified one known mutation, Arg76Lys (227G<bold>→</bold>A), that was reported as a polymorphism by Alward et al. [##REF##9535666##15##], and two novel mutations, Pro13Leu (38 C<bold>→</bold>T) and Gln337Stop (1009C del), that are likely responsible for the pathogenesis of POAG since these mutations result in either a change in the amino acid sequence or a frame shift. The three mutations were summarized in ##TAB##3##Table 4##. The results of PCR-RFLP and gene sequencing are shown in ##FIG##2##Figure 3##. Other mutations i.e., Gln48His (144G<bold>→</bold>T), Gly246Arg (736G<bold>→</bold>A), Gln337Arg (1009C<bold>→</bold>G), Ile345Met (1036 A<bold>→</bold>G), Pro370Leu (1109C<bold>→</bold>T), Asp380Asn (1138G<bold>→</bold>A), Asp380Ala (1139A<bold>→</bold>C), Ile477Ser (1430T<bold>→</bold>A), Pro481Thr (1441C<bold>→</bold>A), and Pro481Leu (1442C<bold>→</bold>T), were not detected.</p>", "<title>Presymptomatic diagnosis for consulters</title>", "<p>Of the 26 consulters, two (III:16 and IV: 25) were classified as suspects because their maximum IOPs were higher than 21 mmHg. Another two (V:2 and V:10) at high risk for POAG since they carried the mutation considered to be disease-causing. One adolescent (V:2) carrying the Arg76Lys (38 C<bold>→</bold>T) mutation and another adolescent (V:10) carrying the Gln337Stop (1009 C del) mutation were defined as preclinical status and as having a high risk of developing glaucoma. To prescribe appropriate medication to carriers at an early stage of glaucoma, follow-up plans were established. The adolescents were asked to accept applanation tonometry and funduscopy every month and perimetry every six months.</p>", "<title>Prediction of two-dimensional structure</title>", "<p>Protein analysis using <ext-link ext-link-type=\"uri\" xlink:href=\"http://antheprot-pbil.ibcp.fr/\">Antheprot</ext-link> suggested that Pro13Leu (38 C<bold>→</bold>T) and Arg76Lys (227 G<bold>→</bold>A) mutations resulted in the modification of the corresponding amino acid, but the predicted secondary structures of the encoded proteins were not different from those of the wild-types (##TAB##4##Table 5##). However, the frame shift introduced by Gln337stop (1009C del) creates a premature termination codon thereby resulting in a truncated product.</p>" ]
[ "<title>Discussion</title>", "<p><italic>MYOC</italic> consists of three exons separated by two introns and encodes a protein of 504 amino acids. Myocilin is a secreted, 55–57 kDa glycoprotein that forms dimers and multimers and has several characteristic structural motifs including a myosin-like domain, a leucine zipper region, and an olfactomedin domain [##REF##16466712##16##]. More than 70 mutations in <italic>MYOC</italic> have been reported, 90% of which occur within exon 3. Mutations in <italic>MYOC</italic> are found in 3.86% of Caucasian patients with POAG, including normal tension glaucoma or ocular hypertension, 3.30% of patients of African descendants including African Americans and black residents in Africa, and 4.44% of Asian patients [##REF##14764620##17##].</p>", "<p>We have screened a Chinese POAG family for <italic>MYOC</italic> base-pair variants and identified three allelic variants, Pro13Leu (38 C<bold>→</bold>T), Arg76Lys (227G<bold>→</bold>A), and Gln337Stop (1009C del). Since the Arg76Lys mutation in POAG was first reported in 1998 [##REF##9535666##15##], numerous studies have investigated the role of <italic>MYOC</italic> in the etiology of POAG in various ethnic groups and found that the mutation rate of <italic>MYOC</italic> ranges from 12% to 18% [##REF##10798654##18##, ####REF##12356829##19##, ##REF##15795224##20##, ##REF##16431959##21##, ##REF##15547491##22####15547491##22##]. In contrast to 0% in normal controls, a mutation frequency of 5.3% (3/56) in Arg76Lys was identified in the present study, which is lower than previously reported. Moreover, these mutations do not result in significant alterations in either the predicted secondary structure or the physico-chemical property. Of course, more samples should be collected and analyzed to draw a conclusion to precisely represent the data generated from the present study.</p>", "<p>Here, we have identified two novel mutations, Pro13Leu (38C<bold>→</bold>T) and Gln337stop (1009 C del), in prevalence rates of 8.9% and 21.4%, respectively, in this family. POAG in one patient carrying the Pro13Leu mutation eventually developed into blindness. One subject (IV:25; without the Pro13Leu mutation) who showed a normal appearance without visible optic head damage and visual field defects had an IOP of 21 mmHg. Whether this switch of amino acid residue has a dominant negative effect remains unknown and must be further studied. The other mutation, Gln337stop, was identified in all clinically confirmed patients and an asymptomatic subject V:10 (Gln337stop genotype frequency: 21.4%, 12/56). In this family, Gln337stop was shown to be one of the most severe and common gene defects that result in POAG. The present study reveals that the Gln337stop mutation of <italic>MYOC</italic> is deemed to largely contribute to the early onset glaucoma in this pedigree. Median onset age of affected individuals with Gln337stop was 24.9 years. Nearly all patients had to accept surgery due to intraocular pressure. In addition, the one suspect (V:10) harboring the Gln337stop mutation was only four years old but had a high IOP and cup-disc ratio, which made it highly likely to be predisposed to POAG. Genetically, the loss of cytosine at 1009 creates a premature stop codon. The resulting truncated product may have a severe dominant negative effect, which plays a critical role in etiology of POAG.</p>", "<p>Ethnic difference in the frequency and types of <italic>MYOC</italic> mutations among patients with POAG has been examined by case control studies. At present, screening tests in whole populations for <italic>MYOC</italic> defects are not feasible due to the low prevalence of <italic>MYOC</italic>-associated glaucoma. However, people at high risk of developing glaucoma may benefit from genetic testing, especially in early onset type of glaucoma pedigrees.</p>", "<p>In summary, mutations in <italic>MYOC</italic> strongly correlate with the pathogenicity of POAG. Future studies on glaucoma will be focused on developing an early genetic diagnosis. Such a test will allow evaluating the penetrance of <italic>MYOC</italic> and bypassing limitations of the present clinical based methods, thus providing a chance to prevent the irreversible damages.</p>" ]
[]
[ "<p>This is an open-access article distributed under the terms of the\n Creative Commons Attribution License, which permits unrestricted use,\n distribution, and reproduction in any medium, provided the original\n work is properly cited.</p>", "<title>Purpose</title>", "<p>To investigate the genetic linkage of primary open-angle glaucoma (POAG) in a Chinese family.</p>", "<title>Methods</title>", "<p>We have screened for myocilin (<italic>MYOC</italic>) gene mutations in a glaucoma family of five generations. There are fifty-six members of whom 11 were confirmed to have POAG , two with ocular hypertension were considered as POAG suspect, and the remaining 43 were asymptomatic. We also recruited 200 unrelated healthy Chinese subjects as normal control. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis and DNA sequencing were used to identify mutations in the three exons of <italic>MYOC</italic>. Presymptomatic diagnoses were made for the family members seeking consultation based on the results of both clinical examination and genetic analysis.</p>", "<title>Results</title>", "<p>Among three allelic variants identified in this pedigree (Pro13Leu [38 C<bold>→</bold>T], Arg76Lys [227G<bold>→</bold>A], and Gln337Stop [1009C del]), Pro13Leu and Gln337Stop were reported to be novel mutations while Arg76Lys has been previously documented. Our results show that all 11 POAG patients carry the Gln337Stop mutation and that four POAG patients and one POAG suspect (V:2) were found to have the Pro13Leu mutation. In addition, Arg76Lys polymorphism was identified in two patients and a POAG suspect (V:5).</p>", "<title>Conclusions</title>", "<p>Pro13Leu and Gln337Stop mutations of <italic>MYOC</italic> are likely responsible for the etiology of POAG in this pedigree, but the causative mechanism needs further research.</p>" ]
[]
[ "<title>Acknowledgments</title>", "<p>We sincerely appreciate the help of the patients, their family members, and the control subjects for participating in this study. This work is supported by grants from the National Natural Science Foundation of China (30672012) and the Wuhan Municipal Department of Science and Technology (20016009107) to X.Z. It is also supported by the Hunan Provincial Natural Science Foundation of China (07JJ6047) to X.X.</p>" ]
[ "<fig id=\"f1\" fig-type=\"figure\" position=\"float\"><label>Figure 1</label><caption><p>Pedigree with variances of <italic>MYOC.</italic> Solid symbols and open symbols represent affected and unaffected disease status, respectively. Suspects are marked with a question mark inside the squares. Roman numerals and Arabian numerals indicate generations and orders, respectively. Squares and circles represent males and females, respectively. A slash indicates a deceased family member. The arrow indicates the proband.</p></caption></fig>", "<fig id=\"f2\" fig-type=\"figure\" position=\"float\"><label>Figure 2</label><caption><p>Optic disc and visual field of individual IV:22. Glaucomatous optic disc atrophy is seen with visual field defects in the right eye. Panel <bold>A</bold> shows the optic disc atrophy of the right eye while panel <bold>B</bold> shows visual field defects of the right eye.</p></caption></fig>", "<fig id=\"f3\" fig-type=\"figure\" position=\"float\"><label>Figure 3</label><caption><p>Electrophoretic mobility assays of PCR-RFLP and sequence results. <bold>A</bold>: PCR products were separated on 12% polyacrylamide gel from three representative samples, and the genotypes are shown for (1) Arg76Lys (227G<bold>→</bold>A), (2) Pro13Leu (38 C<bold>→</bold>T), and (3) Gln337Stop (1009C del). The sizes of the molecular weight marker and DNA fragments are shown in the left and the right sides of the gel, respectively. <bold>B</bold>: The representative chromatogram contains the sequence from the mutant DNA sequence strand for (1) Arg76Lys and (2) Pro13Leu as well as from the (3) wild-type and (4) mutant DNA sequence of Gln337Stop.</p></caption></fig>" ]
[ "<table-wrap id=\"t1\" position=\"float\"><label>Table 1</label><caption><title>Primers and sequences used in this study.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"71\" span=\"1\"/><col width=\"74\" span=\"1\"/><col width=\"53\" span=\"1\"/><col width=\"211\" span=\"1\"/><col width=\"53\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>ID</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Position*</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Tm</bold>
<bold>(°C)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sequence (5′→3′)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Size (bp)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">353–509<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: GGCTGGCTCCCC AGTATATA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">174<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: ACAGCTGGCATCTCAGGC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">491–658<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: ACG TTG CTG CAG CTT TGG<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">196<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: GATGACTGACATGGCCTGG<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">603–772<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: AGTGGCCGATGCCAGTATAC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">189<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CTGGTCCAAGGTCAATTGGT<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">670–864<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: AGGCCATGTCAGTCATCCAT<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">214<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: TCTCTGGTTTGGGTTTCCAG<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">778–958<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TGACCTTGGACCAGGCTG<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CCTGGCCAGATTCTCATTTT<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">928–1095<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TGGAGGAAGAGAAGAAGCGA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">187<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CTGCTGAACTCAGAGTCCCC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC7<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1416–1630<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: AACATAGTCAATCCTTGGGCC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">230<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: TAAAGACCATGTGGGCACA<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1933–2091<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TTATGGATTAAGTGGTGCTTCG<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">177<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: ATTCTCCACGTGGTCTCCTG<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2069–2232<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: AAGCCCACCTACCCCTACAC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">184<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: AATAGAGGCTCCCCGAGTACA<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2195–2366<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: ATACTGCCTAGGCCACTGGA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">190<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CAATGTCCGTGTAGCCACC<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2335–2512<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: TGGCTACCACGGACAGTTC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">197<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CATTGGCGACTGACTGCTTA<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC12<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2480–2653<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: GAACTCGAACAAACCTGGGA<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">195<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: CATGCTGCTGTACTTATAGCGG<hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">MYOC13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2624–2783<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F: AGCAAGACCCTGACCATCC<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">179<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R: AGCATCTCCTTCTGCCATTG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"t2\" position=\"float\"><label>Table 2</label><caption><title>Clinical appearance of the affected members</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"59\" span=\"1\"/><col width=\"36\" span=\"1\"/><col width=\"54\" span=\"1\"/><col width=\"54\" span=\"1\"/><col width=\"52\" span=\"1\"/><col width=\"62\" span=\"1\"/><col width=\"35\" span=\"1\"/><col width=\"49\" span=\"1\"/><col width=\"67\" span=\"1\"/><col width=\"53\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Family member</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sex</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age (years)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age of onset (years)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Visual acuity</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Maximum IOP (mmHg)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Cup-disc ratio</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>VF grading score</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Severity</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Medical therapy</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery##<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery##<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:14<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: 0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.7<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mild<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: 0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mild<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD:0.01<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Severe<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:14<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: NLP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">*<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">**<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">End stage<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Surgery<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:22<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OD: 1.0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mild<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">OS: 0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Moderate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">No</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t3\" position=\"float\"><label>Table 3</label><caption><title>Clinical features and gene screening results of the consulters.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"66\" span=\"1\"/><col width=\"56\" span=\"1\"/><col width=\"62\" span=\"1\"/><col width=\"68\" span=\"1\"/><col width=\"65\" span=\"1\"/><col width=\"65\" span=\"1\"/><col width=\"68\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Family member</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sex</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Age (years)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Maximum IOP (mmHg)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Cup-disc ratio (OD/OS)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Visual field score (OD/OS)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Diagnosis</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:12<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Suspect<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">III:17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:12<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Suspect<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:27<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:29<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">IV:30<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:1<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:2<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:6<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:7<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:8<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4/0.3<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">M<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5/0.5<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:11<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2/0.4<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">V:12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3/0.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0/0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Unaffected</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t4\" position=\"float\"><label>Table 4</label><caption><title>Summary of <italic>MYOC</italic> mutations found in this study.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"140\" span=\"1\"/><col width=\"105\" span=\"1\"/><col width=\"57\" span=\"1\"/><col width=\"90\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Mutation</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Pedigree members</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Controls</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Reference</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Arg76Lys (227G<bold>→</bold>A)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/56 (5.3%)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> [##REF##9535666##15##]<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Pro13Leu (38 C<bold>→</bold>T)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5/56 (8.9%)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Present study<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">Gln337Stop (1009C del)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12/56 (21.4%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Present study</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"t5\" position=\"float\"><label>Table 5</label><caption><title>Antheprot analysis results of the wild-type and mutant protein comparison using different secondary structure prediction methods.</title></caption><table frame=\"hsides\" rules=\"groups\"><col width=\"76\" span=\"1\"/><col width=\"79\" span=\"1\"/><col width=\"74\" span=\"1\"/><col width=\"73\" span=\"1\"/><col width=\"76\" span=\"1\"/><col width=\"72\" span=\"1\"/><thead><tr><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Methods</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold><italic>MYOC</italic> mutations</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Alpha</bold>
<bold>Helix (%)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Sheet</bold>
<bold>(%)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Beta turn Random (%)</bold></th><th valign=\"top\" align=\"center\" scope=\"col\" rowspan=\"1\" colspan=\"1\"><bold>Coil (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">GOR<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Arg76Lys (227G-&gt;A)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Pro13Leu (38 C-&gt;T)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gln337Stop (1009C del)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">DMP<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Arg76Lys (227G-&gt;A)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Pro13Leu (38 C-&gt;T)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gln337Stop (1009C del)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24<hr/></td></tr><tr><td valign=\"top\" align=\"center\" scope=\"row\" rowspan=\"1\" colspan=\"1\">PRD<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wild-type<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Arg76Lys (227G-&gt;A)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"><hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Pro13Leu (38 C-&gt;T)<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0<hr/></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47<hr/></td></tr><tr><td valign=\"top\" align=\"left\" scope=\"row\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Gln337Stop (1009C del)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>The asterisk indicates that all numbers correspond to the numbering scheme of GenBank <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/sites/entrez?db=nucleotide&amp;cmd=search&amp;term=AB006688\">AB006688</ext-link></p></table-wrap-foot>", "<table-wrap-foot><p>The asterisk indicates that the maximum intraocular pressure (IOP) was not certain because of relatively advanced glaucoma at the time of first presentation. The double asterisk indicate that the cup/disc ratio was not certain because of eyeball atrophy or cornea opacity. The sharp (hash mark) indicates that the visual field was not available because the patient had no light perception. The double sharp (hash marks) indicates that the member had surgery twice. The visual fields (VF) were graded for severity of visual field loss with a level grading scale. Severity classification is leveled by the visual field score: mild (0–2), moderate (3–5), severe (6–8), and end stage (no light perception). NLP, no light perception.</p></table-wrap-foot>", "<table-wrap-foot><p>The abbreviations in the “Method” column are; DPM: Double Prediction Method [##REF##3508279##23##], PRD: Predator method [##REF##9005434##24##], GOR: Garnier, Osguthorpe, Robson [##REF##642007##25##].</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"mv-v14-1666-f1\"/>", "<graphic xlink:href=\"mv-v14-1666-f2\"/>", "<graphic xlink:href=\"mv-v14-1666-f3\"/>" ]
[]
[]
{ "acronym": [], "definition": [] }
25
CC BY
no
2022-01-12 14:47:26
Mol Vis. 2008 Sep 5; 14:1666-1672
oa_package/a0/8a/PMC2530518.tar.gz
PMC2530858
18598352
[ "<title>Background</title>", "<p>It is widely recognized that gene duplications, by providing DNA material for evolutionary innovations, have contributed significantly to the complexity of primate genomes. Characterization of the human genome has highlighted the prevalence of segmental duplications (SDs), defined as continuous blocks of DNA that map to two or more genomic locations [##REF##11381028##1##,##REF##16770338##2##]. Previous studies have identified 25,000-30,000 pairs of SD regions (≥90% sequence identity, ≥1 kb), which occupy 5-6% of the human genome and arise primarily from duplication events that occurred after the divergence of the New World and Old World monkeys [##REF##16770338##2##,##REF##12169732##3##]. Detailed characterization of these SDs indicates that several molecular mechanisms might have been involved in the origin and propagation of SDs; in particular, repetitive sequences (for example, Alu elements) seem to have a major role in many segmental duplications [##REF##16770338##2##].</p>", "<p>While the contribution of SDs to the architectural complexity of the human genome has been appreciated, the functional and evolutionary consequences of these duplications remain poorly understood. Although studies have begun to define the important roles of SDs in generating novel genes through adaptive evolution, gene fusion or exon exaptation [##REF##16770338##2##,##REF##11073452##4##,##REF##15568988##5##], it remains a mystery how duplicated copies have evolved from an initial state of complete redundancy (immediately after duplications) to a stable state where both copies are maintained by natural selection. On the other hand, recent investigations of duplicated protein coding genes or gene families have provided a glimpse into this important evolutionary process. Those studies have shown that duplicated genes can evolve different expression patterns, leading to increased diversity and complexity of gene regulation, which in turn can facilitate an organism's adaptation to environmental change [##REF##15647348##6##, ####REF##15122255##7##, ##REF##17928851##8##, ##REF##17928853##9####17928853##9##]. For example, the expression of yeast duplicated genes appears to have evolved asymmetrically, with one copy changing its expression more rapidly than the other [##REF##15647348##6##].</p>", "<p>Initiating from these intriguing observations, the current study explores whether the sequence pairs of SDs are subject to different types and levels of molecular regulation, in particular whether the derived sequences are 'less' functional and are more likely to degenerate. As the majority of SDs are not protein coding, whole genome data unbiased towards genic regions is required to address these questions. Furthermore, such data must have sufficiently high resolution but minimal artifacts, which can often be attributed to high sequence similarity (such as cross-hybridization in microarray analysis), in order to reliably identify distinct signals belonging to each of the two individuals in an SD.</p>", "<p>The human genome is organized into arrays of nucleosomes composed of different histone proteins and higher order chromatin structures. Complex profiles of post-translational modifications (for example, acetylation and methylation) of histone proteins are implicated in regulating gene expression and many other important DNA-based biological functions [##REF##17320507##10##, ####REF##11498575##11##, ##REF##10638745##12####10638745##12##]. For example, acetylation and H3K4 methylation are often implicated in gene activation while H3K27 methylation and H3K9 methylation are associated with gene repression. As histone modifications can be viewed, to a great extent, as a characteristic of functional chromatin domains, it will be interesting to know how histone modifications between copies of SDs are different. Furthermore, such a study may shed light on the evolution of SDs since histone modifications can modulate the accessibility of SD regions for DNA transcription, replication, and repair [##REF##17320507##10##,##REF##17320509##13##].</p>", "<p>This study systematically examined histone modifications in the human SD regions. Using data from a recent chromatin immunoprecipitation and direct sequencing (ChIP-Seq) study [##REF##17512414##14##], the current analysis reveals for the first time that a divergent pattern of modifications exists between the two loci in a pair of SDs, when all SDs are considered collectively. The modifications with an asymmetrical pattern include the methylation of H3K9, H3K27, H3K36, and H3K79. This discovery is very interesting because these modifications have been implicated in a wide range of epigenetic-mediated events, including gene activation, gene repression, and heterochromatin formation [##REF##17320507##10##,##REF##17512414##14##]. Moreover, characterization of SDs emerging after the split of the human and macaque lineages found that the parental copies generally exhibit a higher level of modifications than the derived ones. Intriguingly, parental regions have a greater degree of H3K27me1 and H3K9me1 modifications, but not di- or tri-methylations. Furthermore, the parental loci also differ from the derived loci with respect to gene density, pseudogene density, and the abundance of RNA polymerase II (pol II) association. In short, this study demonstrates that the parental and derived copies of SDs are not functionally identical even though they share ≥90% identity in their primary sequences, suggesting that the descendants in a new genomic environment are more likely the candidates for sequence degeneration or functional innovation.</p>" ]
[ "<title>Materials and methods</title>", "<p>The SD regions were obtained from the UCSC browser [##REF##12519945##15##,##REF##12045153##16##]. The hg18 version contained 51,809 pairs of SDs. After redundant entries were removed, as a pair of segmental duplications was usually listed twice by switching the order of the two regions, 25,914 non-redundant SD pairs were used for this study. RefSeq genes [##REF##15608248##25##] were also downloaded from the UCSC browser and then overlapping transcripts were collapsed into a gene. Human pseudogenes were obtained from the Pseudogene.org database [##REF##17099229##26##]. The identification of these pseudogenes has been described previously [##REF##16574694##27##,##REF##16925835##28##]. Processed and duplicated pseudogenes were separated from the rest, which usually do not contain obvious sequence features of retrotranspositions or exon-intron structures [##REF##17099229##26##,##REF##16925835##28##].</p>", "<p>Two sets of histone profiling data were used, one for the human resting CD4<sup>+</sup> T cells [##REF##17512414##14##] and the other for the human embryonic stem cells [##REF##18371363##17##]. These data (or tags) identified the human genomic regions where modifications of nucleosomes or binding of pol II and CTCF were detected. In both cases, the genomic coordinates of ChIP tags were obtained from the original authors and this study did not re-align ChIP sequencing reads to the human genome. Figure ##FIG##0##1## describes the general strategy of counting ChIP tags for individual segmental duplications. For statistical analysis, the number of tags per 1 kb genomic region was used to represent the level of each modification. A similar approach was applied to map genes or pseudogenes into SDs, but a gene or pseudogene was assigned to an SD if it overlaps this SD by at least 1 bp.</p>", "<p>Figure ##FIG##1##2## illustrates the approach for identifying segmental duplications that arose after the split of human and macaque ancestors. Its principle is chromosomal synteny between the human genome and the macaque genome. Using a very strict criterion, this method recognized 2,654 SD events after the divergence; however, it only resolved the direction of duplications for 1,646 SD pairs. This strategy was designed to only extract (post-macaque) SDs with an easily identifiable direction of duplication.</p>", "<p>In order to characterize the distribution of histone modifications within SDs in detail, a non-statistical method was applied to 185 large (&gt;15 kb) post-macaque SD pairs. Each of these SDs was divided into a set of continuous but disjointed blocks (5 kb), which was in turned represented by a vector describing ChIP tags. Thus, the parental vector was P = [p<sub>1</sub>, p<sub>2</sub>, ..., p<sub>m</sub>] and the derivative vector was D = [d<sub>1</sub>, d<sub>2</sub>, ..., d<sub>n</sub>], where P<sub>i </sub>and D<sub>i </sub>were the numbers of ChIP-Seq tags in the i-th block. 1..n was ordered with respect to 1..m, and m = n for most SDs. Let x = y = 0; and for i in 1..b (b be the smaller of m and n), x increased 1 if P<sub>i </sub>&gt; D<sub>i </sub>but y increased 1 if P<sub>i </sub>&lt; D<sub>i</sub>. Then, for each pair of SDs, its parental locus was considered to have a higher level of a histone modification if x &gt; 2/3 * b, otherwise, the derivative locus was higher if y &gt; 2/3 * b. The result of this analysis is shown in Table ##TAB##2##3##, and the P and D for four pairs of SDs with their duplication directions known are illustrated in Figure ##FIG##3##4##.</p>", "<p>The ChIP-Seq signal 'peaks' in these large SDs were also identified. Many software and algorithms exist for calling peaks from ChIP-Seq reads; however, they were not used here because ChIP-Seq reads in SDs have a distribution quite different from those in non-SD regions (tag density is much lower; Table ##TAB##0##1##). Instead, a peak here was simply defined as a block (5 kb) with &gt;5 ChIP-Seq reads and the read count was also two standard deviations above the average read in this SD. This method correctly reported the apparent H3K4me3 peaks in the first and forth example of Figure ##FIG##3##4##. The numbers of such peaks for the 185 large SD pairs are plotted in Figure ##FIG##4##5## for H3K4me3, H3K27me3, and H3K36me3 because these three methylations are well characterized in the literature.</p>" ]
[ "<title>Results</title>", "<title>Histone modification data in segmental duplications</title>", "<p>The segmental duplications in the human genome were downloaded from the UCSC browser [##REF##12519945##15##,##REF##12045153##16##]. They include 25,914 non-redundant pairs of genomic regions (referred to as SD pairs here) in the released version (hg18) used for this study. The identification of these SDs has been described before [##REF##11381028##1##] and the two sequences in each SD pair have a length of ≥1 kb and share ≥90% sequence identity.</p>", "<p>Histone modification data were primarily obtained from a recent ChIP-Seq study, which mapped the genome-wide distributions of 20 histone lysine (K) or arginine (R) methylations, as well as H2A.Z, pol II and CTCF (an insulator binding protein) across the human genome [##REF##17512414##14##]. These data are summarized in Table ##TAB##0##1##, which shows a good number of ChIP-Seq tags (25 nucleotide sequencing reads) from human SDs. Since only tags that can be mapped uniquely to individual SD loci were used, the data in Table ##TAB##0##1## indicate that ChIP-Seq can resolve signals from each of the two duplicates in an SD pair. The numbers of tags in SDs, however, decrease as the pairwise similarity within individual SD pairs increases (data not shown). Another set of histone modification data generated by ChIP coupled with paired-end ditags sequencing [##REF##18371363##17##] was also obtained for this study (Table ##TAB##0##1##). From these two sets of ChIP data, a value measuring the level of a particular nucleosome modification in an SD was derived using a straightforward strategy (Figure ##FIG##0##1##).</p>", "<title>Asymmetric profiles of histone modifications in the two regions of segmental duplication</title>", "<p>To assess whether two copies of an SD pair exhibit different levels of histone modifications, this study first conducted a paired <italic>t</italic>-test with the null hypothesis that there is no difference. The Wilcoxon signed rank test was also performed to address a concern that ChIP tag differences between the two loci in SD pairs might not distribute normally. The two statistical tests yielded similar results and, therefore, only <italic>t</italic>-test data are discussed. After adjusting multiple testing by the Bonferroni method, 7 of the 20 histone marks showed a difference (adjusted <italic>p </italic>&lt; 0.001; Table ##TAB##1##2##, all SDs), which include H3K9me2, H3K36me1, H3K79me1, H3R2me1 and the three states of H3K27 methylation. The original ChIP-Seq study also probed the bindings of CTCF and pol II, but the tags for them were distributed between the two loci of SDs without a bias. Similar analysis of the data from human stem cells [##REF##18371363##17##] further indicated that histone modifications are asymmetric between the two copies of SDs (Table ##TAB##1##2##).</p>", "<title>Higher level of histone modifications in the parental versus derivative loci of segmental duplications</title>", "<p>Next, I investigated whether the asymmetry is due to uneven histone modifications between the parental and the derivative regions. Although it has been previously found that two duplicated genes can evolve distinct functions, no systematic study to date has addressed which copy diverges away from its ancestral function. Unfortunately, current SD data do not contain the directionality of duplications, and accurate identification of duplication direction remains a challenge. This study thus adopted a strategy that was recently applied to identify ancestral duplication loci [##REF##17922013##18##]. As illustrated in Figure ##FIG##1##2##, this approach relies largely on chromosomal synteny (that is, order of sequences on a chromosome) and uses macaque as an outgroup species to assign duplication directions for SDs. It produced more accurate parental-derivative relationships than other methods that were based entirely on mutual best hits established by sequence comparison, because a synteny-based strategy is more appropriate for identifying evolutionarily equivalent sequences in mammalian genomes. Macaque was chosen here because its genome has been sequenced and the average human-macaque sequence identity is approximately 93% [##REF##17431167##19##], which is near the 90% used in identifying SDs. The current approach is not meant to systematically assign SD directions but to select SDs for subsequent analyses, because it can be applied only to SDs that arose after the split of human and macaque lineages. Nevertheless, it was able to determine the parental-derivative relationship for 1,646 SD pairs, referred to here as post-macaque SD pairs.</p>", "<p>A paired <italic>t</italic>-test for these 1,646 pairs of post-macaque SDs revealed that 14 histone modifications are different between parental sequences and their derivative copies, including H3K36me1, H3K79me1, H3R2me1 and H3K27me1, which also showed asymmetries in the above analysis of all SDs (Table ##TAB##1##2##). In particular, histones in the parental loci exhibited a higher level of mono-methylation of H3K27 and H3K9 than those in the derivative regions (Table ##TAB##1##2##), but no difference was detected for di- and tri-methylations. Data from stem cells further supported a difference in H3K4me3 but no difference in H3K27me3. Interestingly, pol II and CTCF were relatively abundant in the parental versus the derivative loci. Noticeably, the analysis of post-macaque SDs yielded a list of histone marks that is quite different from what was obtained for all SDs (Table ##TAB##1##2##), suggesting that duplication direction is an important factor to include in examining disparate features of duplicated genes.</p>", "<p>The distribution of ChIP-Seq tags was further examined for human segmental duplications with known duplication directions. Previously, Eichler's research group have determined the duplication directions of nine human SDs by comparative fluorescent <italic>in situ </italic>hybridization (FISH), using genomic sequences in a human derivative locus as a probe against chromosomes from an outgroup primate species [##REF##17922013##18##]. Four of those nine pairs are depicted in Figure ##FIG##2##3##. Analysis of ChIP-Seq data found that the levels of histone modifications were in fact quite biased between the two loci of most of these SD pairs. Especially, the parental regions were statistically higher for the following methylations: H2BK5me1, H3K4me2, H3K9me1, H3K27me1, H3K36me3, and H3K79me1. Mono-methylation seems to make up the bulk of the differences. Figure ##FIG##3##4## shows the distributions of ChIP-Seq tags for four of these nine SDs.</p>", "<p>The paired <italic>t</italic>-test described above, in principal, compared the sums of ChIP tags in the two copies of an SD pair, but overlooked the intra-SD tag distributions. Thus, a non-statistical method was developed to address this through analyzing ChIP tags in a set of large SDs (&gt;15 kb). Briefly, these SDs were first divided into non-overlapping blocks. Then, for each pair of SDs, one locus was determined to have a higher level of a histone modification if at least two-thirds of its blocks contained more tags of this modification than the corresponding blocks of the other locus. The results not only show that SD loci with a greater degree of modification were three to six times more likely to be parental (Table ##TAB##2##3##), but also indicated that asymmetry often existed across an SD locus, rather than in one or few narrow sub-regions. Interestingly, all modifications exhibited some degree of asymmetry by this measurement. The second and third examples in Figures ##FIG##2##3## and ##FIG##3##4## illustrate such a pattern of asymmetrical modifications of histones.</p>", "<title>More parental loci of segmental duplications exhibit 'peak' signals of histone modifications</title>", "<p>'Peaks' of histone modifications in these large SD pairs were also studied. In agreement with the above observations, the peaks of ChIP-Seq signals were more frequently located within the parental SDs than the derivative SDs, especially for the three marks H3K4me3, H3K9me1, and H2A.Z, which have been previously shown to be enriched in promoters [##REF##17512414##14##]. Data for H3K4me3, H3K27me3, and H3K36me3 are shown in Figure ##FIG##4##5## because these methylations are known characteristic marks of promoters and transcribed regions, with H3K4me3 correlating with active genes and H3K27me3 relatively enriched at silent promoters [##REF##17320507##10##,##REF##10638745##12##,##REF##17512414##14##,##REF##18250624##20##]. As shown (Figure ##FIG##4##5##), SDs with an H3K4me3 peak were 1.5 times more likely to be parental. Such a bias, however, was not detected for H3K27me3. Only approximately 50% of either parental or derivative SDs with H3K4me3 peaks contained genes, suggesting that more functional elements (including novel protein coding and non-coding genes) are yet to be annotated in the human SDs. Interestingly, 9 of the 16 parental SDs versus 4 of the 16 derivative SDs with H3K27me3 peaks contained annotated genes, but these numbers were not statistically significant enough to claim that fewer genes in the derived SDs were repressed in CD4<sup>+</sup> T cells. Parental SDs appeared more likely to have H3K36me3 and pol II peaks; however, those peaks did not seem to co-exist in the same SDs as frequently as expected from the correlation previously reported between H3K36me3 and actively transcribed regions [##REF##17512414##14##,##REF##18250624##20##]. This inconsistency needs to be studied in the future. Additionally, it needs to be mentioned that the known correlations between histone methylations and transcription start sites (TSSs) [##REF##17512414##14##] were observed for the TSSs within SDs, and the patterns for parental SDs and derivative SDs were mostly indistinguishable (data not shown).</p>", "<p>In summary, characterization of the pattern of histone modifications by various measurements consistently revealed an asymmetrical pattern of histone modifications, with higher levels biased to the parental regions of SDs, demonstrating that two seemingly 'identical' genomic copies are actually distinct in their epigenomic properties.</p>", "<title>Parental loci of segmental duplications contain more genes but fewer pseudogenes</title>", "<p>It has been reported that SDs are generally enriched with genes [##REF##16770338##2##,##REF##12169732##3##]. This is confirmed by the current survey of genes and pseudogenes in human SDs (Table ##TAB##0##1##); note that SDs occupy approximately 5% of the human genome. Moreover, Table ##TAB##0##1## shows that human SDs are more enriched with pseudogenes than genes, as 36.8% of human pseudogenes and 17.8% of human genes are located in SDs (<italic>p </italic>&lt;&lt; 0.001). Duplicated pseudogenes appear more likely to be associated with SDs than processed pseudogenes, as 50% of human duplicated pseudogenes versus 33.8% of processed pseudogenes are in SDs (<italic>p </italic>&lt;&lt; 0.001). This is consistent with the fact that duplicated pseudogenes are generated by gene duplications whereas processed pseudogenes are from retrotranspositions.</p>", "<p>A subsequent examination of genes and pseudogenes in the 1,646 post-macaque SDs revealed that 656 parental and 192 derivative loci contain genes (Table ##TAB##3##4##), while significantly more pseudogenes (all types) are in the derived regions. The numbers of genes and pseudogenes for large SDs are also shown in Figure ##FIG##4##5##, which clearly illustrates that genes and pseudogenes are enriched in the parental and derived loci, respectively. These data suggest that duplicated sequences in the derived loci are more frequently subject to degeneration and pseudogenization than the parental sequences. It is also possible that duplications yield mostly 'broken' genes in the new locations. However, the combined number of genes and pseudogenes is also higher in the parental SDs. Moreover, when both parental and derived loci were compared to their 'ancestral' locus in the macaque genome (Figure ##FIG##1##2##), the average sequence identity was 89.8% (±5.9%) and 88.8% (±6.1%) for the parental and derivative, respectively. This difference is statistically significant (<italic>p </italic>= 3e-10), further suggesting a faster degeneration of derived sequences.</p>", "<title>Pseudogenization and asymmetry in histone modifications</title>", "<p>How does the asymmetry in histone modifications relate to gene content and gene death in human SDs? The asymmetry of pol II ChIP tags is certainly consistent with the biased distribution of genes because more pol II tags usually indicate higher degrees of transcriptional activity. This correlation is further supported by the observation that most histone modifications enriched at promoters are higher in parental SDs (Tables ##TAB##1##2## and ##TAB##2##3##).</p>", "<p>The asymmetric distribution of genes, however, cannot fully account for the asymmetric profiles of histone modifications described above. Firstly, the asymmetrical pattern remained present, though consisted of fewer marks, when the above <italic>t</italic>-test was restricted to 623 post-macaque SD pairs containing neither genes nor pseudogenes in both loci. The significantly different modifications are H3K9me1, H3K27me1, H3K4me1, H3K4me2, H3K79me1, and H3K79me2. Secondly, analysis of SDs without genes also detected a skew for the histone marks H3K9me1, H3K27me1, H3K79me2, H4K20me3, and the three states of H3K4 methylation. All of these modifications occurred more frequently on the parental loci, except H4K20me3, which was previously found to associate with repressive chromatin [##REF##15145825##21##]. Thirdly, an analysis restricted to 419 SD pairs that did not exhibit a difference in pol II between their two copies (defined as difference of pol II &lt;0.3 tag per kb) found several marks with significant asymmetry, including H3K9me1, H3K27me1, H3K27me2, H3K36me1, H3K36me3, and H3K79me2. It is interesting to see that H3K79me2, which was found without a significant preference toward either active or silent genes [##REF##17512414##14##], shows a difference here. In this analysis, the statistics for pol II is a <italic>p</italic>-value of 0.46.</p>", "<p>Gene and pseudogene contents, nevertheless, have an influence on the asymmetrical pattern of epigenomic modifications (Figure ##FIG##4##5##). Not only did fewer marks exhibit a difference in the characterizations of 'gene-depleted' SDs, but also the pattern was less biased to the parental copies. For example, the difference of mean tag densities was 1.215, 0.897, 1.562, 0.703, and 0.427 for H3K9me1, H3K27me1, H3K4me1, H3K4me2, and H3K79me1, respectively (Table ##TAB##1##2##). These numbers decreased to 0.461, 0.389, 0.741, 0.271, and 0.357, respectively, for the SD pairs without genes or pseudogenes. In addition, a characterization of SD pairs (n = 103) with genes in both of their loci did not find a modification with a significantly asymmetrical pattern, though a difference was observed for H3K36me3 and H4R3me2 (unadjusted <italic>p</italic>-value &lt; 0.001).</p>", "<title>Shift in the patterns of differences in histone modification as segmental duplications age</title>", "<p>Finally, in order to address the dynamics of the above asymmetries during evolution, the post-macaque SDs were split into four groups based on pairwise nucleotide sequence identity of SD pairs (Table ##TAB##4##5##). The parental and derivative copies of young SDs (sequence identity ≥0.975) exhibited uneven H3K27me1, H3K36me3, H3K9me1, and H4R3me2 modifications. The first two marks were both enriched downstream of transcription start sites [##REF##17512414##14##]. As SD sequences age, more modifications with an asymmetric pattern emerge and then potentially disappear, but differences in H3K27me1 and H3K9me1 modifications persist. Although a difference in gene content was observed across all age groups, this analysis found that as SDs evolve more genes in the derivative loci have been lost, presumably becoming pseudogenes (Table ##TAB##4##5##). Pseudogenes (of all three types) were always more abundant in the derivative than the parental loci. This is true even for the oldest SDs, though the difference becomes statistically less significant; for example, the means of duplicated pseudogenes were 0.157 and 0.238 for the parental and derivative regions (<italic>p</italic>-value = 0.02), respectively.</p>" ]
[ "<title>Discussion</title>", "<p>Duplication of genomic sequence is an important evolutionary process that supplies raw genetic material for architectural as well as functional innovations. Its prevalence has been observed in all three kingdoms of life, with several distinct mechanisms leading to their abundance [##REF##16770338##2##,##REF##15568988##5##,##REF##15510166##22##]. A duplication occurring in a single individual can be fixed or lost in the population, but the most common consequence seems to be the loss of all or part of the newly duplicated sequences through deletion or degeneration. Nonetheless, a novel biochemical function can sometimes arise from the redundant sequences.</p>", "<p>The asymmetrical distributions of histone modifications, genes, pseudogenes, and transcription (with pol II as the proxy) between parental and derivative loci of human SDs support that degeneration (or pseudogenization) is more common than innovation (or neofunctionalization) after gene duplications. One important discovery here is the depletion of genes and, conversely, the enrichment of pseudogenes in the derivative loci. This implies either that most duplications are incomplete when occurring - that is, only part of a gene is duplicated to the new location, resulting in a pseudogene at birth - or that deletion plays a large role in disabling the descendant sequences. The former is supported by more non-processed pseudogenes in derivative regions, while the latter is probably related to the difference in the sum of genes and duplicated pseudogenes in the two copies (Table ##TAB##3##4##), though it may be influenced by incomplete gene annotation in SDs as well. The results suggest that the original copy is evolutionarily constrained to maintain its functional status while the descendant is relatively free to mutate and can eventually become a 'non-functional' sequence. It is kind of amazing to see that an organism can achieve this given that the two copies are seemly identical in their primary sequences. The current report of gene difference is also consistent with a recent finding that core duplicons, the common DNA subunits sharing by multiple SDs, are enriched for genes and spliced expressed sequence tags [##REF##17922013##18##]. Unfortunately, due to the limitation of the current strategy for identifying the direction of duplication, not enough SD data were produced to address precisely the different rates of pseudogenization in the parental and derived loci. This issue will be addressed in the future when more primate genomes are sequenced and improved algorithms are developed for reliably identifying SDs of sequence identity &lt;90%.</p>", "<p>The asymmetry of histone modifications can be a direct consequence of more genes and fewer pseudogenes in the parental loci as histone modification is a process often occurring near genes that can lead to either gene activation (for example, H3K4 methylation) or repression (for example, H3K27 methylation). Such a correlation is apparent for H3K4me3 in large SDs (Figure ##FIG##4##5##). It is also supported by the analysis of SD pairs containing functional genes in both of their loci, whereas almost no modifications exhibited a significantly unsymmetrical pattern. The small sample size, however, could be an issue for generalizing that result.</p>", "<p>Alternatively, the current findings may suggest that the chromatins in derivative SDs are looser relative to those in the parental. Under this scenario, the genomic sequences in the derived loci are prone to mutations because of their greater exposure, leading to more pseudogenes in evolution, and the turnover rate of nucleosomes in the derivative regions is higher (that is, exchange faster with free histones), resulting in fewer modified histones being detected experimentally. This can explain why higher levels of various modifications were always seen in the parental SDs. Likewise, loose chromatins are more vulnerable to retrotranspositions; as a result, more processed pseudogenes were inserted into the derived loci of SDs (Tables ##TAB##3##4## and ##TAB##4##5##). Along the same line, it is worth noticing that derived loci containing duplicated pseudogenes often have processed pseudogenes too (Figure ##FIG##4##5##). Furthermore, this hypothesis is particularly supported by the data from a recent study [##REF##18329373##23##] that mapped nucleosome positions using the Solexa sequencing technique. Analysis of those reads indeed revealed that nucleosomes were relatively depleted (<italic>p </italic>&lt;&lt; 0.001) in the derived SDs.</p>", "<p>Other biological processes may have also contributed to the asymmetries reported here. First of all, the derived SDs may have ended up in regions of repressive chromatins. The genome distribution of post-macaque SDs showed that centromeres contained slightly more derivative SDs than parental SDs (data not shown). However, characterization of post-macaque SD pairs (n = 1,313) whose two loci were at least 5 Mb away from heterochromatin regions found essentially the same asymmetrical histone modifications that were observed for all post-macaque SD pairs. For example, the study of those restricted SD pairs also showed that mono-methylations of H3K9 and H3K27 were higher in the parental SDs but not di- and tri-methylations of H3K9 and H3K27. Since H3K27 and H3K9 methylations are often associated with chromatin repression [##REF##17320507##10##,##REF##17512414##14##,##REF##18250624##20##] but they did not exhibit an enrichment in the derived SDs, the impact of repressive chromatin on the observed asymmetry of histone modifications is small but warrants further investigations. On the other hand, these results cannot rule out that histone modifications may have been directly involved in the initial regulation of the descendant sequences by keeping the extra genomic copies in a silent chromatin state (for example, by not modifying histones). Conversely, histone modifications may have facilitated degeneration of the descendant sequences by increasing the accessibility of those DNAs for a greater rate of mutations. Both scenarios are very important for understanding SD evolution; however, they cannot be confidently separated in the current analysis.</p>", "<p>In any case, the characterization of either SDs without genes or SDs without pol II asymmetry shows that asymmetrical distributions of several histone modifications were not entirely entangled with gene/pseudogene asymmetries. It is very difficult to really resolve the interaction between sequence degeneration (or pseudogenization) and epigenomic changes, largely because almost all histone marks that have been characterized were investigated in the context of gene expression (either activation or repression). Analysis of post-macaque SDs in different age groups did not help untangle this issue either. If pseudogenization is the cause of asymmetric histone modifications, we would expect to see more asymmetries emerge as SDs age; conversely, we would see asymmetries fade away if they facilitate pseudogenization. The data in Table ##TAB##4##5## provide evidence for both or neither, depending on one's interpretation. Further studies are required to address all these questions, and more generally to fully appreciate the potential importance of epigenetic modifications in the initial regulation and subsequent evolution of duplicated sequences. As shown in Figure ##FIG##3##4##, not all parental loci of SDs exhibited a higher level of histone modification than their derivative regions. Such SD pairs may contain asymmetrical histone acetylations or phosphorylations, or the derivative loci have newly emerging functional elements. In the future, integrated analysis of different types of modifications is certainly necessary as the effect of an individual histone modification is likely context-dependent and cannot simply be referred to as either activating or repressing a chromatin domain [##REF##17320507##10##,##REF##11498575##11##,##REF##17320509##13##,##REF##17512414##14##].</p>", "<p>Finally, the asymmetry in histone modifications may be relevant to the established view that divergence in regulatory elements is the first step of functional divergence between duplicated genes. Several previous studies have suggested that duplicated genes often evolve at different rates; for instance, one study found that expression of duplicated genes tends to evolve asymmetrically [##REF##15647348##6##]. The expression of one copy evolves rapidly, likely through changes in its regulation, whereas the other one largely maintains the ancestral expression profile. It will be interesting to see if the changing copy is the parental or the derivative and whether histone modifications are involved in establishing the disparate profile of expression and in facilitating subsequent functional divergence. A separate study has found that retrotransposed genes tend to undergo accelerated evolution relative to their parental genes [##REF##17179139##24##]. The discovery of asymmetrical histone modifications here is consistent with these early results and points to a new direction to explore those early findings.</p>" ]
[ "<title>Conclusion</title>", "<p>This study is important for understanding both the functional influence and evolutionary fate of SDs because it indicates that derivative sequences of SDs become non-functional more often than the originals, as measured by histone modifications, transcription, and density of genes or pseudogenes. This finding is significant because it pinpoints, for the first time, derived sequences as the main locations of divergent evolution between duplicated genomic regions, suggesting that evolution selects a parental locus to maintain its original biological property but allows its derivative sequence to mutate freely, eventually leading to either degeneration or functional innovation.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A systematic analysis of histone modifications between human segmental duplications shows that two seemingly identical genomic copies have distinct epigenomic properties.</p>", "<title>Background</title>", "<p>Sequencing and annotation of several mammalian genomes have revealed that segmental duplications are a common architectural feature of primate genomes; in fact, about 5% of the human genome is composed of large blocks of interspersed segmental duplications. These segmental duplications have been implicated in genomic copy-number variation, gene novelty, and various genomic disorders. However, the molecular processes involved in the evolution and regulation of duplicated sequences remain largely unexplored.</p>", "<title>Results</title>", "<p>In this study, the profile of about 20 histone modifications within human segmental duplications was characterized using high-resolution, genome-wide data derived from a ChIP-Seq study. The analysis demonstrates that derivative loci of segmental duplications often differ significantly from the original with respect to many histone methylations. Further investigation showed that genes are present three times more frequently in the original than in the derivative, whereas pseudogenes exhibit the opposite trend. These asymmetries tend to increase with the age of segmental duplications. The uneven distribution of genes and pseudogenes does not, however, fully account for the asymmetry in the profile of histone modifications.</p>", "<title>Conclusion</title>", "<p>The first systematic analysis of histone modifications between segmental duplications demonstrates that two seemingly 'identical' genomic copies are distinct in their epigenomic properties. Results here suggest that local chromatin environments may be implicated in the discrimination of derived copies of segmental duplications from their originals, leading to a biased pseudogenization of the new duplicates. The data also indicate that further exploration of the interactions between histone modification and sequence degeneration is necessary in order to understand the divergence of duplicated sequences.</p>" ]
[ "<title>Abbreviations</title>", "<p>ChIP-Seq, chromatin immunoprecipitation and direct sequencing; pol II, RNA polymerase II; SD, segmental duplication; TSS, transcription start site.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The author would like to thank I Qureshi, M Mehler, K Zhao and M Gerstein for useful comments and valuable discussions in the preparation of this manuscript. This work was supported by a starting fund from Albert Einstein College of Medicine of Yeshiva University.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Histone modification ChIP tags in human SDs. A pair of SDs with 91.7% sequence identity was found in chr1:54,212,891-54,214,303 (top) and chr4:83,268,767-83,270,192 (bottom). The top region contained six H3K27me3 and two H3K4me3 ChIP-Seq tags, while the bottom contained two H3K27me3 and seven H3K4me3 tags. Thus, the number of H3K27me3 and H3K4me3 tags per 1 kb are 4.25 and 1.42, respectively, for the top and 1.4 and 4.91 for the bottom region.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>A cartoon illustrating the method used here for identifying post-macaque SDs based on chromosomal synteny. Using the liftOver tool [##REF##14500911##29##] from the UCSC genome browser group, a pair of human SDs (A and B) is mapped to the same location (A') in the macaque genome. A and B (large block) are thus considered the product of an SD event that occurred after the split of human from macaque lineages. Then 1 kb sequences (small block) adjacent to A or B were aligned to the macaque genome. If only the sequence next to A was mapped next to A', then A is designated as the parental copy and B as the derivative.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Gene and pseudogene annotations in four pairs of human SDs with known duplication directions. The parental locus of each pair is depicted first, followed immediately by its derivative.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Pattern of histone modifications for the four SD pairs in Figure 3, ordered left to right to match their order from top to bottom in Figure 3. Each point represents the number of ChIP-Seq tags in a 5 kb genomic region, with red for parental and blue for derivative SDs. Horizontal axes are the position relative to the 5' end of a parental locus. Data for a derivative region is ordered with respect to its parent.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>The peaks of ChIP-Seq signals in large post-macaque SDs. The numbers of peaks (see Materials and methods) for H3K4me3, H3K27me3, H3K36me3, and pol II are plotted for each of the large SD pairs (from top to bottom), along with the numbers of genes and pseudogenes. The numbers on the left (red) and right (blue) are for parental and derivative SDs, respectively. The H3K4me3 peaks in the first and forth example of Figure 4 are marked by an arrow and labeled with 1 and 4, respectively.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Summary of source data</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Data type</td><td align=\"right\">Total data points for the human genome</td><td align=\"right\">Points within SDs</td></tr></thead><tbody><tr><td align=\"left\">H2AZ</td><td align=\"right\">7,536,100</td><td align=\"right\">152,848</td></tr><tr><td align=\"left\">H2BK5me1</td><td align=\"right\">8,942,880</td><td align=\"right\">184,251</td></tr><tr><td align=\"left\">H3K27me1</td><td align=\"right\">10,047,279</td><td align=\"right\">196,347</td></tr><tr><td align=\"left\">H3K27me2</td><td align=\"right\">9,070,882</td><td align=\"right\">180,054</td></tr><tr><td align=\"left\">H3K27me3</td><td align=\"right\">8,970,141</td><td align=\"right\">176,060</td></tr><tr><td align=\"left\">H3K36me1</td><td align=\"right\">8,077,127</td><td align=\"right\">164,151</td></tr><tr><td align=\"left\">H3K36me3</td><td align=\"right\">13,572,575</td><td align=\"right\">313,579</td></tr><tr><td align=\"left\">H3K4me1</td><td align=\"right\">11,322,526</td><td align=\"right\">213,535</td></tr><tr><td align=\"left\">H3K4me2</td><td align=\"right\">5,447,902</td><td align=\"right\">100,330</td></tr><tr><td align=\"left\">H3K4me3</td><td align=\"right\">16,845,478</td><td align=\"right\">361,316</td></tr><tr><td align=\"left\">H3K79me1</td><td align=\"right\">10,041,806</td><td align=\"right\">213,775</td></tr><tr><td align=\"left\">H3K79me2</td><td align=\"right\">2,058,068</td><td align=\"right\">40,023</td></tr><tr><td align=\"left\">H3K79me3</td><td align=\"right\">8,114,474</td><td align=\"right\">240,709</td></tr><tr><td align=\"left\">H3K9me1</td><td align=\"right\">9,311,627</td><td align=\"right\">170,633</td></tr><tr><td align=\"left\">H3K9me2</td><td align=\"right\">9,782,127</td><td align=\"right\">188,748</td></tr><tr><td align=\"left\">H3K9me3</td><td align=\"right\">6,348,997</td><td align=\"right\">147,639</td></tr><tr><td align=\"left\">H3R2me1</td><td align=\"right\">9,560,224</td><td align=\"right\">208,646</td></tr><tr><td align=\"left\">H3R2me2</td><td align=\"right\">6,521,560</td><td align=\"right\">147,126</td></tr><tr><td align=\"left\">H4K20me1</td><td align=\"right\">11,015,873</td><td align=\"right\">205,009</td></tr><tr><td align=\"left\">H4K20me3</td><td align=\"right\">5,720,089</td><td align=\"right\">370,598</td></tr><tr><td align=\"left\">H4R3me2</td><td align=\"right\">7,357,597</td><td align=\"right\">173,684</td></tr><tr><td align=\"left\">Pol II</td><td align=\"right\">4,150,378</td><td align=\"right\">85,849</td></tr><tr><td align=\"left\">CTCF</td><td align=\"right\">2,947,043</td><td align=\"right\">65,080</td></tr><tr><td/><td/><td/></tr><tr><td align=\"left\">H3K4me3, ES</td><td align=\"right\">478,213</td><td align=\"right\">37,413</td></tr><tr><td align=\"left\">H3K27me3, ES</td><td align=\"right\">257,574</td><td align=\"right\">24,480</td></tr><tr><td/><td/><td/></tr><tr><td align=\"left\">RefSeq genes</td><td align=\"right\">18,957</td><td align=\"right\">3,366</td></tr><tr><td align=\"left\">Duplicated pseudogenes</td><td align=\"right\">2,550</td><td align=\"right\">1,276</td></tr><tr><td align=\"left\">Processed pseudogenes</td><td align=\"right\">8,234</td><td align=\"right\">2,786</td></tr><tr><td align=\"left\">Other pseudogenes</td><td align=\"right\">6,809</td><td align=\"right\">2,412</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Statistics for ChIP tag differences in the two copies of human SDs</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"2\">All SDs (n = 25,914)</td><td align=\"center\" colspan=\"7\">Post-macaque SDs (n = 1,646)</td></tr><tr><td/><td colspan=\"2\"><hr/></td><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\">Factors</td><td align=\"right\">Paired <italic>t</italic>-test <italic>p</italic>-values</td><td align=\"right\">Wilcoxon signed rank test <italic>p</italic>-values</td><td align=\"right\">Mean of parental</td><td align=\"right\">Standard deviation of parental</td><td align=\"right\">Mean of derivative</td><td align=\"right\">Standard deviation of derivative</td><td align=\"right\">Paired <italic>t</italic>-test <italic>p</italic>-values</td><td align=\"right\">Mean of difference</td><td align=\"right\">Wilcoxon signed rank test <italic>p</italic>-values</td></tr></thead><tbody><tr><td align=\"left\">H2AZ</td><td align=\"right\"><bold>3.64E-05</bold></td><td align=\"right\">2.86E-07</td><td align=\"right\">1.319</td><td align=\"right\">2.388</td><td align=\"right\">1.114</td><td align=\"right\">1.987</td><td align=\"right\">5.51E-03</td><td align=\"right\">0.205</td><td align=\"right\">1.58E-03</td></tr><tr><td align=\"left\">H2BK5me1</td><td align=\"right\">2.92E-01</td><td align=\"right\">2.32E-02</td><td align=\"right\">2.600</td><td align=\"right\">6.582</td><td align=\"right\">1.224</td><td align=\"right\">3.237</td><td align=\"right\"><bold>1.49E-15</bold></td><td align=\"right\">1.377</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K27me1</td><td align=\"right\"><bold>2.12E-05</bold></td><td align=\"right\">5.92E-03</td><td align=\"right\">2.147</td><td align=\"right\">2.415</td><td align=\"right\">1.250</td><td align=\"right\">1.786</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">0.897</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K27me2</td><td align=\"right\"><bold>9.71E-10</bold></td><td align=\"right\">1.74E-11</td><td align=\"right\">1.526</td><td align=\"right\">1.670</td><td align=\"right\">1.355</td><td align=\"right\">1.668</td><td align=\"right\">5.60E-04</td><td align=\"right\">0.170</td><td align=\"right\">5.08E-05</td></tr><tr><td align=\"left\">H3K27me3</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">4.60E-14</td><td align=\"right\">1.492</td><td align=\"right\">1.727</td><td align=\"right\">1.460</td><td align=\"right\">2.003</td><td align=\"right\">6.09E-01</td><td align=\"right\">0.031</td><td align=\"right\">2.95E-01</td></tr><tr><td align=\"left\">H3K36me1</td><td align=\"right\"><bold>1.48E-05</bold></td><td align=\"right\">2.28E-10</td><td align=\"right\">1.533</td><td align=\"right\">1.392</td><td align=\"right\">1.242</td><td align=\"right\">1.638</td><td align=\"right\"><bold>8.66E-10</bold></td><td align=\"right\">0.291</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K36me3</td><td align=\"right\">1.29E-02</td><td align=\"right\">1.57E-05</td><td align=\"right\">3.755</td><td align=\"right\">6.347</td><td align=\"right\">1.796</td><td align=\"right\">3.027</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">1.959</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K4me1</td><td align=\"right\">3.63E-01</td><td align=\"right\">8.01E-03</td><td align=\"right\">2.700</td><td align=\"right\">6.895</td><td align=\"right\">1.139</td><td align=\"right\">2.741</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">1.562</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K4me2</td><td align=\"right\">8.76E-01</td><td align=\"right\">9.66E-06</td><td align=\"right\">1.354</td><td align=\"right\">2.290</td><td align=\"right\">0.651</td><td align=\"right\">1.458</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">0.703</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K4me3</td><td align=\"right\">6.68E-01</td><td align=\"right\">7.46E-11</td><td align=\"right\">4.144</td><td align=\"right\">16.473</td><td align=\"right\">1.987</td><td align=\"right\">6.256</td><td align=\"right\"><bold>7.06E-07</bold></td><td align=\"right\">2.157</td><td align=\"right\">4.44E-16</td></tr><tr><td align=\"left\">H3K79me1</td><td align=\"right\"><bold>6.49E-12</bold></td><td align=\"right\">2.20E-16</td><td align=\"right\">1.911</td><td align=\"right\">1.644</td><td align=\"right\">1.484</td><td align=\"right\">1.781</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">0.427</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K79me2</td><td align=\"right\">3.12E-02</td><td align=\"right\">1.26E-02</td><td align=\"right\">0.476</td><td align=\"right\">0.520</td><td align=\"right\">0.356</td><td align=\"right\">0.499</td><td align=\"right\"><bold>1.94E-08</bold></td><td align=\"right\">0.120</td><td align=\"right\">3.45E-11</td></tr><tr><td align=\"left\">H3K79me3</td><td align=\"right\">9.62E-04</td><td align=\"right\">2.06E-06</td><td align=\"right\">1.823</td><td align=\"right\">2.595</td><td align=\"right\">1.671</td><td align=\"right\">2.941</td><td align=\"right\">5.76E-02</td><td align=\"right\">0.153</td><td align=\"right\">3.76E-05</td></tr><tr><td align=\"left\">H3K9me1</td><td align=\"right\">2.42E-02</td><td align=\"right\">6.41E-04</td><td align=\"right\">2.262</td><td align=\"right\">3.827</td><td align=\"right\">1.046</td><td align=\"right\">2.171</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">1.215</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3K9me2</td><td align=\"right\"><bold>1.67E-07</bold></td><td align=\"right\">3.73E-14</td><td align=\"right\">1.618</td><td align=\"right\">1.865</td><td align=\"right\">1.489</td><td align=\"right\">1.936</td><td align=\"right\">1.88E-02</td><td align=\"right\">0.130</td><td align=\"right\">5.80E-03</td></tr><tr><td align=\"left\">H3K9me3</td><td align=\"right\">2.40E-03</td><td align=\"right\">3.90E-06</td><td align=\"right\">1.380</td><td align=\"right\">2.323</td><td align=\"right\">1.357</td><td align=\"right\">2.357</td><td align=\"right\">7.25E-01</td><td align=\"right\">0.023</td><td align=\"right\">3.61E-01</td></tr><tr><td align=\"left\">H3R2me1</td><td align=\"right\"><bold>2.19E-05</bold></td><td align=\"right\">4.41E-09</td><td align=\"right\">1.878</td><td align=\"right\">1.751</td><td align=\"right\">1.440</td><td align=\"right\">1.866</td><td align=\"right\"><bold>2.20E-16</bold></td><td align=\"right\">0.438</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H3R2me2</td><td align=\"right\">4.60E-01</td><td align=\"right\">4.41E-01</td><td align=\"right\">1.292</td><td align=\"right\">1.263</td><td align=\"right\">1.079</td><td align=\"right\">1.500</td><td align=\"right\"><bold>1.40E-06</bold></td><td align=\"right\">0.213</td><td align=\"right\">1.18E-11</td></tr><tr><td align=\"left\">H4K20me1</td><td align=\"right\">5.72E-04</td><td align=\"right\">6.77E-05</td><td align=\"right\">4.865</td><td align=\"right\">18.476</td><td align=\"right\">1.257</td><td align=\"right\">5.664</td><td align=\"right\"><bold>9.85E-13</bold></td><td align=\"right\">3.608</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">H4K20me3</td><td align=\"right\">3.23E-01</td><td align=\"right\">5.78E-01</td><td align=\"right\">1.687</td><td align=\"right\">6.408</td><td align=\"right\">1.953</td><td align=\"right\">5.677</td><td align=\"right\">1.26E-01</td><td align=\"right\">-0.266</td><td align=\"right\">2.03E-01</td></tr><tr><td align=\"left\">H4R3me2</td><td align=\"right\">2.16E-01</td><td align=\"right\">5.43E-01</td><td align=\"right\">1.439</td><td align=\"right\">1.404</td><td align=\"right\">1.165</td><td align=\"right\">1.666</td><td align=\"right\"><bold>1.91E-10</bold></td><td align=\"right\">0.275</td><td align=\"right\">6.66E-16</td></tr><tr><td align=\"left\">Pol II</td><td align=\"right\">4.24E-01</td><td align=\"right\">2.45E-04</td><td align=\"right\">1.538</td><td align=\"right\">4.476</td><td align=\"right\">0.507</td><td align=\"right\">0.812</td><td align=\"right\"><bold>6.47E-16</bold></td><td align=\"right\">1.031</td><td align=\"right\">2.20E-16</td></tr><tr><td align=\"left\">CTCF</td><td align=\"right\">9.31E-02</td><td align=\"right\">6.45E-05</td><td align=\"right\">0.757</td><td align=\"right\">1.560</td><td align=\"right\">0.521</td><td align=\"right\">1.226</td><td align=\"right\"><bold>1.41E-06</bold></td><td align=\"right\">0.236</td><td align=\"right\">2.20E-16</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">H3K4me3, ES</td><td align=\"right\"><bold>0.0008</bold></td><td align=\"right\">0.294</td><td align=\"right\">5.673</td><td align=\"right\">9.20</td><td align=\"right\">1.423</td><td align=\"right\">4.89</td><td align=\"right\"><bold>2.30E-06</bold></td><td align=\"right\">4.25</td><td align=\"right\">1.91E-10</td></tr><tr><td align=\"left\">H3K27me3, ES</td><td align=\"right\">0.0024</td><td align=\"right\">7.63E-06</td><td align=\"right\">2.203</td><td align=\"right\">3.255</td><td align=\"right\">1.958</td><td align=\"right\">3.314</td><td align=\"right\">0.71</td><td align=\"right\">0.245</td><td align=\"right\">0.534</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Numbers of large (&gt;15 kb) post-macaque SDs with higher histone modifications in either parental or derivative loci</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Factors</td><td align=\"right\">Higher in parental loci</td><td align=\"right\">Higher in derivative loci</td></tr></thead><tbody><tr><td align=\"left\">H2AZ</td><td align=\"right\">68</td><td align=\"right\">23</td></tr><tr><td align=\"left\">H2BK5me1</td><td align=\"right\">92</td><td align=\"right\">15</td></tr><tr><td align=\"left\">H3K27me1</td><td align=\"right\">96</td><td align=\"right\">17</td></tr><tr><td align=\"left\">H3K27me2</td><td align=\"right\">85</td><td align=\"right\">19</td></tr><tr><td align=\"left\">H3K27me3</td><td align=\"right\">85</td><td align=\"right\">29</td></tr><tr><td align=\"left\">H3K36me1</td><td align=\"right\">97</td><td align=\"right\">14</td></tr><tr><td align=\"left\">H3K36me3</td><td align=\"right\">90</td><td align=\"right\">23</td></tr><tr><td align=\"left\">H3K4me1</td><td align=\"right\">83</td><td align=\"right\">14</td></tr><tr><td align=\"left\">H3K4me2</td><td align=\"right\">67</td><td align=\"right\">12</td></tr><tr><td align=\"left\">H3K4me3</td><td align=\"right\">93</td><td align=\"right\">15</td></tr><tr><td align=\"left\">H3K79me1</td><td align=\"right\">87</td><td align=\"right\">19</td></tr><tr><td align=\"left\">H3K79me2</td><td align=\"right\">37</td><td align=\"right\">9</td></tr><tr><td align=\"left\">H3K79me3</td><td align=\"right\">82</td><td align=\"right\">23</td></tr><tr><td align=\"left\">H3K9me1</td><td align=\"right\">84</td><td align=\"right\">16</td></tr><tr><td align=\"left\">H3K9me2</td><td align=\"right\">83</td><td align=\"right\">24</td></tr><tr><td align=\"left\">H3K9me3</td><td align=\"right\">73</td><td align=\"right\">15</td></tr><tr><td align=\"left\">H3R2me1</td><td align=\"right\">103</td><td align=\"right\">19</td></tr><tr><td align=\"left\">H3R2me2</td><td align=\"right\">93</td><td align=\"right\">18</td></tr><tr><td align=\"left\">H4K20me1</td><td align=\"right\">81</td><td align=\"right\">14</td></tr><tr><td align=\"left\">H4K20me3</td><td align=\"right\">72</td><td align=\"right\">27</td></tr><tr><td align=\"left\">H4R3me2</td><td align=\"right\">88</td><td align=\"right\">18</td></tr><tr><td align=\"left\">Pol II</td><td align=\"right\">57</td><td align=\"right\">15</td></tr><tr><td align=\"left\">CTCF</td><td align=\"right\">51</td><td align=\"right\">13</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Numbers of post-macaque SD loci with genes or pseudogenes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">Parental</td><td align=\"center\">Derivative</td></tr></thead><tbody><tr><td align=\"left\">RefSeq genes</td><td align=\"center\">656 (716)</td><td align=\"center\">192 (213)</td></tr><tr><td align=\"left\">Duplicated pseudogenes</td><td align=\"center\">113 (131)</td><td align=\"center\">251 (279)</td></tr><tr><td align=\"left\">Processed pseudogenes</td><td align=\"center\">161 (219)</td><td align=\"center\">269 (331)</td></tr><tr><td align=\"left\">Other pseudogenes</td><td align=\"center\">124 (143)</td><td align=\"center\">209 (232)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Features with asymmetric distribution between the parental and derivative loci of post-macaque SDs grouped by sequence identity</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"4\">Sequence identity</td></tr><tr><td/><td colspan=\"4\"><hr/></td></tr><tr><td/><td align=\"center\">&lt;0.925 (n = 330)</td><td align=\"center\">0.925-0.95 (n = 444)</td><td align=\"center\">0.95-0.975 (n = 570)</td><td align=\"center\">≥0.975 (n = 302)</td></tr></thead><tbody><tr><td align=\"left\">Significantly different modifications (paired <italic>t</italic>-test, adjusted <italic>p</italic>-value &lt;0.001)</td><td align=\"center\">H3K27me1</td><td align=\"center\">H3K27me1</td><td align=\"center\">H2BK5me1</td><td align=\"center\">H3K27me1</td></tr><tr><td/><td align=\"center\">H3K4me2</td><td align=\"center\">H3K36me1</td><td align=\"center\">H3K27me1</td><td align=\"center\">H3K36me3</td></tr><tr><td/><td align=\"center\">H3K9me1</td><td align=\"center\">H3K36me3</td><td align=\"center\">H3K36me3</td><td align=\"center\">H3K9me1</td></tr><tr><td/><td align=\"center\">H3R2me1</td><td align=\"center\">H3K4me1</td><td align=\"center\">H3K4me1</td><td align=\"center\">H4R3me2</td></tr><tr><td/><td/><td align=\"center\">H3K4me2</td><td align=\"center\">H3K4me2</td><td/></tr><tr><td/><td/><td align=\"center\">H3K79me1</td><td align=\"center\">H3K79me1</td><td/></tr><tr><td/><td/><td align=\"center\">H3K79me2</td><td align=\"center\">H3K9me1</td><td/></tr><tr><td/><td/><td align=\"center\">H3K9me1</td><td align=\"center\">H3R2me1</td><td/></tr><tr><td/><td/><td align=\"center\">H3R2me1</td><td align=\"center\">H4K20me1</td><td/></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Genes/pseudogenes (<italic>p</italic>-value &lt;0.001)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> RefSeq genes</td><td align=\"center\">0.375/0.087</td><td align=\"center\">0.341/0.087</td><td align=\"center\">0.367/0.127</td><td align=\"center\">0.410/0.191</td></tr><tr><td align=\"left\"> Duplicated pseudogenes</td><td align=\"center\">None</td><td align=\"center\">0.089/0.223</td><td align=\"center\">0.043/0.135</td><td align=\"center\">None</td></tr><tr><td align=\"left\"> Processed pseudogenes</td><td align=\"center\">None</td><td align=\"center\">0.238/0.413</td><td align=\"center\">0.104/0.367</td><td align=\"center\">0.063/0.296</td></tr><tr><td align=\"left\"> Other pseudogenes</td><td align=\"center\">None</td><td align=\"center\">0.089/0.253</td><td align=\"center\">0.056/0.238</td><td align=\"center\">0.055/0.285</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
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[]
[ "<table-wrap-foot><p>In the analyses of histone modifications and transcription factor binding, a data point is a read (that is, tag) from ChIP sequencing. The third column lists the numbers of ChIP tags (or genes, or pseudogenes) within the human SDs.</p></table-wrap-foot>", "<table-wrap-foot><p>The <italic>p</italic>-values are before adjustment for multiple testing; statistically significant results (by <italic>t</italic>-test) are in bold.</p></table-wrap-foot>", "<table-wrap-foot><p>For reference, the numbers of genes/pseudogenes are also listed in parentheses as some loci can have more than one gene or pseudogene.</p></table-wrap-foot>", "<table-wrap-foot><p>The values for genes/pseudogenes are the average number of genes (or pseudogenes) per 1 kb for parental/derivative sequences. Only features with statistical significance are listed.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"gb-2008-9-7-r105-1\"/>", "<graphic xlink:href=\"gb-2008-9-7-r105-2\"/>", "<graphic xlink:href=\"gb-2008-9-7-r105-3\"/>", "<graphic xlink:href=\"gb-2008-9-7-r105-4\"/>", "<graphic xlink:href=\"gb-2008-9-7-r105-5\"/>" ]
[]
[]
{ "acronym": [], "definition": [] }
29
CC BY
no
2022-01-12 14:47:26
Genome Biol. 2008 Jul 3; 9(7):R105
oa_package/68/d1/PMC2530858.tar.gz
PMC2530859
18606003
[ "<title>Background</title>", "<p>The coordination of protein roles to achieve specific biological functions requires the spatial/temporal concurrence of proteins so that they can form complexes [##REF##10688190##1##,##REF##16429126##2##] or, in general, operate within a module [##REF##16429126##2##, ####REF##10591225##3##, ##REF##12202830##4####12202830##4##]. In turn, this concurrence is tightly coordinated through the regulation of gene expression, as suggested by established correlations between the transcriptome and the interactome [##REF##11694880##5##,##REF##11779829##6##]. However, structure-encoded factors that may quantitatively control such correlations have not been identified. So far, protein structure has not provided organizing clues for the integration of large-scale descriptions of the molecular phenotype.</p>", "<p>As reported in this work, by exploiting a structure-based analysis of protein associations [##REF##14681378##7##,##REF##17185604##8##] and their correlated expression patterns, we identify a structural attribute, protein vulnerability, and show that it commits gene expression patterns in a quantifiable manner. More specifically, protein vulnerability is shown to determine the extent of co-expression of genes containing protein-encoding interactive domains in metabolic adaptation phases [##REF##9008165##9##,##REF##16569694##10##] or tissue types [##REF##15075390##11##,##REF##17693573##12##], while extreme vulnerability promotes significant post-transcriptional regulatory control.</p>", "<p>Soluble proteins maintain the integrity of their functional structures provided the amide and carbonyl groups paired through hydrogen bonds are adequately shielded from water attack, preventing backbone hydration and, generally, the concurrent total or partial denaturation of the soluble structure [##REF##12518060##13##,##UREF##0##14##]. As shown in this work, this integrity is often ensured through the formation of protein complexes, which become more or less obligatory depending on the extent of structure vulnerability and the level of backbone protection provided by the association [##REF##12518060##13##]. By adopting vulnerability as a structural indicator of dosage imbalance effects, the extent of reliance on binding partnerships is precisely quantified and shown to be an organizing factor for the yeast and human transcriptome.</p>" ]
[ "<title>Materials and methods</title>", "<title>Expression data sources</title>", "<p>Yeast expression data were obtained from the comprehensive <italic>Saccharomyces </italic>Genome Database [##REF##16741729##22##]. This complete dataset contains mRNA expression levels during a transition from glucose-fermentative to glycerol-based respiratory growth. Human expression data were taken from the comprehensive Novartis Gene Expression Atlas [##REF##15075390##11##]. This dataset includes 158 array images composed of 79 samples, each of which has two replicates hybridized on the human genome HG-U133A array. We discarded six samples of cancer tissues: ColorectalAdenocarcinoma, leukemialymphoblastic(molt4), lymphomaburkittsRaji, leukemiapromyelocytic, lymphomaburkittsDaudi, and leukemiachronicmyelogenous (k562).</p>", "<title>Interaction data sources</title>", "<p>Protein interaction curation based on structure provides direct physical interactions [##REF##17185604##8##]. Two proteins were considered to interact with each other when their respective domains or homologs of their respective domains were found in a complex with PDB-reported structure. We obtained curated yeast protein domain interactions from the Structural Interaction Network [##REF##17185604##8##], and filtered them using recently published yeast interaction data [##REF##16554755##21##]. For human, we focused on interactions within complexes. The complex data were obtained from the MIPS/Mammalian Protein Complex Database [##REF##16381839##20##]. We used the protein domain descriptions in the Pfam database [##REF##14681378##7##], and searched for domain-domain interactions using iPfam [##REF##15353450##39##].</p>", "<title>Expression correlation η</title>", "<p>The expression correlation for a protein-protein interaction is a normalized quantity defined as the Pearson correlation of the expression vectors of the genes encoding for the interacting domains divided by the mean correlation over all gene pairs encoding for interacting domains. The normalization is necessary for comparative analysis across species because different species have different mean expression correlations and, hence, the significance of a correlation is necessarily a relative attribute. Given its statistical nature, the denominator is non-zero for any species since, in a statistical sense, protein pairs that interact are expected to be positively correlated in their expression. We use the Pearson correlation coefficients of expression vectors to determine similarity between expression profiles. For two expression vectors <bold>X </bold>and <bold>Y</bold>, the Pearson correlation coefficient Corr(<bold>X</bold>, <bold>Y</bold>) is given by:</p>", "<p></p>", "<p>where <italic>X</italic>, <italic>Y </italic>are generic coordinates in the vectors <bold><italic>X </italic></bold>and <bold><italic>Y</italic></bold>, respectively, and &lt; &gt; indicates mean over the 73 normal tissues (human) [##REF##15075390##11##] or over the 5 metabolic adaptation phases (yeast) [##REF##16741729##22##].</p>", "<title>Calculation of vulnerability ν and identification of SEBHs for soluble proteins</title>", "<p>To determine the extent of solvent exposure of a backbone hydrogen bond in a soluble protein structure, we determine the extent of bond protection from atomic coordinates. This parameter, denoted ρ, is given by the number of side-chain nonpolar groups contained within a desolvation domain (hydrogen-bond microenvironment) defined as two intersecting balls of fixed radius (the approximate thickness of three water layers) centered at the α-carbons of the residues paired by the hydrogen bond. In structures of PDB-reported soluble proteins, at least two-thirds of the backbone hydrogen bonds are protected on average by ρ = 26.6 ± 7.5 side-chain nonpolar groups for a desolvation ball radius of 6 Å. Thus, SEBHs lie in the tails of the distribution, that is, their microenvironment contains 19 or fewer nonpolar groups, so their ρ-value is below the mean (ρ = 26.6) minus one standard deviation (= 7.5).</p>", "<p>In cases where the protein structures were unavailable from the PDB, we generated atomic coordinates through homology threading adopting the Pfam homolog as template and using the program Modeller [##REF##8254673##40##, ####REF##10940251##41##, ##REF##12824331##42####12824331##42##]. Modeller is a computer program that models three-dimensional structures of proteins subject to spatial constraints [##REF##8254673##40##], and was adopted for homology and comparative protein structure modeling. We thus generate the alignment of the target sequence to be modeled with the Pfam-homolog structure reported in the PDB and the program computes a model with all non-hydrogen atoms. The input for the computation consists of the set of constraints applied to the spatial structure of the amino acid sequence to be modeled and the output is the three-dimensional structure that best satisfies these constraints. The three-dimensional model is obtained by optimization of a molecular probability density function with a variable target function procedure in Cartesian space that employs methods of conjugate gradients and molecular dynamics with simulated annealing.</p>", "<title>Homolog PDB sources</title>", "<p>Yeast PDB homologs were obtained from the <italic>Saccharomyces </italic>Genome Database [##UREF##1##43##], and human PDB homologs were from Pfam [##UREF##2##44##].</p>", "<title>Micro-RNA targeting analysis</title>", "<p>For 17,444 human genes, we identified putative target sites for 162 conserved miRNA families using TargetScanS (version 4.0), a leading target-prediction program [##REF##14697198##45##]. Thus, we obtained the number of target-site types in the 3' UTR of each gene [##REF##17652130##31##]. Among the genes in our analysis: 105 genes were identified as encoding extremely vulnerable proteins; 7,927 out of 17,444 genes (45.4%) are predicted to be miRNA targets (containing at least one type of miRNA target site); and 87 out of 105 genes encoding extremely vulnerable proteins (82.9%) are predicted to be target genes. Thus, genes encoding extremely vulnerable proteins tend to be miRNA target genes (<italic>P </italic>&lt;&lt; 1.31 × 10<sup>-5</sup>, binomial test).</p>", "<p>In terms of miRNA regulation complexity, the average number of miRNA target-site types for a human gene is 2.66 and the median number is 0; while the average number for a prion gene is 6.01 and the median is 5. Again, this is highly significant (<italic>P </italic>&lt; 10<sup>-16</sup>, Wilcox rank test).</p>", "<title>Prediction of native disorder of protein domains</title>", "<p>The highly accurate predictor of native disorder PONDR VSL2 [##REF##16618368##34##,##REF##16187360##35##] exploits the length-dependent (heterogenous) amino acid compositions and sequence properties of intrinsically disordered regions to improve prediction performance. Unlike previous PONDR predictors for long disordered regions (&gt;30 residues), it is applicable to disordered regions of any length. The disorder score (<italic>0 </italic>≤ <italic>f</italic><sub><italic>d </italic></sub>≤ <italic>1</italic>) is assigned to each residue within a sliding window, representing the predicted propensity of the residue to be in a disordered region (<italic>f</italic><sub><italic>d </italic></sub>= <italic>1</italic>, certainty of disorder; <italic>f</italic><sub><italic>d </italic></sub>= <italic>0</italic>, certainty of order). The disorder propensity is quantified by a sequence-based score that takes into account residue attributes such as hydrophilicity, aromaticity, and their distribution within the window interrogated.</p>" ]
[ "<title>Results</title>", "<title>Protection of a vulnerable protein and co-expression demands</title>", "<p>We start by defining vulnerability <italic>ν </italic>of a soluble protein structure as the ratio of solvent-exposed backbone hydrogen bonds (SEBHs) to the overall number of such bonds (Figure ##FIG##0##1##). The SEHBs may be computationally identified from atomic coordinates (Materials and methods). Thus, while backbone hydrogen bonds are determinants of the basic structural motifs [##REF##14816373##15##,##REF##14834147##16##], the SEHBs represent local weaknesses of the structure.</p>", "<p>Figure ##FIG##0##1a## shows the vulnerability pattern of a well protected soluble protein, the yeast SH3 signaling domain [##REF##11668184##17##], with <italic>ν </italic>= 19.0%. Figure ##FIG##0##1b## shows the most vulnerable protein structure for an autonomous folder in the Protein Data Bank (PDB) (<italic>ν </italic>= 63.0%), the cellular form of the 90-230 fragment of the human prion protein PrP<sup>C </sup>(PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1QM0\">1QM0</ext-link>) [##REF##10618385##18##]. This extreme case was detected after exhaustive computation of the <italic>ν </italic>parameter for all conformations of isolated (those not in a complex) polypeptide chains reported in the PDB (Materials and methods). Figure ##FIG##1##2## shows the most vulnerable structure adopted by a protein chain within a yeast complex: subunit 1 from the cytochrome b-c1 complex (<italic>COR1/YBL045C</italic>). Unlikely to be found in isolation, this structure is found within the mitochondrial respiratory chain complex III [##REF##11726495##19##].</p>", "<p>A vulnerable soluble structure gains extra protection of its backbone hydrogen bonds through forming complexes, as nonpolar groups of a binding partner contribute to expel water molecules from the microenvironment of the preformed bonds [##REF##12518060##13##]. On the other hand, the SEBHs promote their own dehydration as a means to stabilize and strengthen the hydrogen bond [##UREF##0##14##].</p>", "<p>To delineate the role of structure vulnerability as an organizing integrative factor in large-scale descriptions of the molecular phenotype, we first examined the Pfam-filtered [##REF##14681378##7##] protein complexes for yeast [##REF##17185604##8##] and human [##REF##16381839##20##]. These associations involve domains whose PDB-reported homologs are involved in complexes.</p>", "<p>This work quantitatively examines the relationship between the structural vulnerability of a protein and the extent of co-expression of genes encoding its binding partners. Thus, the extent of co-expression, <italic>η </italic>(<italic>i</italic>, <italic>j</italic>), for two genes <italic>i</italic>, <italic>j </italic>encoding interacting proteins is measured by the expression correlation of the two genes normalized to the average correlation over the interactome (Materials and methods). In consonance, the expression correlation of a complex, <italic>η (complex)</italic>, may be defined by the maximum expression correlation over its constitutive underlying pairwise interactions (see Additional data files 7-9 for alternative definitions).</p>", "<p>Thus, the most highly correlated yeast complex (overall <italic>η (complex) </italic>= 3.61) with full PDB-reported representation is the mitochondrial respiratory chain complex III shown in Figure ##FIG##2##3a## (PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1KB9\">1KB9</ext-link>[##REF##11726495##19##]). The most vulnerable protein within the complex (<italic>ν </italic>= 57%) is subunit 1 from the cytochrome b-c1 complex (Gene/ORF = <italic>COR1/YBL045C</italic>, shown in red). Its peptide chain conformation, with the SEBH pattern described in Figure ##FIG##1##2##, is involved in the most highly correlated interaction (<italic>η </italic>= 3.61) within the complex (Figure ##FIG##2##3b,c##). The binding partner in this interaction is subunit 2 of cytochrome b-c1 (Gene/ORF = <italic>QCR2/YPR191W</italic>, blue chain in Figure ##FIG##2##3a##). Figure ##FIG##2##3c## shows the mutual protection of preformed SEBHs in the two subunits along part of their association interface (red, <italic>COR1 </italic>residues 42-119; blue, <italic>QCR2 </italic>residues 250-331). This intermolecular mutual 'wrapping' of local weaknesses illustrates the fact that the association contributes to maintain structural integrity (Figure ##FIG##2##3c##).</p>", "<p>We examined the role of structure vulnerability as a factor governing the extent of co-expression of binding partners in illustrative yeast complexes (Figure ##FIG##3##4a##; Additional data file 1). Structure-based protein-protein interactions were curated through the Pfam database, so that two proteins were considered to interact with each other if their respective domains (or homolog domains) were reported in a PDB complex [##REF##17185604##8##,##REF##16554755##21##]. The expression correlation, <italic>η</italic>, for each interaction pair within a complex was determined at the mRNA level of the genes whose open reading frames (ORFs) contained the interacting domains (Materials and methods). Vulnerabilities were computed either directly from PDB files, when available, as described in Figure ##FIG##0##1##, or from atomic coordinates generated by homology threading using the Pfam-homolog domain as template (Materials and methods). In the latter case, side-chain equilibration, constrained by a fixed homology-threaded backbone, was obtained from constrained molecular dynamics simulations (Materials and methods). We then determined the maximum <italic>ν</italic>-value for each interactive pair and, using the comprehensive microarray database for <italic>Saccharomyces cerevisiae </italic>glucose→ glycerol metabolic adaptation [##REF##16741729##22##], we computed the expression correlation <italic>η </italic>for each Pfam interaction. A tight <italic>(η-ν) </italic>correlation (R<sup>2 </sup>= 0.891) is obtained and shown to hold across the illustrative yeast complexes (Figure ##FIG##3##4a##) and, furthermore, to hold across all 1,354 pairs of interacting proteins in the yeast interactome with Pfam representation (Figure ##FIG##3##4b,c##; Additional data file 2). The <italic>(η-ν) </italic>correlation implies that the protection of a functionally competent protein structure in yeast drives co-expression of its binding partners to an extent that is determined by the structure vulnerability.</p>", "<p>In selecting the yeast transcriptome [##REF##16741729##22##], particular attention was focused on the 'perturbative' nature of the change triggering the structural remodeling of the proteomic network across different phases. A more extensive remodeling on a vastly larger scale, as in the complete yeast developmental cycle [##REF##9843569##23##], cannot be treated as a perturbation since it clearly alters the modular structure of the proteome network [##REF##12202830##4##] and, consequently, yields a weaker <italic>(η-ν) </italic>correlation (Additional data file 10).</p>", "<p>Structure vulnerability is not only an organizing factor for the metabolic-adaptation transcriptome but also steers the organization of tissue-based transcriptomes. This is revealed by a similar comparative analysis of the most comprehensive protein-encoding gene-expression data for human [##REF##15075390##11##] and the structure-represented interactome [##REF##16381839##20##]. Thus, a clear <italic>(η-ν) </italic>correlation is apparent between the co-expression of 607 gene pairs and the maximum structure vulnerability for each pair of interacting domains encoded in the ORFs of the respective genes (Figure ##FIG##4##5##; Additional data file 3).</p>", "<p>Other human transcriptomes based on normal tissue expression were examined (see, for example, [##REF##11752297##24##]), but none provided statistically significant (&gt;&gt;10 genes pairs) representation for the gene pairs for which interactome data also exist [##REF##16381839##20##], as needed for the present study.</p>", "<title>Post-transcriptional regulation of the expression of highly vulnerable proteins</title>", "<p>In contrast with the tighter yeast correlation, a few but significant outlier pairs (Figure ##FIG##4##5##, red data points) are found beyond the confidence band defined by a width of two Gaussian dispersions from the linear <italic>(η-ν) </italic>fit. To rationalize this fact, we identified 115 human genes with ORFs encoding extremely vulnerable proteins (Additional data file 4). Consistent with the definition of structure vulnerability (Figure ##FIG##0##1##), the latter proteins are identified by large sequences (≥ 30 residues) of amino acids that are poor protectors of backbone hydrogen bonds. In principle, a sizable window of residues unable to protect backbone hydrogen bonds produces a poor folder, yielding a highly vulnerable structure [##UREF##0##14##,##REF##12743379##25##]. Thus, these sequences are either probably unable to sustain a stable soluble structure, or prone to relinquish the folding information encoded in the amino acid sequence in favor of self-aggregation [##REF##12743379##25##]. The poor protectors (G, A, S, Y, N, Q, P) are amino acids possessing side chains with insufficient nonpolar groups, with polar groups too close to the backbone (thus precluding hydrogen-bond protection through clustering of nonpolar groups) [##UREF##0##14##] or with amphiphilic aggregation-nucleating character (Y) [##REF##15944694##26##, ####REF##17495929##27##, ##REF##12073366##28####12073366##28##]. Charged backbone de-protecting side chains (D, E) are excluded since they would entail negative design relative to protein self-aggregation. All outlier interactions in the human <italic>(η-ν) </italic>correlation involve genes with extreme vulnerability (Figure ##FIG##4##5##; Additional data file 4). Significantly, when the same criterion for extreme vulnerability is used to scan the yeast genome (Additional data file 5), 85 genes are identified whose ORFs encode the five confirmed prion proteins for this organism [##REF##15944694##26##, ####REF##17495929##27##, ##REF##12073366##28##, ##REF##18362884##29####18362884##29##]: PSI+ (<italic>SUP35</italic>), NU+ (<italic>NEW1</italic>), PIN+ (<italic>RNQ1</italic>), URE3 (<italic>URE2</italic>) and SWI+ (<italic>SWI1</italic>). This fact is statistically significant (<italic>P </italic>&lt; 10<sup>-10</sup>, hypergeometric test) and supports the presumed relationship between structural vulnerability of the soluble fold and aggregation propensity [##REF##12743379##25##].</p>", "<p>The <italic>(η-ν) </italic>correlation reported in Figure ##FIG##4##5## for human is weaker than the yeast counterpart likely because, in contrast with yeast, mRNA levels are not a reliable surrogate for protein expression levels in human [##REF##14744438##30##,##REF##17652130##31##]. This observation led us to examine post-transcriptional regulation in human genes, to analyze the microRNA (miRNA) targeting of the predicted 115 extremely vulnerable human genes (Additional data files 4 and 6), and to contrast the miRNA-targeting statistics with the generic values across the human genome [##REF##17652130##31##]. To obtain statistics on miRNA targeting, we identified putative target sites in the 3' UTR (untranslated region) of each gene for 162 conserved miRNA families (Materials and methods) [##REF##17652130##31##]. Thus, 7,927 out of 17,444 genes (45.4%) are predicted to contain at least one miRNA target site (Additional data file 6), while 87 out of 105 (82.9%) extremely vulnerable genes are predicted to be targeted genes. Thus, human genes containing extremely vulnerable regions are more frequently targeted by miRNA (<italic>P </italic>&lt;&lt; 1.31 × 10<sup>-5</sup>, binomial test). In regards to miRNA regulation complexity, the mean number of miRNA target sites for human genes is 2.66 and the median is 0, while the mean number for extremely vulnerable genes is 6.01 and the median is 5. This significant difference (<italic>P </italic>&lt; 10<sup>-16</sup>, Wilcox rank test) strongly suggests that the deviation of extremely vulnerable genes from the <italic>(η-ν) </italic>correlation (Figure ##FIG##4##5##), with expression correlation evaluated at the level of mRNA expression, can be explained by post-transcriptional miRNA regulation. This type of regulation influences the final protein expression level. In a broad sense, this analysis highlights the connection between protein structure and gene regulation: extremely vulnerable genes require tight control at the post-transcriptional level.</p>", "<title>Protein intrinsic disorder and transcriptome organization</title>", "<p>The inability of an isolated protein fold to protect specific intramolecular hydrogen bonds from water attack may lead to structure-competing backbone hydration with concurrent local or global dismantling of the structure [##UREF##0##14##,##REF##12743379##25##,##REF##17672484##32##]. This view of structural vulnerability suggests a strong correlation between the degree of solvent exposure of intramolecular hydrogen bonds and the local propensity for structural disorder [##REF##17158572##33##, ####REF##16618368##34##, ##REF##16187360##35####16187360##35##]: in the absence of binding partners, the inability of a protein domain to exclude water intramolecularly from pre-formed hydrogen bonds may be causative of a loss of structural integrity, and this tendency is marked by the disorder propensity of the domain [##REF##17672484##32##]. These findings led us to regard the predicted extent of disorder in a protein domain as a likely surrogate for its vulnerability and to contrast it with the extent of expression correlation with its interactive partners. The disorder propensity may be determined by a sequence-based score, <italic>f</italic><sub><italic>d</italic></sub>(<italic>f</italic><sub><italic>d </italic></sub>= 1, certainty of disorder; <italic>f</italic><sub><italic>d </italic></sub>= 0, certainty of order), assigned to each residue. In this work, this parameter is generated by the highly accurate predictor of native disorder PONDR-VSL2 [##REF##16618368##34##,##REF##16187360##35##]. The extent of intrinsic disorder of a domain may be defined as the percentage of residues predicted to be disordered relative to a predetermined <italic>f</italic><sub><italic>d </italic></sub>threshold (<italic>f</italic><sub><italic>d </italic></sub>= 0.5).</p>", "<p>Reexamination of the expression correlations in the yeast and human transcriptomes was carried out, taking into account a proteome-wide sequence-based attribution of the extent of disorder (percentage of residues predicted to be disordered, or 'disorder content') in interacting protein domains. The disorder predictions did not include any structural information on induced fits arising upon forming a complex, and hence, unlike structure vulnerability, the percent predicted disorder is independent of the complex under consideration. This fact introduces deviations in the estimation of vulnerability through disorder content for proteins with extensive disorder content since their conformational plasticity may enable diverse induced-fit conformations with different vulnerabilities (Figure ##FIG##5##6a##). In yeast, the extent of disorder of the most disordered domain for each pair of interacting domains captures the degree of correlation in the expression patterns required for structure protection (Figure ##FIG##5##6a##). This is revealed by the correlation between the extent of disorder of the most disordered domain in an interacting pair and the expression correlation <italic>η </italic>of the two genes encoding the respective interacting domains. While weaker than the <italic>η</italic>-<italic>ν </italic>correlation (Figure ##FIG##3##4##), the <italic>η</italic>-disorder correlation is still relatively strong for yeast proteins (R<sup>2 </sup>= 0.752; Figure ##FIG##5##6a##), implying that disorder content determines the degree of coexpression of binding partners to a significant extent. The large dispersion in disorder extent at high levels of coexpression (approximately 45% dispersion versus approximately 15% for proteins with low disorder/low expression correlation) is indicative that highly disordered chains may adopt structures with very different levels of vulnerability depending on the complex in which they are involved (the <italic>η</italic>-<italic>ν </italic>correlation does not widen so significantly for smaller <italic>η</italic>-values). Thus, the more disordered the chain, the more multi-valued the correspondence between disorder extent and vulnerability, conferring higher dispersion to the <italic>η</italic>-disorder correlation.</p>", "<p>The <italic>η</italic>-disorder correlation in human is considerably weaker (R<sup>2 </sup>= 0.304; Figure ##FIG##5##6b##) than in yeast. This is partly due to the fact that human proteins have a higher degree of disorder propensity than their yeast orthologs [##REF##15347802##36##] and, hence, they are capable of significantly diversifying their structural adaptation (induced folding) in different complexes. In this context, the extent of disorder becomes a poor surrogate of structural vulnerability, as different ν-values may correspond to a single percent predicted disorder. In addition, post-transcriptional regulation in humans implies that expression correlations at the mRNA level are not reflective of the protein concurrencies modulated by tissue type, as indicated above.</p>", "<p>To conclude, Figure ##FIG##5##6## reveals the role of intrinsic protein disorder in transcriptome organization suggested by exploring the interrelationship between protein vulnerability and disorder propensity.</p>" ]
[ "<title>Discussion</title>", "<p>Soluble protein structures may be more or less vulnerable to water attack depending on their packing quality. As shown in this work, one way of quantifying the structure vulnerability is by determining the extent of solvent exposure of backbone hydrogen bonds. Within this scheme, local weaknesses in the protein structure may become protected upon forming a complex, as exposed backbone hydrogen bonds become exogenously dehydrated. Vulnerable structures are thus quantitatively reliant on binding partnerships to maintain their integrity, suggesting that vulnerability may be regarded as a structure-based indicator of gene dosage sensitivity [##REF##15219392##37##,##REF##15680512##38##]. This observation is validated by establishing the significance of protein vulnerability or structure protection as an organizing factor in temporal phases (yeast) and tissue-based (human) transcriptomes. Specifically, this role was established by examining the degree of co-expressions of a protein with its binding partners in structure-represented interactions. Thus, for each Pfam-filtered binding partnership, the extent of co-expression across metabolic adaptation phases (yeast) or tissue types (human) was found to depend quantitatively on the structure vulnerability of the proteins involved. Hence, vulnerability may be regarded as an organizing factor encoded in the structure of gene products.</p>", "<p>Furthermore, as shown in this work, the tight coordination between translation regulation and gene function dictates that extremely vulnerable, and hence 'highly needy', proteins are subject to significant levels of post-transcriptional regulation. In human, this extra regulation is achieved through extensive miRNA targeting of genes coding for extremely vulnerable proteins. In yeast, on the other hand, our results imply that such a regulation is likely achieved through sequestration of the extremely vulnerable proteins into aggregated states. Intriguingly, the 85 yeast genes encoding extremely vulnerable proteins included those for the five confirmed yeast prions [##REF##15944694##26##, ####REF##17495929##27##, ##REF##12073366##28##, ##REF##18362884##29####18362884##29##]. This statistically significant result implies that if the extremely vulnerable proteins are themselves translational regulators, this sequestration may directly lead to epigenetic consequences and phenotypic polymorphism [##REF##15944694##26##, ####REF##17495929##27##, ##REF##12073366##28####12073366##28##].</p>" ]
[ "<title>Conclusion</title>", "<p>In this work we adopted a structural biology perspective to reassess the fundamental notion of 'dosage imbalance effect' and examine the implications for gene expression, specifically for transcriptomal organization and post-transcriptional regulation. Thus, vulnerability of protein structures and the concurrent need to maintain structural integrity for functional reasons prove to be quantifiers of dosage imbalance: proteins with a high degree of reliance on binding partnerships to maintain their structural integrity are naturally expected to yield high dosage sensitivity in their respective gene expressions. Hence, structural vulnerability is shown to be a determinant of transcriptome organization across tissues and temporal phases: the need for protein structure protection compels gene co-expression in a quantifiable manner. Extreme vulnerability is shown to require significant additional regulation at the post-transcriptional level, manifested by epigenetic aggregation in yeast and miRNA targeting in human. These latter observations will likely inspire further study of structure-encoded signals that govern post-transcriptional regulation.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A proteomic association study between protein three-dimensional structure and transcriptional and post-transcriptional regulation in yeast and human.</p>", "<title>Background</title>", "<p>Gene co-expressions often determine module-defining spatial and temporal concurrences of proteins. Yet, little effort has been devoted to tracing coordinating signals for expression correlations to the three-dimensional structures of gene products.</p>", "<title>Results</title>", "<p>We performed a global structure-based analysis of the yeast and human proteomes and contrasted this information against their respective transcriptome organizations obtained from comprehensive microarray data. We show that protein vulnerability quantifies dosage sensitivity for metabolic adaptation phases and tissue-specific patterns of mRNA expression, determining the extent of co-expression similarity of binding partners. The role of protein intrinsic disorder in transcriptome organization is also delineated by interrelating vulnerability, disorder propensity and co-expression patterns. Extremely vulnerable human proteins are shown to be subject to severe post-transcriptional regulation of their expression through significant micro-RNA targeting, making mRNA levels poor surrogates for protein-expression levels. By contrast, in yeast the expression of extremely under-wrapped proteins is likely regulated through protein aggregation. Thus, the 85 most vulnerable proteins in yeast include the five confirmed prions, while in human, the genes encoding extremely vulnerable proteins are predicted to be targeted by microRNAs. Hence, in both vastly different organisms protein vulnerability emerges as a structure-encoded signal for post-transcriptional regulation.</p>", "<title>Conclusion</title>", "<p>Vulnerability of protein structure and the concurrent need to maintain structural integrity are shown to quantify dosage sensitivity, compelling gene expression patterns across tissue types and temporal adaptation phases in a quantifiable manner. Extremely vulnerable proteins impose additional constraints on gene expression: They are subject to high levels of regulation at the post-transcriptional level.</p>" ]
[ "<title>Abbreviations</title>", "<p>miRNA, micro RNA; ORF, open reading frame; PDB, Protein Data Bank; SEBH, solvent-exposed backbone hydrogen bonds; UTR, untranslated region.</p>", "<title>Authors' contributions</title>", "<p>JC provided theoretical insight, designed methodology, generated and collected data, and co-wrote the paper. HL provided theoretical insight, and generated and collected data. AF provided the fundamental concepts and insights, designed methodology and wrote the paper.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data file ##SUPPL##0##1## provides raw data for Figure ##FIG##3##4a##. Additional data file ##SUPPL##1##2## provides Raw data for Figure ##FIG##3##4b,c##. Additional data file ##SUPPL##2##3## provides raw data for Figure ##FIG##4##5##. Additional data file ##SUPPL##3##4## lists extremely vulnerable proteins in human. Additional data file ##SUPPL##4##5## lists extremely vulnerable yeast proteins. Additional data file ##SUPPL##5##6## lists the predicted number of miRNA targets for human genes. Additional data file ##SUPPL##6##7## outlines the robustness of results with respect to alternative graph-theoretic definitions of co-expression similarity. Additional data file ##SUPPL##7##8## outlines how vulnerability correlates with co-expression similarity in protein complexes. Additional data file ##SUPPL##8##9## provides Raw data: yeast (a) and human (b) complexes examined in Additional data file ##SUPPL##7##8##. Additional data file ##SUPPL##9##10## shows the (η-ν) plot obtained for the yeast developmental-phase transcriptome obtained from a comprehensive identification of cell cycle-regulated genes by microarray hybridization [##REF##9843569##23##].</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The research of AF is supported through NIH grant R01 GM72614 (NIGMS). The input of Drs Kristina Rogale Plazonic, Pedro Romero and Florin Despa is gratefully acknowledged.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Hydrogen-bond pattern and structural vulnerabilities (SEBHs) of the yeast SH3 domain and the human prion protein PrP<sup>C</sup>. <bold>(a) </bold>Hydrogen-bond pattern and structural vulnerabilities (SEBHs) of the yeast SH3 domain from a <italic>S. cerevisiae </italic>40.4 kDa protein (PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1SSH\">1SSH</ext-link>) [##REF##11668184##17##]. The ribbon display is included as a visual aid. The protein backbone is shown as virtual bonds (blue) joining consecutive α-carbons in the peptide chain. Light-grey segments represent well protected backbone hydrogen bonds, and green segments represent SEBHs. The extent of solvent-exposure extent of a hydrogen bond was determined from atomic coordinates by calculating the number of nonpolar groups within its microenvironment (Materials and methods). SEBHs are those backbone hydrogen bonds protected by an insufficient number of nonpolar groups as statistically defined in Materials and methods. The level of structure vulnerability <italic>ν</italic>, defined as the ratio of SEBHs to the overall number of backbone hydrogen bonds, is 19.0% (<italic>ν </italic>= 4/21). <bold>(b) </bold>SEBH-pattern for the cellular structure of the human prion protein PrP<sup>C </sup>(PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1QM0\">1QM0</ext-link>) [##REF##10618385##18##]. Its vulnerability parameter is <italic>ν </italic>= 63.0%, making it the most vulnerable soluble folder of all structures of unbound proteins reported in the PDB.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Ribbon representation and vulnerability (SEBH) pattern of subunit 1 from the cytochrome b-c1 complex. <bold>(a) </bold>Ribbon representation and <bold>(b) </bold>vulnerability (SEBH) pattern of subunit 1 from the cytochrome b-c1 complex (PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1KB9\">1KB9</ext-link>) [##REF##11726495##19##]. In b, red segments represent virtual protein backbone bonds, light-grey segments represent well protected backbone hydrogen bonds, and those green segments represent SEBHs. In the cytochrome complex, this protein adopts a highly vulnerable (<italic>ν </italic>= 57.3%) conformation.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Mutual protection of SEBHs in the two subunits of mitochondrial respiratory chain complex III. <bold>(a) </bold>Ribbon representation of mitochondrial respiratory chain complex III (PDB.<ext-link ext-link-type=\"pdb\" xlink:href=\"1KB9\">1KB9</ext-link>). The high structure vulnerability of subunit 1 (red; compare Figure 2) renders it highly needy for interaction with other subunits of the complex to maintain its structural integrity. <bold>(b) </bold>SEBH pattern for subunit 1 (red) and subunit 2 (blue). The interacting pair is characterized by a very high expression correlation <italic>η </italic>= 3.61. The yellow square highlights the part of the interface shown in detail in (c). <bold>(c) </bold>Illustration of mutual protections of SEBHs in the two subunits along part of their interface. One side-chain bond (between α and β carbons) is displayed. The thin blue lines, which connect β-carbons in one protein with centers of hydrogen bonds in the other protein, represent mutual protections of hydrogen bonds across the protein-association interface. Thus, a thin line is shown whenever the side chain of one protein is contributing with nonpolar groups to the microenvironment of a preformed hydrogen bond in its binding partner.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Correlation between maximum structure vulnerability <italic>ν </italic>and co-expression similarity <italic>η </italic>for yeast protein interactions. <bold>(a) </bold>Correlation between maximum structure vulnerability <italic>ν </italic>and co-expression similarity <italic>η </italic>for interactions within specific yeast complexes. The <italic>ν</italic>-parameter of an interaction is defined as the maximum vulnerability between the two interacting partners, and the <italic>η</italic>-parameter is the ratio of their expression correlation to the (non-zero) expected correlation over all interacting pairs in the proteome. <bold>(b) </bold>(<italic>η</italic>-<italic>ν</italic>) correlation for all Pfam-filtered yeast protein interactions. Red points represent interactions involving extremely vulnerable proteins, including confirmed yeast prions (Additional data file 5). <bold>(c) </bold>(<italic>η</italic>-<italic>ν</italic>) correlation of Pfam-filtered yeast protein interactions involving only PDB-reported proteins. The red data point represents an interaction involving an extremely vulnerable protein, and the green point represents an interaction involving an extremely vulnerable protein reported to be a prion protein (ERF2) [##REF##11752297##24##, ####REF##12743379##25##, ##REF##15944694##26####15944694##26##].</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>(<italic>η </italic>- <italic>ν</italic>) correlation for human protein interactions. <bold>(a) </bold>The (<italic>η</italic>-<italic>ν</italic>) correlation for all Pfam-filtered human protein interactions. Red points represent interactions involving extremely vulnerable proteins (Additional data file 4). <bold>(b) </bold>The correlation over Pfam-filtered human protein interactions that involve only PDB-reported proteins. The red point represents an interaction containing an extremely vulnerable protein.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><italic>(η</italic>-disorder) correlation for yeast and human protein interactions. Correlation between <italic>η</italic>-parameter and percent predicted disorder (disorder content) of the most disordered domain for each of <bold>(a) </bold>the 1,354 Pfam-filtered protein-interaction pairs in yeast and <bold>(b) </bold>the 607 pairs in human.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Data in column A indicate the expression correlation <italic>η </italic>associated with protein interactions, and data in column B indicate the structure vulnerability <italic>ν </italic>for interactions within specific complexes. The rest of the columns contain the ORF, domain and structure information (PDB accession code of interacting domain or its Pfam-homologs), respectively, for every pair of interacting proteins.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Sheet 1 contains all Pfam-filtered yeast protein interactions, while sheet 2 contains only those interactions with both partners having PDB structures. In each sheet, column A lists the expression correlation h of interactions, and columns B and C list the structure vulnerability n of interactions not involving or involving, respectively, extremely vulnerable proteins. The remaining columns contain ORF, domain and structure information (PDB accession code of interacting domain or of its Pfam-homologs) for every pair of interacting proteins.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Sheet 1 contains all Pfam-filtered human protein interactions, while sheet 2 contains only those interactions with both partners having PDB structures. In each sheet, column A contains the expression correlation h for each interaction, and columns B and C list the structure vulnerability n of interactions not involving or involving, respectively, extremely vulnerable proteins, and the rest of the columns list gene name, protein ID, domain and structure information (PDB accession code of interacting domain or of its Pfam-homologs) of every pair of interacting proteins.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>The extremely vulnerable proteins in human are identified from genome-wide scanning of protein-encoding regions with sequence windows (length ≥ 30) containing mainly amino acids (G, A, S, Y, N, Q, P) that are poor protectors of the protein backbone. An extremely vulnerable protein contains at least one such window with a threshold of three amino acids allowed to be outside the group of poor protectors.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Extremely vulnerable yeast proteins are determined in the same way as for human (Additional data file ##SUPPL##3##4##). The rows marked in green correspond to the five confirmed yeast prions [##REF##15944694##26##, ####REF##17495929##27##, ##REF##12073366##28##, ##REF##18362884##29####18362884##29##]: SUP35 (ERF2), URE2, NEW1, RNQ1 and SWI1.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>The number of putative target-site types corresponding to 162 conserved miRNA families determined for 17,444 human genes by interrogation of the 3' UTR using TargetScanS (version 4.0) [##REF##14697198##45##].</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>The co-expression similarity for genes <italic>i</italic>, <italic>j</italic>, encoding a pair of interacting proteins is alternatively measured as the adjacency <italic>a</italic><sub><italic>ij</italic></sub>(β) = (0.5 + 0.5 <italic>η </italic>(<italic>i</italic>, <italic>j</italic>))<sup>β</sup>, where <italic>η </italic>(<italic>i</italic>, <italic>j</italic>) is the expression correlation for the gene pair <italic>i</italic>, <italic>j </italic>and β is a soft threshold [##UREF##3##46##]. Similarly, the structure vulnerability is alternatively defined as ν<sub><italic>i</italic>, <italic>j</italic></sub><italic>(β) </italic>= ν (<italic>i</italic>, <italic>j</italic>)<sup>β</sup>, where ν (<italic>i</italic>, <italic>j</italic>) is the maximum ν-value for the interacting pair. <bold>(a, b) </bold>(ν <italic>(β)</italic>-<italic>a(β)) </italic>correlations for yeast for exponents β = 0.5 (a) and 10 (b). The adjacencies for β = 1 correspond simply to a linear rescaling of η already correlated with ν in Figure ##FIG##3##4##. <bold>(c, d) </bold>The same as (a, b) but for human. Notice that high exponents (β &gt; 1) tend to amplify differences in co-expression, yielding lower correlation coefficients (R<sup>2 </sup>in (ν <italic>(β)</italic>-<italic>a(β)) </italic>plots).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>A normalized co-expression similarity <italic>γ (β, complex) </italic>for all genes encoding proteins that form a complex is obtained from the adjacencies of the pairwise interactions within the complex as: <italic>γ (β, complex) </italic>= [median<sub>i, j ∈ complex</sub><italic>a</italic><sub><italic>ij</italic></sub>(β)]/median<sub>i, j </sub><italic>a</italic><sub><italic>ij</italic></sub>(β)], where the median in the denominator extends over all interactive pairs in the interactome. Similarly, the normalized structure vulnerability Λ <italic>(β, complex) </italic>for complexes is defined as Λ <italic>(β, complex) </italic>= [median<sub>i, j ∈ complex</sub><italic>ν</italic><sub><italic>ij</italic></sub>(β)]/median<sub>i, j </sub><italic>ν</italic><sub><italic>ij</italic></sub>(β)]. <bold>(a-c) </bold>(Λ <italic>(β, complex)-γ (β, complex)) </italic>correlation over all 98 yeast complexes with transcriptome representation for exponents β = 0.5 (a), 1 (b) and 10 (c). <bold>(d-f) </bold>The same as (a-c) but for 53 human complexes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p><bold>(a) </bold>Yeast complexes. <bold>(b) </bold>Human complexes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional data file 10</title><p>(<italic>η</italic>-<italic>ν</italic>) plot obtained for the yeast developmental-phase transcriptome obtained from a comprehensive identification of cell cycle-regulated genes by microarray hybridization [##REF##9843569##23##]</p></caption></supplementary-material>" ]
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[{"surname": ["Fern\u00e1ndez"], "given-names": ["A"], "article-title": ["Keeping dry and crossing membranes."], "source": ["Nat Biot"], "year": ["2004"], "volume": ["22"], "fpage": ["1081"], "lpage": ["1084"]}, {"italic": ["Saccharomyces "]}, {"article-title": ["The Pfam database"]}, {"surname": ["Zhang", "Horvath"], "given-names": ["B", "S"], "article-title": ["A general framework for weighted gene co-expression network analysis."], "source": ["Stat Appl Gen Mol Biol"], "year": ["2005"], "volume": ["4"], "fpage": ["Article 17"]}]
{ "acronym": [], "definition": [] }
46
CC BY
no
2022-01-12 14:47:26
Genome Biol. 2008 Jul 7; 9(7):R107
oa_package/25/0b/PMC2530859.tar.gz
PMC2530860
18611267
[ "<title>Background</title>", "<p>The role of horizontal gene transfer (HGT) in prokaryotic evolution has long been documented in numerous studies, from bacterial pathogenesis to the spread of antibiotic resistance and nitrogen fixation [##REF##2685126##1##, ####REF##11352062##2##, ##REF##14645288##3####14645288##3##]. The proportion of genes affected by HGT has been estimated from an average of 7% to over 65% in prokaryotic genomes [##REF##10830951##4##, ####REF##10360571##5##, ##REF##16899658##6##, ##REF##17213324##7##, ##REF##16176988##8####16176988##8##]. The pervasive occurrence of gene transfer has revolutionized our view of microbial evolution - microbial evolution must be considered reticulate and cooperative by sharing genes and resources among organisms in the community [##REF##3336435##9##,##REF##17251963##10##].</p>", "<p>Reticulate evolution and gene transfer have long been known in eukaryotes. Hybridization, which occurs frequently in seed plants [##UREF##0##11##], can be viewed as a form of HGT. However, since eukaryotic genomes are relatively stable, hybridization between closely related taxa rarely involves acquisition of novel genes and its impact is mainly limited to lower taxonomic levels. Symbioses that generate new phenotypes can also be considered a form of reticulate evolution. Primary endosymbioses with an α-proteobacterium and a cyanobacterium gave rise to mitochondria and plastids, respectively [##REF##8118213##12##], whereas secondary endosymbioses contributed greatly to the evolution of several major eukaryotic groups [##UREF##1##13##, ####REF##14696040##14##, ##REF##11178253##15####11178253##15##]. Such endosymbiotic events are often accompanied by gene transfer from the endosymbiont to the nucleus, a process termed intracellular gene transfer (IGT) [##REF##2076556##16##,##REF##10570164##17##] or endosymbiotic gene transfer [##REF##11560168##18##]. However, the distinction between IGT and HGT is fluid - once an endosymbiont becomes obsolete, the IGTs have to be considered a form of HGT [##REF##17547748##19##].</p>", "<p>Apparently, the residence of mitochondria and plastids in eukaryotic cells provides ample opportunities for IGT and this has been supported by several genome analyses [##REF##15155797##20##, ####REF##12777624##21##, ##REF##14761653##22##, ##REF##12218172##23####12218172##23##]. On the other hand, the role of HGT in eukaryotic evolution was poorly appreciated until recently. Thus far, an increasing amount of data shows that HGT events do exist in eukaryotes - HGT from prokaryotes to eukaryotes not only is frequent in unicellular eukaryotes of various habitats and lifestyles [##REF##12546782##24##, ####REF##12801413##25##, ##REF##15003488##26##, ##REF##15535864##27##, ##REF##17086451##28##, ##REF##15947202##29##, ##REF##16472398##30##, ##REF##15729342##31##, ##REF##17298675##32####17298675##32##], but occurred multiple times in multicellular eukaryotes as well [##REF##12386340##33##, ####REF##13678641##34##, ##REF##17761848##35####17761848##35##]. In many cases, acquisition of foreign genes has significantly impacted the evolution of the biochemical system of the recipient organism [##REF##12546782##24##,##REF##14973196##36##].</p>", "<p>A critical question regarding the role of HGT is whether and how HGT contributed to the evolution of major eukaryotic groups. Given the scope of HGT in unicellular eukaryotes and that multicellularity is derived from unicellularity, the unicellular ancestors of modern multicellular eukaryotes might have been subject to frequent HGT [##REF##16730850##37##]. Most importantly, the anciently acquired genes, if retained among descendants, are likely to shape the long-term evolution of recipients [##REF##16730850##37##,##REF##16049196##38##]. In this study, we provide an analysis for genes that were introduced to the ancestor of plants (we use the term to denote the taxonomic group Plantae that includes glaucophytes, red algae, and green plants [##REF##9809012##39##,##REF##16701456##40##]). Such an analysis is possible because of the availability of sequence data of <italic>Cyanidioschyzon</italic>, the only red algal species whose nuclear genome has been completely sequenced. Our data indicate that ancient HGT events indeed occurred during early plant evolution and that the vast majority of the acquired genes are related to the biogenesis and functionality of plastids. In light of these findings, we also discuss the implications of concerted gene recruitment as a mechanism for the origin and optimization of key evolutionary novelties in eukaryotes.</p>" ]
[ "<title>Materials and methods</title>", "<title>Data sources</title>", "<p>Protein sequences for the red alga <italic>Cyanidioschyzon merolae </italic>were obtained from the <italic>Cyanidioschyzon </italic>Genome Project [##REF##15071595##42##,##UREF##3##74##]. Expressed sequence tag (EST) sequences were obtained from TBestDB [##REF##17202165##75##] and the NCBI EST database. All other sequences were from the NCBI protein sequence database.</p>", "<title>Identification of ancient HGT</title>", "<p>Anciently acquired genes in this study include those horizontally acquired prior to the split of red algae and green plants. A list of ancient HGT candidates was first generated based on phylogenomic screening of the <italic>Cyanidioschyzon </italic>genome using PhyloGenie [##REF##15459293##41##] and the NCBI non-redundant protein sequence database. The vast majority of the genes on this list are predominantly identified in bacteria and archaea, and therefore are likely of prokaryotic origin. To reduce the complications arising from potential cases of IGT, we adopted an approach combining sequence comparison, phylogenetic analyses, and statistical tests. Each gene on the list was first used to search the NCBI protein sequence database. Because of the cyanobacterial origin of plastids and the α-proteobacterial origin of mitochondria, genes with cyanobacterial and plastid-containing eukaryotic homologs as top hits were considered as likely plastid-derived; those with α-proteobacterial and other eukaryotic homologs as top hits were considered as likely mitochondrion-derived. These potentially organelle-derived genes were removed from the candidate list and the remaining genes were subject to detailed phylogenetic analyses. Gene tree topologies generated through detailed phylogenetic analyses were subject to careful inspections; any genes that formed a monophyly with cyanobacterial and plastid-containing eukaryotic homologs or with proteobacterial and other eukaryotic sequences were also eliminated from further consideration. Additionally, alternative topologies representing various evolutionary scenarios for each gene were statistically evaluated based on AU tests [##REF##12079646##43##]. Genes for which a straightforward IGT scenario (versus IGT followed by secondary transfers) could not be rejected (<italic>p</italic>-value &gt; 0.05) were also removed from the HGT candidate list. For a few genes, the gene tree topology may be explained by either a straightforward HGT or an IGT followed by secondary HGT events to other organisms; we prefer the scenario of straightforward HGT in these cases to that of secondary HGT, based on an assumption that chances for the same gene being repeatedly transferred among different organismal groups are relatively rare. In several other cases (for example, Figures ##FIG##0##1## and ##FIG##1##2d##), the distribution of the subject gene may also be explained by either multiple independent HGT events or a single HGT followed by differential gene losses. In such cases, we prefer the gene loss scenario based on an assumption that independent acquisitions of the same gene, by closely related taxa, from the same donor are rare. Because identification of HGT heavily relies on an accurate organismal phylogeny and because the relationships among many major eukaryotic lineages remain unsolved [##REF##16701456##40##,##REF##17194223##47##], HGT events among eukaryotes were not included in our analyses in most cases, except for those between photosynthetic eukaryotes where secondary or tertiary endosymbioses and subsequent gene transfer to host cells have been frequently documented [##REF##12777624##21##,##REF##15003488##26##,##REF##15746017##76##].</p>", "<title>Detailed phylogenetic analyses</title>", "<p>Sequences were sampled from representative groups (including major phyla of bacteria and major groups of eukaryotes) within each domain of life (bacteria, archaea, and eukaryotes). Because of the potential for sequence contaminations, eukaryotic EST sequences whose authenticity is suspicious (for example, high nucleotide sequence percent identity with bacterial homologs and/or absence of homologs from genomes of closely related taxa) were not included in the analyses. Multiple protein sequence alignments were performed using MUSCLE [##REF##15034147##77##] and clustalx [##REF##9396791##78##], and only unambiguously aligned sequence portions were used. Such unambiguously aligned positions were identified by cross-comparison of alignments generated using MUSCLE and clustalx, followed by manual refinement. The alignments are available in Additional data file 1. Phylogenetic analyses were performed with a maximum likelihood method using PHYML [##REF##14530136##79##], a Bayesian inference method using MrBayes [##REF##12912839##80##], and a distance method using the program <italic>neighbor </italic>of PHYLIP version 3.65 [##UREF##4##81##] with maximum likelihood distances calculated using TREE-PUZZLE [##REF##11934758##82##]. All maximum likelihood calculations were based on a substitution matrix determined using ProtTest [##REF##15647292##83##] and a mixed model of four gamma-distributed rate classes plus invariable sites. Maximum likelihood distances for bootstrap analyses were calculated using TREE-PUZZLE [##REF##11934758##82##] and PUZZLEBOOT v1.03 (by Michael E Holder and Andrew J Roger, available on the web [##UREF##5##84##]). Branch lengths and topologies of the trees depicted in all figures (Figures ##FIG##0##1## and ##FIG##1##2##; Additional data file 1) were calculated with PHYML. For the convenience of presentation, gene trees were rooted using archaeal (or archaeal plus eukaryotic) sequences, or paralogous gene copies if ancient gene families were involved, as outgroups; otherwise, trees were rooted in a way that no top hits of the sequence similarity search were used as an outgroup. Nevertheless, all gene trees should be strictly interpreted as unrooted.</p>", "<title>AU tests on alternative tree topologies</title>", "<p>Following detailed phylogenetic analyses, alternative tree topologies for each remaining HGT candidate were assessed for their statistical confidence using Treefinder [##UREF##6##85##]. In most cases, multiple constraint trees for each HGT candidate were generated using Treefinder by enforcing: monophyly of all eukaryotic sequences; monophyly of cyanobacterial, plant and other plastid-containing eukaryotic sequences; and monophyly of cyanobacterial, plant, and closely related bacterial sequences. These alternative topologies assumed that the subject gene in plants is not HGT-derived; they served as null hypotheses that all eukaryotic sequences have the same eukaryotic or mitochondrial origin or that plants acquired the subject gene from plastids, sometimes followed by secondary HGT to other bacterial groups. AU tests, which have been recommended for general tree tests [##REF##12079646##43##], were performed on alternative tree topologies (non-HGT hypotheses) and the tree generated from detailed phylogenetic analyses (HGT hypothesis). In this study, topologies with a <italic>p</italic>-value &lt; 0.05 were rejected.</p>", "<title>Prediction of protein localization</title>", "<p>Targeting signal of identified protein sequences was predicted using ChloroP [##REF##10338008##86##] and TargetP [##REF##10891285##87##]. Additional information about protein localization in green plants was obtained from The <italic>Arabidopsis </italic>Information Resource (TAIR).</p>" ]
[ "<title>Results</title>", "<p>To better understand the scope of HGT, one would like to eliminate complications arising from cases of IGT, in particular those from mitochondria. The ancient origin of mitochondria may translate into difficulties to uncover the α-proteobacterial nature of mitochondrion-derived genes and, therefore, identification of cases of HGT. Because of the ubiquitous distribution of mitochondria in eukaryotes, it is also often difficult to distinguish mitochondrion-derived genes from those transmitted from the ancestral eukaryotic nucleocytoplasm or anciently acquired from other prokaryotes. In this study, we removed genes that potentially are of organellar origin based on sequence comparison, phylogenetic analyses and statistical tests on alternative tree topologies. With only a few exceptions (for example, 2-methylthioadenine synthetase and isoleucyl-tRNA synthetase), anciently acquired genes identified in this study are predominantly found in prokaryotes and photosynthetic eukaryotes, suggesting a likely prokaryotic origin of these genes.</p>", "<p>Using PhyloGenie [##REF##15459293##41##], 2,605 trees were generated in the analyses of the <italic>Cyanidioschyzon </italic>genome [##REF##15071595##42##], which were subject to further screening and detailed phylogenetic analyses (see Materials and methods). We previously reported 14 genes anciently acquired from the obligate intracellular bacterial chlamydiae (mostly the environmental <italic>Protochlamydia</italic>) [##REF##17547748##19##] and two other genes, one each from crenarchaeotes and δ-proteobacteria [##REF##16730850##37##]. In this study, an additional 21 anciently acquired genes are reported. Therefore, a total of 37 genes (Table ##TAB##0##1##; Additional data file 1) have been identified as likely acquired from non-organellar sources prior to the split of red algae and green plants (genome sequences of glaucophytes are not currently available) or earlier. For all these newly reported genes, approximately unbiased (AU) tests [##REF##12079646##43##] for alternative tree topologies representing an organellar origin were performed, and an organellar origin of the subject gene was rejected (<italic>p</italic>-value &lt; 0.05) if no scenario of secondary HGT was invoked. For only a few genes, the scenario of an IGT event in plants followed by secondary HGT to other organismal groups cannot be confidently rejected (Additional data file 1); in these cases, we prefer the simpler scenario of straightforward HGT rather than secondary HGT, based on an assumption that the chance is increasingly rare for the same acquired gene being repeatedly transferred to other organisms. Notably among the newly reported genes, six are related to proteobacteria and two to chloroflexi. The multiplicity of HGT from the same donor groups (for example, proteobacteria) may, in part, have resulted from the over-representation of their genomes in current sequence databases or past physical associations between the donors and the ancestral plant.</p>", "<p>The dynamics of ancient HGT may be illustrated with the gene encoding 2-methylthioadenine synthetase (<italic>miaB</italic>), a tRNA modification enzyme involved in translation (Figure ##FIG##0##1##). The evolution of this gene involves gene duplication, transfer, and differential losses. Three versions of this gene exist in bacteria, likely resulting from ancient duplications. Likewise, at least two gene copies (<italic>miaB1</italic>, <italic>miaB2</italic>) are distributed among several major eukaryotic lineages. The eukaryotic <italic>miaB1 </italic>sequences form a monophyletic group with archaeal homologs as expected [##REF##2528146##44##,##REF##2531898##45##]. On the other hand, eukaryotic <italic>miaB2 </italic>sequences and their homologs from bacteroidetes and chlorobi share the highest percent identity (42-45%; using Flavobacteria: <ext-link ext-link-type=\"gen\" xlink:href=\"ZP_01734273\">ZP_01734273</ext-link> and <italic>Arabidopsis</italic>: <ext-link ext-link-type=\"gen\" xlink:href=\"NP_195357\">NP_195357</ext-link> as queries). These sequences cluster together with high support within the otherwise bacterial group. To investigate if <italic>miaB2 </italic>is derived from mitochondria, we performed an AU test on a constraint tree enforcing a monophyly of proteobacterial and <italic>miaB2 </italic>sequences. Results of the AU test suggest that <italic>miaB2 </italic>is not very likely of mitochondrial origin (<italic>p</italic>-value &lt; 0.001). Although the molecular phylogeny of this gene (Figure ##FIG##0##1##) is theoretically compatible with the scenario of a eukaryotic origin through genome fusion, no current data suggest a bacteriodete or chlorobi partner in the putative ancient fusion event. Therefore, it is more likely that eukaryotic <italic>miaB2 </italic>resulted from an ancient HGT from a bacteroidetes or chlorobi-related organism prior to the divergence of most major eukaryotic lineages. In addition to <italic>miaB1 </italic>and <italic>miaB2</italic>, two other <italic>miaB </italic>copies are also found in plants, one of which is related to cyanobacterial homologs, likely resulting from IGT from plastids, whereas the other copy is related to planctomycete homologs with modest support. Therefore, a total of four copies of the 2-methylthioadenine synthetase gene are found in plants, three of which were likely acquired via independent IGT and ancient HGT events.</p>", "<p>An anciently acquired gene might possess novel functions or merely displace existing homologs (either of eukaryotic or organellar origin) in the recipient. Among the 37 anciently acquired genes identified in our analyses, seven are largely absent from cyanobacteria and other eukaryotes and three already have cyanobacteria-related (or plastid-derived) homologs in plants (Table ##TAB##0##1##); these genes likely are not derived from homolog displacement. The gene encoding glycerol-3-phosphate acyltransferase (ATS1 and ATS2) has identifiable homologs only in chlamydiae and plastid-containing eukaryotes [##REF##17547748##19##]. Similarly, the gene encoding monogalactosyldiacylglycerol (MGDG) synthases is predominantly found in chloroflexi and firmicutes, with sporadic occurrence in other bacterial groups (including the cyanobacterium <italic>Gloeobacter</italic>). Phylogenetic analyses suggest that plant MGDG synthases are derived from a single HGT event from bacteria, followed by subsequent spread to other photosynthetic eukaryotes (for example, cryptophytes) as well as gene duplication and functional differentiation in flowering plants (Figure ##FIG##1##2a##).</p>", "<p>For the remaining genes, the possibility of them resulting from displacement of existing homologs, especially those that were previously acquired from plastids, cannot be excluded. Notably, at least four of these genes are essential to lysine biosynthesis in plants. The gene encoding aspartate aminotransferase was acquired from a <italic>Protochlamydia</italic>-related organism whereas donors of two other acquired genes, dihydrodipicolinate reductase (<italic>dapB</italic>) and diaminopimelate decarboxylase (<italic>lysA</italic>), cannot be unambiguously determined (Figure ##FIG##1##2b,c##; Additional data file 1). For another essential gene in lysine biosynthesis, dihydrodipicolinate synthase (<italic>dapA</italic>), sequences from green plants and glaucophytes cluster with γ-proteobacterial homologs, but the cyanobacterial (plastidic) copy is still retained in red algae (Figure ##FIG##1##2d##). The different evolutionary origins of <italic>dapA </italic>among primary photosynthetic eukaryotes may be explained by a HGT event in the ancestral plant, followed by differential gene losses (that is, displacements of a plastid-derived gene copy in green plants and glaucophytes, or displacement of an HGT-derived gene copy in <italic>Cyanidioschyzon</italic>). It is also theoretically possible that green plants and glaucophytes acquired the gene through independent HGT events, though the chance for closely related taxa acquiring the same gene from the same donor is conceivably lower. A similar scenario has also been observed for several other chlamydiae-related genes involved in isoprenoid and type II fatty acid biosyntheses [##REF##17547748##19##,##REF##11078528##46##].</p>" ]
[ "<title>Discussion</title>", "<title>Scope of ancient HGT</title>", "<p>We use the term HGT loosely in this study for any transfer events from non-organellar sources. Although the timing of HGT cannot be accurately calibrated in most cases, it can be inferred based on gene distribution in the recipient lineage. If the acquired gene is found in most taxa of a major lineage, it is likely that the gene was acquired prior to the divergence of the lineage. Given the paucity of sequence data from representatives of many major eukaryotic groups and the lack of consensus on eukaryotic phylogeny [##REF##17194223##47##], identification of ancient HGT often becomes more difficult as phylogenetic depth increases.</p>", "<p>A major issue related to the role of HGT in macroevolution is the scale of ancient HGT. Our analyses identified 37 anciently acquired genes in plants that account for 1.42% (37/2,605) of all generated gene trees (Table ##TAB##0##1##; Additional data file 1). It should be cautioned that HGT identification is affected by many factors, in particular taxonomic sampling, method of analysis, complications arising from IGT, and lineage-specific gains or losses (see [##REF##16730850##37##,##REF##17562012##48##,##REF##17521426##49##] for more discussions). For studies based on phylogenetic approaches, long-branch attraction arising from biased sequence data is also a particular concern [##REF##12167360##50##,##UREF##2##51##]. Additionally, if the α-proteobacterial or the cyanobacterial nature of IGT-derived genes has been erased, due to either frequent HGT among prokaryotes or the loss of phylogenetic signal over time, these genes will not be properly identified and may be mistaken as HGT-derived. It should also be noted that this study is based on the genome analyses of the red alga <italic>Cyanidioschyzon</italic>, which inhabits an extreme environment in acidic hot springs and maintains a streamlined genome [##REF##15459293##41##]. Some anciently acquired genes might have been lost from the <italic>Cyanidioschyzon </italic>genome, but are retained in other red algal species. This could potentially underestimate the HGT frequency in plants. With the rapid accumulation of sequence data, in particular those from other red algae and under-represented eukaryotic groups, a broader taxonomic sampling will be possible and the number of anciently acquired genes identified in the plant lineage will likely change. Therefore, the data presented in this study should only be interpreted as our current understanding of the scale of ancient HGT, rather than an exhaustive list of all anciently acquired genes in plants.</p>", "<p>Despite the difficulties in HGT identification, the multiple introductions of the same gene from various prokaryotic sources (for example, 2-methylthioadenine synthetase; Figure ##FIG##0##1##) suggest that HGT is a continuous and dynamic process. Given that phylogenetic signal tends to become obscure over time and that eukaryote-to-eukaryote transfer, which has been recorded in multiple studies [##REF##16551352##52##,##REF##16979565##53##], is largely not covered in this study, it is possible that the identified genes in our analyses represent only the tip of an iceberg for the overall scope of ancient HGT in eukaryotes. In particular, during early eukaryotic evolution when the ancestral nucleocytoplasmic lineage emerged from prokaryotes (either by a split from archaea or by fusion of archaeal and bacterial partners) and began to diverge into extant groups, these early eukaryotes might bear more biochemical and physiological similarities to their prokaryotic relatives. Because HGT tends to occur among organisms of similar biological and ecological characters [##REF##12777514##54##], the barriers to interdomain gene transfer during early eukaryotic evolution might not be as significant as observed today. Therefore, although our data suggest that HGT indeed existed in early plant evolution, many other anciently acquired genes in plants might have escaped our detection because of the limitations of current phylogenetic approaches. These genes might have shaped the genome composition of the recipient lineages and may also be, in part, responsible for the lack of resolution of relationships among major eukaryotic groups [##REF##16701456##40##,##REF##17194223##47##].</p>", "<title>Functional recruitment and plant adaptation</title>", "<p>A significant insight from prokaryotic genome analyses is the role of HGT in microbial adaptation. By acquiring ready-to-use genes from other sources, HGT avoids a slow process of gene generation and might confer to the recipient organisms immediate abilities to explore new resources and niches [##REF##12446813##55##, ####REF##15851667##56##, ##REF##17288581##57####17288581##57##]. This may be crucial for organisms inhabiting shifting environments, where acquisition of beneficial genes from local communities is necessary for recipient organisms to avoid extinction or to optimize their adaptation. Therefore, lineage continuity and ecological stability can be achieved by increasing the genetic repertoire through recruitment of foreign genes.</p>", "<p>An acquired gene may be novel to the recipient or homologous to an endogenous copy. In the latter case, the newly acquired homolog may be retained (for example, 2-methylthioadenine synthetase; Figure ##FIG##0##1##) and the acquisition of an additional gene copy will provide opportunities for functional differentiation and enriches the genetic repertoire of the recipient. Although all acquired genes affect genome composition and evolution, only those that potentially provide new functions will most likely induce biochemical or phenotypic changes, and consequently adaptation in recipient organisms. Some anciently acquired novel genes identified in our analyses appear to be critical for plant development or adaptation. For example, the gene encoding topoisomerase VI beta subunit (TOP6B) in plants was likely acquired from a crenarchaeote [##REF##16730850##37##]. TOP6B in green plants is required for endoreplication, a process of DNA amplification without cell division and a mechanism to increase cell size in plants. <italic>Top6b </italic>mutants display extreme dwarf phenotypes (about 20% the height of wild types), chloroplast degradation, and early senescence [##REF##12401175##58##, ####REF##12401176##59##, ##REF##12119417##60####12119417##60##].</p>", "<p>Several other novel genes are functionally related to the biogenesis and development of plastids. These include genes acquired from different bacterial groups. For example, MGDG synthases are responsible for the generation of MGDG, a major lipid component of plant photosynthetic tissues. MGDG synthases appear to be encoded by a single-copy gene in red and green algae, but three copies exist in <italic>Arabidopsis </italic>and they are further classified into two types (type A, including MGD1, and type B, including MGD2 and MGD3). In <italic>Arabidopsis</italic>, MGD1 is localized in the inner membrane of chloroplasts and it is responsible for the majority of MGDG biosynthesis. No <italic>mgd1 </italic>null mutants are found in <italic>Arabidopsis</italic>, suggesting that MGD1 is essential for chloroplast development and plant growth [##REF##11553816##61##]. In contrast, MGD2 and MGD3 are highly expressed in non-photosynthetic tissues and likely provide an alternative route for MGDG biosynthesis under phosphate starvation conditions [##REF##11553816##61##, ####REF##15590685##62##, ##REF##8990209##63####8990209##63##]. Therefore, ancient HGT, gene duplication and subsequent functional differentiation provide a mechanism for specialized MGDG production in different tissues and growing conditions. As another example, knocking down the expression of the chlamydiae-related ATS1 and ATS2 in <italic>Arabidopsis </italic>will lead to small, pale-yellow plants, suggesting that the chloroplast development has been seriously impeded [##REF##16774646##64##].</p>", "<title>Homolog displacement</title>", "<p>Not all acquired genes may bring new biochemical functions to the recipient organism. The acquired gene may displace the existing homolog and, if they are functionally equivalent, the impact of gene transfer on the adaptation of the recipient may be limited. Such homolog displacement may be considered selectively neutral [##REF##16138096##65##,##REF##10900003##66##], though their contributions to genome evolution should not be ignored.</p>", "<p>Although the role of HGT in eukaryotic evolution is gaining increasing appreciation, there are very few studies available on the number of acquired genes resulting from homolog displacement without introducing new functions. According to the gene transfer ratchet mechanism proposed by Doolittle [##REF##9724962##67##], homolog displacement might be pervasive in unicellular eukaryotes and bacterial genes, either intracellularly or horizontally derived, may gradually replace all endogenous copies over time. Although our analyses only address anciently acquired genes prior to the split of red algae and green plants, homolog displacement indeed appears to be frequent compared to the acquisition of genes with novel functions. For example, at least three genes encoding organellar aminoacyl-tRNA synthetases (that is, leuRS, tyrRS, and ileRS) were likely acquired from other prokaryotic sources (Table ##TAB##0##1##; Additional data file 1). These aminoacyl-tRNA synthetases are often shared by both mitochondria and plastids [##REF##16251277##68##], suggesting that both plastidic and mitochondrial aminoacyl-tRNA synthetases might have been frequently displaced in plant evolution.</p>", "<p>It should be noted that the displacement of aminoacyl-tRNA synthetases is relatively easy to identify because these genes have low substitution rates and they are universally present in all organisms [##REF##16049196##38##,##REF##10704480##69##, ####REF##10447505##70##, ##REF##15356278##71##, ##REF##10486006##72####10486006##72##]. Many other cases of homolog displacement may not be as easily detected because of complications arising from possible independent gene losses/gains or lack of phylogenetic information retained in the acquired gene [##REF##16730850##37##,##REF##16138096##65##]. In our analyses, homologs for most identified genes can be found in multiple extant cyanobacteria. Given the cyanobacterial origin of plastids, a cyanobacterial copy of these genes might have existed when the plastids were first established; therefore, an IGT event and subsequent displacement of the original plastidic genes by later non-cyanobacterial homologs cannot be excluded, though such a scenario is highly unlikely to have occurred to all these genes. Overall, our data show that many acquired genes may have resulted from homolog displacement without introducing new functions, suggesting that the number of acquired genes does not predict the role of HGT in the adaptation of recipient organisms. It is unclear whether such a gene displacement pattern also exists in non-photosynthetic eukaryotes.</p>", "<title>Concerted gene recruitment and the origin of evolutionary novelties</title>", "<p>Plastids are the key evolutionary novelty that defines photosynthetic eukaryotes. Aside from photosynthesis, some other important biochemical activities, including biosyntheses of fatty acids and isoprenoids, are also carried out in plastids. Intriguingly, over 78% (29/37) of the anciently acquired genes identified in our analyses are either predicted or experimentally determined to be related to the biogenesis and functionality of plastids (Table ##TAB##0##1##); these include genes possessing novel functions and those resulting from homolog displacement. Because of the extremophilic lifestyle of <italic>Cyanidioschyzon </italic>and its streamlined genome, some acquired genes related to non-photosynthetic activities might have been eliminated from the genome. It remains to be investigated whether such a high density of acquired genes that are functionally related to plastids also exists in other photosynthetic eukaryotes, including mixotrophs and those inhabiting broader niches. Nevertheless, given the total number of these plastid-related genes identified in our analyses, it appears that concerted gene recruitment from multiple sources or selective retention of the acquired genes occurred to optimize the functionality of plastids during early plant evolution. The observation that some independently acquired bacterial genes are functionally related to plastids has also been reported in the chlorarachniophyte <italic>Bigelowiella natans</italic>, which contains plastids derived from a secondary endosymbiont [##REF##12777624##21##].</p>", "<p>This phenomenon of concerted gene recruitment for the origin and optimization of key evolutionary novelties of the recipient also exists in other eukaryotic groups. In the protozoan group diplomonads, about half (7/15) of the acquired genes are related to the anaerobic lifestyle of the organisms. These genes were interpreted to have been acquired from various organisms, including other eukaryotes, and might be responsible for the lifestyle transition from aerobes to anaerobes in diplomonads [##REF##12546782##24##]. Another example is related to ciliates that live in the rumen of herbivorous animals. In this case, over 140 genes were transferred from diverse bacterial groups to rumen ciliates, the vast majority of which are related to degradation of carbohydrates derived from plant cell walls [##REF##16472398##30##]. A third example is the evolution of nucleotide biosynthesis in the apicomplexan parasite <italic>Cryptosporidium</italic>, where two independently acquired genes, one each from γ- and ε-proteobacteria, and likely two other plant-like genes facilitated the establishment of salvage nucleotide biosynthetic pathways [##REF##14973196##36##,##REF##11959921##73##], allowing the parasite to obtain nucleotides from their hosts. Therefore, concerted recruitment or selective retention of foreign genes apparently is not a unique phenomenon in the origin and optimization of evolutionary novelties of unicellular eukaryotes. In the case of plants, ancient endosymbioses and HGT events in concert drove the establishment of plastids. In the cases of diplomonads, rumen ciliates and <italic>Cryptosporidium </italic>parasites, multiple independent HGTs from other organisms contributed to the major lifestyle transitions in the recipient organisms. In all these cases, the origin of evolutionary novelties may be viewed as a result of gene sharing with other organisms.</p>", "<p>Although the current data suggest that HGT events are frequent in unicellular eukaryotes [##REF##12777624##21##,##REF##12546782##24##,##REF##15003488##26##,##REF##16472398##30##], how and to what degree they have affected the evolution of the recipients remain largely unclear. An interesting observation from the studies of HGT in eukaryotes is that the vast majority of well-documented cases involve prokaryotes as donors [##REF##15003488##26##,##REF##16472398##30##,##REF##15729342##31##]. Given the ubiquitous distribution of prokaryotes and their greater species and metabolic diversity, the gene pool of prokaryotes conceivably was significantly larger than that of eukaryotes, in particular during early eukaryotic evolution. Therefore, it is interesting to speculate whether early eukaryotes continuously obtained genes from a larger prokaryotic gene pool [##REF##9724962##67##], either individually or occasionally in large chunks, through HGT events in response to the environment, as we have now observed in many prokaryotes and unicellular eukaryotes. Such changes in genetic background and biochemical system would likely induce shifts in ecology, physiology, morphology or other traits of the recipient lineage. Concerted gene recruitment in plants, diplomonads, rumen ciliates, <italic>Cryptosporidium </italic>parasites and possibly many other organisms suggests that independently acquired genes are able to generate and optimize key evolutionary novelties in recipient organisms. Whether such ancient gene recruitment events and the novelties they generated were ultimately responsible for the emergence and adaptive radiation of some major eukaryotic groups warrants further investigations.</p>" ]
[ "<title>Conclusion</title>", "<p>Phylogenetic analyses, sequence comparisons, and statistical tests indicate that at least 1.42% of the genome of the red alga <italic>Cyanidioschyzon </italic>is derived from ancient HGT events prior to the split of red algae and green plants. Although many acquired genes may represent displacement of existing homologs, other genes introduced novel functions essential to the ancestor of red algae and green plants. The vast majority of the anciently acquired genes identified in our analyses are functionally related to plastids, suggesting an important role of concerted gene recruitment in the generation and optimization of major evolutionary novelties in some eukaryotic groups.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Analyses of the red algal <italic>Cyanidioschyzon</italic> genome identified 37 genes that were acquired from non-organellar sources prior to the split of red algae and green plants.</p>", "<title>Background</title>", "<p>Horizontal gene transfer occurs frequently in prokaryotes and unicellular eukaryotes. Anciently acquired genes, if retained among descendants, might significantly affect the long-term evolution of the recipient lineage. However, no systematic studies on the scope of anciently acquired genes and their impact on macroevolution are currently available in eukaryotes.</p>", "<title>Results</title>", "<p>Analyses of the genome of the red alga <italic>Cyanidioschyzon </italic>identified 37 genes that were acquired from non-organellar sources prior to the split of red algae and green plants. Ten of these genes are rarely found in cyanobacteria or have additional plastid-derived homologs in plants. These genes most likely provided new functions, often essential for plant growth and development, to the ancestral plant. Many remaining genes may represent replacements of endogenous homologs with a similar function. Furthermore, over 78% of the anciently acquired genes are related to the biogenesis and functionality of plastids, the defining character of plants.</p>", "<title>Conclusion</title>", "<p>Our data suggest that, although ancient horizontal gene transfer events did occur in eukaryotic evolution, the number of acquired genes does not predict the role of horizontal gene transfer in the adaptation of the recipient organism. Our data also show that multiple independently acquired genes are able to generate and optimize key evolutionary novelties in major eukaryotic groups. In light of these findings, we propose and discuss a general mechanism of horizontal gene transfer in the macroevolution of eukaryotes.</p>" ]
[ "<title>Abbreviations</title>", "<p>ATS, glycerol-3-phosphate acyltransferase; AU, approximately unbiased; EST, expressed sequence tag; HGT, horizontal gene transfer; IGT, intracellular gene transfer; MGDG, monogalactosyldiacylglycerol; TOP6B, topoisomerase VI beta subunit.</p>", "<title>Authors' contributions</title>", "<p>JH conceived the study, performed the data analyses, and drafted the manuscript. JPG participated in data interpretation and manuscript writing. Both authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available. Additional data file ##SUPPL##0##1## contains protein sequence alignments used for phylogenetic analyses, resulting gene trees, tree interpretations, and AU tests on alternative topologies.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank three anonymous reviewers for their insightful comments and suggestions, and Olga Zhaxybayeva for critical reading of the manuscript. This study was supported in part by a Research and Creative Activity Award from the East Carolina University to JH and through the NASA AISRP program to JPG (NNG04GP90G).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Phylogenetic analyses of 2-methylthioadenine synthetase. The numbers above the branch show bootstrap values for maximum likelihood and distance analyses, and posterior probabilities from Bayesian analyses, respectively. Asterisks indicate values lower than 50%. Colors show taxonomic affiliations.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Phylogenetic analyses of anciently acquired genes. Numbers above the branch show bootstrap values from maximum likelihood and distance analyses, and posterior probabilities from Bayesian analyses, respectively. Asterisks indicate values lower than 50%. Colors show taxonomic affiliations. <bold>(a) </bold>MGDG synthase; <bold>(b) </bold>dihydrodipicolinate reductase (<italic>dapB</italic>); <bold>(c) </bold>diaminopimelate decarboxylase (<italic>lysA</italic>); <bold>(d) </bold>dihydrodipicolinate synthase (<italic>dapA</italic>). <italic>DapA</italic>, <italic>dapB </italic>and <italic>lysA </italic>are related to lysine biosynthesis in plants. Please note in (d) that green plant and glaucophyte sequences are of γ-proteobacterial origin whereas the red alga <italic>Cyanidioschyzon </italic>retains the cyanobacterial (plastidic) copy. The <italic>Dehalococcoides </italic>sequence in the cyanobacterial cluster in (d) was likely acquired from cyanobacteria. Another gene (aspartate aminotransferase) related to lysine biosynthesis in plants was likely acquired from chlamydiae [##REF##17547748##19##]. Also see the text and Additional data file 1 for more discussion.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Genes acquired from non-organellar sources prior to the split of red algae and green plants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Gene name</td><td align=\"left\">Putative donor</td><td align=\"left\">Localization</td><td align=\"left\">Putative functions</td></tr></thead><tbody><tr><td align=\"left\"><italic>GCN5-related N-acetyltransferase</italic>*</td><td align=\"left\">β,γ-Proteobacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">Arginine biosynthesis</td></tr><tr><td align=\"left\"><italic>Glycyl-tRNA synthetase</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid/mitochondria</td><td align=\"left\">Translation</td></tr><tr><td align=\"left\"><italic>Dihydrodipicolinate synthase </italic>(<italic>dapA</italic>)</td><td align=\"left\">γ-Proteobacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Lysine biosynthesis</td></tr><tr><td align=\"left\"><italic>ThiC family protein</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Thiamine biosynthesis</td></tr><tr><td align=\"left\"><italic>2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Isoprenoid biosynthesis</td></tr><tr><td align=\"left\"><italic>Polynucleotide phosphorylase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">RNA degradation</td></tr><tr><td align=\"left\"><italic>ATP/ADP translocase</italic><sup>†</sup></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">ATP/ADP transport</td></tr><tr><td align=\"left\"><italic>MGDG synthase</italic><sup>†</sup></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Lipid biosynthesis</td></tr><tr><td align=\"left\"><italic>Glycerol-3-phosphate acyltransferase</italic><sup>†</sup></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Phospholipid biosynthesis</td></tr><tr><td align=\"left\"><italic>Alpha amylase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Carbohydrate metabolism</td></tr><tr><td align=\"left\"><italic>Sodium:hydrogen antiporter</italic><sup>†</sup></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Ion transport</td></tr><tr><td align=\"left\"><italic>3-Dehydroquinate synthase</italic></td><td align=\"left\">β,γ-Proteobacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Amino acid biosynthesis</td></tr><tr><td align=\"left\"><italic>2-Methylthioadenine synthetase</italic></td><td align=\"left\">Bacteroidetes</td><td align=\"left\">Plastid</td><td align=\"left\">tRNA modification</td></tr><tr><td align=\"left\"><italic>Uroporphyrinogen-III synthase</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Porphyrin biosynthesis</td></tr><tr><td align=\"left\"><italic>ACT domain-containing protein</italic><sup>†</sup></td><td align=\"left\">γ-Proteobacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Amino acid binding</td></tr><tr><td align=\"left\"><italic>4-Hydroxy-3-methylbut-2-en-1-yl diphosphate synthase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Isoprenoid biosynthesis</td></tr><tr><td align=\"left\"><italic>Queuine tRNA-ribosyltransferase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">tRNA modification</td></tr><tr><td align=\"left\"><italic>SAM-dependent methyltransferase</italic><sup>†</sup></td><td align=\"left\">Bacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">RNA binding</td></tr><tr><td align=\"left\"><italic>Beta-ketoacyl-ACP synthase </italic>(<italic>fabF</italic>)</td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Fatty acid biosynthesis</td></tr><tr><td align=\"left\"><italic>Semialdehyde dehydrogenase</italic></td><td align=\"left\">α-Proteobacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">Amino acid metabolism</td></tr><tr><td align=\"left\"><italic>Diaminopimelate decarboxylase </italic>(<italic>lysA</italic>)</td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Lysine biosynthesis</td></tr><tr><td align=\"left\"><italic>Dihydrodipicolinate reductase </italic>(<italic>dapB</italic>)</td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid</td><td align=\"left\">Lysine biosynthesis</td></tr><tr><td align=\"left\"><italic>Aspartate aminotransferase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Lysine biosynthesis</td></tr><tr><td align=\"left\"><italic>Leucyl-tRNA synthetase</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid/mitochondria</td><td align=\"left\">Translation</td></tr><tr><td align=\"left\"><italic>Tyrosyl-tRNA synthetase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid/mitochondria</td><td align=\"left\">Translation</td></tr><tr><td align=\"left\"><italic>Ribosomal protein L11 methyltransferase</italic></td><td align=\"left\">β,γ-Proteobacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">Amino acid methylation</td></tr><tr><td align=\"left\"><italic>2-Methylthioadenine synthetase</italic>*</td><td align=\"left\">Bacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">tRNA modification</td></tr><tr><td align=\"left\"><italic>GTP binding protein, typA</italic></td><td align=\"left\">Chloroflexi</td><td align=\"left\">Plastid</td><td align=\"left\">Translation elongation</td></tr><tr><td align=\"left\"><italic>Cu-ATPase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Ion transport</td></tr><tr><td align=\"left\"><italic>4-Diphosphocytidyl-2-C-methyl-D-erythritol kinase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Isoprenoid biosynthesis</td></tr><tr><td align=\"left\"><italic>Enoyl-ACP reductase </italic>(<italic>fabI</italic>)</td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">Fatty acid biosynthesis</td></tr><tr><td align=\"left\"><italic>Histidinol-phosphate transaminase</italic></td><td align=\"left\">Chloroflexi</td><td align=\"left\">Plastid</td><td align=\"left\">Histidine biosynthesis</td></tr><tr><td align=\"left\"><italic>Florfenicol resistance protein</italic>*</td><td align=\"left\">δ-Proteobacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">Fe-S-cluster binding</td></tr><tr><td align=\"left\"><italic>23S rRNA </italic>(<italic>Uracil-5-)-methyltransferase</italic></td><td align=\"left\">Chlamydiae</td><td align=\"left\">Plastid</td><td align=\"left\">RNA modification</td></tr><tr><td align=\"left\"><italic>Topoisomerase 6 subunit B</italic><sup>†</sup></td><td align=\"left\">Crenarchaea</td><td align=\"left\">Cytosol</td><td align=\"left\">Protein binding</td></tr><tr><td align=\"left\"><italic>tRNA methyltransferase</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Plastid/cytosol</td><td align=\"left\">RNA processing</td></tr><tr><td align=\"left\"><italic>Isoleucyl-tRNA synthetase</italic></td><td align=\"left\">Bacteria</td><td align=\"left\">Cytosol</td><td align=\"left\">Translation</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Each sequence name includes a GenBank GI number followed by the species name.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*Genes for which plastid-derived homologs already exist in plants. <sup>†</sup>Genes that likely possessed novel functions and whose homologs are rarely found in cyanobacteria. For all other genes, the possibility of them resulting from displacement of an endogenous homolog cannot be excluded. The putative donors of these genes are determined without invoking secondary HGT events. Alternative explanations for each gene are discussed in the text and Additional data file 1.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"gb-2008-9-7-r109-1\"/>", "<graphic xlink:href=\"gb-2008-9-7-r109-2\"/>" ]
[ "<media xlink:href=\"gb-2008-9-7-r109-S1.pdf\" mimetype=\"application\" mime-subtype=\"pdf\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Arnold"], "given-names": ["ML"], "source": ["Evolution Through Genetic Exchange Press"], "year": ["2006"], "publisher-name": ["New York: Oxford University"]}, {"surname": ["Keeling"], "given-names": ["PJ"], "article-title": ["Diversity and evolutionary history of plastids and their hosts."], "source": ["Am J Botany"], "year": ["2004"], "volume": ["91"], "fpage": ["1481"], "lpage": ["1493"], "pub-id": ["10.3732/ajb.91.10.1481"]}, {"surname": ["Bergsten"], "given-names": ["J"], "article-title": ["A review of long-branch attraction."], "source": ["Cladistics"], "year": ["2005"], "volume": ["21"], "fpage": ["163"], "lpage": ["193"], "pub-id": ["10.1111/j.1096-0031.2005.00059.x"]}, {"italic": ["Cyanidioschyzon merolae "]}, {"surname": ["Felsenstein"], "given-names": ["J"], "source": ["PHYLIP (Phylogeny Inference Package) version 365"], "year": ["2005"], "publisher-name": ["Seattle: Distributed by the author, Department of Genome Sciences, University of Washington"]}, {"article-title": ["TREE-PUZZLE 5.2"]}, {"article-title": ["TREEFINDER version of March 2008"]}]
{ "acronym": [], "definition": [] }
87
CC BY
no
2022-01-12 14:47:26
Genome Biol. 2008 Jul 8; 9(7):R109
oa_package/4b/39/PMC2530860.tar.gz
PMC2530865
18598358
[ "<title>Background</title>", "<p>Viruses are ubiquitous and the most numerous microbes in marine environments. Previous analyses using electron microscopy, epifluorescence microscopy and flow cytometry revealed the existence of 10<sup>6 </sup>to 10<sup>9 </sup>virus-like particles per milliliter of sea water [##REF##2755508##1##, ####REF##15109783##2##, ##REF##16163346##3####16163346##3##]. Infecting marine organisms from oxygen-producing phytoplankton to whales, viruses regulate the population of many sea organisms and are important effectors of global biogeochemical fluxes [##REF##10376593##4##,##UREF##0##5##]. It is also becoming clear that viruses hold a great genetic diversity; comparative genomics [##REF##17652424##6##,##REF##18205946##7##] and virus-targeted metagenomics studies [##REF##15886693##8##, ####REF##17090214##9##, ##REF##12384570##10####12384570##10##] revealed a large amount of viral sequences having no detectable homologs in the databases. As a reservoir of 'new' genes as well as vectors of 'old' genes, viruses may significantly contribute to the evolution of microorganisms in marine ecosystems.</p>", "<p>Despite this progress in characterizing the environmental significance of viruses, a quantitative description of the marine virosphere remains to be done. This includes the determination of the relative abundance of virus families and the assessment of the level of their genetic diversity. In this context, large viruses, whose particle sizes can exceed those of small bacteria [##REF##16469402##11##], are of particular concern. Most of them, such as <italic>Acanthamoeba polyphaga </italic>[##REF##15486256##12##], may be retained on the 0.16-0.2 μpore filters specifically used in virus-targeted metagenomic studies and may not be gathered in the fraction traditionally associated with viral sequences [##REF##16469402##11##]. A recently released marine microbial metagenomic sequence data set, produced by the first phase of the Sorcerer II Global Ocean Sampling (GOS) Expedition [##REF##17355176##13##], provides an opportunity to quantitatively investigate viral diversity in marine environments. The GOS data comprise a large environmental shotgun sequence collection, with 7.7 million sequencing reads assembled into 4.9 billion bp contigs. In the GOS expedition, microbial samples were collected mainly from surface sea waters, and some others were collected from non-marine aquatic environments. Most DNA samples were extracted from the 0.1-0.8 μsized fraction, which is dominated by bacteria. Williamson <italic>et al</italic>. [##REF##18213365##14##] recently reported that at least 3% of the predicted proteins contained within the GOS data are of viral origin. Notably, a number of sequences most similar to the genome of the giant mimivirus have been found in the Sargasso Sea metagenomic data set [##REF##16105173##15##], produced by a pilot study of the GOS expedition [##REF##15001713##16##], as well as in the new GOS metagenomic data set [##REF##18215256##17##].</p>", "<p>Determining taxonomic distribution, referred to as 'binning', is the first step to analyze microbial populations in metagenomic sequences [##REF##17355177##18##]. One simple binning approach uses database search programs such as BLAST to find the best scoring sequence of known species. A majority rule can be used to assign a taxonomic group to a metagenomic sequence [##REF##18213365##14##,##REF##17355171##19##]. Similar to the best hit criterion used to define orthologous genes in complete genomes [##REF##8816789##20##,##REF##9395406##21##], two-way BLAST searches were used to detect 'mimivirus-like' sequences in metagenomic data [##REF##16105173##15##,##REF##18215256##17##]. Such a post-processing of homology search results can improve the accuracy of taxonomic assignment. However, the use of homology search programs has serious drawbacks [##REF##11443357##22##]. For instance, BLAST scores are highly sensitive to alignment sizes and to insertions/deletions. Further, it is difficult to infer evolutionary distances among high scoring hits only from the BLAST scores.</p>", "<p>Phylogenetic analysis remains the most powerful way to determine taxonomic distribution of metagenomic sequences. Short and Suttle [##REF##11872479##23##] used phylogenetic methods to classify PCR-amplified gene sequences and suggested the existence of previously unknown algal viruses in coastal waters. Similar phylogenetic studies were performed to assess the diversity of T4-type phages [##REF##16116082##24##] or RNA viruses [##REF##12944967##25##,##REF##17644642##26##] in marine environments. In these studies, different markers, such as the major capsid genes or RNA-dependent RNA polymerase gene sequences, were amplified by PCR or RT-PCR and analyzed by phylogenetic methods. To examine taxonomic distribution of large DNA viruses in a metagenomic sequence collection, B-family DNA polymerase (PolB) is a useful marker [##REF##11872479##23##,##REF##8451181##27##,##REF##12029358##28##]. PolB sequences are conserved in all known members of nucleocytoplasmic large DNA viruses (NCLDVs) [##REF##11689653##29##], which include 'Mimiviridae' [##REF##16514500##30##], Phycodnaviridae, Iridoviridae, Asfarviridae, and Poxviridae. PolB genes are also found in other eukaryotic viruses, such as herpesviruses, baculoviruses, ascoviruses and nimaviruses, in some bacteriophages (for example, T4-phage, cyanophage P-SSM2), and in some archaeal viruses (for example, Halovirus HF1). Eukaryotes have four PolB paralogs (catalytic subunits of α, δ, ε and ζ DNA polymerases). PolB genes are found in all of the main archaeal lineages (Nanoarchaeota, Crenarchaeota and Euryarchaeota). The presence of PolB homologs in bacteria (the prototype being <italic>Escherichia coli </italic>DNA polymerase II) is limited; PolBs are found in Proteobacteria, Acidobacteria, Firmicutes, Chlorobi and Bacteroidetes. PolB genes are suitable for the classification of large DNA viruses [##REF##8702280##31##,##REF##17359269##32##] thanks to their strong sequence conservation and an apparently low frequency of recent horizontal transfer [##REF##12029358##28##,##REF##10888648##33##].</p>", "<p>When applying phylogenetic methods to environmental shotgun sequences, the treatment of short sequences requires special attention. These sequences show large variation in size and possibly correspond to different parts of a selected marker gene. Piling up multiple short sequences on representative markers from known organisms does not provide an appropriate alignment (whatever software is used) with enough signals for the subsequent phylogenetic analysis. In this study we developed a new phylogeny-based method. The method called 'phylogenetic mapping' analyzes individual metagenomic sequences one by one and determines their phylogenetic positions using a reference multiple sequence alignment (MSA) and a reference tree. As an attempt to investigate the presence, the taxonomic richness and the relative abundance of different large DNA viruses in marine environments, we analyzed the GOS data set using PolB sequences as our reference. Our study does not address the abundances of small DNA viruses or RNA viruses [##REF##18213365##14##,##REF##16794078##34##].</p>" ]
[ "<title>Materials and methods</title>", "<title>Extraction of PolB fragments from the GOS metagenomic data set</title>", "<p>We retrieved the combined assemblies of the GOS metagenomic data through the CAMERA website [##REF##17355175##60##]. The data set was composed of 3,081,849 scaffolds. We extracted all the stop-to-stop ORFs (≥ 60 amino acid residues) from the assembled sequences using EMBOSS/GETORF [##REF##10827456##61##]. We obtained a set of 21,406,171 ORFs. Those ORFs were translated into corresponding amino acid sequences. To identify PolB-like fragments in this set, we used the Pfam profile (PF00136, both long and fragment search versions: 'ls' and 'fs') [##REF##16381856##62##] and the HMMER software as a search engine [##REF##9918945##63##] using an E-value threshold of 0.001. We then removed redundancy (due to the double use of 'ls' and 'fs' versions of the Pfam profile) and false positive detections (having the best hit against non-PolB sequences in the NRDB) by BLASTP [##REF##9254694##64##] using an E-value threshold of 10<sup>-5</sup>). We extracted only the parts of metagenomic amino acid sequences that were aligned on the Pfam profile representing the polymerase domains of PolB. Thus, additional domains (such as endonuclease domains) were not included in our PolB sequence set. No contig was found to contain more than one PolB homolog. As a result of these processes, we obtained 1,947 distinct PolB-like sequences (from 23-562 amino acid residues); these sequences are referred to as PolB fragments in this study. We parsed the GOS PolB fragments to find intein insertions by the TIGRFAM profiles TIGR01445 (intein amino terminus) and TIGR01443 (intein carboxyl terminus) [##REF##12520025##65##], but none of these fragments had a detectable intein domain. In this study, we did not include the protein priming subfamily of the B family DNA polymerase [##REF##12029358##28##], which is represented by the Pfam profile PF03175. The members of this subfamily are found in eukaryotic linear plasmids of mitochondrion, phages and adenoviruses.</p>", "<title>PolB homologs from the NRDB</title>", "<p>We retrieved PolB homologs from the NRDB, RefSeq [##REF##17130148##66##] and KEGG [##REF##16381885##67##] databases using BLAST using multiple query sequences (E-value &lt; 10<sup>-5</sup>) and the PolB Pfam profile (E-value &lt; 0.001). We removed species redundancy using BLASTCLUST [##REF##9254694##64##] while keeping the widest possible taxonomic/paralog coverage (but with a non-exhaustive sampling for closely related species). This resulted in a set of 120 PolB homologs (Additional data file 1). We removed intein sequences in the PolBs of mimivirus [##REF##15707490##68##], HaV [##REF##16000767##69##] and CeV01 (GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"ABU23716\">ABU23716</ext-link>).</p>", "<title>Construction of the reference alignment and the reference tree</title>", "<p>We next constructed an alignment of PolB homologs from known organisms (that is, the reference MSA) and generated a phylogenetic tree of PolB homologs (that is, the reference tree). There is a tradeoff between the number of distant homologs included in the reference MSA (contributing to a wider taxonomic/paralog coverage) and the quality of the resulting MSA and tree (contributing to a reliable classification of metagenomic sequences). Among the 120 PolB homologs, we identified 19 highly divergent sequences that decrease the quality of the resulting PolB alignment and tree but that show no close homologs in the GOS PolB fragments. This process was performed through multiple trials of building alignments by T-Coffee [##REF##10964570##70##] and phylogenetic trees by PhyML for the PolB homologs. These 19 sequences correspond to six groups of PolB homologs: eukaryotic DNA polymerase ε, a <italic>Trichomonas vaginalis </italic>DNA polymerase α-like paralog, PolBs of unclassified herpesviruses (Ostreid, Ictalurid and Ranid herpesviruses), <italic>Heliothis zea </italic>virus, a nimavirus (shrimp white spot syndrome virus), and PolBs of a group of bacteria related to <italic>Prosthecochloris vibrioformis </italic>and <italic>Chlorobium tepidum</italic>. There was no PolB-like fragment in the GOS data exhibiting a best BLAST hit against these groups of PolB homologs. Therefore, the removal of the six groups of PolB homologs from our reference data set does not affect the interpretation of the results described in this manuscript. After discarding these 19 sequences, the final PolB set was composed of 101 sequences. We aligned the 101 PolB sequences using M-Coffee accessible from a public server [##REF##17526519##71##] with the use of default options. M-Coffee is a meta-method for assembling multiple sequence alignments [##REF##16556910##72##]. We extracted only the polymerase domain sequences from the alignment (that is, the reference MSA; Additional data file 2). The reference alignment showed four conserved regions (numbered from I to IV) previously described as the signatures of the PolB polymerase domains [##REF##10888648##33##]. We next built a maximum likelihood tree based on the reference MSA (that is, the reference tree) using PhyML after removing gap-containing sites [##REF##14530136##73##] with JTT substitution model and a gamma low (four rate categories). Bootstrap values were obtained after 100 bootstrap replicates. We used the phylogeny.fr platform [##REF##18424797##74##] to generated scalable vector graphics from newick formatted trees.</p>", "<title>Phylogenetic mapping</title>", "<p>Each of the metagenomic PolB fragments was taxonomically assigned by aligning it against the reference MSA and by examining its phylogenetic position in the reference tree. In order to reduce the computation time and to avoid unnecessary complications in summarizing results within too dense a tree, we reduced the size of the reference MSA and the reference tree. Specifically, we selected 51 PolBs from the 101 PolBs contained in the initial set. We kept the selected 51 PolBs in the reduced set, and deleted the remaining PolBs. The selection of the 51 representatives was carried out in the following way. First, we selected all the PolBs (that is, ASFV, EhV86, HaV, Phage RM378) that were not grouped with other PolBs with a statistical support (≥ 70% bootstrap value) in the initial reference tree (Figure ##FIG##0##1##). Second, we selected two or three representatives from each of the statistically supported monophyletic groups (≥ 70% bootstrap value). The choice of representatives from a monophyletic group was arbitrary. We simply selected two or three relatively distant sequences from the members of the monophyletic group. To obtain a reduced reference MSA composed of the selected 51 sequences, we extracted a part (that is, lines) of the initial reference MSA (containing gaps). The initial reference tree (composed of 199 branches including internal ones) was also reduced by pruning branches leading to the non-selected leaves using BAOBAB [##REF##12075029##75##].</p>", "<p>The reduced reference tree has 99 branches (including internal branches); the constraint on the topology of the reduced reference tree thus defined 99 possible branching positions for each PolB-like fragment extracted from the metagenomic data set. The reduced reference MSA and the reduced reference tree are the basis for our phylogenetic mapping in this study. Each of the PolB fragments from the GOS data set was aligned on the reduced reference MSA (containing gaps) using T-Coffee [##REF##10964570##70##] with a profile alignment option. For the T-Coffee profile alignment, we used the option '-profile comparison = full10'. If a GOS PolB fragment generates an alignment with less than 50 sites after removing gap-containing sites, we discarded the GOS PolB fragment from our analysis. Based on the resulting alignment (51 reference sequences and one GOS PolB fragment), the likelihoods of all 99 possible branching positions (thus 99 different topologies) for the PolB fragment were computed by ProtML [##UREF##1##35##]. A statistical significance for the best tree among the 99 topologies was assessed by the RELL method [##UREF##2##36##,##REF##14571377##37##]. We considered the branching position of a PolB fragment to be supported when the RELL bootstrap value for the best topology was ≥ 75%.</p>", "<title>Read coverage</title>", "<p>Read coverage for a contig was defined by dividing the cumulated size of reads contributing to the contig by the size of the contig.</p>", "<title>Relative abundance of PolBs</title>", "<p>For the analysis of the relative abundance of PolB sequences, we used the same approach used by Williamson <italic>et al</italic>. [##REF##18213365##14##]. Briefly, we first estimated the average number of reads overlapping with a part of a contig where a PolB domain was encoded, by taking into account the length of the PolB domain (as defined by the Pfam hit) and the length of the contig. The abundance of the PolB-sequences for each viral group for a given sample site was then quantified by the total number of reads associated with the relevant set of PolB-sequences (that is, the sum of the estimated read numbers). For a given site, the viral PolB proportion was computed by dividing the total number of viral PolB reads (for all viral groups) by the total number of reads obtained from the site.</p>" ]
[ "<title>Results</title>", "<title>Phylogenetic mapping</title>", "<p>We searched the GOS data set for PolB-like sequences using the Pfam hidden Markov profile (PF00136). This resulted in a set of 1,947 sequences (from 23-562 amino acid residues). These sequences are referred to as 'PolB fragments' in this study. We next built a reference MSA of PolB homologs from known organisms (Additional data file 1). The reference MSA (Additional data file 2) corresponds to the polymerase domains of PolB homologs and contains 101 sequences, which were selected to achieve the widest possible taxonomic/paralog coverage (but with a non-exhaustive sampling for closely related species) for the analysis of the GOS metagenomic data. The reference MSA was used to generate a maximum likelihood tree (that is, the reference tree; Figure ##FIG##0##1##). Although the phylogenetic reconstruction did not provide statistical support for most of the basal branches, many peripheral groupings (supported by bootstrap values ≥ 70%) were coherent with the current taxonomy of viruses and cellular organisms. In this tree, we identified eight viral groups: poxviruses; chloroviruses; phaeoviruses; mimivirus and related algal viruses (<italic>Pyramimonas orientalis </italic>virus PoV01, <italic>Chrysochromulina ericina </italic>virus CeV01 and <italic>Phaeocystis pouchetii</italic> virus PpV01); iridoviruses grouped with ascoviruses; herpesviruses; baculoviruses; and one phage group. The PolB homologs from African swine fever virus (ASFV, Asfarviridae), <italic>Emiliania huxleyi </italic>virus 86 (EhV-86, Phycodnaviridae), <italic>Heterosigma akashiwo</italic> virus 1 (HaV, Phycodnaviridae) and the phage RM378 did not show well supported clustering with other PolB sequences. We also identified eleven groups in the reference tree for cellular PolB homologs: seven archaeal groups, one bacterial group and three eukaryotic groups (α, δ and ζ subtypes). Each of the GOS PolB fragments was then examined for its phylogenetic position using the reference MSA and the reference tree. To reduce the computation time and to streamline tprocess of summarizing results, we reduced the size of the reference MSA. Specifically, we selected 51 representatives from the 101 reference sequences and removed the remaining sequences. The reference tree was also reduced so that the resulting tree contains only the selected 51 representatives, while we conserved the original topology of the full reference tree shown in Figure ##FIG##0##1##. The reduced reference tree has 99 branches (including internal branches). A constraint on this topology defines 99 possible branching positions for each of the GOS PolB fragments. We aligned, one by one, each of the PolB fragments on the reduced reference MSA using the T-Coffee profile method. Based on the resulting profile MSA containing 52 sequences, the likelihoods for all 99 possible branching positions (thus 99 different topologies) were computed by ProtML [##UREF##1##35##]. A statistical significance for the best tree among the 99 topologies was assessed by the RELL (resampling of estimated log likelihoods) bootstrap method [##UREF##2##36##,##REF##14571377##37##]. We considered the branching position of a PolB fragment to be supported when the RELL bootstrap value for the best topology was ≥ 75%.</p>", "<title>Diversity of large DNA viruses in the GOS data set</title>", "<p>Our phylogenetic mapping method could assign the best branching position for 1,423 PolB fragments, of which 1,224 (86%) were mapped on viral branches. The best branching position was statistically supported by the RELL method for 869 PolB fragments, of which 811 (93%) were mapped on viral branches. Figure ##FIG##1##2## and Additional data file 3 show the taxonomic distribution of the GOS PolB fragments. The largest fraction of the PolB fragments was mapped on the phage group. Of 866 cases of mapping within the phage group, 633 were supported. This appears consistent with the current estimate of the large number of phage-like particles and their genetic richness in marine environments [##REF##16163346##3##]. The second largest number of supported mappings was found to fall into large eukaryotic viruses commonly found in aquatic environments. Among them, the 'Mimiviridae group' (mimivirus, PoV01 and CeV01 [##REF##18215256##17##]) represented the largest fraction, with 115 supported cases. The chlorovirus group gathered 51 supported cases of mapping. The iridovirus/ascovirus group and the branch leading to HaV showed five supported mappings each. In contrast, no PolB fragment was mapped for the groups for baculoviruses or herpesviruses commonly found in terrestrial animals. Interestingly, we found two PolB fragments mapped with good support on the ASFV branch (JCVI SCAF 1101668126451, JCVI SCAF 1101668152950). When these two PolB fragments were compared to the NCBI non-redundant amino acid sequence database (NRDB) using BLASTP, they were most similar to the ASFV PolB sequence. ASFV is pathogenic to domestic pigs and is currently the sole representative of the Asfarviridae family [##REF##18198370##38##]. Concerning cellular organisms, eukaryotic homologs gathered few mappings, as expected from the sample filtration threshold used in the GOS metagenomic study. Two archaeal groups - the group III containing crenarchaeotes (for example, <italic>Pyrobaculum aerophilum, Cenarchaeum symbiosum</italic>) and the group IV containing euryarchaeotes (for example, <italic>Thermoplasma acidophilum</italic>, an uncultured euryarchaeote Alv-FOS1) - had 23 and 17 supported cases of mapping, respectively. The bacterial group presented ten supported mappings.</p>", "<title>Validation of the mapping results using long PolB fragments</title>", "<p>We examined the phylogenetic mapping result and the sequence diversity of the PolB fragments classified in large eukaryotic virus groups (that is, NCLDVs). From those mapped on NCLDV branches, we selected long PolB fragments that generated a profile MSA showing at least 150 non-gapped sites. We computed a single alignment of these long PolB fragments together with the reference PolB sequences from large eukaryotic virus groups. A maximum likelihood tree (Figure ##FIG##2##3##) based on the alignment was perfectly consistent with our one-by-one mapping result (Figure ##FIG##1##2##) in terms of taxonomic assignment. The Mimiviridae group contained 16 PolB fragments showing substantial sequence variations. Twelve of them were significantly closer (bootstrap 100%) to CeV01 or PpV01 (both viruses of haptophytes) than to mimivirus or PoV01 (a green algal virus). Three of the rest were grouped with either mimivirus (bootstrap 89%) or PoV01 (bootstrap 96%). The last one (JCVI SCAF 1096627348452) was placed at the basal position of the Mimiviridae group. Although this basal positioning was not statistically supported, it was consistent with our one-by-one phylogenetic mapping result. The mimivirus PolB shared 47% identical amino acid residues with its closest homolog (JCVI SCAF 1101668170038). A large and diverse group containing 27 PolB fragments (bootstrap 92%) was also found beside the chlorella virus group (<italic>Paramecium bursaria </italic>chlorella viruses 1, K2 and NY2A). The DNA polymerase gene from the recently released <italic>Ostreococcus </italic>virus OtV5 genome (GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"EU304328\">EU304328</ext-link>) [##REF##18509524##39##] was found grouped together with these PolB fragments. The grouping of a PolB fragment with ASFV PolB was also confirmed (bootstrap 100%).</p>", "<title>Viral PolBs are more diverse than bacterial PolBs</title>", "<p>We investigated the abundance of viral PolB genes relative to bacterial PolB genes in the GOS data set. Here, we used read coverage as a proxy to measure the abundance of the cognate DNA molecules in the samples. We computed the read coverage of each contig harboring a PolB fragment mapped on the reference tree with significant support, and then obtained the median of the read coverage values for each set of contigs mapped on the same branch (Additional data file 3). PolB sequences mapped on viral branches exhibited low median coverage values ranging from 1.31 for the ASFV branch to 2.00 for a phage branch. The median coverage value for the contigs mapped on the mimivirus branch (12 contigs) was 1.32. The viral contig with the largest read coverage (6.68) was the one mapped on the cyanophage P-SSM4 branch. In contrast, a higher median coverage value (8.40) was found for bacterial contigs mapped on the branch leading to <italic>Shewanella frigidimarina</italic>. One of the bacterial contigs exhibited a read coverage of 29.17. Viral branches were thus characterized by a large number of mapped contigs exhibiting a low coverage. This is consistent with numerous and very diverse viral populations [##REF##12705861##40##]. On the other hand, the bacterial branches exhibited a lower number of mapped contigs with a larger read coverage. This is consistent with numerous but less diverse populations of bacterial species, although our results concern only bacteria having PolB homologs.</p>", "<title>Geographic distributions of viral PolBs</title>", "<p>GOS metadata provide physicochemical and biological parameters associated with each sampling site, such as water temperature, salinity, chlorophyll <italic>a </italic>concentration, and sample's water depth. These data offer additional dimensions to analyze the viral PolB fragments identified by our phylogenetic mapping. Here we compared the relative abundance of the predicted viral PolB fragments and the associated metadata across different GOS sampling sites (Figure ##FIG##3##4a##).</p>", "<p>Predicted viral PolB fragments were detected in all of 44 GOS sampling sites (Figure ##FIG##3##4b##). The relative abundance of different virus groups showed substantial variation across these samples. This is consistent with the diverse ecosystems covered by the GOS expedition.</p>", "<p>PolB fragments classified in the phage group were found in 42 (95%) of the 44 sample sites; the two samples without phage PolB fragments were GS08 (Newport Harbor, Richmond, USA) and GS32 (mangrove). In most samples (32 sites), putative phage PolBs exhibited a higher abundance relative to putative eukaryotic viral PolBs. On the other hand, the relative abundance of eukaryotic viral PolBs was higher than that of phage PolBs in 12 sampling sites. We found a significant positive correlation between the relative abundance of phage PolBs and water temperature (<italic>p </italic>= 0.001; Fischer's exact test with no correction for multiple testing): phage-type PolBs showed a higher relative abundance than eukaryotic viral PolBs in tropical waters (T ≥ 20°C), while a reversed tendency was observed in temperate water (T &lt; 20°C). Interestingly, among eukaryotic viral PolBs, putative Mimiviridae PolBs showed the most widespread distribution, being detected in 38 (86%) of the total sites. One of these sampling sites (mangrove located on Isabella, Ecuador) exhibits only viral PolBs classified in the Mimiviridae group. This is the sole mangrove site of all the GOS sampling locations. Mimiviridae PolBs were also relatively abundant in two of the three samples from a hydrostation located in the Sargasso Sea. Three samples correspond to different size fractions: 3.0-20.0 μm for GS01a; 0.8-3.0 μm for GS01b; and 0.1-0.8 μm for GS01c. Putative Mimiviridae PolBs were identified in the GS01a and GS01c samples. The GS01a sample, which was targeted to small eukaryotes, might have contained host species infected by putative viruses of the Mimiviridae group. PolB fragments grouped with chloroviruses were also widely distributed. They were detected in 16 (36%) samples. The relative abundance of this putative eukaryotic virus group showed a significant positive correlation with chlorophyll <italic>a </italic>concentration, a measure of primary productivity in oceanic regions (<italic>p </italic>= 0.00002; Fisher's exact test with no correction for multiple testing).</p>", "<p>The sample exhibiting the broadest taxonomic richness of viral PolBs was from Chesapeake Bay (GS12, MD, USA), which is an estuary. The GOS metagenomic sequences from this site exhibited PolB fragments classified in phages, chloroviruses, Asfarviridae and Mimiviridae. Notably, this site is a highly eutrophic estuary with an extremely high chlorophyll <italic>a </italic>concentration. PolBs classified in Asfarviridae were also detected in another estuary site (GS11, Delaware Bay, NY, USA), which is close to Chesapeake Bay.</p>", "<title>Prediction of putative 'new' viral genes</title>", "<p>Contigs harboring putative viral PolB homologs were relatively small, ranging from 0.4-12.5 kb (average 1,874 bp) for contigs mapped on eukaryotic viral branches and 0.5-8.8 kb (average 1,885 bp) for phages. To examine the presence of additional open reading frames (ORFs) in these contigs, these putative viral contigs were searched against NRDB using BLASTX. We detected several genes or gene fragments that are usually specific to viruses. For example, several contigs (for example, JCVI SCAF 1096626858151, JCVI SCAF 1096626920680) containing PolB fragments assigned to the chlorovirus group also harbor an ORF most similar to the OtV5 putative major capsid gene. Several putative phage-type contigs (for example, JCVI SCAF 1096628232224, JCVI SCAF 1096626847406) mapped on the cyanophage P-SSM4 branch exhibited ORFs similar to <italic>regA </italic>(translation repressor of early genes) or <italic>uvsX </italic>(<italic>recA</italic>-like recombination and DNA repair protein genes). The presence of such 'virus-specific' genes next to the 'virus-like' PolB homologs corroborates the validity of our phylogenetic mapping approach.</p>", "<p>During this search, we found an ORF similar to RimK, a protein involved in post-translational modification of the ribosomal protein S6, in a contig (JCVI SCAF 1096626956347) having a PolB fragment mapped on the cyanophage P-SSM4 branch. In this contig, the <italic>rimK </italic>homolog was flanked by a phage-specific <italic>regA </italic>homolog (Figure ##FIG##4##5##). <italic>rimK </italic>homologs are found in bacteria, archaea and eukaryotes [##REF##9416615##41##]. To our knowledge, no <italic>rimK </italic>homolog has been found in a viral genome. Using this putative viral RimK homolog as a query of TBLASTN, we screened the entire GOS data set. We identified more than a hundred contigs harboring RimK homologs with higher similarities (BLAST score from 137 up to 732; E-value &lt; 10<sup>-30</sup>) than those exhibited by cellular homologs (BLAST score &lt; 132; E-value &gt; 10<sup>-29</sup>) in NRDB. The sequences of those putative phage RimK homologs were readily aligned with <italic>Escherichia coli </italic>RimK along its entire length (not shown), and showed amino acid residues highly conserved in the ATP-graps domain of bacterial RimK [##REF##9416615##41##]. Several GOS RimK sequences showed an additional domain of unknown function (DUF785, PF05618, E-value &lt; 0.001) at the carboxy-terminal side of the ATP-graps domain. A DUF785 domain is present also in RimK of some bacteria (at the amino-terminal side of the ATP-graps domain) such as <italic>Synechococcus </italic>sp. (Q7U6F4) and euryarchaeotes (at the carboxy-terminal side of the ATP-graps domain) such as <italic>Halobacteria </italic>(for example, Q5V351). Furthermore, many of the GOS contigs encoding RimK homologs exhibited additional ORFs usually specific to phages such as T4-like clamp loader subunit genes, contractile tail sheath protein genes or T4-like DNA packaging large subunit terminase genes (Figure ##FIG##4##5##). Our phylogenetic analysis indicates that those RimK homologs are closely related to each other and distantly related to bacterial RimK (Figure ##FIG##5##6##). These results suggest the existence of phages carrying <italic>rimK </italic>homologs in marine environments.</p>" ]
[ "<title>Discussion</title>", "<p>Until recently, the marine virosphere was <italic>terra incognita</italic>. The increasing amount of environmental sequence data now provides unprecedented opportunities to explore the viral world. Previous studies characterized the abundance and the genetic richness of marine viruses using environmental sequencing approaches [##REF##15886693##8##,##REF##18213365##14##,##REF##17355171##19##,##REF##11872479##23##,##REF##16116082##24##]. However, the extent of species diversity within individual viral groups is still unclear. This is especially the case for large DNA viruses. Large DNA viruses were often overlooked or were not the specific focus of marine metagenomic projects. In this study, we used a new phylogenetic mapping approach to identify viral PolB sequences contained in the GOS metagenomic data set and assessed their taxonomic distribution. This study does not concern small viruses, including RNA viruses. Beyond BLAST searches, our phylogenetic mapping approach provided a somewhat unexpected picture of the taxonomic distribution of viral sequences in the metagenomic data.</p>", "<p>In the GOS data we identified 811 PolB-like sequences closely related to known viral PolB sequences. This is consistent with the existence of a wide taxonomic spectrum of PolB-containing DNA viruses in marine environments [##REF##11872479##23##]. As previously noted [##REF##18213365##14##], phages are the main contributors to this diversity; our method predicted that 78% (633/811) of the viral PolB fragments were of phage origin. This proportion is likely an underestimate of the actual taxonomic diversity of double-stranded DNA phages in the GOS sampling areas as only a subset of DNA phages carry PolB genes.</p>", "<p>Interestingly, the mimivirus group was the second largest in terms of the number of assigned PolB fragments (that is, 115 cases of mapping). Previous studies revealed the existence of mimivirus-like sequences in the GOS metagenomic data set [##REF##16105173##15##,##REF##18215256##17##]. Our data now suggest that the species/strain richness contained in the GOS metagenomic samples for this viral group may be comparable to those exhibited by other groups of eukaryotic large DNA viruses, including most of the previously characterized phycodnaviruses. The amoeba infecting mimivirus has the largest known viral genome (1.2 Mb). Its particle size is approximately 0.7 m in diameter including its filamentous layer [##REF##16469402##11##]. In addition, the mimivirus group contains two haptophyte viruses (CeV01 (510 kb), and PpV01 (485-kb)) and a virus infecting a green algal species (PoV01 (560 kb)) [##REF##18215256##17##,##REF##18359826##42##]. Their genomes are also larger than any other eukaryotic viruses sequenced so far [##REF##11883191##43##,##REF##15994818##44##]. The particle sizes of these three algal viruses are 0.16-0.22 μm, being compatible with the filter sizes used in the GOS sampling. Notably, their particle sizes are comparable to those of classic phycodnaviruses with a mean diameter of 0.16 ± 0.06 μm [##REF##16877063##45##,##REF##16099989##46##]. By counting overlapping PolB fragments mapped on the mimivirus group, we estimated that at least 85 distinct species/strains of Mimiviridae are present in the GOS metagenomic samples. Within the mimivirus group, two haptophyte viruses (PpV1 and CeV01) were clustered together with a high bootstrap value (Figure ##FIG##2##3##). Most (84%; 97/115) of the Mimiviridae-like PolB fragments were mapped within this subgroup. Haptophyte species may thus be the major hosts of putative viruses corresponding to the PolB subgroup. Overall, these data suggest that large DNA viruses composing the Mimiviridae group represent one of the main components of marine eukaryotic large DNA viruses.</p>", "<p>The branch leading to the chloroviruses presented 51 cases of GOS PolB fragment mapping. These GOS sequences were closely related to the recently determined PolB sequence from OtV5. OtV5 infects <italic>Ostreococcus tauri</italic>, a small green algal species of prasinophyte (approximately 1 μm in diameter) found in diverse geographic locations [##REF##17460045##47##]. Short and Suttle identified a group of viral sequences closely related to prasinoviruses (<italic>Micromonas pusilla </italic>viruses) through sequencing PCR products targeted to algal virus PolBs [##REF##11872479##23##]. We found that some of the sequences studied in their work were also highly similar to the OtV5 PolB sequence. For instance, the sequence named BSA99-5 (GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"AF405581\">AF405581</ext-link>) in their study exhibited 93% amino acid sequence identity to the OtV5 PolB sequence. This suggests that the major hosts for this putative viral group may be prasinophytes.</p>", "<p>Surprisingly, we identified two PolB fragments most closely related to the ASFV PolB. ASFV is currently the sole isolated member of the Asfarviridae family. The known natural hosts of ASFV are terrestrial animals, including warthogs, bush pigs and soft ticks [##REF##18198370##38##]. ASFV causes a persistent but asymptomatic infection in these hosts. In domestic pigs, ASFV causes an acute hemorrhagic infection with mortality rates up to 100% depending on different viral isolates. We now predict the existence of additional Asfarviridae in marine environments, although the contamination from terrestrial origin cannot be excluded. In a recent metagenomic study, Marhaver <italic>et al</italic>. [##REF##18479440##48##] analyzed the viral communities associated with healthy and bleaching corals. They showed that alphaherpesvirus-like and gammaherpesvirus-like sequences accounted for 4-8% of the analyzed environmental sequences. GOS sampling sites include a coral reef atoll site (GS51). No herpesvirus-type PolB fragment was detected in our study.</p>", "<p>Through the analysis of geographic distribution, we found that putative viral PolB fragments were identified in all of the 44 GOS samples. This suggests a wide presence of PolB-encoding viruses in diverse marine environments. Interestingly, phage PolB sequences were more abundant than eukaryotic viral PolB sequences in samples from tropical areas; conversely, many samples from temperate areas were enriched in eukaryotic viral PolBs. Further, most of the samples showing a great taxonomic richness of viral PolB sequences corresponded to those from temperate areas. This observation is consistent with the current understanding of the distribution of eukaryotic and bacterial phytoplankton in oceans. Gibb <italic>et al</italic>. [##UREF##3##49##] surveyed the spatial distributions of phytoplankton pigments across the Atlantic Ocean over 100° of latitude (from 50°N to 50°S). They showed a major transition in pigment characteristics from temperate to tropical/sub-tropical waters; temperate waters were characterized by larger phyto-biomass enriched in eukaryotic phytoplankton, while tropical/sub-tropical waters exhibited smaller phyto-biomass enriched in prokaryotic phytoplankton such as prochlorophytes [##UREF##3##49##].</p>", "<p>The relatively high abundance of eukaryotic viral PolBs in samples from temperate areas (showing high chlorophyll <italic>a </italic>concentrations) was mainly due to the abundance of the GOS PolB sequences grouped with chlorovirus PolBs. This again suggests that the hosts of these putative viruses are green algae (such as prasinophytes). In contrast, Mimiviridae-like PolB fragments showed a wider geographical distribution. They were identified in sequences from most of the GOS sampling sites, from northeast Atlantic Ocean to southwest Pacific Ocean. These sites correspond to a variety of habitat types, such as coastal seas, open oceans, fresh water sites (GS20, Lake Gatun, Panama; GS32, mangrove, Isabella, Ecuador) and even hypersaline waters (GS33, Punta Cormorant Lagoon, Floreana, Ecuador). The detection of Mimiviridae-like PolB fragments was not correlated with chlorophyll <italic>a </italic>concentration. Hence, the hosts of these putative Mimiviridae viruses are not limited in temperate/eutrophic waters. In fact, species of haptophyte have been found and known to occasionally form blooms in waters from sub-polar to (sub-)tropical latitudes, including oligotrophic areas [##UREF##4##50##, ####UREF##5##51##, ##UREF##6##52####6##52##]. <italic>Acanthamoeba</italic>, the host of mimivirus, also have the ability to survive in diverse environments [##REF##16774587##53##].</p>", "<p>Finally, our study allowed the identification of putative phage <italic>rimK</italic>. In <italic>E. coli</italic>, RimK catalyzes the post-translational addition of glutamic acid residues to the amino terminus of ribosomal protein S6 [##REF##2570347##54##]. A resistance to antibiotics was suggested for the <italic>E. coli </italic>mutant lacking the activity of the S6-modification [##UREF##7##55##]. Reeh and Pedersen [##REF##386035##56##] showed that the relative level of the S6-modification was not affected by the growth rate in culture. Besides these observations, however, much is unknown for the functional consequence of the S6 modification in <italic>E. coli</italic>. Bacteriophage T7 modifies ribosomal protein S6, S1 and translational initiation factors by phosphorylation upon infection of <italic>E. coli </italic>[##REF##8022276##57##]. The modifications of host translational proteins are performed by a T7-encoded kinase, and enhance phage reproduction under sub-optimal growth conditions. It was suggested that the phosphorylation of these proteins serves to stimulate translation of the phage late mRNAs. The RimK homologs found in phage-like contigs may be involved in a similar process. Unexpected homologs of cellular genes are continuously identified in viral genome sequences [##REF##15486256##12##,##REF##18065537##58##,##REF##16222247##59##]. We believe that our phylogenetic mapping approach will be useful to identify further occurrences of unexpected viral genes in environmental sequences.</p>" ]
[ "<title>Conclusion</title>", "<p>The use of a phylogenetic approach provided a comprehensive picture of the taxonomic distribution of large viruses enclosed in the GOS metagenomic data. As expected, the highest genetic richness corresponded to phages. Interestingly, our data suggest that Mimiviridae represent a major and ubiquitous component of large eukaryotic DNA viruses in diverse marine environments.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Phylogenetic mapping of metagenomics data reveals the taxonomic distribution of large DNA viruses in the sea, including giant viruses of the Mimiviridae family.</p>", "<title>Background</title>", "<p>Viruses are ubiquitous and the most abundant biological entities in marine environments. Metagenomics studies are increasingly revealing the huge genetic diversity of marine viruses. In this study, we used a new approach - 'phylogenetic mapping' - to obtain a comprehensive picture of the taxonomic distribution of large DNA viruses represented in the Sorcerer II Global Ocean Sampling Expedition metagenomic data set.</p>", "<title>Results</title>", "<p>Using DNA polymerase genes as a taxonomic marker, we identified 811 homologous sequences of likely viral origin. As expected, most of these sequences corresponded to phages. Interestingly, the second largest viral group corresponded to that containing mimivirus and three related algal viruses. We also identified several DNA polymerase homologs closely related to Asfarviridae, a viral family poorly represented among isolated viruses and, until now, limited to terrestrial animal hosts. Finally, our approach allowed the identification of a new combination of genes in 'viral-like' sequences.</p>", "<title>Conclusion</title>", "<p>Albeit only recently discovered, giant viruses of the Mimiviridae family appear to constitute a diverse, quantitatively important and ubiquitous component of the population of large eukaryotic DNA viruses in the sea.</p>" ]
[ "<title>Abbreviations</title>", "<p>ASFV, African swine fever virus; CeV, <italic>Chrysochromulina ericina </italic>virus; EhV86, <italic>Emiliania huxleyi </italic>virus 86; GOS, Global Ocean Sampling; HaV, <italic>Heterosigma akashiwo </italic>virus 1; MSA, multiple sequence alignment; NCLDV, nucleocytoplasmic large DNA virus; NRDB, NCBI non-redundant amino-acid sequence database; ORF, open reading frame; PolB, B-family DNA polymerase; PoV, <italic>Pyramimonas orientalis </italic>virus; PpV, <italic>Phaeocystis pouchetii </italic>virus; RELL, resampling of estimated log likelihoods.</p>", "<title>Authors' contributions</title>", "<p>AM performed the analyses. HO designed the experiments. All authors analyzed the data and contributed to the writing of the manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data file ##SUPPL##0##1## is a table listing the PolB sequences used in the study. Additional data file ##SUPPL##1##2## is a multiple sequence alignment of 101 PolB sequences retrieved from databases. Additional data file ##SUPPL##2##3## is a figure summarizing the results of the phylogenetic mapping of the GOS PolB fragments, which are displayed for each of the 99 branches tested.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We are thankful to Colomban de Vargas for fruitful discussions and to anonymous referees for useful suggestions. We are also thankful to Alexis Dereeper for graphic support. AM is partially supported by the EuroPathoGenomics European network of excellence. This work was partially supported by Marseille-Nice Genopole and the French National Network (RNG).</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Maximum likelihood tree of 101 PolB sequences in the complete reference set. The phylogenetic tree was built using PhyML [##REF##14530136##73##] (Jones-Taylor-Thornton substitution model [##REF##8112466##76##], 100 bootstrap replicates) based on a multiple sequence alignment generated using M-Coffee [##REF##16556910##72##]. This tree is unrooted <italic>per se</italic>. The phage group was arbitrarily chosen as an outgroup for presentation purposes. The lengths of branches do not represent sequence divergence. Bootstrap values lower than 70% are not shown. The selected 51 representatives for the phylogenetic mapping and the associated branches are highlighted in bold face and black lines, respectively. Different colors correspond to different taxa: viruses (blue), eukaryotes (orange), bacteria (green) and archaea (pink).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Phylogenetic mapping results of the GOS PolB fragments. Results of the phylogenetic mapping are summarized and displayed for each group in the reference tree. Numbers in parentheses (<italic>X</italic>/<italic>Y</italic>) are the total number of mapped PolB fragments (<italic>Y</italic>) and the number of supported cases (<italic>X</italic>). The tree topology is the same as the one shown in Figure 1. Branches with bootstrap values ≥ 70% are marked with filled circles. The 99 branches examined by our phylogenetic mapping are shown with black lines; other peripheral branches are shown with gray lines. The length of the scale bar corresponds to 0.5 substitutions per site. colors correspond to different taxa: viruses (blue), eukaryotes (orange), bacteria (green) and archaea (pink).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Maximum likelihood tree of PolB sequences belonging to NCLDVs. The phylogenetic tree was built using PhyML [##REF##14530136##73##] (Jones-Taylor-Thornton substitution model [##REF##8112466##76##], 100 bootstrap replicates) based on a multiple sequence alignment generated using MUSCLE [##REF##15318951##77##]. Bootstrap values lower than 50% are not shown. GOS sequences are marked with filled circles and displayed in purple. The tree was mid-point rooted. The DNA polymerase gene from the recently released <italic>Ostreococcus </italic>virus OtV5 (GenBank: <ext-link ext-link-type=\"gen\" xlink:href=\"EU304328\">EU304328</ext-link>) was included in this tree. The OtV5 PolB was not included in our reference set as it was not available at the time of our phylogenetic mapping study. The length of the scale bar corresponds to 0.5 substitutions per site.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Geographic localization. <bold>(a) </bold>The different sampling sites of the Sorcerer II Global Sampling expedition. The samples 00 and 01 are part of the Sargasso Sea pilot study [##REF##15001713##16##]. The inset shows samples 27 to 36, which were sampled in the Galapagos Islands. The sampling sites displayed in light gray were not analyzed in the GOS original study, nor in this study. This part of Figure 1 was reproduced from [##REF##17355176##13##]. <bold>(b) </bold>Relative abundance of PolB fragments for virus groups across GOS sampling sites. The left-most panel shows the relative abundance of viral PolBs in difierent GOS samples. The mimivirus group clearly appears as the most ubiquitous after phages. Four area plots (second to fifth panels from the left) show water temperature, chlorophyll <italic>a </italic>concentration (no information was available for GS20, GS30, GS32, GS33, GS47 and GS51 sites), salinity (no information was available for GS06, GS11, GS13, GS14, GS28, GS30, GS31, GS32, GS34 and GS37 sites) and sample depth, respectively. Two far right histograms (sixth and seventh panels) show the proportion and the estimated number of reads associated with the viral PolB fragments among total reads for a given sample.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Gene organization of GOS contigs with putative phage RimK sequences. Putative phage <italic>rimK </italic>genes are shown in red. Other predicted genes are color coded according to their best BLAST hit taxonomies in NRDB as shown in the inset panel. MT-A70 corresponds to the adenine-specific methyltransferase. gp17 is a T4-like DNA packaging large subunit terminase homolog. gp18 is a contractile tail sheath protein homolog. The crystal structure of a GOS homolog for the protein encoded by the hypothetical gene (gray) has been determined and is available in the Protein Data Bank (3BY7).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Maximum likelihood tree of RimK sequences. RimK sequences were retrieved from UniProt [##REF##16381842##78##] and from the GOS metagenomic data set using BLASTP. The phylogenetic reconstruction was performed using PhyML [##REF##14530136##73##] (Jones-Taylor-Thornton substitution model [##REF##8112466##76##], 100 bootstrap replicates) based on a multiple sequence alignment generated with MUSCLE [##REF##15318951##77##]. Bootstrap values lower than 50% are not shown. The tree was mid-point rooted. GOS sequences are marked with filled circles and displayed in purple. The length of the scale bar corresponds to 0.4 substitutions per site.</p></caption></fig>" ]
[]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>The IDs and species names of the PolB sequences retrieved from databases are given. Sequences used in the reference multiple alignment are in bold.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Sequences used in the final reduced reference multiple alignment are displayed with an asterisk.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>The GOS PolB fragments are displayed for each of the 99 branches tested. Numbers in parentheses (<italic>V/W</italic>) are the total number of mapped PolB fragments (<italic>W</italic>) and the number of supported cases (<italic>V</italic>) (displayed in red). Read coverage values are presented as follows: [<italic>X</italic>-<italic>Y</italic>]-(<italic>Z</italic>) where X and Y are the read coverage value range (minimum/maximum) and <italic>Z </italic>the read coverage median value.</p></caption></supplementary-material>" ]
[]
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[{"surname": ["Wilhelm", "Suttle"], "given-names": ["SW", "CA"], "article-title": ["Viruses and nutrient cycles in the sea."], "source": ["BioScience"], "year": ["1999"], "volume": ["49"], "fpage": ["781"], "lpage": ["788"], "pub-id": ["10.2307/1313569"]}, {"surname": ["Adachi", "Hasegawa"], "given-names": ["J", "M"], "article-title": ["MOLPHY version 2.3: programs for molecular phylogenetics based on maximum likelihood."], "source": ["Computer Science Monographs"], "year": ["1996"], "volume": ["28"], "publisher-name": ["Tokyo: Institue of Statistical Mathematics"]}, {"surname": ["Kishino", "Miyata", "Hasegawa"], "given-names": ["H", "T", "M"], "article-title": ["Maximum likelihood inference of protein phylogeny and the origin of chloroplasts."], "source": ["J Mol Evol"], "year": ["1990"], "volume": ["31"], "fpage": ["151"], "lpage": ["160"], "pub-id": ["10.1007/BF02109483"]}, {"surname": ["Gibb", "Barlow", "Cummings", "Rees", "Trees", "Holligan", "Suggett"], "given-names": ["S", "R", "D", "N", "C", "P", "D"], "article-title": ["Surface phytoplankton pigment disributions in the Atlantic Ocean: an assessment of basin scale between 50\u00b0N and 50\u00b0S."], "source": ["Prog Oceanography"], "year": ["2000"], "volume": ["45"], "fpage": ["368"]}, {"surname": ["Massana", "Balagu\u00e9", "Guillou", "Pedr\u00f3s-Ali\u00f3"], "given-names": ["R", "L", "C"], "article-title": ["Picoeukaryotic diversity in an oligotrophic coastal site studied by molecular and culturing approaches."], "source": ["FEMS Microbiol Ecol"], "year": ["2004"], "volume": ["3"], "fpage": ["231"], "lpage": ["243"]}, {"surname": ["Brown", "Yoder"], "given-names": ["C", "J"], "article-title": ["Blooms of "], "italic": ["Emiliania huxleyi "], "source": ["J Plankton Res"], "volume": ["15"], "fpage": ["1438"]}, {"surname": ["Haidar", "Thierstein"], "given-names": ["AT", "HR"], "article-title": ["Coccolithophore dynamics off Bermuda (N. Atlantic)."], "source": ["Deep Sea Res II"], "year": ["2001"], "volume": ["48"], "fpage": ["1925"], "lpage": ["1956"], "pub-id": ["10.1016/S0967-0645(00)00169-7"]}, {"surname": ["Kade", "Dabbs", "Wittmann-Liebold"], "given-names": ["B", "E", "B"], "article-title": ["Protein-chemical studies on "], "italic": ["Escherichia coli "], "source": ["FEBS Lett"], "year": ["1980"], "volume": ["121"], "fpage": ["313"], "lpage": ["316"], "pub-id": ["10.1016/0014-5793(80)80371-1"]}]
{ "acronym": [], "definition": [] }
78
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 3; 9(7):R106
oa_package/bd/f2/PMC2530865.tar.gz
PMC2530866
18611264
[ "<title>Background</title>", "<p>The historical roots of our understanding of the intimate connection between tumorigenesis and developmental processes reach back to 1858, when Rudolf Virchow first suggested that neoplasms arise \"in accordance with the same law, which regulates embryonic development\" [##UREF##0##1##]. Since then, his idea has profoundly influenced medicine and still remains highly relevant today. The similarities between cancer and development are evident on many levels of observation: microscopically, cancerous tissues appear as undifferentiated masses, with some tumor types even exhibiting embryonic tissue organization. The increased mobility of malignant cells, leading to invasion of the local environment with the potential for subsequent travel to distant organs (representing one of the most problematic clinical aspects of cancer), is reminiscent of migratory behavior during development. On the molecular level, the shared characteristics between certain malignant tumors and developing tissues with respect to transcription factor activity [##REF##17142318##2##], regulation of chromatin structure [##REF##17060944##3##] and signaling [##REF##16778178##4##] have been documented. In particular, several studies have suggested that part of the cancer transcriptome represents a 'developmental signature', that is, it contains a set of genes that are collectively active during development. For lung cancer [##REF##14578194##5##,##REF##16800721##6##], liver cancer [##REF##15885357##7##], Wilms' tumor [##REF##16778176##8##], colon cancer [##REF##16204040##9##,##REF##17615082##10##] and medulloblastoma [##REF##15075291##11##], gene expression patterns resembling early developmental stages of the corresponding organ have been identified in the tumor profile. The results of these transcriptome-scale analyses are important because they offer a glimpse into fundamental biological processes underlying tumorigenesis and provide a natural framework for understanding complex cancer gene expression signatures that are difficult to interpret otherwise. Moreover, developmental signatures harbor a clinical relevance that we are only beginning to discover. For example, lung cancers can be risk-stratified by their similarity to lung development and pluripotency gene signatures can be used to predict outcome in breast cancer [##REF##16800721##6##,##REF##17229949##12##].</p>", "<p>In the present study, we paint a novel picture of the oncological landscape by comparing a variety of human cancers based on their developmental signature. Our analysis was inspired by the following questions: to which extent can the transcriptome of a tumor, which is oftentimes perceived as an aberration, be 'explained' by developmental gene expression? Does the developmental signature represent a feature of most, and possibly all, human cancers or does gene expression in different tumors fall into distinct groups with respect to development? Is recapitulation of developmental gene expression programs a tissue-specific phenomenon or is the developmental signature largely composed of general transcriptional modules that play a ubiquitous role in developmental processes? The answers to these open questions have therapeutic implications [##REF##16862190##13##]. If a broad range of tumors employs primitive developmental mechanisms that are shared across tissues to sustain their growth and survival, a certain drug or class of drugs could be capable of affecting them all. If, on the other hand, highly lineage-specific mechanisms govern malignant growth and behavior, focus has to be put on identifying and targeting tissue-specific regulators.</p>", "<p>The results from the integrative analysis of gene expression in cancer and development presented here suggest that the developmental information content of most human cancers indeed is significant. The developmental signature of cancers originating from various tissues exhibits low tissue-specificity, indicating that a large portion of the cancer transcriptome is composed of general developmental modules. Furthermore, we describe three developmentally distinct groups of cancer, validate the class distinction on a new time series of embryonic development in the mouse and show that the behavior of genes in lung development is predictable by their expression across the three groups. We explore the biological themes dominating the expression profiles of these classes and demonstrate that one group recapitulates early developmental gene expression patterns and is characterized by an 'individualistic' signature with upregulation of pluripotency genes and suppression of genes involved in cell-cell communication and signal transduction. A second group exhibits a 'communicative' gene expression signature that is active in late development, is enriched in genes involved in immune response, cell-cell and cell-matrix interactions and resembles a wound healing signature. A third group connects the previous two with a transition phenotype. While social and anti-social aspects of cancer have been widely popularized, this study points out the possibility of a more subtle classification of different cancers that tend to evoke different types of 'survival mechanisms'. Finally, we identify a core program of genes that are upregulated in most cancers and show that these genes are coexpressed in early development.</p>" ]
[ "<title>Materials and methods</title>", "<title>Data</title>", "<p>All gene expression data with the exception of the lung development validation series came from the public domain. Developmental time courses were profiled on several different Affymetrix chips (MG-430 2.0, MG-430A, MG-U74, Mu11K, HG-U133A). To exclude potential platform-related bias, we restricted ourselves to Affymetrix HG-U133A or HG-U133 Plus 2.0 arrays for cancer gene expression profiles. A detailed description of all data sets can be found in Additional data file 6.</p>", "<title>Data preprocessing</title>", "<p>When available, .CEL files were downloaded and arrays were normalized and expression measures calculated using the robust multi-array average [##REF##12925520##36##,##REF##15461798##37##]. When raw data were not available, MAS5 preprocessed expression values were downloaded, quantile-normalized and log2-transformed.</p>", "<title>Cross-platform comparison and homology mapping</title>", "<p>On Affymetrix arrays, a gene is often assayed by several probe sets. We first reduced each platform to unique Entrez Gene IDs. To avoid artifacts in downstream analyses caused by biased probe set selection, we randomly chose the probe set that would represent a gene on a particular platform. Probe sets with no Entrez ID were removed. In the next step, we used the homologene database (NCBI) to define orthologs between the human and the mouse. Entrez IDs with no ortholog were removed from all platforms. Finally, we matched orthologous genes across platforms. The resulting 'consensus' between the different platforms consists of 5,166 unique genes.</p>", "<title>Construction of the developmental timeline: principal components analysis</title>", "<p>We used principal components analysis to construct a DT for each developmental time series [##REF##15075291##11##]. Principal components analysis is a linear data transformation technique that allows representation of the original data in a new coordinate system in which the axes (principal components (PCs)) are chosen to capture as much variation in the data as possible in a decreasing order, that is, PC1 accounts for x% variability, PC2 for y%, PC3 for z% and so on, with x &gt; y &gt; z. We first normalized the expression values of each gene across conditions (time points) to mean 0 and standard deviation 1. Principal components analysis was carried out on the normalized data using the R language and environment for statistical computing [##UREF##2##38##], with genes representing objects and time points representing the features whose dimensionality is to be reduced. For the purposes of our analysis, we were interested in the PC that is most significantly associated with time. For each developmental time series M (5,166 rows/genes and <italic>k</italic> columns/time points), we therefore computed the correlation between a time vector (1,2,3...,<italic>k</italic>) and the component loadings for each of the <italic>k </italic>PCs. For all time series with the exception of liver regeneration, PC1 was most significantly correlated with the time vector (&gt;0.8). For liver regeneration, the highest correlation (~0.6) was found for PC3, indicating that the major changes in gene expression during liver development do not occur in a continuum from time point 1 to <italic>k</italic>, as in development. The most active stage of hepatocyte regeneration occurs approximately 48 h after hepatectomy, while our time series spans 0-72 h (Seth Karp, personal communication).</p>", "<p>The DT of a developmental time series is the original data matrix M (5,166 rows/genes and <italic>k </italic>columns/time points) after the transformation:</p>", "<p></p>", "<p>where <italic>v</italic><sup><italic>T </italic></sup>denotes the <italic>k</italic>-dimensional PC of M that is most significantly correlated with time.</p>", "<title>Analysis of differential gene expression and construction of developmental profiles</title>", "<p>Differential gene expression between tumors and corresponding controls was determined using significance analysis of microarrays (SAM) [##REF##11309499##39##]. All genes with a q-value &lt;0.05 were considered differentially expressed. For purposes of consistency, SAM based on an unpaired <italic>t</italic>-test was used for all data sets, even though paired data were available in four cases.</p>", "<title>Frequency plots and probability distributions</title>", "<p>Frequency plots were constructed by dividing the DTs in 13 equally sized (approximately 400 genes) compartments and plotting the compartment index against the number of upregulated and downregulated genes mapping to that compartment.</p>", "<p>Probability distributions show the probability <italic>P</italic>(<italic>DEV</italic>[1,2,...<italic>i</italic>]|<italic>cancer</italic>) of being among the first <italic>i</italic> genes on the DT (positions are numbered 1-5,166, starting on the left, early side) if deregulated in cancer for up- and downregulated genes. Specifically, we plot:</p>", "<p></p>", "<p>We then quantified the shape of the distribution by fitting two independent linear models to the data, one regression line representing the probability distribution on the early end of the DT and another one approximating the distribution on the late end (illustrated in Figure ##FIG##0##1b## and top right corner of Figure ##FIG##2##3##). The goal was to find two regression lines that best approximate the probability distribution and use their slopes as a two-dimensional summary of how the cancer genes map to the DT. Since each probability distribution has a unique shape, it has to be determined in each individual case at which point on the DT the breakpoint between the early and late model should occur to achieve an optimal approximation. We computed a series of F statistics (Chow test) for each potential breakpoint in the probability distribution; that is, we tested how different the coefficients of the two regression lines are if we choose the breakpoint at <italic>DEV</italic>[<italic>i</italic>] for <italic>i </italic>= 774,775,...4,391 (this excludes the earliest and latest parts of the DT because the linear model should represent a sufficiently large segment). The optimal breakpoint is defined as the maximal value in the series of F statistics. All computations were done using the strucchange package available at [##UREF##3##40##]. The fit of the regression lines to all probability distributions (blue lines) can be viewed in Additional data files 9 (before cell cycle subtraction) and 10 (after cell cycle subtraction). For each combination of cancer and DT, this approach yields four regression lines: two models representing early and late probabilities for upregulated genes and two models for downregulated genes. For each cancer, we can thus summarize the relationship to the 10 DTs in a 40-dimensional vector (4 regression line slopes × 10 DTs). These vectors were for used for clustering using Euclidian distance and Ward's linkage (Figure ##FIG##2##3##).</p>", "<title>Cell cycle subtraction</title>", "<p>We downloaded cell cycle scores for 38,578 probes [##UREF##4##41##]. The scaled Fourier periodicity scores ranged from 0.1-58; the cutoff for being considered cell cycle regulated in the original publication was 3.2. In our CC subtracted data sets, we allowed only genes with a defined score &lt;1.</p>", "<title>Meta-developmental signatures, consensus gene sets and GO characterization</title>", "<p>The meta-developmental signatures were determined by computing the average rank of each gene across all ten DTs and selecting the x genes with earliest (eDEVx) and latest (lDEVx) expression. Enrichment of GO categories in meta-developmental signatures and deregulated genes in cancer was determined against the background of all 5,166 genes in our analysis using Bioconductor's GOstats package [##UREF##5##42##]. Consensus gene sets for tumors in groups 1-3 were defined as those genes that are upregulated (downregulated) in at least 60% of datasets belonging to a given group, that is, 11/15 for group 1, 5/6 for group 2 and 8/13 for group 3.</p>", "<title>C2 gene set enrichment</title>", "<p>We downloaded all C2 gene sets from the Broad Institute website [##UREF##6##43##], eliminated the fraction that had an overlap of less than 15 genes with our data sets, and augmented the C2 collection with 10 meta-developmental gene signatures (DEV30, 50, 100, 200, 500 and LATEDEV30, 50, 100, 200, 500). We then tested the enrichment of each of these 999 gene signatures in the up- and downregulated genes of our 32 data sets using Fisher's exact test (one-sided). Clustering of all data sets by the <italic>p</italic>-values for the top 20 enriched gene sets in groups 1-3 was accomplished using Manhattan distance and Ward's linkage.</p>", "<p>R scripts for all above-mentioned analyses are provided on the website accompanying this paper [##UREF##7##44##].</p>", "<title>Validation time series: embryonic lung development</title>", "<p>Whole lungs were dissected from C57BL/6J mice at E11.5, E13.5, E14.5, E16.5 and postnatal day 5 and stored in RNAlater (Ambion, Austin, TX, USA). All time points represent gene expression patterns of individuals; only E11.5 was a pooled sample (seven pups). Total RNA was extracted using Ambion's mirVana miRNA isolation kit and tested for quality using a bioanalyzer (Agilent, Santa Clara, CA, USA). RNA integrity numbers ranged from 9.2-9.7. The samples were prepared for hybridization to Affymetrix Mouse 430 2.0 arrays according to the manufacturer's instructions. Processed and raw data have been submitted to Gene Expression Omnibus [##UREF##8##45##] under accession number GSE11539 and are also available in RMA-normalized form as Additional data file 7.</p>" ]
[ "<title>Results</title>", "<title>Placing human cancers on a developmental landscape</title>", "<p>Our analysis is based on a large-scale comparison of gene expression in 10 developmental processes and 32 cancer data sets. To paint an unbiased picture of the association between gene expression in development and oncogenesis, we selected data from a wide biological range. Our development database encompasses gene expression time series characterizing processes as diverse as heart development in the mouse, human T cell development and <italic>in vitro </italic>differentiation of murine embryonic stem cells (see Additional data file 6 for a list of all data sets). Cancer gene expression data include tumors from most commonly affected anatomical locations and corresponding normal tissue as a reference.</p>", "<p>The approach for analysis of this large data compendium (consisting of 1,094 individual arrays) is depicted in Figure ##FIG##0##1##. We first simplified the complex, high-dimensional expression profiles characterizing each developmental process into a one-dimensional developmental timeline (DT). To understand the DT, it is necessary to first consider some general properties of gene expression dynamics during a continuous developmental process: starting at the earliest (least differentiated) instance of a series of conditions, genes that are characteristic of an immature state will be active. As development progresses, the expression of these genes will gradually abate. Concomitantly, the expression of genes that are specific for the mature state will continuously intensify until it reaches its peak at the latest (most differentiated) point in time. On average, about 30% of the measured genes will follow this pattern. The construction of the DT takes advantage of this behavior, ordering the genes in a linear array based on their temporal pattern of expression. Early genes are localized on the left end of the DT, genes with no bias towards early or late expression center in the middle and late genes occupy the right end. Thus, the unique order of genes on the DT represents a summary of early and late states for each developmental process.</p>", "<p>In the next step, we determined the relationship of gene expression in cancer to each of the ten DTs. We identified the genes that were up- and downregulated in a cancer relative to its corresponding normal tissue and tracked their position (or the position of their mouse ortholog for murine developmental processes) on the DTs [##REF##15075291##11##]. In the following, we will use two kinds of plots to summarize the resulting distribution: a frequency plot (Figure ##FIG##0##1a##) for an intuitive overview of where deregulated cancer genes fall on the DT and a probability density plot (Figure ##FIG##0##1b##) that allows a more accurate quantification of the cancer-development relationship. The frequency plot is divided into two panels: on the left side, the frequency of upregulated genes on the DT is shown; on the right side, the DT is depicted again with the distribution of downregulated genes (Figure ##FIG##0##1##).</p>", "<p>The probability density plot shows how likely genes in different segments of the DT are to be expressed/suppressed in cancer (see the Figure ##FIG##0##1## legend for details). If there was no correlation between gene expression in cancer and development, the probability distributions would follow a straight line with slope 1. However, if certain parts of the DT contain genes that are up- or downregulated in cancer with a higher frequency than expected by chance, the slope of the probability density increases. Conversely, if cancer genes are depleted in a particular segment of the DT, the slope becomes flatter. For the deregulated genes in Figure ##FIG##0##1b##, this results in an 'open eye' shape of the probability density (the legend to Figure ##FIG##0##1## details the quantification of this shape).</p>", "<title>A variety of cancers have activated a predominantly tissue-independent developmental signature</title>", "<p>We will discuss some general principles emerging from the comparison of all our data sets to the ten DTs on a subset of instances and progress to a global overview thereafter. Figure ##FIG##1##2## shows the frequency plots and probability distributions for lung adenocarcinoma, Wilms' tumor, glioblastoma, ovarian cancer and liver cirrhosis with respect to the DTs of lung development, atrial chamber development, embryonic stem (ES) cell differentiation and T cell development. The distribution of lung adenocarcinoma genes on the lung development DT represents a good starting point for discussion, given that the recapitulation of embryonal pulmonary gene expression in lung cancer has been reported repeatedly [##REF##16778178##4##,##REF##14578194##5##]. The frequency plot shows an early peak for upregulated genes, followed by a gradual decline towards the late end of the DT, implying that genes that are active in lung adenocarcinoma are preferentially expressed in early lung development. The pattern is inversed for downregulated genes, meaning that genes that are characteristic for the mature, differentiated state of the lung are suppressed in lung cancer. The probability density confirms this observation with a sharp rise of <italic>P</italic>(<italic>DEV</italic>[1-<italic>i</italic>] | <italic>cancer</italic>) for low values of <italic>i</italic> (early development) for upregulated genes and high values of <italic>i</italic> (late development) for downregulated genes.</p>", "<p>Perhaps unexpectedly, the specificity of upregulated lung cancer genes for early development (and downregulated genes for late development) can be reproduced on DTs derived from atrial chamber development, ES cell differentiation and T cell development (more examples can be found in Additional data file 1). Apparently, gene expression programs that are exploited during lung tumorigenesis play a ubiquitous role in processes involving differentiation and morphogenesis. This result is in contrast to the prevailing notion that recapitulation of developmental gene expression in cancer is a tissue-specific phenomenon [##REF##16204040##9##,##REF##15075291##11##].</p>", "<p>Examination of the developmental distribution of Wilms' tumor genes suggests that this property is not unique to lung cancers. The segregation of up- and downregulated genes in Wilms' tumor on lung development occurs even more convincingly than the separation of lung cancer genes. A similar result for many other tumor types (Additional data file 1) suggests that this is unlikely to be solely attributable to the embryonal nature of Wilms' tumor. Instead, a general developmental signature that shows very little evidence of tissue-specificity seems to be a hallmark of many cancers. However, there are several notable exceptions.</p>", "<p>Upregulated genes in glioblastoma (2c) follow a similar pattern to lung adenocarcinoma and Wilms' tumor in early development, but an additional peak prominently occurs on the late end of the DTs. Beyond expressing early genes, glioblastomas have activated other, distinct transcriptional programs that are characteristic of later developmental stages. The developmental gradient in this case is not capable of 'explaining' the glioblastoma gene expression signature unambiguously. An even more striking example is ovarian cancer (Figure ##FIG##1##2d##), a tumor that is in many respects the developmental complement of glioblastoma: upregulated genes tend to avoid early and late development, while downregulated genes have a preference for the extremes of the DT. Apparently, transcriptional states in different cancers map to distinct domains of physiological gene expression. These divergent developmental patterns are unlikely to be random fluctuations. First, their recurrence with respect to changing developmental backgrounds suggests a robust association. Second, up- and downregulated genes have complementary patterns; where upregulated genes are abundant on the DT, downregulated genes are infrequent and vice versa. The expression of certain sets of genes seems to be mutually exclusive; if one set is active, the other set is invariably turned off. Third, a limited number of patterns consistently recurs in different data sets.</p>", "<p>Finally, Figure ##FIG##1##2e## shows the developmental profile of a disease that does not directly belong to the cancer family: liver cirrhosis. The developmental timing of deregulated genes in cirrhosis is strikingly different from most cancers. Upregulated genes have a preference for late development, downregulated genes tend to be enriched on the early end of the DTs. This example illustrates that the distribution of deregulated genes in development indeed is a pathophysiology-specific phenomenon.</p>", "<title>Three distinct groups of tumors emerge from the developmental landscape</title>", "<p>The cases discussed in Figure ##FIG##1##2## are a collection of representative examples highlighting some fundamental properties of the association between cancer and development. By visual inspection it is already clear that the developmental profiles of lung adenocarcinoma and Wilms' tumor are more similar to each other than to ovarian cancer, for example. However, if we want to extend this assessment of similarity to a larger number of tumors, a quantitative description of the 'shape' of the developmental profile is required. We realized this quantification by fitting two linear curves to each probability distribution, one curve representing its slope in the early part of the DT and the other one approximating the late slope (Figure ##FIG##0##1b##). Thus, each combination of cancer and developmental process is summarized by a unique set of four values, consisting of two slopes for upregulated and two slopes for downregulated genes.</p>", "<p>We next used this set of values to establish a high-level overview of the developmental information in all our datasets. Clustering by the probability distribution slope values (Figure ##FIG##2##3##) reveals at least three distinct groups of tumors that exhibit disparate developmental patterns. Group 1 contains tumors with 'early' developmental profiles comparable to lung adenocarcinoma and Wilms' tumor (Figure ##FIG##1##2##). This group represents 46% of all datasets and contains tumors from a diversity of anatomical locations, including lung carcinomas, bladder cancers, hepatocellular carcinomas and the hematological malignancy T-cell lymphoma. Clearly, early developmental gene expression is a widespread feature in cancer. An important observation is that the early developmental signature in all these tumors is only minimally tissue-specific. Many cancers have approximately equal slope values across diverse developmental backgrounds, meaning that deregulated genes map with the same specificity to the early and late segments of many DTs.</p>", "<p>Group 2 contains several tumors with an ambiguous correlation with developmental gene expression. Glioblastoma is part of this group, next to several other central nervous system tumors, breast cancer, and the more aggressive forms of papillary renal cell carcinoma (subtypes 1.2A and 2). Examination of the frequency plots and probability distributions for these cancers (Additional data file 1) shows that two types of tumors are found in this group: those that do recapitulate early developmental gene expression, but also exhibit additional transcriptional programs that are not consistent with the developmental gradient (for example, glioblastoma); and tumors that are consistent with the gradient, but whose deregulated genes show a less dramatic preference for the extremes of the DTs (for example, breast carcinoma).</p>", "<p>Group 3, featuring several subtypes of ovarian cancer, prostate cancer, two independent data sets of papillary thyroid carcinoma (PTC) and two independent instances of renal cell carcinoma, displays a transcriptional phenotype that is completely distinct from groups 1 and 2. Upregulated genes have no clear preference for early development. In fact, in some instances they accumulate on the late end of the DTs, co-clustering with liver cirrhosis, dysplastic liver and ulcerative colitis. The behavior of downregulated genes varies considerably. In some cases - most notably the ovarian cancers - they complement upregulated genes, but in PTC 3 for example, up- and downregulated genes peak in similar DT segments, hinting at active regulatory mechanisms that are not found in normal developmental processes. It is apparent that group 3 is a much more heterogeneous collection of diseases than groups 1 or 2.</p>", "<p>Of note, two data sets in group 3 have counterparts of histologically similar tumors located in group 1. PTC is represented with three, and clear cell renal cell carcinoma (CRCC) with two independent data sets in our database. Two of the PTC data sets belong to group 3; a third data set, which is divided in three histological subtypes of PTC (follicular, tall cell and conventional variant) is part of group 1. Possibly, the lacking histological subclassification of PTCs belonging to group 3 emphasizes a different transcriptional theme in those tumors. Even more likely, the paired experimental design of the two group 3 PTC data sets - in both cases, tissue from the same patient served as a normal control - influences the gene expression signature. We will address this issue in more detail in the discussion.</p>", "<p>The CRCC data sets are concordant as far as the top third of differentially expressed genes is concerned. Considering only the 450 most differentially expressed genes reveals a pronounced preference of upregulated genes for the late part of DTs in both data sets (Additional data file 3), making CRCC more similar to diseases like liver cirrhosis and ulcerative colitis and implying that the early peak that places CRCC 1 among the 'early developmental' tumors is a less significant addition to a prominent 'late' transcriptional program.</p>", "<p>While groups 1 and 3 are clearly distinct, it is debatable whether group 2 should be treated as its own entity. It is apparent that there is a spectrum of developmental signatures, with most cancer types clustering at its early or late end and a few intermediate cases that cannot be classified unambiguously. Examining the distribution of probability distribution slope values for upregulated genes in the early segment of the DTs (the most distinguishing feature) exemplifies this point (Additional data file 8). The distribution is bimodal, with most cancers falling into the early or late peak and group 2 tumors occupying the middle. To achieve a clear biological separation in subsequent analyses, we decided to treat these intermediate cases as a distinct class; it remains to be determined in more comprehensive studies whether this group can be identified reproducibly.</p>", "<title>The contribution of proliferation-related genes to the developmental pattern in cancer</title>", "<p>Since early stages of most developmental processes involve massive proliferation, part of the similarity between early development and cancer can most certainly be attributed to cell cycle (CC)-related genes. Also, the clinical behavior of the cancers constituting the three groups raises the question whether a proliferation signature could be driving their developmental profile. Group 1 mostly consists of aggressive tumors with low doubling times (for example, urinary bladder cancer, lung cancer, Wilms' tumor), while group 3 contains more indolent forms. Tumors like ovarian and renal cancer are associated with poor outcome because they metastasize frequently and do not respond well to chemotherapy, but their growth rate tends to be relatively low [##REF##7845434##14##, ####REF##12534920##15##, ##REF##7553291##16####7553291##16##]. Also, prostate and thyroid cancers are well-known for their slow growth [##REF##18045949##17##,##REF##7685704##18##].</p>", "<p>In order to determine whether the developmental component in cancer is more than a proliferation signature, we rigorously eliminated genes that are correlated with progression through the CC in HeLa cells [##REF##12058064##19##] from the deregulated genes of all cancers (see Materials and methods), discounting approximately 50% of differentially expressed genes in many data sets. Figure ##FIG##3##4## shows selected developmental profiles before and after this CC subtraction. Group 1 tumors are largely unaffected. Their profiles become noisier due to the reduction of the number of differentially expressed genes, but the shape remains qualitatively unchanged. In group 2, however, the early peaks in the frequency distribution disappear, suggesting that the CC is a dominant factor in the upregulated genes mapping to early development here, which does not seem to be the case in group 1. The profiles of group 3 tumors also remain constant. To see whether this surprising robustness to CC subtraction is a cancer-specific phenomenon, we constructed a developmental profile for proliferating endometrium (PEN) versus early secretory endometrium (ESEN) as a model for a proliferating, but non-malignant tissue. Similarly to tumors in group 1, most genes upregulated in PEN map to early development. In contrast to cancer, however, the effects of CC subtraction are much more pronounced. Figure ##FIG##3##4c## shows a quantitative assessment of these effects, defined as the difference of the probability density slope for early upregulated genes before and after CC subtraction. Clearly, the developmental component in cancer is less CC dominated than in the PEN. This becomes particularly visible on the background of ES cell differentiation (Figure ##FIG##3##4b##). Discounting CC-regulated genes completely eliminates the early peak in the frequency distribution for PEN, while the profile for squamous cell lung carcinoma and other group 1 tumors (Additional data file 2) does not change. This demonstrates that cancer shares a common gene expression signature with stem cells that cannot be found in normal PEN tissue. Finally, clustering all data sets by their probability distribution slope values after CC subtraction results in the same distinction between groups 1, 2 and 3 as the one shown in Figure ##FIG##2##3## (Additional data file 4). We therefore conclude that the CC is not the main determinant of the disparate gene expression programs in these tumors.</p>", "<title>Gene expression in groups 1, 2 and 3 is dominated by different biological processes</title>", "<p>We next used Gene Ontology (GO) to compare the dominant biological processes in groups 1, 2 and 3 with two developmental meta-signatures, eDEV500 and lDEV500, representing tissue-independent early and late programs. eDEV500 is defined as the 500 genes that are most consistently expressed early across all time series (analogous definition for lDEV500). Table ##TAB##0##1## shows that upregulated genes in groups 1 and 2 are enriched for the same processes as eDEV500, most prominently CC, RNA splicing and DNA repair. Indeed, DNA repair genes are active in pre-implantation and late gestational development and have been shown to be essential for embryonic viability and development of extra-embryonic tissues [##REF##17141556##20##]. Downregulated genes in group 1 belong to processes that are underrepresented in eDEV500 and enriched in lDEV500. These include cell communication, signal transduction and system development, processes that are required for the establishment and maintenance of a structured tissue organization. It is noteworthy that downregulated genes in group 2 diverge from this theme. The prominent observation here is that genes required for aerobic respiration are reduced; this could either point at hypoxic conditions or the Warburg effect (a shift towards lactate production in cancer cells even under normal oxygen supply). From a developmental perspective, upregulated genes in group 3 represent a mirror image of group 1. They map to similar terms as lDEV500, namely immune response, cell adhesion and multicellular organismal process. While the latter two processes clearly gain importance in the course of organogenesis, immune response is less obviously associated with late developmental stages. The role of cytokine signaling in hematopoiesis is well-established, but its function in the development of other tissues is incompletely understood. However, it is becoming clear that chemokines do not only function as chemoattractants for immune cells during inflammation, but also fulfill essential roles in embryogenesis and tissue homeostasis [##REF##17473367##21##]. For example, inhibition of signaling through the chemokine receptor CXCR4 leads to defects in migration and differentiation in the developing chick limb [##REF##16958136##22##]. In cancer, chemokine signaling can also affect migratory behavior. For instance, mesenchymal stem cells in the tumor stroma are able to increase breast cancer cell motility through paracrine CCL5 signaling [##REF##17914389##23##]. The expression of inflammation-related genes in cancer tissue is frequently interpreted as a consequence of an immune response against the tumor. Interestingly, the developmental perspective suggests that a similar gene expression signature exists during the normal development of several tissues without the involvement of an inflammatory reaction.</p>", "<p>The difference between early and late developmental genes, and consequently genes activated in group 1 versus group 3, is also evident when comparing the cellular localization of their gene products. Proteins that are produced in early development and group 1 are predominantly located in the nucleus. Similarly, upregulated genes in group 2 have products with nuclear localization and specific involvement in the CC. Gene products of lDEV500 and group 3, however, are chiefly membrane-associated or secreted into the extracellular space.</p>", "<p>Finally, we compared the PEN to development and cancer. As expected, upregulated genes were mostly CC-related. However, they were not depleted for cell communication or signal transduction genes like eDEV500 and cancers in groups 1 and 2, suggesting that proliferating cells of the endometrium retain a higher level of communication with their surroundings than those in cancer or early development. Downregulated genes were associated with lipid metabolism and showed no enrichment for organogenesis or multicellular processes like lDEV500 and downregulated genes in group 1. Taken together, these results suggest a unique relationship between malignancy and development that is not fully recapitulated in normal proliferating tissues.</p>", "<title>Among hundreds of curated gene sets, the developmental signature is the best descriptor of approximately 50% of interrogated tumor types</title>", "<p>We next wanted to determine how well our developmental signatures describe the difference between cancer and normal tissue in a direct comparison with other gene sets. We downloaded the C2 database from MSigDB [##REF##16199517##24##], a collection of gene sets derived from gene expression studies and known pathways, and tested the enrichment of approximately 1,000 gene sets in the up- and downregulated genes of our data sets. Subsequently, we compared the results with the performance of eDEV500, lDEV500 and four smaller gene sets that were defined analogously, eDEV200/lDEV200 and eDEV100/lDEV100.</p>", "<p>Table ##TAB##1##2## shows the gene sets that were most significantly enriched in the up- and downregulated genes of the three groups. Upregulated genes in group 1 are best represented by eDEV500, which is a remarkable result because no cancer gene expression data were used in deriving this gene set, but solely time courses of mouse development (all DTs except for T cell development are murine). Many data sets in MSigDB, on the other hand, are directly derived from gene expression profiles of human cancers. Of course, the groups were defined by the distribution of deregulated genes in development, but group 1 is not a specialized subset, but comprises almost 50% of our data sets. Two of the top ranks next to eDEV500 and eDEV200 are occupied by sets of genes that are upregulated in stem cells, implying a close connection between early development and pluripotency that is also evident in the cancer gene expression profile. CC gene sets are not among the most enriched signatures, but the imprint of 'stemness' can clearly be distinguished in group 1 tumors, even though our data sets represent heterogeneous tissues containing a variety of cell types. Conversely, lDEV500 is the most significant gene set in the downregulated genes of group 1, next to genes that are downregulated in various tumor models (SANSOM_APC_5_DN, LEE_DENA_DN, LEE_ACOX1_DN) and signatures found in activated mast cells (NAKAJIMA_MCS_UP), confirming the aforementioned association of late developmental genes and downregulated genes in group 1 cancers with the immune response.</p>", "<p>eDEV500 is less significant in group 2 than in group 1. This is consistent with previous results showing a less pronounced clustering of upregulated genes in early development for group 2. Instead, two independent serum response signatures are enriched in the upregulated genes (SERUM_FIBROBLAST_CORE_UP, CHANG_SERUM_RESPONSE_UP). Besides stimulating proliferation, serum exposure induces a wound healing response in fibroblasts, involving the activation of genes that play a role in intercellular signaling and remodeling of the extracellular matrix [##REF##9872747##25##]. These are both processes that map to late development in our analysis. Indeed, group 2 tumors tend to have both an early and a late peak in the frequency distribution of upregulated genes (Figure ##FIG##1##2##).</p>", "<p>As already noted in the context of GO classification, gene sets enriched in group 3 are a counterpart of group 1. eDEV500 does not rank among the top gene sets, nor do any of the stem cell signatures. Instead, three signatures that are enriched in group 1 downregulated genes are overrepresented in the upregulated genes of group 3 (TARTE_MATURE_PC, SANSOM_APC_5_DN, NAKAJIMA_MCS_UP). The combination of serum-induced cell division (SERUM_FIBROBLAST_CELLCYCLE) and immune response gene sets again suggests an association with wound healing, but the early developmental component that is so prominent in group 1 and also present in group 2 is lacking in group 3.</p>", "<p>To visualize how well the tumors inside of a group agree on the significance of a gene set, we clustered all data sets by the <italic>p</italic>-values for the top 20 signatures in the upregulated genes of the three groups (Figure ##FIG##4##5##). Group 1 presents very homogeneously with only few exceptions such as the thyroid carcinomas and renal carcinoma. Both of these cancers have counterparts in group 3 and have already been mentioned as ambiguous cases. The variation in group 2 is also low. Its position as a transition state between groups 1 and 3 is clearly visible in the heatmap as a general agreement with group 1, but simultaneous activation of a cluster of gene sets (hypoxia response, immune response, cell adhesion receptor activity) that are enriched in group 3 and insignificant in group 1. Group 3 clearly represents a distinct entity, but intra-group variation is substantial, confirming a greater heterogeneity among these tumors. Notwithstanding, they are all characterized by the lack of a pronounced developmental/stemness component and activation of inflammatory signatures. An analogous heatmap for gene sets enriched in downregulated genes (Additional data file 5) shows that the distinction of groups 1-3 is also present in genes that are suppressed in these cancers.</p>", "<title>The class distinction is reproducible on an independent time series</title>", "<p>To test whether we could validate the segregation of tumors into distinct developmental classes on an independent time series, we generated expression profiles of the developing mouse lung at embryonic day (E) 11.5, E13.5, E14.5, E16.5 and postnatal day 5. A heatmap of probability distribution slope values based on the DT constructed from these data (Figure ##FIG##5##6##) shows that the segregation of tumors into the previously defined groups can be fully recapitulated. This result further corroborates that the relationship between a cancer type and developmental gene expression is highly robust. Given that groups 1(2) and 3 display such disparate developmental patterns, we next asked whether the fact that a gene is upregulated in group 1, 2 or 3 is enough to predict its behavior during embryonic lung development. Based on our previous results, we would expect genes that are commonly upregulated in group 1 to be expressed in early lung development, group 2 genes to have a more ambiguous expression pattern and genes activated in group 3 to be expressed late. We defined three consensus signatures by selecting those genes that are expressed in at least 60% of the data sets in each group (80% for group 2 to account for smaller group size). Figure ##FIG##6##7## shows the average expression value for the three consensus gene sets at each time point in our lung developmental time series. Indeed, consensus genes for groups 1 and 2 are upregulated in early development (with a more pronounced decline of group 1 genes in late development), while group 3 genes are active late. Remarkably, the fact that a set of genes is expressed in a particular group of tumors is enough to predict the average temporal expression pattern of these genes during embryonic development in a different species, further highlighting the deep-rooted connection between development and tumorigenesis.</p>", "<p>Table ##TAB##2##3## shows some examples of the consensus genes, sorted by their average rank across all DTs. Consensus genes in group 1 are almost exclusively expressed very early on (average rank &lt;1,500) and include molecular chaperones (<italic>CCT3</italic>, <italic>CCT7</italic>), proliferation-related genes (<italic>RACGAP1</italic>, <italic>PCNA </italic>and its associated factor KIA1001 [##REF##11313979##26##]) and DNA repair genes (<italic>UNG</italic>, <italic>H2AFX</italic>). The consensus set of group 2 includes genes that are expressed early and late. Early genes fall into similar categories as the group 1 consensus, that is, DNA repair (<italic>NUDT1</italic>, <italic>DTL</italic>), proliferation (<italic>DTMYK</italic>, <italic>MELK</italic>) and DNA methylation (<italic>HELLS</italic>). Late genes are involved in signal transduction (<italic>DTNA</italic>) and antigen processing (<italic>TAP1</italic>). <italic>PSMB8 </italic>and <italic>PSMB9 </italic>are part of the immunoproteasome, a special form of the proteasome that is active after stimulation of cells with interferon-γ and is constitutively expressed in dendritic cells. The immunoproteasome exhibits modified cleavage properties that have been shown to affect tumor antigen processing and consequently cytotoxic T cell responses [##UREF##1##27##]. Consensus genes in group 3 mainly map to late development and are involved in antigen processing (<italic>HLA-C</italic>, <italic>TAP1</italic>), extracellular matrix remodeling (<italic>MMP7</italic>), proteolysis (<italic>PPGB</italic>), and cytokine signaling (<italic>IL7R</italic>). However, the group 3 consensus also contains a fraction of early genes that overlap with early consensus genes in groups 1 and 2 (<italic>CKS2</italic>, <italic>MELK</italic>, KIAA0101*, <italic>RACGAP1</italic>). Considering the small size of the consensus gene sets - 20, 58 and 29 genes for groups 1-3, respectively - this level of unanimity is striking and suggests the existence of a 'core program' that is active in all cancers, regardless of large-scale differences in the global gene expression program.</p>", "<title>A core program of genes expressed in most cancers is active in early development</title>", "<p>To further explore the notion of a tissue-independent core program in cancer, we scored each gene by how many times it is upregulated in all cancer data sets (here, we excluded liver cirrhosis, dysplastic liver and ulcerative colitis) and compared this score to the average rank of the gene across all DTs. Figure ##FIG##7##8## shows a highly significant inverse relationship between the developmental rank of a gene and its overexpression frequency (<italic>p </italic>&lt; 2.2e-16). The top-scoring genes in this comparison are related to proliferation and DNA repair and also include transcripts coding for chromatin remodeling proteins (<italic>EZH2</italic>), a histone variant that has recently been linked with stem cell proliferation [##REF##18185516##28##] (<italic>H2AFX</italic>) and RNA-interacting proteins (<italic>ELAVL1</italic>, <italic>SNRPA</italic>). To see whether the relationship between developmental rank and overexpression in cancer is robust towards CC subtraction, we excluded all CC-regulated genes as previously described. The association remains highly significant (<italic>p </italic>&lt; 2.2e-16). Top-scoring genes are mainly involved in RNA processing (<italic>CSTF2</italic>, <italic>SNRPA</italic>, <italic>SNRPA1</italic>, <italic>SNRPE</italic>, <italic>USP39</italic>, <italic>HNRPAB</italic>), which could either be a secondary effect of proliferation or reflect the increased metabolic activity of cancer cells, and chromatin remodeling (<italic>ACTL6A</italic>, <italic>SMARCC1</italic>), indicating that epigenetic mechanisms may be involved in the maintenance of an embryonic phenotype in many cancers.</p>" ]
[ "<title>Discussion</title>", "<p>We have presented a comprehensive, tissue-spanning comparison of gene expression in normal development and human cancer. Main conclusions emerging from this analysis are that a large percentage of tumors recapitulate early developmental gene expression and that the developmental signature in these cancers exhibits low tissue-specificity. Furthermore, we have identified three groups of cancers distinguished by disparate developmental signatures. One group has an early developmental phenotype and expresses genes that are characteristic of stem cells. From a developmental perspective, this group presents very homogeneously. This is all the more surprising as it contains cancer types with complex karyotypes, which are currently thought to lead to more 'chaotic' gene expression. A second, more heterogeneous group tends to be more similar to late development and is characterized by an inflammatory signature. A small group of cancers presents as a transition phenotype between these two extremes and displays both characteristics. This group distinction is reproducible with respect to a new time series of embryonic lung development in the mouse. Finally, we have identified a core program of genes that are expressed in most cancers and mapped the activity of this transcriptional program to early development.</p>", "<p>An unexpected result is the low tissue-specificity of the developmental signature, contrasting with previous reports [##REF##16204040##9##,##REF##15075291##11##]. We cannot exclude the possibility that cancer types that were not included in our database recapitulate more tissue-specific developmental patterns. However, our findings suggest that comprehensive comparisons against a diverse set of developmental backgrounds are required before a specific association between a cancer and the development of its cognate tissue can be established on the gene expression level. It is likely that a lineage-specific aspect does exist in cancer gene expression [##REF##16862190##13##], but its magnitude seems to be small in comparison with more generic developmental modules. Possibly, microRNA profiles might be better suited for detection of such subtle signals because they reflect more specific processes than mRNA profiles [##REF##15944708##29##].</p>", "<p>Given the type of analysis conducted here, intended to reveal broad brush strokes rather than subtleties, the clear segregation of tumors into three groups with distinct expression patterns is surprising. Clearly, the developmental trajectory provides a meaningful background for capturing large-scale differences in gene expression across diverse conditions. That said, we can only speculate as to what the biological determinants of the observed segregation might be as they are potentially as broad as the contexts in which a proliferative response is 'normal' and physiological.</p>", "<p>The capability to divide in response to certain conditions is an inherent property of most cells. Epithelia can augment the production of new cells in response to mechanical irritation [##REF##11041204##30##], fibroblasts divide to reconstitute injured tissue [##REF##17280897##31##], the microvasculature of the female reproductive system periodically expands [##REF##1371260##32##], hepatocytes reconstitute liver tissue after hepatectomy [##REF##17227769##33##] and, of course, cells divide to form a new organism during embryogenesis. The transcriptional programs driving these processes might be as diverse as the contexts that trigger proliferation. Cancers likely exploit endogenous cellular mechanisms to sustain their growth, but our understanding of which of the available paths towards proliferation is chosen in different types of cancers is rudimentary. Our analysis suggests that tumors in group 1 recapitulate an embryonic phenotype: they express early developmental and stem cell genes, suppress genes characteristic of mature tissues and they have downregulated messages required for intercellular communication and signaling. Cell cycle in these tumors might be fueled through the same mechanisms that are employed in rapidly proliferating blastemal cells. Group 3, on the other hand, presents a different picture. Differentially expressed genes imply that proliferation here could occur in the context of wound healing, which is associated with all the processes that are relevant in group 3 (inflammatory reaction, proliferation, tissue remodeling). The conception of cancer as a 'wound that does not heal' has often been cited [##REF##3537791##34##]. Our analysis suggests that it might be more applicable to some tumors than to others. Indeed, the clinical behavior of tumors in group 3 seems to exhibit some special features. Even though ovarian cancer is known as a malignancy with poor prognosis, its growth rate often is slow and patients can live with metastases for years [##REF##7845434##14##]. Renal cell carcinoma also has a poor prognosis when metastatic; however, most renal cell carcinomas have an indolent growth rate [##REF##12534920##15##]. Finally, thyroid cancers are also recognized for their slow growth [##REF##18045949##17##]. It is not clear whether the inflammatory gene expression signature we observe in these tumors is a cause or consequence of this particular behavior, but further investigation of this question has profound clinical implications. If tumors truly rely on distinct programs for proliferation and survival, a classification system that takes these differences into consideration could provide valuable guidelines for therapeutic decisions. Based on our study, for example, we would predict that a drug interfering with the wound healing program might be effective against both ovarian and renal carcinoma, but not against Wilms' tumor or lung adenocarcinoma. Interestingly, a recent paper that examined gene expression in mouse models of colon carcinoma in a developmental context revealed a distinction between <italic>Smad3-/- </italic>and <italic>Tgfb1-/-; Rag2-/- </italic>models (both exhibiting a strong inflammatory component and showing similarity to late colon development) and <italic>ApcMin/+ </italic>and <italic>AOM </italic>models, which recapitulated early colon development [##REF##17615082##10##]. This result implies that different genetic alterations might underlie the distinct gene expression signatures in group 1 and 3 cancers.</p>", "<p>To refine the distinction between the developmental groups of cancers emerging from our analysis, more data - ideally acquired under standardized conditions - are necessary. While the embryonic cancers in group 1 seem to represent a fairly homogeneous population with respect to their developmental component, diseases in group 3 are far more heterogeneous and more reliable data would probably lead to further sub-classification of these cancers. Standardized data would also likely help to resolve the group affiliation of ambiguous cases like thyroid carcinoma. Both PTC data sets mapping to group 3 are paired experiments, with tumor and normal tissue coming from the same patient, while the PTC data set in group 1 is unpaired. Such differences seem to have a larger impact on the genes that are identified as differentially expressed than commonly assumed. A recent study elegantly proves this point by showing an altered gene expression signature in 'normal' tissue adjacent to lung tumors [##REF##18172294##35##]. Other possibly confounding factors like the degree of lymphocyte infiltration in different samples and the already mentioned specification of histological subtype might play important roles in determining the developmental profile of a tumor and should be accounted for in future studies.</p>", "<p>To gain a better understanding of the biology underlying different loci on the developmental landscape, it might also be helpful to include more pathological conditions unrelated to cancer in the analysis. For many diseases, we have sufficiently good understanding of etiology and pathophysiology to be able to use them as 'landing lights' on the developmental surface.</p>" ]
[ "<title>Conclusion</title>", "<p>The results presented here suggest that there is great potential for better understanding of human disease in a 'macrobiological' approach to analyzing high-throughput data. Shifting our focus from single sets of genes or processes to the biology of aggregates on the order of the entire transcriptome is likely to be useful in establishing highly robust molecular correlations between seemingly unrelated disease phenotypes.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A systematic analysis of the relationship between the neoplastic and developmental transcriptome provides an outline of global trends in cancer gene expression.</p>", "<title>Background</title>", "<p>In recent years, the molecular underpinnings of the long-observed resemblance between neoplastic and immature tissue have begun to emerge. Genome-wide transcriptional profiling has revealed similar gene expression signatures in several tumor types and early developmental stages of their tissue of origin. However, it remains unclear whether such a relationship is a universal feature of malignancy, whether heterogeneities exist in the developmental component of different tumor types and to which degree the resemblance between cancer and development is a tissue-specific phenomenon.</p>", "<title>Results</title>", "<p>We defined a developmental landscape by summarizing the main features of ten developmental time courses and projected gene expression from a variety of human tumor types onto this landscape. This comparison demonstrates a clear imprint of developmental gene expression in a wide range of tumors and with respect to different, even non-cognate developmental backgrounds. Our analysis reveals three classes of cancers with developmentally distinct transcriptional patterns. We characterize the biological processes dominating these classes and validate the class distinction with respect to a new time series of murine embryonic lung development. Finally, we identify a set of genes that are upregulated in most cancers and we show that this signature is active in early development.</p>", "<title>Conclusion</title>", "<p>This systematic and quantitative overview of the relationship between the neoplastic and developmental transcriptome spanning dozens of tissues provides a reliable outline of global trends in cancer gene expression, reveals potentially clinically relevant differences in the gene expression of different cancer types and represents a reference framework for interpretation of smaller-scale functional studies.</p>" ]
[ "<title>Abbreviations</title>", "<p>CC, cell cycle; CRCC, clear cell renal cell carcinoma; DT, developmental timeline; E, embryonic day; ES, embryonic stem; ESEN, early secretory endometrium; GO, Gene Ontology; PC, principal component; PEN, proliferative endometrium; PTC, papillary thyroid carcinoma; SAM, significance analysis of microarrays.</p>", "<title>Authors' contributions</title>", "<p>ISK, SK and KN conceived of and designed the study. KN carried out all analyses and wrote the manuscript. ISK, SK, CJB, AP and BBK provided guidance and participated in the preparation of the manuscript. CJB, AP, KF and KN performed the experimental work.</p>", "<title>Additional data files</title>", "<p>The following additional data are available. Additional data file ##SUPPL##0##1## contains the frequency plots for all cancer types and all developmental time series. Additional data file ##SUPPL##1##2## contains the frequency plots for all cancers and all time series after CC subtraction. Additional data file ##SUPPL##2##3## shows the frequency plots for the top 450 differentially expressed genes in CRCC1 and CRCC2. Additional data file ##SUPPL##3##4## contains a heatmap of probability distribution slopes after CC subtraction (analogously to Figure ##FIG##2##3##). Additional data file ##SUPPL##4##5## is a heatmap of enrichment <italic>p</italic>-values for gene sets that ranked among the 20 most enriched in the downregulated genes of either group 1, 2 or 3. Additional data file ##SUPPL##5##6## is a spreadsheet containing detailed annotation for all data sets used in this study. Additional data file ##SUPPL##6##7## contains the raw data for the lung development validation time series (after RMA-normalization). Additional data file ##SUPPL##7##8## contains a smooth histogram of early upregulated (UpE) probability distribution slopes for all cancer data sets. Additional data file ##SUPPL##8##9## contains the probability distribution plots and linear regression fits for all cancers and all time series. Additional data file ##SUPPL##9##10## shows the same data as additional data file ##SUPPL##8##9##, but after CC subtraction.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Zoltan Szallasi, Vania Nose, Iris Eisenberg, Wilhelmine DeVries, Judah Folkman, Michael Galdzicki and Alal Eran for fruitful discussions, valuable comments on the manuscript and technical assistance.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Approach to data analysis. A developmental timeline (DT), which is a linear number ray on which each of 5,166 genes has a definite position, is constructed from a time course of gene expression during development (top left panel), positioning genes that are expressed in early development on the left end, genes that are upregulated in late development on the right end and neutral genes in the middle. The DT is integrated with genes that are deregulated in a population of tumors versus corresponding normal tissues (top right panel). <bold>(a) </bold>Frequency plot showing a histogram-like representation of the frequency of upregulated (red) and downregulated (green) cancer genes in different portions of the DT. The height of each bar indicates how many deregulated genes map to one of 13 equally sized segments of the DT. Each segment corresponds to approximately 400 genes. Up- and downregulated genes are depicted on separate DTs, that is, the first red bar refers to the same DT segment as the first green bar. Stated differently, the height of the first red bar signifies the number of upregulated cancer genes that map to the first 400 developmental genes and the height of the first green bar signifies the number of downregulated cancer genes that map to the same set of 400 developmental genes. <bold>(b) </bold>Probability density plot showing <italic>P</italic>(<italic>DEV</italic>[<italic>1</italic>,<italic>2</italic>,<italic>3...i</italic>]<italic> | cancer</italic>) for <italic>i </italic>= 2,3...5,166 for upregulated and downregulated cancer genes. The probability of being among the first <italic>i </italic>genes on the DT (genes are numbered 1-5,166 from left/early to right/late) if deregulated in cancer directly reflects the preference of cancer genes for different segments of the DT. The shape of each probability distribution is summarized by two linear functions that are fitted to its early and late portions (blue lines). The slopes of these functions are subsequently used as a quantification of the developmental profile of a cancer.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Frequency plots and probability distributions for <bold>(a) </bold>lung adenocarcinoma, <bold>(b) </bold>Wilms' tumor, <bold>(c) </bold>glioblastoma, <bold>(d) </bold>clear cell ovarian cancer and <bold>(e) </bold>liver cirrhosis. These cases were selected because they are representative of most tumors in our database.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Heatmap of probability distribution slopes. Thirty-two expression data sets of neoplasia versus corresponding normal tissue (and liver cirrhosis versus normal liver, dysplastic liver versus normal liver and ulcerative colitis versus non-inflamed colon) are compared against all 10 DTs. Each comparison is characterized by a four-dimensional vector of slopes derived from the probability distributions (example in top left corner). Two slope values stem from the distribution of upregulated genes on the DT, two are derived from the distribution of downregulated genes (Figure 1). UpE = slope for upregulated genes in the early part of the DT; UpL = slope for upregulated genes in the late part of the DT; DownE = slope for downregulated genes in the early part of the DT; DownL = slope for downregulated genes in the late part of the DT. Red indicates a steep slope (high specificity of up- or downregulated genes for that segment of the DT), green indicates a flat slope (depletion of up- or downregulated genes in that segment).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Effects of CC subtraction. Frequency plots of selected cancer types on the backdrop of lung development (left panel) and ES cell differentiation (middle panel) are depicted before and after the dismissal of hundreds of CC regulated genes. The corresponding probability distributions can be viewed in Additional data files 9 and 10. The right panel shows the effects of this CC subtraction on all data sets, quantified as the difference of the early probability distribution slope value (UpE) before and after elimination of CC regulated genes. PEN versus ESEN = proliferating endometrium versus early secretory endometrium; PEN versus MSEN = proliferating endometrium versus mid secretory endometrium.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Heatmap of enrichment <italic>p</italic>-values. The <italic>p</italic>-values for gene sets that ranked among the 20 most enriched in the upregulated genes of either group 1, 2 or 3 are shown for all data sets. Red indicates low <italic>p</italic>-values, green high <italic>p</italic>-values.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Heatmap of probability distribution slopes for all data sets with respect to the lung development validation time series. Abbreviations and colors are the same as in Figure 3.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>Average expression level of consensus gene sets in the lung development validation time series. Consensus group 1 = genes overexpressed in 11/16 data sets belonging to group 1; consensus group 2 = genes overexpressed in 5/6 data sets belonging to group 2; consensus group 3 = genes overexpressed in 8/13 data sets belonging to group 3.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p>Cancer core program genes before and after cell cycle subtraction. Genes overexpressed in &gt;20/32 data sets and with an average DT rank &lt;1,000 are marked in red and their names are listed below the table (left panel). Analogously for the right panel, with the parameter relaxed to overexpression in &gt;15/32 data sets to account for the reduced number of genes after elimination of CC genes. Genes belonging to the GO category 'cell cycle' are marked as orange asterisks (and with orange boxes in the right panel) to allow a better assessment of the effects of CC subtraction.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>GO category enrichment</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">BP - overrepresented</td><td align=\"left\">BP - underrepresented</td><td align=\"left\">CC - overrepresented</td></tr></thead><tbody><tr><td align=\"left\">eDEV500</td><td align=\"left\">DNA replication<break/>Cell cycle<break/>RNA splicing<break/>DNA repair<break/>Chromatin modification</td><td align=\"left\">Multicellular organismal process<break/>Cell communication<break/>Signal transduction<break/>System development<break/>Ion transport</td><td align=\"left\">Intracellular<break/>Nuclear part<break/>Membrane-bound organelle<break/>Spliceosome<break/>Ribonucleoprotein complex</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">lDEV500</td><td align=\"left\">Immune response<break/>Antigen processing and presentation<break/>Cytokine and chemokine mediated signaling pathway<break/>Cell adhesion<break/>Multicellular organismal process</td><td align=\"left\">Biopolymer metabolic process<break/>Biosynthetic process<break/>RNA processing<break/>Cell cycle phase<break/>DNA repair</td><td align=\"left\">Membrane<break/>Extracellular region<break/>MHC protein complex<break/>Lysosome<break/>Secretory granule</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Group 1 (16)</td><td/><td/><td/></tr><tr><td align=\"left\"> Up</td><td align=\"left\">DNA repair (15)<break/>Cell cycle (15)<break/>RNA splicing (13)</td><td align=\"left\">Multicellular organismal process (16)<break/>G-protein coupled receptor protein signaling pathway (16)<break/>Neurological process (16)</td><td align=\"left\">Intracellular (16)<break/>Organelle (15)<break/>Nuclear part (15)</td></tr><tr><td align=\"left\"> Down</td><td align=\"left\">Multicellular organismal process (15)<break/>Organ development (14)<break/>Cell communication (11)</td><td align=\"left\">Primary metabolic process (14)<break/>RNA processing (14)<break/>DNA metabolic process (14)</td><td align=\"left\">Plasma membrane (16)<break/>Extracellular region (13)<break/>Voltage-gated potassium channel complex (8)</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Group 2 (6)</td><td/><td/><td/></tr><tr><td align=\"left\"> Up</td><td align=\"left\">Cell cycle (6)<break/>DNA replication (6)<break/>Response to DNA damage stimulus (6)</td><td align=\"left\">Multicellular organismal development (5)<break/>Anatomical structure development (5)<break/>System development (4)</td><td align=\"left\">Chromosome (6)<break/>Protein complex (5)<break/>Replication fork (5)</td></tr><tr><td align=\"left\"> Down</td><td align=\"left\">Monovalent inorganic cation transport (5)<break/>ATP synthesis coupled proton transport (5)<break/>Oxidative phosphorylation (4)</td><td align=\"left\">DNA recombination (6)<break/>Immune response (5)<break/>Macromolecule metabolic process (5)</td><td align=\"left\">Proton-transporting two-sector ATPase complex (5)<break/>Membrane (5)<break/>Extracellular matrix (3)</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Group 3 (13)</td><td/><td/><td/></tr><tr><td align=\"left\"> Up</td><td align=\"left\">Immune response (10)<break/>Multicellular organismal process (8)<break/>Cell adhesion (6)<break/>Response to wounding (5)</td><td align=\"left\">Cellular metabolic process (10)<break/>Nucleobase, nucleoside, nucleotide and nucleic acid metabolic process (9)<break/>RNA metabolic process (8)</td><td align=\"left\">Plasma membrane (10)<break/>Extracellular region (10)<break/>Lysosome (5)</td></tr><tr><td align=\"left\"> Down</td><td align=\"left\">Cellular metabolic process (10)<break/>Protein metabolic process (6)<break/>RNA processing (5)</td><td align=\"left\">Multicellular organismal process (10)<break/>Immune response (10)<break/>Cell activation (8)</td><td align=\"left\">Cytoplasm (10)<break/>Intracellular (8)<break/>Organelle (8)</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">PEN versus ESEN</td><td/><td/><td/></tr><tr><td align=\"left\"> Up</td><td align=\"left\">DNA replication<break/>Cell cycle phase<break/>DNA metabolic process</td><td align=\"left\">Biosynthetic process<break/>Generation of precursor metabolites and energy<break/>Translation</td><td align=\"left\">Chromosome<break/>Replication fork<break/>Microtubule cytoskeleton</td></tr><tr><td align=\"left\"> Down</td><td align=\"left\">Lipid metabolic process<break/>Lipid biosynthetic process<break/>Cofactor metabolic process</td><td align=\"left\">Macromolecule metabolic process<break/>Intracellular signaling cascade<break/>M phase of mitotic cell cycle</td><td align=\"left\">Desmosome<break/>Membrane fraction<break/>Microsome</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>MSigDB C2 gene sets most significantly enriched in groups 1-3</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Upregulated genes</td><td align=\"left\">Downregulated genes</td></tr></thead><tbody><tr><td align=\"left\">Group 1</td><td/></tr><tr><td align=\"left\"> eDEV500</td><td align=\"left\">lDEV500</td></tr><tr><td align=\"left\"> STEMCELL_NEURAL_UP</td><td align=\"left\">SANSOM_APC_5_DN</td></tr><tr><td align=\"left\"> eDEV200</td><td align=\"left\">NAKAJIMA_MCS_UP</td></tr><tr><td align=\"left\"> TARTE_PLASMA_BLASTIC</td><td align=\"left\">TARTE_MATURE_PC</td></tr><tr><td align=\"left\"> STEMCELL_EMBRYONIC_UP</td><td align=\"left\">CALCIUM_REGULATION_IN_CARDIAC_CELLS</td></tr><tr><td align=\"left\"> PRMT5_KD_UP</td><td align=\"left\">LEE_DENA_DN</td></tr><tr><td align=\"left\"> CANCER_NEOPLASTIC_META_UP</td><td align=\"left\">SMOOTH_MUSCLE_CONTRACTION</td></tr><tr><td align=\"left\"> LI_FETAL_VS_WT_KIDNEY_DN</td><td align=\"left\">YAO_P4_KO_VS_WT_UP</td></tr><tr><td align=\"left\"> eDEV100</td><td align=\"left\">lDEV200</td></tr><tr><td align=\"left\"> MOREAUX_TACI_HI_IN_PPC_UP</td><td align=\"left\">LEE_ACOX1_DN</td></tr><tr><td/><td/></tr><tr><td align=\"left\">Group 2</td><td/></tr><tr><td align=\"left\"> HOFFMANN_BIVSBII_BI_TABLE2</td><td align=\"left\">FLECHNER_KIDNEY_TRANSPL_REJ_DN</td></tr><tr><td align=\"left\"> YU_CMYC_UP</td><td align=\"left\">AGEING_KIDNEY_SPECIFIC_DN</td></tr><tr><td align=\"left\"> DNA_REPLICATION_REACTOME</td><td align=\"left\">CHANG_SERUM_RESPONSE_DN</td></tr><tr><td align=\"left\"> eDEV500</td><td align=\"left\">LE_MYELIN_DN</td></tr><tr><td align=\"left\"> SERUM_FIBROBLAST_CORE_UP</td><td align=\"left\">AGEING_KIDNEY_DN</td></tr><tr><td align=\"left\"> CMV_IE86_UP</td><td align=\"left\">VENTRICLES_UP</td></tr><tr><td align=\"left\"> CHANG_SERUM_RESPONSE_UP</td><td align=\"left\">CARIES_PULP_DN</td></tr><tr><td align=\"left\"> G1_TO_S_CELL_CYCLE_REACTOME</td><td align=\"left\">UVB_NHEK1_UP</td></tr><tr><td align=\"left\"> PEART_HISTONE_DN</td><td align=\"left\">SMOOTH_MUSCLE_CONTRACTION</td></tr><tr><td align=\"left\"> GENOTOXINS_ALL_4HRS_REG</td><td align=\"left\">BRCA_ER_POS</td></tr><tr><td/><td/></tr><tr><td align=\"left\">Group 3</td><td/></tr><tr><td align=\"left\"> SERUM_FIBROBLAST_CELLCYCLE</td><td align=\"left\">FLECHNER_KIDNEY_TRANSPL_REJ_DN</td></tr><tr><td align=\"left\"> BRCA_ER_NEG</td><td align=\"left\">AGEING_KIDNEY_DN</td></tr><tr><td align=\"left\"> TARTE_MATURE_PC</td><td align=\"left\">IDX_TSA_UP_CLUSTER6</td></tr><tr><td align=\"left\"> DAC_PANC_UP</td><td align=\"left\">AGEING_KIDNEY_SPECIFIC_DN</td></tr><tr><td align=\"left\"> SANSOM_APC_5_DN</td><td align=\"left\">DIAB_NEPH_DN</td></tr><tr><td align=\"left\"> NAKAJIMA_MCS_UP</td><td align=\"left\">CARIES_PULP_DN</td></tr><tr><td align=\"left\"> CANCER_UNDIFFERENTIATED_META_UP</td><td align=\"left\">MITOCHONDRIA</td></tr><tr><td align=\"left\"> HIF1_TARGETS</td><td align=\"left\">BRCA_ER_POS</td></tr><tr><td align=\"left\"> LEE_TCELLS3_UP</td><td align=\"left\">VENTRICLES_UP</td></tr><tr><td align=\"left\"> GENOTOXINS_ALL_4HRS_REG</td><td align=\"left\">HEARTFAILURE_ATRIA_DN</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Example genes from the consensus sets of groups 1-3 ordered by their average rank across all DTs</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">ProbeID</td><td align=\"right\">Average rank</td><td align=\"left\">Gene symbol</td><td align=\"left\">Description</td></tr></thead><tbody><tr><td align=\"left\">Consensus group 1</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Early</td><td align=\"left\">201577_at</td><td align=\"right\">627.1</td><td align=\"left\">NME1*</td><td align=\"left\">Non-metastatic cells 1, protein (NM23A)</td></tr><tr><td/><td align=\"left\">200812_at</td><td align=\"right\">633.8</td><td align=\"left\"><bold>CCT7</bold></td><td align=\"left\">Chaperonin containing TCP1, subunit 7 (eta)</td></tr><tr><td/><td align=\"left\">201202_at</td><td align=\"right\">875.7</td><td align=\"left\"><bold>PCNA</bold></td><td align=\"left\">Proliferating cell nuclear antigen</td></tr><tr><td/><td align=\"left\">205436_s_at</td><td align=\"right\">890.0</td><td align=\"left\"><bold>H2AFX</bold></td><td align=\"left\">H2A histone family, member X</td></tr><tr><td/><td align=\"left\">201476_s_at</td><td align=\"right\">904.6</td><td align=\"left\"><bold>RRM1</bold></td><td align=\"left\">Ribonucleotide reductase M1 polypeptide</td></tr><tr><td/><td align=\"left\">200910_at</td><td align=\"right\">922.9</td><td align=\"left\"><bold>CCT3</bold></td><td align=\"left\">Chaperonin containing TCP1, subunit 3 (gamma)</td></tr><tr><td/><td align=\"left\">202330_s_at</td><td align=\"right\">924.1</td><td align=\"left\"><bold>UNG</bold></td><td align=\"left\">Uracil-DNA glycosylase</td></tr><tr><td/><td align=\"left\">202503_s_at</td><td align=\"right\">1060.0</td><td align=\"left\">KIAA0101*</td><td align=\"left\">KIAA0101</td></tr><tr><td/><td align=\"left\">222077_s_at</td><td align=\"right\">1146.2</td><td align=\"left\"><bold>RACGAP1</bold></td><td align=\"left\">Rac GTPase activating protein 1</td></tr><tr><td/><td align=\"left\">204170_s_at</td><td align=\"right\">1188.5</td><td align=\"left\"><bold>CKS2</bold></td><td align=\"left\">CDC28 protein kinase regulatory subunit 2</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Consensus group 2</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Early</td><td align=\"left\">204766_s_at</td><td align=\"right\">530.6</td><td align=\"left\"><bold>NUDT1</bold></td><td align=\"left\">Nudix (nucleoside diphosphate linked moiety X)-type motif 1</td></tr><tr><td/><td align=\"left\">204825_at</td><td align=\"right\">629.0</td><td align=\"left\"><bold>MELK</bold></td><td align=\"left\">Maternal embryonic leucine zipper kinase</td></tr><tr><td/><td align=\"left\">203270_at</td><td align=\"right\">636.6</td><td align=\"left\"><bold>DTYMK</bold></td><td align=\"left\">Deoxythymidylate kinase (thymidylate kinase)</td></tr><tr><td/><td align=\"left\">218585_s_at</td><td align=\"right\">768.5</td><td align=\"left\">DTL</td><td align=\"left\">Denticleless homolog (<italic>Drosophila</italic>)</td></tr><tr><td/><td align=\"left\">220085_at</td><td align=\"right\">772.6</td><td align=\"left\"><bold>HELLS</bold></td><td align=\"left\">Helicase, lymphoid-specific</td></tr><tr><td align=\"left\"> Late</td><td align=\"left\">205741_s_at</td><td align=\"right\">3587.9</td><td align=\"left\">DTNA</td><td align=\"left\">Dystrobrevin, alpha</td></tr><tr><td/><td align=\"left\">204279_at</td><td align=\"right\">3816.5</td><td align=\"left\"><italic>PSMB9</italic></td><td align=\"left\">Proteasome (prosome, macropain) subunit, beta type, 9 (large multifunctional peptidase 2)</td></tr><tr><td/><td align=\"left\">204416_x_at</td><td align=\"right\">3944.6</td><td align=\"left\">APOC1</td><td align=\"left\">Apolipoprotein C-I</td></tr><tr><td/><td align=\"left\">202307_s_at</td><td align=\"right\">3987.2</td><td align=\"left\"><italic>TAP1</italic></td><td align=\"left\">Transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)</td></tr><tr><td/><td align=\"left\">209040_s_at</td><td align=\"right\">4243.6</td><td align=\"left\"><italic>PSMB8</italic></td><td align=\"left\">Proteasome (prosome, macropain) subunit, beta type, 8 (large multifunctional peptidase 7)</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Consensus group 3</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Early</td><td align=\"left\">204825_at</td><td align=\"right\">629.0</td><td align=\"left\"><bold>MELK</bold></td><td align=\"left\">Maternal embryonic leucine zipper kinase</td></tr><tr><td/><td align=\"left\">202503_s_at</td><td align=\"right\">1060.0</td><td align=\"left\">KIAA0101*</td><td align=\"left\">KIAA0101</td></tr><tr><td/><td align=\"left\">202705_at</td><td align=\"right\">1095.9</td><td align=\"left\"><bold>CCNB2</bold></td><td align=\"left\">Cyclin B2</td></tr><tr><td/><td align=\"left\">222077_s_at</td><td align=\"right\">1146.2</td><td align=\"left\"><bold>RACGAP1</bold></td><td align=\"left\">Rac GTPase activating protein 1</td></tr><tr><td/><td align=\"left\">204170_s_at</td><td align=\"right\">1188.5</td><td align=\"left\"><bold>CKS2</bold></td><td align=\"left\">CDC28 protein kinase regulatory subunit 2</td></tr><tr><td align=\"left\"> Late</td><td align=\"left\">208997_s_at</td><td align=\"right\">3296.1</td><td align=\"left\"><italic>UCP2</italic></td><td align=\"left\">Uncoupling protein 2 (mitochondrial, proton carrier)</td></tr><tr><td/><td align=\"left\">205798_at</td><td align=\"right\">3499.9</td><td align=\"left\"><italic>MMP7</italic></td><td align=\"left\">Matrix metallopeptidase 7 (matrilysin, uterine)</td></tr><tr><td/><td align=\"left\">202307_s_at</td><td align=\"right\">3724.7</td><td align=\"left\"><italic>PPGB</italic></td><td align=\"left\">Protective protein for beta-galactosidase (galactosialidosis)</td></tr><tr><td/><td align=\"left\">209166_s_at</td><td align=\"right\">3946.3</td><td align=\"left\"><italic>IL7R</italic></td><td align=\"left\">Interleukin 7 receptor</td></tr><tr><td/><td align=\"left\">206707_x_at</td><td align=\"right\">3987.2</td><td align=\"left\"><italic>TAP1</italic></td><td align=\"left\">Transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)</td></tr><tr><td/><td align=\"left\">208812_x_at</td><td align=\"right\">4485.7</td><td align=\"left\"><italic>HLA-C</italic></td><td align=\"left\">Major histocompatibility complex, class I, C</td></tr></tbody></table></table-wrap>" ]
[ "<disp-formula><italic>y </italic>= <italic>v</italic><sup><italic>T </italic></sup>× <italic>d</italic><sup><italic>T</italic></sup></disp-formula>", "<disp-formula><mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\" name=\"gb-2008-9-7-r108-i1\" overflow=\"scroll\">\n <mml:semantics>\n <mml:mrow>\n <mml:mi>P</mml:mi>\n <mml:mrow>\n <mml:mo>(</mml:mo>\n <mml:mrow>\n <mml:mi>D</mml:mi>\n <mml:mi>E</mml:mi>\n <mml:mi>V</mml:mi>\n <mml:mrow>\n <mml:mo>[</mml:mo>\n <mml:mrow>\n <mml:mn>1</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>2</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>...</mml:mn>\n <mml:mi>i</mml:mi>\n </mml:mrow>\n <mml:mo>]</mml:mo>\n </mml:mrow>\n <mml:mo>|</mml:mo>\n <mml:mi>c</mml:mi>\n <mml:mi>a</mml:mi>\n <mml:mi>n</mml:mi>\n <mml:mi>c</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>r</mml:mi>\n </mml:mrow>\n <mml:mo>)</mml:mo>\n </mml:mrow>\n <mml:mo>=</mml:mo>\n <mml:mfrac>\n <mml:mrow>\n <mml:mi>n</mml:mi>\n <mml:mrow>\n <mml:mo>(</mml:mo>\n <mml:mrow>\n <mml:mi>d</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>r</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>g</mml:mi>\n <mml:mi>u</mml:mi>\n <mml:mi>l</mml:mi>\n <mml:mi>a</mml:mi>\n <mml:mi>t</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>d</mml:mi>\n <mml:mo>∩</mml:mo>\n <mml:mi>D</mml:mi>\n <mml:mi>E</mml:mi>\n <mml:mi>V</mml:mi>\n <mml:mrow>\n <mml:mo>[</mml:mo>\n <mml:mrow>\n <mml:mn>1</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>2</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>...</mml:mn>\n <mml:mi>i</mml:mi>\n </mml:mrow>\n <mml:mo>]</mml:mo>\n </mml:mrow>\n </mml:mrow>\n <mml:mo>)</mml:mo>\n </mml:mrow>\n </mml:mrow>\n <mml:mrow>\n <mml:mi>n</mml:mi>\n <mml:mrow>\n <mml:mo>(</mml:mo>\n <mml:mrow>\n <mml:mi>d</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>r</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>g</mml:mi>\n <mml:mi>u</mml:mi>\n <mml:mi>l</mml:mi>\n <mml:mi>a</mml:mi>\n <mml:mi>t</mml:mi>\n <mml:mi>e</mml:mi>\n <mml:mi>d</mml:mi>\n </mml:mrow>\n <mml:mo>)</mml:mo>\n </mml:mrow>\n </mml:mrow>\n </mml:mfrac>\n <mml:mtext> for </mml:mtext>\n <mml:mi>i</mml:mi>\n <mml:mo>=</mml:mo>\n <mml:mn>2</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>3</mml:mn>\n <mml:mo>,</mml:mo>\n <mml:mn>4...5,166.</mml:mn>\n </mml:mrow>\n \n </mml:semantics>\n </mml:math></disp-formula>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Frequency plots for all cancer types and all developmental time series.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Frequency plots for all cancers and all time series after CC subtraction.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Frequency plots for the top 450 differentially expressed genes in CRCC1 and CRCC2.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Heatmap of probability distribution slopes after CC subtraction (analogously to Figure ##FIG##2##3##).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Heatmap of enrichment <italic>p</italic>-values for gene sets that ranked among the 20 most enriched in the downregulated genes of either group 1, 2 or 3.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Detailed annotation for all data sets used in this study.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Raw data for the lung development validation time series (after RMA-normalization).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>This shows a bimodal distribution with the left peak containing group 3 tumors, the right peak containing group 1 tumors and intermediate cases (group 2) falling in between.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>Probability distribution plots and linear regression fits for all cancers and all time series.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional data file 10</title><p>The same data as additional data file 9, but after CC subtraction.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>Next to the most significant GO categories for eDEV500, lDEV500 and PEN versus ESEN, the GO categories that are most frequently enriched in the up- and downregulated genes of group 1, 2 and 3 data sets are listed with the number of occurrences in parentheses. BP, biological process; CC, cellular component. For example, DNA repair is enriched in the upregulated genes of 15 out of 16 data sets belonging to group 1.</p></table-wrap-foot>", "<table-wrap-foot><p>To identify only the most relevant genes, the definition of 'consensus gene set' was tightened from the definition employed in Figure 7. Consensus group 1 = 20 genes overexpressed in at least 15/16 data sets belonging to group 1; consensus group 2 = 58 genes overexpressed in all data sets (6/6) belonging to group 2; consensus group 3 = 29 genes overexpressed in 9/13 data sets belonging to group 3. Bold entries are those expressed more than three times above median in at least one of murine E6, E7, E8, E9, E10 (Symatlas). Italicized entries are those expressed more than three times above median in at least one of the following human cell types: CD4+ T cells, CD8+ T cells, CD19+ B cells (peripheral blood), BDCA4+ dendritic cells, B lymphoblasts (peripheral blood). *Expression data not available in Symatlas.</p></table-wrap-foot>" ]
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[{"surname": ["Virchow"], "given-names": ["RLK"], "source": ["Cellular Pathology"], "year": ["1859"], "publisher-name": ["Berlin"]}, {"surname": ["Kufe", "Pollock", "Weichselbaum", "Bast", "Gansler", "Holland", "Frei"], "given-names": ["D", "R", "R", "R", "T", "J", "E"], "source": ["Cancer Medicine"], "year": ["2003"], "publisher-name": ["Hamilton, Canada: BC Decker Inc."]}, {"article-title": ["The R Project for Statistical Computing"]}, {"article-title": ["strucchange"]}, {"article-title": ["Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors"]}, {"article-title": ["Bioconductor"]}, {"article-title": ["C2 Gene Sets"]}, {"article-title": ["Macrobiology at the Children's Hospital Informatics Program"]}, {"article-title": ["Gene Expression Omnibus"]}]
{ "acronym": [], "definition": [] }
45
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 8; 9(7):R108
oa_package/10/ad/PMC2530866.tar.gz
PMC2530867
18611278
[ "<title>Background</title>", "<p>Enterococci have emerged over the past few decades as the second to third most common cause of nosocomial infections, including urinary tract and soft tissue infections, bacteremia, and endocarditis [##REF##2404568##1##, ####REF##17609597##2##, ##REF##17889954##3####17889954##3##]. They are well equipped to thrive in environments with heavy antibiotic usage due to both their intrinsic resistance to antibiotics and their talent for swapping genetic information, which allows them to gain and share resistance determinants. Entecococcal infections are predominantly caused by <italic>E. faecalis </italic>and <italic>E. faecium</italic>. However, many, if not most, strains of these species are harmless commensals, with some enterococci being marketed in Europe to alleviate symptoms of irritable bowel syndrome and recurrent chronic sinusitis or bronchitis (Cylactin<sup>® </sup>and Fargo688<sup>® </sup>(<italic>E. faecium</italic>) and Symbioflor 1 (<italic>E. faecalis</italic>)). To differentiate the two faces of this organism, genome-wide comparisons are necessary. Although hundreds of microbial genomes have been sequenced, only two <italic>E. faecalis </italic>genomes have been reported (V583 as a clinical isolate [##REF##12663927##4##] and Symbioflor 1 as a commensal isolate [##REF##17466591##5##]), but only the V583 genome has been made publicly available. In this strain, more than one-quarter of the genome is mobile DNA, more than any other sequenced bacterial genome [##REF##12663927##4##]. The occurrence of multiple antibiotic resistance determinants in V583 [##REF##2554802##6##] makes it difficult to manipulate genetically. Moreover, the vancomycin resistance phenotype makes this strain more of a risk to handle in the laboratory. To avoid these issues, most laboratories use strain OG1 or its close derivatives. OG1 is a human isolate subsequently shown to cause dental caries in rats [##REF##807189##7##]. OG1RF is a <underline>r</underline>ifampicin and <underline>f</underline>usidic acid resistant derivative of OG1 [##REF##98769##8##,##REF##122512##9##]. By pulsed-field gel electrophoresis, Murray <italic>et al</italic>. [##REF##8349561##10##] estimated the size of the OG1RF genome as 2,825 kb and created a restriction map of the chromosome. Multilocus sequence typing (MLST) showed that OG1RF is clonally distinct from V583 (differs in six out of seven alleles of housekeeping genes) [##REF##16077117##11##] and characterization of regions flanking transposon insertions in OG1RF suggested that approximately 10% of their sequences differed [##REF##15489440##12##].</p>", "<p>OG1 and its derivatives have been successfully used over the past 20 years in various animal models, starting with the demonstration that it can cause caries in germ-free rats [##REF##807189##7##], and later to characterize factors important for <italic>E. faecalis </italic>virulence in a mouse model of peritonitis [##REF##9753005##13##], a rabbit model of endophthalmitis [##REF##12117982##14##], a rat model of endocarditis [##REF##16041002##15##] and in a mouse urinary tract infection model [##REF##17471437##16##]. OG1RF was also shown to be as virulent as V583 in the model host <italic>Caenorhabditis elegans </italic>[##REF##11535834##17##]. In addition to its virulence, the main reasons for the extensive use of OG1RF as a laboratory strain are that it does not carry plasmids, is readily transformable by electroporation, and is not resistant to commonly used antibiotics, other than rifampicin and fusidic acid. These resistances were serially selected in OG1 to provide strain markers [##REF##122512##9##]. The lack of resistance to common antibiotics facilitates the selection of plasmids, transposons, and allelic replacement markers introduced into the strain.</p>", "<p>Numerous factors important for virulence have been characterized in OG1RF. A recently described example are the Ebp pili, whose subunits are encoded by the <italic>ebp </italic>operon [##REF##17016560##18##] and whose genes are regulated by EbpR [##REF##17586623##19##]. A non-piliated mutant produces less biofilm than the parent strain and is attenuated in a rat model of endocarditis [##REF##17016560##18##] and in a murine urinary tract infection model [##REF##17471437##16##]. Also present is Ace, a member of the MSCRAMM (microbial surface component recognizing adhesive matrix molecules) family. The <italic>ace </italic>gene, like the <italic>ebp </italic>locus, is ubiquitous in <italic>E. faecalis </italic>and it occurs in at least four different forms that vary in the number of repeats of the B domain [##REF##10948146##20##]. Ace mediates conditional (that is, after growth at 46°C or in the presence of serum or collagen) adherence of <italic>E. faecalis </italic>to collagen type IV and to laminin [##REF##16926389##21##] and, in unpublished data, influences the ability of OG1RF to cause experimental endocarditis (KV Singh and BE Murray, unpublished observation). Finally, the Fsr system, a major positive and negative transcriptional regulator in OG1RF [##REF##16585749##22##], affects expression of several virulence factor genes, including <italic>gelE</italic>, which encodes gelatinase [##REF##10768947##23##], and contributes to infection in various animal models [##REF##16041002##15##,##REF##12228293##24##].</p>", "<p>The distinct MLST profile and the wide range of phenotypic and genotypic analyses of OG1RF, including many molecular genetic studies and experiments in various animal models, suggested that genomic analysis of this strain would prove insightful and would be useful to future studies. Thus, we analyzed the sequence of <italic>E. faecalis </italic>OG1RF. This revealed approximately 232 kb encoding 227 open reading frames (ORFs) that are unique to this important strain compared to V583. The unique regions were then characterized further.</p>" ]
[ "<title>Materials and methods</title>", "<title>Strains</title>", "<p><italic>E. faecalis </italic>OG1 is a strain of human origin (formerly designated 2SaR [##REF##807189##7##]) and was subsequently selected on rifampicin and fusidic acid [##REF##98769##8##,##REF##122512##9##] to generate OG1RF (deposited at the American Type Culture Collection (ATCC) under ATCC accession number 47077). V583 is a vancomycin resistant <italic>E. faecalis </italic>strain [##REF##2554802##6##], recovered from a blood culture of a patient hospitalized at the Barnes Hospital, St Louis, MO, USA in February 1987 (ATCC accession number 700802, NCBI complete genome accession number NC_004668). Bacteria were grown routinely at 37°C in BHI broth (Difco Laboratories, Detroit, MI, USA) or BHI agar unless otherwise indicated. Comparisons of OG1RF and V583 grown in broth (BHI, tryptic soy broth with glucose (TSBG), or BHI with 40% serum) did not reveal any obvious differences.</p>", "<title>DNA sequencing and genome assembly</title>", "<p>Genomic DNA was purified from cesium chloride (CsCl) gradients of whole cell lysates [##REF##8349561##10##]. DNA sequencing was performed by a combined approach using 454 Life Sciences pyrosequencing strategies [##REF##16056220##59##] and the Solexa approach [##REF##15165179##60##]. Read-pair information was used to create higher order scaffolds. Sanger sequencing was used for OG1RF whole gun sequencing and finishing. The coverage was 28× by the 454, 104× by Solexa, and 4.5× by Sanger sequencing technique. The assembly was done using Atlas [##REF##15060016##61##].</p>", "<title>Gene identification and annotation</title>", "<p>Gene prediction and manual annotation were performed as previously described [##REF##17895969##62##]. Glimmer [##REF##10556321##63##] and GeneMark [##REF##9461475##64##] were used independently to predict ORFs. Visualization of gene predictions was performed using the Genboree system [##UREF##0##65##] and the CONAN database [##REF##15317790##66##]. OG1RF-unique ORFs were analyzed with BLASTN and BLASTX. Protein sequences were analyzed by BLASTP versus the nr database at NCBI [##REF##15608222##67##]. When appropriate, other predictive tools were used as described previously [##REF##17895969##62##]. This whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the project accession ABPI00000000. The version described in this paper is the first version, ABPI01000000. This project includes also the annotation of the ORFs unique to OG1RF. The OG1RF-unique ORFs are listed in Additional data file 1.</p>", "<title>Transposon mutations in OG1RF-unique sequences</title>", "<p>Following the creation of an <italic>E. faecalis </italic>Tn<italic>917 </italic>library [##REF##15489440##12##], 6,237 sequences representing the flanking regions of the transposon insertion sites were obtained and compared to the V583 genome by BLASTN. A total of 196 sequences were unique to OG1RF. Thirty-seven of the unique genes contained a transposon insertion. The locations of the transposon insertions are listed in Additional data file 1.</p>", "<title>Carbohydrate fermentation tests</title>", "<p>Forty-eight <italic>E. faecalis </italic>isolates, including OG1RF and V583, having different MLST profiles, pulsed field gel electrophoresis types or from various geographical origins, were streaked onto BHI agar and incubated overnight at 37°C. Five to ten colonies of each strain were resuspended in 100 μl of 0.9% saline in a microtiter plate and tested for fermentation using BBL™ Phenol Red Broth Base (Diagnostic Systems, Sparks, MD, USA) supplemented with agar and either 10 mM glucose (positive control), 10 mM dulcitol (negative control), or 10 mM myo-inositol (Sigma, St Louis, MO, USA). Plates were read after incubation at 37°C for 24 h; a yellow halo around the colony was considered positive for fermentation. <italic>iolB </italic>and <italic>iolG2 </italic>transposon mutants [##REF##15489440##12##] were also tested.</p>", "<title>PCR</title>", "<p>PCR was performed using genomic DNA purified using Bactozol™ (Molecular Research Center, Inc., Cincinnati, OH, USA), as recommended by the manufacturer. Specific PCR primer pairs (Additional data file 4) were used to assess the presence of the OG1RF-unique sequences and for confirmation of flanking DNA regions in common with V583.</p>", "<title>Competence assays</title>", "<p>To test strains for competence, overnight cultures, grown at 37°C in Todd-Hewitt broth, were diluted in Todd-Hewitt broth to an OD<sub>600 nm </sub>of 0.05 and then further diluted 10,000-fold in Todd-Hewitt broth to a final volume of 100 ml. After 30 minutes at 37°C, with shaking at 150 rpm, and every hour for 10 h, 0.5 ml samples were removed and 2.5 μg of plasmid DNA were added. The plasmids tested were pAM401 [##REF##3005240##48##] and pMSP3535VA [##REF##10964628##49##]. The samples were incubated for 2 h before plating on BHI or BHI plus antibiotic (chloramphenical 10 μg/ml for pAM401 or kanamycin 2 mg/ml for pMSP3535VA). Following overnight incubation at 37°C, the total numbers of CFU/ml recovered on selective agar for the plasmid were compared to the total number of CFU/ml (plated on BHI agar) for each time point.</p>", "<title>Biofilm assay and statistical analysis</title>", "<p>The biofilm assay was performed as described by Mohamed <italic>et al</italic>. [##REF##15155680##68##]. Each assay was performed using 16 wells on three occasions. The median was calculated using the 48 OD<sub>570 nm </sub>readings on data pooled from all experiments and statistical analysis was performed using a non-parametric <italic>t</italic>-test.</p>", "<title>Mouse peritonitis model</title>", "<p><italic>E. faecalis </italic>strains OG1RF and V583 [##REF##2554802##6##] were tested using a previously published method [##REF##9753005##13##]. Mice were injected intraperitoneally with appropriate dilutions of premixed bacteria/sterile rat fecal extract and were observed for five days. Two-fold dilutions of test bacteria in the range 10<sup>7</sup>-10<sup>9 </sup>CFU were used as the inocula for LD<sub>50 </sub>determination using 6-9 mice per inoculum group. Inocula CFU geometric mean values were obtained and used for LD<sub>50 </sub>calculation by the method of Reed and Muench [##UREF##1##69##].</p>", "<title>UTI model for competition assay and ID50 determination</title>", "<p><italic>E. faecalis </italic>strains OG1RF and V583 were tested in the UTI model as previously described [##REF##17471437##16##]. For the mixed inoculum experiments, an approximately 1:1 ratio of <italic>E. faecalis </italic>OG1RF:V583 at approximately 10<sup>3 </sup>CFU each was used. Two independent experiments, using 10 and 11 mice, respectively, were performed and the results were combined. The log<sub>10</sub>(CFU) of OG1RF and V583 per gram of tissue of each animal (kidney or bladder) from mixed infection were analyzed for significance by the paired <italic>t</italic>-test. For mono-infection, approximately 10<sup>3 </sup>CFU organisms grown in BHI + 40% horse serum were used for each strain independently and CFU obtained from kidney pairs (nine mice per strain) were analyzed for significance by the unpaired <italic>t</italic>-test. The minimum detectable limits of recovered bacteria were 10<sup>1 </sup>and 10<sup>2 </sup>CFU/gm of kidney pairs and urinary bladder homogenates, respectively.</p>" ]
[ "<title>Results and discussion</title>", "<title>General genome features</title>", "<p>The complete circular chromosome of OG1RF was found to be 2,739,625 bp with an average G+C content of 37.8%. The complete OG1RF sequence was obtained using three independent techniques (Solexa, the 454, and Sanger sequencing technique) with a higher than classic coverage (more than 100 times), diminishing the likelihood of sequencing-related frameshifts, base errors and/or misassembly. A comparison of our assembly of the closed OG1RF genome with the restriction map of OG1RF published by Murray <italic>et al</italic>. [##REF##8349561##10##] showed only minor variations (primarily an overestimation of 30 kb for the <italic>Sfi </italic>I fragment E, 540 kb versus 509 kb predicted from the sequence; Figure ##FIG##0##1##).</p>", "<p>We found 232 kb of OG1RF unique sequences distributed in 48 regions ranging from 101 bp to approximately 49 kb in length (Figure ##FIG##0##1##; Additional data file 1). Using the published DNA sequence of V583 as reference (NC_004668), OG1RF shares 2,474 ORFs as well as the 12 rRNA genes and 58 of 68 tRNA genes (Table ##TAB##0##1##). The 10 missing tRNA are localized in a region in V583 that has been replaced in OG1RF by a 49 kb region (see below). Surprisingly, the genomes align syntenically, as shown in Figure ##FIG##1##2##, despite the fact that 25% of the V583 genome is composed of mobile elements. Similarly, the presence of OG1RF-unique sequences has not affected the overall chromosomal arrangement. Some of the major insertions/deletions in the two genomes are shown in Figure ##FIG##1##2##, such as the absence of the pathogenicity island (PAI) in OG1RF and the presence of an approximately 49 kb fragment unique to OG1RF. However, most of the differences are small and cannot be visualized in this figure. Overall, we found 64 areas of divergence between the genomes that can be divided into 3 classes: an additional sequence present in OG1RF when compared with V583; a sequence replacement where a sequence in OG1RF differs from the sequence in V583; and the absence of a sequence from OG1RF when compared with V583.</p>", "<title>CRISPR loci</title>", "<p>The CRISPR (comprised of regularly interspaced short palindromic repeats) loci encoded by some bacterial strains is a recently described system that protects cells from infection with bacteriophage [##REF##17379808##25##, ####REF##18065539##26##, ##REF##18065545##27####18065545##27##]. The specificity of the phage resistance conferred by the CRISPR elements and CRISPR-associated genes (<italic>cas </italic>genes) is determined by spacer-phage sequence similarity. OG1RF carries two CRISPR elements: CRISPR1 (between the OG1RF homologue of EF0672 and EF0673) and CRISPR2 (between the OG1RF homologue of EF2062 and EF2063); CRISPR1 is linked to <italic>cas</italic>-like genes while CRISPR2 is not (Figure ##FIG##2##3##). Both OG1RF CRISPR elements are composed of 7 repeats of a 37 bp palindromic sequence with a 29 bp spacer. None of the 29 bp spacers (14 total) have homology to any sequences in GenBank. The CRISPR1-associated proteins belong to the Nmeni subtype [##REF##16292354##28##]. Species bearing this CRISPR/<italic>cas </italic>subtype have so far been found exclusively in bacteria that are vertebrate pathogens or commensals. The Nmeni subtype is characterized by the presence of four specific <italic>cas </italic>genes and a single copy of the repeat that is upstream of the first gene in the locus. The four <italic>cas </italic>genes encode Cas_csn1 (possible endonuclease), Cas1 (novel nuclease), Cas2 (conserved hypothetical protein), and Cas_csn2 (conserved hypothetical protein). The repeat upstream of <italic>cas_csn1 </italic>appears to have degenerated since it shares only 23 bp with the 37 bp repeat cluster downstream of the last gene. A unique feature of the OG1RF CRISPR1 locus is the presence of a gene downstream of the element, which encodes a hypothetical 119 amino acid transmembrane protein.</p>", "<p>The presence of the CRISPR loci among <italic>E. faecalis </italic>strains may be a powerful tool to avoid the load of prophage replication. To determine the distribution of the CRISPR1 locus in <italic>E. faecalis </italic>strains, 16 isolates of various MLST types were tested for the presence (PCR with primers specific for <italic>csn1 </italic>and <italic>cas1</italic>) or absence (PCR with primers overlapping the junction between EF0672 and EF0673) of the CRISPR1 locus (Table ##TAB##1##2##). Seven strains were <italic>cas </italic>positive, but negative for the junction and the remaining nine were positive only for the junction. This indicates that the location of the CRISPR1 locus appears to be conserved (between EF0672 and EF0673 when compared with the V583 genome). Interestingly, the two vancomycin resistant strains tested were both <italic>cas </italic>negative. It is appealing to postulate that the presence of the CRISPR locus in OG1RF may be the reason for the absence of prophage in this strain.</p>", "<title>A 14.8 kb region inserted in the 23.9 kb region containing <italic>fsrA </italic>and <italic>fsrB</italic></title>", "<p>Nakayama <italic>et al</italic>. [##REF##12039782##29##] described a conserved 23.9 kb chromosomal deletion when comparing <italic>fsrA-</italic>lacking/<italic>fsrC</italic><sup>+</sup>/<italic>gelE</italic><sup>+ </sup>strains (by PCR) from various origins with V583; the deleted sequences start in the middle of EF1841, include the <italic>fsrAB </italic>genes and end in the middle of the <italic>fsrC </italic>gene (EF1820). Loss of the <italic>fsr </italic>regulatory components results in a gelatinase-negative phenotype under routine test conditions despite the fact that these strains still carry the <italic>gelE </italic>gene [##REF##10768947##23##,##REF##12039782##29##]. The absence/presence of the 23.9 kb region, from EF1820/<italic>fsrC </italic>to EF1841, did not appear to correlate with the clinical origin of the isolates [##REF##15131223##30##]. In a more recent analysis of relationships between various <italic>E. faecalis </italic>strains, the 23.9 kb region was not detected in 86% of the strains of the clonal complex (CC)2, 58% of the CC9 strains, nor in any of the CC8 strains [##REF##17611618##31##]. The Symbioflor 1 strain, used as a probiotic, is one representative of the 7.4% of <italic>E. faecalis </italic>isolates that are missing the <italic>gelE </italic>gene in addition to the 23.9 kb region [##REF##17466591##5##,##REF##15131223##30##]. Our analysis of this area in OG1RF revealed the presence of an additional 14.8 kb fragment inserted between the corresponding EF1826 and EF1827 of OG1RF (confirmed by PCR; results not shown). In OG1RF, this 14.8 kb region contains two loci, a WxL locus (described below) and a seven-gene locus that may encode a possible ABC transporter with similarity to one annotated in <italic>Pediococcus pentosaceus</italic>.</p>", "<title>Components of the cell surface</title>", "<p>It has been shown in <italic>E. faecalis </italic>that at least one cell surface protein (Ace) is subject to domain variation [##REF##10948146##20##] and it has been postulated that domain variation may help bacteria escape the immune system. We found more polymorphisms in two families of <italic>E. faecalis </italic>proteins present on the cell surface: the MSCRAMMs and the WxL domain surface proteins. The MSCRAMMs are composed of two large regions, namely, the non-repeat A region (which is usually the ligand binding region for extracellular matrix molecules such as collagen or fibrinogen) and the B region (which typically contains repeated sub-domains). The B region of Ace contains five repeats in OG1RF, while it contains only four in V583 [##REF##10948146##20##]. We found two other MSCRAMM proteins that show polymorphisms in the number of their B repeats. OG1RF_0186 (corresponding to EF2505 of V583) has four repeats compared to seven in V583, and OG1RF_0165 (corresponding to EF2224 of V583) has eight repeats compared to five in V583. It has been proposed that the repeats are used as a stalk that projects the A region across the peptidoglycan and away from the cell surface [##REF##9799504##32##]. A hypothesis that the number of repeats may be proportional to the depth of the peptidoglycan has been proposed [##REF##9799504##32##]. However, OG1RF_0186 carries fewer repeats than EF2505 while Ace and OG1RF_0165 carry more repeats than their counterparts in V583, suggesting that our observation does not fit this hypothesis or that the peptidoglycan depth is not uniform. Apart from these three MSCRAMMs with B-repeat polymorphisms, we identified two unique MSCRAMM proteins in OG1RF: a homologue of EF0089 (OG1RF_0063, which shares 48% similarity) and a homologue of EF1896 (OG1RF_0039, which shares 75% similarity); both are located in the approximately 49 kb region unique to OG1RF described below (Figure ##FIG##0##1##; Additional data file 1).</p>", "<p>Another family of <italic>E. faecalis </italic>surface proteins includes the newly described WxL domain surface proteins. Siezen <italic>et al</italic>. [##REF##16723015##33##] reported a novel gene cluster encoding exclusively cell-surface proteins that is conserved in a subgroup of Gram-positive bacteria. Each gene cluster has at least one member of three gene families: a gene encoding a small LPxTG protein (approximately 120 amino acids); a gene encoding a member of the DUF916 transmembrane protein family; and a gene encoding a WxL domain surface protein. In addition, members of these gene families were found as singletons or associated with genes encoding other proteins (Additional data file 2). Recently, it was shown that the WxL domain attaches to the peptidoglycan on the cell surface [##REF##16963569##34##] and one member of this WxL domain family, the homologue of EF2686 in OG1RF (a probable internalin protein), was shown to be important for virulence in a mouse peritonitis model and is required for dissemination to the spleen and liver [##REF##17620355##35##]. OG1RF shares five complete WxL loci with V583 (EF0750-7, EF2682-6, EF2970-68, EF3181-8, and EF3248-53). OG1RF does not contain homologues of EF2248-54 (carrying instead the <italic>iol </italic>operon), though it has a novel WxL locus within the 14.8 kb unique region upstream of the <italic>fsr </italic>locus (Additional data file 2). In addition to the variation in the number of WxL loci, we also observed polymorphisms in six of the WxL domain surface proteins. For example, OG1RF_0213 shares 88% similarity with EF3188, while OG1RF_0224, OG1RF_0225, and OG1RF_0227 share 64-68% similarity with their V583 counterparts, EF3248, EF3250, and EF3252, respectively. Also, in place of EF3153, EF3154, and EF3155 (which share 70% similarity among themselves), were found non-distantly related homologues, OG1RF_0209 and OG1RF_0210, which share 60-80% similarity with EF3153, EF3154, and EF3155. It is interesting to note that while several of these WxL loci, including the EF0750 and EF3184 loci, were present by hybridization in all the strains (clinical or food isolates) tested by Lepage <italic>et al</italic>. [##REF##16980489##36##], other loci, including the EF3153 and EF3248 loci, were not detected in the majority of these strains. In addition, it appears that the EF3248 locus diverges in the Symbioflor 1 strain. When compared to V583, the sequence identity in this area between the two strains appears to be as low as 75% (depicted in Figure ##FIG##1##2## from reference [##REF##17466591##5##]).</p>", "<p>However, because the Symbioflor 1 genome sequence is not currently available, it was not possible to compare their respective sequences in more detail. Since these proteins are located at the surface of the cell, the low level of homology shared between them may be the result of antigenic variation. More analyses are required for a better understanding of the number, frequency and function of these WxL domain proteins and their possible relationship with the diversity of <italic>E. faecalis</italic>.</p>", "<p>Finally, as previously found using PCR, the <italic>cpsCDEFGHIJK </italic>operon capsule polysaccharide genes [##REF##15184433##37##] were confirmed here as missing, although OG1RF carries the <italic>cpsA </italic>and <italic>cpsB </italic>genes, which were proposed to be essential for <italic>E. faecalis </italic>since all strains tested by Hufnagel <italic>et al</italic>. [##REF##15184433##37##] carry these two genes. In OG1RF, the region that would encode the <italic>cps </italic>operon is only 59 bp in length and has no homology with V583. Thus, while V583 and OG1RF share much similarity between their surface components, there are unique differences that could potentially be important in affecting the behavior of the strains and might be useful for strain typing.</p>", "<title>Two-component regulatory systems</title>", "<p>OG1RF lacks four two-component systems found in V583. These are histidine kinase-response regulator (HK-RR)08, HK-RR12 located in the PAI, HK-RR16 and the <italic>vanB </italic>regulatory system HK-RR11 [##REF##12374813##38##]. However, an OG1RF-unique two-component system with high homology with the <italic>van</italic><sub>G </sub>locus was found at the location corresponding to the region between EF2860 and EF2861 in V583 (Table ##TAB##2##3##). OG1RF_0193 shares 82% similarity with VanR<sub>G </sub>and 81% similarity with VanR<sub>G2</sub>. Similarly, OG1RF_0192 shares 68% similarity with VanS<sub>G </sub>and VanS<sub>G2</sub>. A gene (OG1RF_0191) encoding an M15 family muramoyl pentapeptide carboxypeptidase is located downstream of these two-component regulatory genes (Figure ##FIG##3##4a##). The predicted carboxypeptidase (OG1RF_0191) shares 69% similarity over 179 amino acids with EF2297, a membrane-associated D, D-carboxypeptidase encoded by the <italic>vanB </italic>operon in V583. However, OG1RF_0191 lacks an identifiable transmembrane domain that is important to the VanY function and it is likely, therefore, that this protein may be a soluble D, D-carboxypeptidase/transpeptidase as seen in <italic>Streptomyces </italic>[##REF##3997832##39##] and <italic>Actinomadura </italic>[##REF##15987687##40##], and thus may not be involved in peptidoglycan metabolism. Consequently, it seems unlikely that this operon is a remnant of a vancomycin resistance operon in OG1RF, but rather part of a still unknown regulatory pathway.</p>", "<title>The <italic>iol </italic>operon</title>", "<p>OG1RF carries an <italic>iol </italic>operon while V583 does not. This operon encodes the factors necessary for the degradation of myo-inositol into glyceraldehyde-3P. Many soil and plant micro-organisms, including <italic>Bacillus subtilis </italic>[##REF##9226270##41##] (first <italic>iol </italic>operon identified), <italic>Klebsiella </italic>spp. [##REF##977784##42##], and cryptococci [##REF##394818##43##], have been reported to use myo-inositol as a sole carbon source. Myo-inositol, one of the nine isomers of the inositol group, belongs to the cyclitol group and is abundant in nature, particularly in the soil. The OG1RF <italic>iol </italic>operon appears to be closely related to ones described in <italic>Clostridium perfringens </italic>[##REF##15183876##44##] and <italic>Lactobacillus casei </italic>[##REF##17449687##45##]. In <italic>L. casei</italic>, the myo-inositol operon consists of ten genes with an upstream divergent regulator gene, <italic>iolR</italic>. In OG1RF, the operon appears to include ten genes, beginning with a probable transcriptional regulator (helix-turn-helix domain protein). Also, the OG1RF operon carries two copies of an <italic>iolG</italic>-like gene, which encodes inositol 2-dehydrogenase, the first enzyme of the myo-inositol degradation pathway (Figure ##FIG##4##5##). However, the order of the genes is not the same between <italic>E. faecalis </italic>and <italic>L. casei</italic>. In addition, <italic>iolH</italic>,<italic>iolJ </italic>and <italic>iolK</italic>, present in <italic>L. casei</italic>, are not present in OG1RF, nor are <italic>iolH </italic>and <italic>iolK </italic>present in the <italic>C. perfringens iol </italic>operon.</p>", "<p>Yebra <italic>et al</italic>. reported that <italic>L. casei </italic>was the sole member of the Lactobacillales to carry a functional <italic>iol </italic>operon [##REF##17449687##45##]. To survey <italic>E. faecalis</italic>, also a member of this order, for the presence of the <italic>iol </italic>operon, 48 isolates with different MLST and/or from various origins (including OG1RF and V583) were tested for myo-inositol fermentation; 23 of 48 isolates were positive. In addition, PCR verified the presence of <italic>iolE </italic>and <italic>iolR </italic>in these strains and in one negative for myo-inositol fermentation, indicating that the <italic>iol </italic>operon is not unique to OG1RF. To verify that the <italic>iol </italic>genes are responsible for the fermentation of myo-inositol in OG1RF, transposon insertion mutants [##REF##122512##9##] in the <italic>iolB </italic>and <italic>iolG2 </italic>genes of OG1RF were tested. Both mutants failed to ferment myo-inositol (data not shown), demonstrating that these genes are essential for myo-inositol fermentation. To investigate whether the <italic>iol </italic>operon was 'inserted into' or 'removed from' a putative ancestral strain, the sequences surrounding the <italic>iol </italic>genes were examined. In OG1RF, the <italic>iol </italic>operon is located between the equivalent of EF2239 and EF2352 when compared with V583. In V583, this region encodes probable prophage proteins and carries the <italic>vanB </italic>transposon, which confers vancomycin resistance. Since we did not identify any remnants of the <italic>iol </italic>operon in V583, it would appear that at least two independent events at the same location differentiate OG1RF and V583, suggesting that it is a hot region for rearrangement. This region between EF2239 and EF2352 (111 Kb) is also missing in the Symbioflor 1 strain (referred to as gap 2) [##REF##17466591##5##]. The possible junction and presence of unique sequence in this region, if investigated, was not mentioned in the publication. Nonetheless, preliminary analysis of other strains' genotypes in this area seemed to confirm the hypothesis of a hot region for rearrangement (data not shown).</p>", "<title>A homologue of Tn<italic>916 </italic>in OG1RF</title>", "<p>An analysis of the G+C content of OG1RF unique regions revealed several loci with a lower G+C content than the 37.8% average content of OG1RF. One of these is an approximately 49 kb fragment with a G+C content of 32.1% located between an rRNA operon and the homologue in OG1RF of EF1053, replacing 10 tRNA genes present in V583 (Figure ##FIG##0##1##). This fragment appears to be a patchwork composed of hypothetical genes, homologues of Tn<italic>916</italic>-associated genes and homologues of genes from other Gram-positive organisms, including <italic>Listeria</italic>, <italic>E. faecium</italic>, staphylococci, or lactococci (Additional data file 1). It is interesting to note that this region contains: a putative adhesin protein gene (OG1RF_0039) at one end of the fragment; homologues of 14 Tn<italic>916</italic>-associated genes (Tn<italic>916</italic>_2 to Tn<italic>916</italic>_12, Tn<italic>916</italic>_18 and Tn<italic>916</italic>_19, with an average of 70% similarity); and a gene encoding a putative integrase (OG1RF_0088) at the other end - these three features are also present in Tn<italic>5386 </italic>in <italic>E. faecium </italic>D344R [##REF##17408741##46##]. However, the approximately 49 kb fragment lacks an excisase gene and the probable lantibiotic ABC transporter genes present in Tn<italic>5386</italic>.</p>", "<title>An uninterrupted competence operon in OG1RF</title>", "<p>OG1RF contains what appears to be an intact competence operon while that of V583 appears to be non-functional. This operon in OG1RF is similar to a nine-gene operon described in <italic>Streptococcus mutans </italic>[##REF##15632435##47##], as shown in Figure ##FIG##5##6##. For example, the homologue in OG1RF of EF2046 shares 61% similarity with ComYA and the OG1RF homologue of EF2045 is 55% similar to ComYB. In <italic>S. mutans</italic>, only the first seven genes of the operon are essential for competence [##REF##15632435##47##]. In V583, the fourth gene of this operon (corresponding to OG1RF_0148) is interrupted by phage 4 (EF1896-EF2043); in addition, EF1984 contains a premature stop codon not found in the corresponding gene in OG1RF (OG1RF_0228).</p>", "<p>Natural competence has not been reported for <italic>E. faecalis</italic>. To assess the functionality of this operon in OG1RF, we evaluated the competence of cells in different phases of growth (early log growth to stationary phase) using pAM401 [##REF##3005240##48##] and pMSP3535VA [##REF##10964628##49##]. We were not able to show natural competence under the conditions tested. We have also noted that V583 is less transformable by electroporation than OG1RF. To investigate the possibility that directly or indirectly the <italic>com </italic>operon might be responsible for this phenotype, we also evaluated transformability by electroporation. When compared with OG1RF, transposon mutants [##REF##15489440##12##] in the OG1RF equivalent of EF2045 (encoding the <italic>comGB </italic>homologue) and in the OG1RF equivalent of EF1986 (encoding the <italic>comGF </italic>homologue) showed similar levels of transformability by electroporation (data not shown), implying that the difference in electroporation efficiency observed between OG1RF and V583 is not related to this locus.</p>", "<p>In <italic>Streptococcus pneumoniae </italic>[##REF##9701804##50##], the competence operon is tightly regulated by a quorum sensing two-component system (ComDE) and a competence-stimulating peptide (CSP; encoded by <italic>comC</italic>). We did not find any homologues of CSP in OG1RF. Two homologous ComDE sensor histidine kinase/response regulators were found in OG1RF, one of which is FsrC/FsrA. Based on our previous microarray data, the Fsr system does not regulate the <italic>comY </italic>operon, at least under our previously used conditions (mid-log phase growth to early stationary phase in brain heart infusion (BHI)) [##REF##16585749##22##]. The other ComDE homology is that with a two-component system unique to OG1RF (OG1RF_0199 and OG1RF_0198, respectively) that lies on a 4,706 bp unique fragment that maps between EF3114 and EF3115 in V583. This fragment also carries two genes (OG1RF_0200 and OG1RF_0201) encoding homologues of the YhaQ and YhaP sodium efflux ATP-binding cassette efflux/transporter proteins (Figure ##FIG##3##4b##). Although they are potential elements of a secretion apparatus, these two proteins do not share any homology at the protein level with the competence secretion apparatus ComAB of <italic>S. pneumoniae </italic>[##REF##11115120##51##] nor CslAB from <italic>S. mutans </italic>[##REF##11154426##52##]. Searching for a possible CSP in the vicinity of these genes, we identified a small ORF encoding 50 amino acids between <italic>yhaP </italic>and OG1RF199 and another encoding 20 amino acids downstream of OG1RF198. More analysis will be required to determine if there are conditions in which the OG1RF <italic>com </italic>operon is expressed and to determine whether or not this two-component system is involved in competence.</p>", "<title>Limited presence of mobile elements</title>", "<p>By probing a microarray of the V583 genome and plasmids with OG1RF genomic DNA, we previously estimated that only 75% of V583 ORFs were also present in OG1RF [##REF##16585749##22##]. Later, Aakra <italic>et al</italic>. [##REF##17220255##53##] compared nine strains, including OG1RF to V583, using comparative genomic hybridization. In these results, OG1RF appears to carry a few genes included in the PAI, and a few prophage genes. Using the complete genome sequence, we have now found that OG1RF lacks 639 genes and the three pTET plasmids described in V583. All but 45 of the missing genes are associated with putative mobile elements, such as the entire PAI, the recently described phages 1, 3, 4, 5, 6, and 7 [##REF##17611618##31##], and the approximately 111 kb area between genes EF2240 and EF2351 (including the <italic>vanB </italic>transposon) present in V583. The absence of these elements appears also to be a characteristic of the commensal strain Symbioflor 1, although because the genome was not completely finished, the possibility remains that some of these regions were not sequenced. In conclusion, other than the approximately 49 kb fragment containing a Tn<italic>916 </italic>homologue, it appears that OG1RF has only one additional possible mobile element derivative, namely the phage 2 proposed to be part of the core genome [##REF##17611618##31##].</p>", "<title>Fusidic acid and rifampicin resistance</title>", "<p>OG1RF was sequentially selected from OG1 for resistance to fusidic acid and rifampicin [##REF##122512##9##]. The mutation leading to rifampicin resistance was identified in the <italic>rpoB </italic>gene by Ozawa <italic>et al</italic>. [##REF##15907539##54##] and is caused by the A1467G mutation, which results in substitution of arginine for histidine at amino acid 489. The mutation also affected the clumping phenotype of <italic>traA </italic>mutants and this effect appears to be specific for the pAM373 system [##REF##15907539##54##]. All of the other 22 differences in <italic>rpoB </italic>between OG1RF and V583 are synonymous. Fusidic acid resistance is associated with mutation(s) in the <italic>fusA </italic>gene, which encodes elongation factor G. We compared <italic>fusA </italic>from OG1RF with that in V583 and identified two differences (C1368A and T1992C). The mutation T1992C is synonymous, while the mutation C1368A leads to the presence of glutamine (histidine in V583) at position 404 in OG1RF. Mutations in this region have been associated with fusidic acid resistance in <italic>Staphylococcus aureus </italic>[##REF##11036042##55##,##REF##11309125##56##], and thus the C1368A mutation is likely the cause of the fusidic acid resistance phenotype in OG1RF.</p>", "<title>Virulence and biofilm comparisons of OG1RF with V583</title>", "<p>When compared in the mouse peritonitis model, the LD<sub>50 </sub>values of V583 in different determinations were lower (4.8 × 10<sup>7 </sup>to 1.1 × 10<sup>8 </sup>colony forming units (CFU)/ml) than the LD<sub>50 </sub>values of OG1RF (1.2 × 10<sup>8 </sup>to 4.8 × 10<sup>8 </sup>CFU/ml). However, at comparable inoculum, OG1RF (4 × 10<sup>8 </sup>CFU/ml) showed more rapid mortality versus V583 (5 × 10<sup>8 </sup>CFU/ml) in the first 48 hours (<italic>P </italic>= 0.0034; Additional data file 3). In a urinary tract infection model administering mixed equal inocula of V583 and OG1RF, OG1RF significantly outnumbered V583 in kidney with geometric means of 1.3 × 10<sup>4 </sup>CFU/gm for OG1RF versus 1.9 × 10<sup>2 </sup>CFU/gm for V583 (<italic>P </italic>= 0.0005); in urinary bladder homogenates, the geometric mean CFU/gm was 1.7 × 10<sup>3 </sup>for OG1RF versus 6.6 × 10<sup>1 </sup>for V583 (<italic>P </italic>= 0.003; Figure ##FIG##6##7a##). Similarly, in mono-infection, the geometric mean CFU/gm of OG1RF in kidneys was 9.4 × 10<sup>3 </sup>versus 4 × 10<sup>1 </sup>for V583 (<italic>P </italic>= 0.0035; Figure ##FIG##6##7b##). We also found that OG1RF produced 20% more biofilm (<italic>P </italic>&lt; 0.01) than V583 at 24 hours (results not shown). These results, together with the previous results in <italic>C. elegans </italic>[##REF##11535834##17##], demonstrate that OG1RF, although lacking what was thought to be important for virulence (PAI, plasmids, prophages), is as pathogenic as V583 in at least three assays.</p>" ]
[ "<title>Results and discussion</title>", "<title>General genome features</title>", "<p>The complete circular chromosome of OG1RF was found to be 2,739,625 bp with an average G+C content of 37.8%. The complete OG1RF sequence was obtained using three independent techniques (Solexa, the 454, and Sanger sequencing technique) with a higher than classic coverage (more than 100 times), diminishing the likelihood of sequencing-related frameshifts, base errors and/or misassembly. A comparison of our assembly of the closed OG1RF genome with the restriction map of OG1RF published by Murray <italic>et al</italic>. [##REF##8349561##10##] showed only minor variations (primarily an overestimation of 30 kb for the <italic>Sfi </italic>I fragment E, 540 kb versus 509 kb predicted from the sequence; Figure ##FIG##0##1##).</p>", "<p>We found 232 kb of OG1RF unique sequences distributed in 48 regions ranging from 101 bp to approximately 49 kb in length (Figure ##FIG##0##1##; Additional data file 1). Using the published DNA sequence of V583 as reference (NC_004668), OG1RF shares 2,474 ORFs as well as the 12 rRNA genes and 58 of 68 tRNA genes (Table ##TAB##0##1##). The 10 missing tRNA are localized in a region in V583 that has been replaced in OG1RF by a 49 kb region (see below). Surprisingly, the genomes align syntenically, as shown in Figure ##FIG##1##2##, despite the fact that 25% of the V583 genome is composed of mobile elements. Similarly, the presence of OG1RF-unique sequences has not affected the overall chromosomal arrangement. Some of the major insertions/deletions in the two genomes are shown in Figure ##FIG##1##2##, such as the absence of the pathogenicity island (PAI) in OG1RF and the presence of an approximately 49 kb fragment unique to OG1RF. However, most of the differences are small and cannot be visualized in this figure. Overall, we found 64 areas of divergence between the genomes that can be divided into 3 classes: an additional sequence present in OG1RF when compared with V583; a sequence replacement where a sequence in OG1RF differs from the sequence in V583; and the absence of a sequence from OG1RF when compared with V583.</p>", "<title>CRISPR loci</title>", "<p>The CRISPR (comprised of regularly interspaced short palindromic repeats) loci encoded by some bacterial strains is a recently described system that protects cells from infection with bacteriophage [##REF##17379808##25##, ####REF##18065539##26##, ##REF##18065545##27####18065545##27##]. The specificity of the phage resistance conferred by the CRISPR elements and CRISPR-associated genes (<italic>cas </italic>genes) is determined by spacer-phage sequence similarity. OG1RF carries two CRISPR elements: CRISPR1 (between the OG1RF homologue of EF0672 and EF0673) and CRISPR2 (between the OG1RF homologue of EF2062 and EF2063); CRISPR1 is linked to <italic>cas</italic>-like genes while CRISPR2 is not (Figure ##FIG##2##3##). Both OG1RF CRISPR elements are composed of 7 repeats of a 37 bp palindromic sequence with a 29 bp spacer. None of the 29 bp spacers (14 total) have homology to any sequences in GenBank. The CRISPR1-associated proteins belong to the Nmeni subtype [##REF##16292354##28##]. Species bearing this CRISPR/<italic>cas </italic>subtype have so far been found exclusively in bacteria that are vertebrate pathogens or commensals. The Nmeni subtype is characterized by the presence of four specific <italic>cas </italic>genes and a single copy of the repeat that is upstream of the first gene in the locus. The four <italic>cas </italic>genes encode Cas_csn1 (possible endonuclease), Cas1 (novel nuclease), Cas2 (conserved hypothetical protein), and Cas_csn2 (conserved hypothetical protein). The repeat upstream of <italic>cas_csn1 </italic>appears to have degenerated since it shares only 23 bp with the 37 bp repeat cluster downstream of the last gene. A unique feature of the OG1RF CRISPR1 locus is the presence of a gene downstream of the element, which encodes a hypothetical 119 amino acid transmembrane protein.</p>", "<p>The presence of the CRISPR loci among <italic>E. faecalis </italic>strains may be a powerful tool to avoid the load of prophage replication. To determine the distribution of the CRISPR1 locus in <italic>E. faecalis </italic>strains, 16 isolates of various MLST types were tested for the presence (PCR with primers specific for <italic>csn1 </italic>and <italic>cas1</italic>) or absence (PCR with primers overlapping the junction between EF0672 and EF0673) of the CRISPR1 locus (Table ##TAB##1##2##). Seven strains were <italic>cas </italic>positive, but negative for the junction and the remaining nine were positive only for the junction. This indicates that the location of the CRISPR1 locus appears to be conserved (between EF0672 and EF0673 when compared with the V583 genome). Interestingly, the two vancomycin resistant strains tested were both <italic>cas </italic>negative. It is appealing to postulate that the presence of the CRISPR locus in OG1RF may be the reason for the absence of prophage in this strain.</p>", "<title>A 14.8 kb region inserted in the 23.9 kb region containing <italic>fsrA </italic>and <italic>fsrB</italic></title>", "<p>Nakayama <italic>et al</italic>. [##REF##12039782##29##] described a conserved 23.9 kb chromosomal deletion when comparing <italic>fsrA-</italic>lacking/<italic>fsrC</italic><sup>+</sup>/<italic>gelE</italic><sup>+ </sup>strains (by PCR) from various origins with V583; the deleted sequences start in the middle of EF1841, include the <italic>fsrAB </italic>genes and end in the middle of the <italic>fsrC </italic>gene (EF1820). Loss of the <italic>fsr </italic>regulatory components results in a gelatinase-negative phenotype under routine test conditions despite the fact that these strains still carry the <italic>gelE </italic>gene [##REF##10768947##23##,##REF##12039782##29##]. The absence/presence of the 23.9 kb region, from EF1820/<italic>fsrC </italic>to EF1841, did not appear to correlate with the clinical origin of the isolates [##REF##15131223##30##]. In a more recent analysis of relationships between various <italic>E. faecalis </italic>strains, the 23.9 kb region was not detected in 86% of the strains of the clonal complex (CC)2, 58% of the CC9 strains, nor in any of the CC8 strains [##REF##17611618##31##]. The Symbioflor 1 strain, used as a probiotic, is one representative of the 7.4% of <italic>E. faecalis </italic>isolates that are missing the <italic>gelE </italic>gene in addition to the 23.9 kb region [##REF##17466591##5##,##REF##15131223##30##]. Our analysis of this area in OG1RF revealed the presence of an additional 14.8 kb fragment inserted between the corresponding EF1826 and EF1827 of OG1RF (confirmed by PCR; results not shown). In OG1RF, this 14.8 kb region contains two loci, a WxL locus (described below) and a seven-gene locus that may encode a possible ABC transporter with similarity to one annotated in <italic>Pediococcus pentosaceus</italic>.</p>", "<title>Components of the cell surface</title>", "<p>It has been shown in <italic>E. faecalis </italic>that at least one cell surface protein (Ace) is subject to domain variation [##REF##10948146##20##] and it has been postulated that domain variation may help bacteria escape the immune system. We found more polymorphisms in two families of <italic>E. faecalis </italic>proteins present on the cell surface: the MSCRAMMs and the WxL domain surface proteins. The MSCRAMMs are composed of two large regions, namely, the non-repeat A region (which is usually the ligand binding region for extracellular matrix molecules such as collagen or fibrinogen) and the B region (which typically contains repeated sub-domains). The B region of Ace contains five repeats in OG1RF, while it contains only four in V583 [##REF##10948146##20##]. We found two other MSCRAMM proteins that show polymorphisms in the number of their B repeats. OG1RF_0186 (corresponding to EF2505 of V583) has four repeats compared to seven in V583, and OG1RF_0165 (corresponding to EF2224 of V583) has eight repeats compared to five in V583. It has been proposed that the repeats are used as a stalk that projects the A region across the peptidoglycan and away from the cell surface [##REF##9799504##32##]. A hypothesis that the number of repeats may be proportional to the depth of the peptidoglycan has been proposed [##REF##9799504##32##]. However, OG1RF_0186 carries fewer repeats than EF2505 while Ace and OG1RF_0165 carry more repeats than their counterparts in V583, suggesting that our observation does not fit this hypothesis or that the peptidoglycan depth is not uniform. Apart from these three MSCRAMMs with B-repeat polymorphisms, we identified two unique MSCRAMM proteins in OG1RF: a homologue of EF0089 (OG1RF_0063, which shares 48% similarity) and a homologue of EF1896 (OG1RF_0039, which shares 75% similarity); both are located in the approximately 49 kb region unique to OG1RF described below (Figure ##FIG##0##1##; Additional data file 1).</p>", "<p>Another family of <italic>E. faecalis </italic>surface proteins includes the newly described WxL domain surface proteins. Siezen <italic>et al</italic>. [##REF##16723015##33##] reported a novel gene cluster encoding exclusively cell-surface proteins that is conserved in a subgroup of Gram-positive bacteria. Each gene cluster has at least one member of three gene families: a gene encoding a small LPxTG protein (approximately 120 amino acids); a gene encoding a member of the DUF916 transmembrane protein family; and a gene encoding a WxL domain surface protein. In addition, members of these gene families were found as singletons or associated with genes encoding other proteins (Additional data file 2). Recently, it was shown that the WxL domain attaches to the peptidoglycan on the cell surface [##REF##16963569##34##] and one member of this WxL domain family, the homologue of EF2686 in OG1RF (a probable internalin protein), was shown to be important for virulence in a mouse peritonitis model and is required for dissemination to the spleen and liver [##REF##17620355##35##]. OG1RF shares five complete WxL loci with V583 (EF0750-7, EF2682-6, EF2970-68, EF3181-8, and EF3248-53). OG1RF does not contain homologues of EF2248-54 (carrying instead the <italic>iol </italic>operon), though it has a novel WxL locus within the 14.8 kb unique region upstream of the <italic>fsr </italic>locus (Additional data file 2). In addition to the variation in the number of WxL loci, we also observed polymorphisms in six of the WxL domain surface proteins. For example, OG1RF_0213 shares 88% similarity with EF3188, while OG1RF_0224, OG1RF_0225, and OG1RF_0227 share 64-68% similarity with their V583 counterparts, EF3248, EF3250, and EF3252, respectively. Also, in place of EF3153, EF3154, and EF3155 (which share 70% similarity among themselves), were found non-distantly related homologues, OG1RF_0209 and OG1RF_0210, which share 60-80% similarity with EF3153, EF3154, and EF3155. It is interesting to note that while several of these WxL loci, including the EF0750 and EF3184 loci, were present by hybridization in all the strains (clinical or food isolates) tested by Lepage <italic>et al</italic>. [##REF##16980489##36##], other loci, including the EF3153 and EF3248 loci, were not detected in the majority of these strains. In addition, it appears that the EF3248 locus diverges in the Symbioflor 1 strain. When compared to V583, the sequence identity in this area between the two strains appears to be as low as 75% (depicted in Figure ##FIG##1##2## from reference [##REF##17466591##5##]).</p>", "<p>However, because the Symbioflor 1 genome sequence is not currently available, it was not possible to compare their respective sequences in more detail. Since these proteins are located at the surface of the cell, the low level of homology shared between them may be the result of antigenic variation. More analyses are required for a better understanding of the number, frequency and function of these WxL domain proteins and their possible relationship with the diversity of <italic>E. faecalis</italic>.</p>", "<p>Finally, as previously found using PCR, the <italic>cpsCDEFGHIJK </italic>operon capsule polysaccharide genes [##REF##15184433##37##] were confirmed here as missing, although OG1RF carries the <italic>cpsA </italic>and <italic>cpsB </italic>genes, which were proposed to be essential for <italic>E. faecalis </italic>since all strains tested by Hufnagel <italic>et al</italic>. [##REF##15184433##37##] carry these two genes. In OG1RF, the region that would encode the <italic>cps </italic>operon is only 59 bp in length and has no homology with V583. Thus, while V583 and OG1RF share much similarity between their surface components, there are unique differences that could potentially be important in affecting the behavior of the strains and might be useful for strain typing.</p>", "<title>Two-component regulatory systems</title>", "<p>OG1RF lacks four two-component systems found in V583. These are histidine kinase-response regulator (HK-RR)08, HK-RR12 located in the PAI, HK-RR16 and the <italic>vanB </italic>regulatory system HK-RR11 [##REF##12374813##38##]. However, an OG1RF-unique two-component system with high homology with the <italic>van</italic><sub>G </sub>locus was found at the location corresponding to the region between EF2860 and EF2861 in V583 (Table ##TAB##2##3##). OG1RF_0193 shares 82% similarity with VanR<sub>G </sub>and 81% similarity with VanR<sub>G2</sub>. Similarly, OG1RF_0192 shares 68% similarity with VanS<sub>G </sub>and VanS<sub>G2</sub>. A gene (OG1RF_0191) encoding an M15 family muramoyl pentapeptide carboxypeptidase is located downstream of these two-component regulatory genes (Figure ##FIG##3##4a##). The predicted carboxypeptidase (OG1RF_0191) shares 69% similarity over 179 amino acids with EF2297, a membrane-associated D, D-carboxypeptidase encoded by the <italic>vanB </italic>operon in V583. However, OG1RF_0191 lacks an identifiable transmembrane domain that is important to the VanY function and it is likely, therefore, that this protein may be a soluble D, D-carboxypeptidase/transpeptidase as seen in <italic>Streptomyces </italic>[##REF##3997832##39##] and <italic>Actinomadura </italic>[##REF##15987687##40##], and thus may not be involved in peptidoglycan metabolism. Consequently, it seems unlikely that this operon is a remnant of a vancomycin resistance operon in OG1RF, but rather part of a still unknown regulatory pathway.</p>", "<title>The <italic>iol </italic>operon</title>", "<p>OG1RF carries an <italic>iol </italic>operon while V583 does not. This operon encodes the factors necessary for the degradation of myo-inositol into glyceraldehyde-3P. Many soil and plant micro-organisms, including <italic>Bacillus subtilis </italic>[##REF##9226270##41##] (first <italic>iol </italic>operon identified), <italic>Klebsiella </italic>spp. [##REF##977784##42##], and cryptococci [##REF##394818##43##], have been reported to use myo-inositol as a sole carbon source. Myo-inositol, one of the nine isomers of the inositol group, belongs to the cyclitol group and is abundant in nature, particularly in the soil. The OG1RF <italic>iol </italic>operon appears to be closely related to ones described in <italic>Clostridium perfringens </italic>[##REF##15183876##44##] and <italic>Lactobacillus casei </italic>[##REF##17449687##45##]. In <italic>L. casei</italic>, the myo-inositol operon consists of ten genes with an upstream divergent regulator gene, <italic>iolR</italic>. In OG1RF, the operon appears to include ten genes, beginning with a probable transcriptional regulator (helix-turn-helix domain protein). Also, the OG1RF operon carries two copies of an <italic>iolG</italic>-like gene, which encodes inositol 2-dehydrogenase, the first enzyme of the myo-inositol degradation pathway (Figure ##FIG##4##5##). However, the order of the genes is not the same between <italic>E. faecalis </italic>and <italic>L. casei</italic>. In addition, <italic>iolH</italic>,<italic>iolJ </italic>and <italic>iolK</italic>, present in <italic>L. casei</italic>, are not present in OG1RF, nor are <italic>iolH </italic>and <italic>iolK </italic>present in the <italic>C. perfringens iol </italic>operon.</p>", "<p>Yebra <italic>et al</italic>. reported that <italic>L. casei </italic>was the sole member of the Lactobacillales to carry a functional <italic>iol </italic>operon [##REF##17449687##45##]. To survey <italic>E. faecalis</italic>, also a member of this order, for the presence of the <italic>iol </italic>operon, 48 isolates with different MLST and/or from various origins (including OG1RF and V583) were tested for myo-inositol fermentation; 23 of 48 isolates were positive. In addition, PCR verified the presence of <italic>iolE </italic>and <italic>iolR </italic>in these strains and in one negative for myo-inositol fermentation, indicating that the <italic>iol </italic>operon is not unique to OG1RF. To verify that the <italic>iol </italic>genes are responsible for the fermentation of myo-inositol in OG1RF, transposon insertion mutants [##REF##122512##9##] in the <italic>iolB </italic>and <italic>iolG2 </italic>genes of OG1RF were tested. Both mutants failed to ferment myo-inositol (data not shown), demonstrating that these genes are essential for myo-inositol fermentation. To investigate whether the <italic>iol </italic>operon was 'inserted into' or 'removed from' a putative ancestral strain, the sequences surrounding the <italic>iol </italic>genes were examined. In OG1RF, the <italic>iol </italic>operon is located between the equivalent of EF2239 and EF2352 when compared with V583. In V583, this region encodes probable prophage proteins and carries the <italic>vanB </italic>transposon, which confers vancomycin resistance. Since we did not identify any remnants of the <italic>iol </italic>operon in V583, it would appear that at least two independent events at the same location differentiate OG1RF and V583, suggesting that it is a hot region for rearrangement. This region between EF2239 and EF2352 (111 Kb) is also missing in the Symbioflor 1 strain (referred to as gap 2) [##REF##17466591##5##]. The possible junction and presence of unique sequence in this region, if investigated, was not mentioned in the publication. Nonetheless, preliminary analysis of other strains' genotypes in this area seemed to confirm the hypothesis of a hot region for rearrangement (data not shown).</p>", "<title>A homologue of Tn<italic>916 </italic>in OG1RF</title>", "<p>An analysis of the G+C content of OG1RF unique regions revealed several loci with a lower G+C content than the 37.8% average content of OG1RF. One of these is an approximately 49 kb fragment with a G+C content of 32.1% located between an rRNA operon and the homologue in OG1RF of EF1053, replacing 10 tRNA genes present in V583 (Figure ##FIG##0##1##). This fragment appears to be a patchwork composed of hypothetical genes, homologues of Tn<italic>916</italic>-associated genes and homologues of genes from other Gram-positive organisms, including <italic>Listeria</italic>, <italic>E. faecium</italic>, staphylococci, or lactococci (Additional data file 1). It is interesting to note that this region contains: a putative adhesin protein gene (OG1RF_0039) at one end of the fragment; homologues of 14 Tn<italic>916</italic>-associated genes (Tn<italic>916</italic>_2 to Tn<italic>916</italic>_12, Tn<italic>916</italic>_18 and Tn<italic>916</italic>_19, with an average of 70% similarity); and a gene encoding a putative integrase (OG1RF_0088) at the other end - these three features are also present in Tn<italic>5386 </italic>in <italic>E. faecium </italic>D344R [##REF##17408741##46##]. However, the approximately 49 kb fragment lacks an excisase gene and the probable lantibiotic ABC transporter genes present in Tn<italic>5386</italic>.</p>", "<title>An uninterrupted competence operon in OG1RF</title>", "<p>OG1RF contains what appears to be an intact competence operon while that of V583 appears to be non-functional. This operon in OG1RF is similar to a nine-gene operon described in <italic>Streptococcus mutans </italic>[##REF##15632435##47##], as shown in Figure ##FIG##5##6##. For example, the homologue in OG1RF of EF2046 shares 61% similarity with ComYA and the OG1RF homologue of EF2045 is 55% similar to ComYB. In <italic>S. mutans</italic>, only the first seven genes of the operon are essential for competence [##REF##15632435##47##]. In V583, the fourth gene of this operon (corresponding to OG1RF_0148) is interrupted by phage 4 (EF1896-EF2043); in addition, EF1984 contains a premature stop codon not found in the corresponding gene in OG1RF (OG1RF_0228).</p>", "<p>Natural competence has not been reported for <italic>E. faecalis</italic>. To assess the functionality of this operon in OG1RF, we evaluated the competence of cells in different phases of growth (early log growth to stationary phase) using pAM401 [##REF##3005240##48##] and pMSP3535VA [##REF##10964628##49##]. We were not able to show natural competence under the conditions tested. We have also noted that V583 is less transformable by electroporation than OG1RF. To investigate the possibility that directly or indirectly the <italic>com </italic>operon might be responsible for this phenotype, we also evaluated transformability by electroporation. When compared with OG1RF, transposon mutants [##REF##15489440##12##] in the OG1RF equivalent of EF2045 (encoding the <italic>comGB </italic>homologue) and in the OG1RF equivalent of EF1986 (encoding the <italic>comGF </italic>homologue) showed similar levels of transformability by electroporation (data not shown), implying that the difference in electroporation efficiency observed between OG1RF and V583 is not related to this locus.</p>", "<p>In <italic>Streptococcus pneumoniae </italic>[##REF##9701804##50##], the competence operon is tightly regulated by a quorum sensing two-component system (ComDE) and a competence-stimulating peptide (CSP; encoded by <italic>comC</italic>). We did not find any homologues of CSP in OG1RF. Two homologous ComDE sensor histidine kinase/response regulators were found in OG1RF, one of which is FsrC/FsrA. Based on our previous microarray data, the Fsr system does not regulate the <italic>comY </italic>operon, at least under our previously used conditions (mid-log phase growth to early stationary phase in brain heart infusion (BHI)) [##REF##16585749##22##]. The other ComDE homology is that with a two-component system unique to OG1RF (OG1RF_0199 and OG1RF_0198, respectively) that lies on a 4,706 bp unique fragment that maps between EF3114 and EF3115 in V583. This fragment also carries two genes (OG1RF_0200 and OG1RF_0201) encoding homologues of the YhaQ and YhaP sodium efflux ATP-binding cassette efflux/transporter proteins (Figure ##FIG##3##4b##). Although they are potential elements of a secretion apparatus, these two proteins do not share any homology at the protein level with the competence secretion apparatus ComAB of <italic>S. pneumoniae </italic>[##REF##11115120##51##] nor CslAB from <italic>S. mutans </italic>[##REF##11154426##52##]. Searching for a possible CSP in the vicinity of these genes, we identified a small ORF encoding 50 amino acids between <italic>yhaP </italic>and OG1RF199 and another encoding 20 amino acids downstream of OG1RF198. More analysis will be required to determine if there are conditions in which the OG1RF <italic>com </italic>operon is expressed and to determine whether or not this two-component system is involved in competence.</p>", "<title>Limited presence of mobile elements</title>", "<p>By probing a microarray of the V583 genome and plasmids with OG1RF genomic DNA, we previously estimated that only 75% of V583 ORFs were also present in OG1RF [##REF##16585749##22##]. Later, Aakra <italic>et al</italic>. [##REF##17220255##53##] compared nine strains, including OG1RF to V583, using comparative genomic hybridization. In these results, OG1RF appears to carry a few genes included in the PAI, and a few prophage genes. Using the complete genome sequence, we have now found that OG1RF lacks 639 genes and the three pTET plasmids described in V583. All but 45 of the missing genes are associated with putative mobile elements, such as the entire PAI, the recently described phages 1, 3, 4, 5, 6, and 7 [##REF##17611618##31##], and the approximately 111 kb area between genes EF2240 and EF2351 (including the <italic>vanB </italic>transposon) present in V583. The absence of these elements appears also to be a characteristic of the commensal strain Symbioflor 1, although because the genome was not completely finished, the possibility remains that some of these regions were not sequenced. In conclusion, other than the approximately 49 kb fragment containing a Tn<italic>916 </italic>homologue, it appears that OG1RF has only one additional possible mobile element derivative, namely the phage 2 proposed to be part of the core genome [##REF##17611618##31##].</p>", "<title>Fusidic acid and rifampicin resistance</title>", "<p>OG1RF was sequentially selected from OG1 for resistance to fusidic acid and rifampicin [##REF##122512##9##]. The mutation leading to rifampicin resistance was identified in the <italic>rpoB </italic>gene by Ozawa <italic>et al</italic>. [##REF##15907539##54##] and is caused by the A1467G mutation, which results in substitution of arginine for histidine at amino acid 489. The mutation also affected the clumping phenotype of <italic>traA </italic>mutants and this effect appears to be specific for the pAM373 system [##REF##15907539##54##]. All of the other 22 differences in <italic>rpoB </italic>between OG1RF and V583 are synonymous. Fusidic acid resistance is associated with mutation(s) in the <italic>fusA </italic>gene, which encodes elongation factor G. We compared <italic>fusA </italic>from OG1RF with that in V583 and identified two differences (C1368A and T1992C). The mutation T1992C is synonymous, while the mutation C1368A leads to the presence of glutamine (histidine in V583) at position 404 in OG1RF. Mutations in this region have been associated with fusidic acid resistance in <italic>Staphylococcus aureus </italic>[##REF##11036042##55##,##REF##11309125##56##], and thus the C1368A mutation is likely the cause of the fusidic acid resistance phenotype in OG1RF.</p>", "<title>Virulence and biofilm comparisons of OG1RF with V583</title>", "<p>When compared in the mouse peritonitis model, the LD<sub>50 </sub>values of V583 in different determinations were lower (4.8 × 10<sup>7 </sup>to 1.1 × 10<sup>8 </sup>colony forming units (CFU)/ml) than the LD<sub>50 </sub>values of OG1RF (1.2 × 10<sup>8 </sup>to 4.8 × 10<sup>8 </sup>CFU/ml). However, at comparable inoculum, OG1RF (4 × 10<sup>8 </sup>CFU/ml) showed more rapid mortality versus V583 (5 × 10<sup>8 </sup>CFU/ml) in the first 48 hours (<italic>P </italic>= 0.0034; Additional data file 3). In a urinary tract infection model administering mixed equal inocula of V583 and OG1RF, OG1RF significantly outnumbered V583 in kidney with geometric means of 1.3 × 10<sup>4 </sup>CFU/gm for OG1RF versus 1.9 × 10<sup>2 </sup>CFU/gm for V583 (<italic>P </italic>= 0.0005); in urinary bladder homogenates, the geometric mean CFU/gm was 1.7 × 10<sup>3 </sup>for OG1RF versus 6.6 × 10<sup>1 </sup>for V583 (<italic>P </italic>= 0.003; Figure ##FIG##6##7a##). Similarly, in mono-infection, the geometric mean CFU/gm of OG1RF in kidneys was 9.4 × 10<sup>3 </sup>versus 4 × 10<sup>1 </sup>for V583 (<italic>P </italic>= 0.0035; Figure ##FIG##6##7b##). We also found that OG1RF produced 20% more biofilm (<italic>P </italic>&lt; 0.01) than V583 at 24 hours (results not shown). These results, together with the previous results in <italic>C. elegans </italic>[##REF##11535834##17##], demonstrate that OG1RF, although lacking what was thought to be important for virulence (PAI, plasmids, prophages), is as pathogenic as V583 in at least three assays.</p>" ]
[ "<title>Conclusion</title>", "<p><italic>E. faecalis </italic>OG1RF carries a number of unique loci compared to V583. Those of particular interest include new surface proteins (MSCRAMMs and WxL domain proteins), an operon encoding myo-inositol utilization, an intact competence operon, and two CRISPR elements. The CRISPR elements may be of particular significance when one considers that most of what is missing from OG1RF compared to V583 consists of mobile genetic elements (MGEs), including 6 phages or remnants thereof. The presence of the CRISPR elements in OG1RF provides a tantalizing, but as yet unproven, explanation for the discordance in the number of mobile elements between these two strains.</p>", "<p>The acquisition of MGE is believed to be an important mechanism by which the species <italic>E. faecalis </italic>had been able to generate genetic diversity and, thereby, highly variable phenotypes [##REF##12663927##4##]. It has been proposed that the ability of <italic>E. faecalis </italic>to cause healthcare related infections is associated with these MGEs [##REF##12663927##4##,##REF##17466591##5##]. This hypothesis was supported by several studies that have highlighted the importance of virulence determinants carried by these mobile elements, such as cytolysin [##REF##15638770##57##] by the PAI. However, more recent results from Aakra <italic>et al</italic>. [##REF##17220255##53##] and Lepage <italic>et al</italic>. [##REF##16980489##36##] demonstrate that these factors may be present in harmless strains while absent in clinical isolates, indicating that <italic>E. faecalis </italic>virulence is not dependent on any single virulence factor. Indeed, few studies have compared the virulence pattern of strains from various origins. The increased ability of OG1RF to infect kidneys and to produce biofilm, despite the absence of MGEs and their associated virulence factors, was surprising. Different possibilities can be proposed relating to the factors important for these differences in enterococcal infections. One of these is that virulence in the assays used may be linked to the shared core genome of these two strains, with the differences arising from the unique genes. On the other hand, virulence could be associated primarily with the genes unique to each strain, but with each set being able to complement the absence of the other. It seems most likely that virulence, and/or some combination of virulence and fitness, is caused by the expression of a mixture of both the core and unique genes. It is also important to remember that <italic>E. faecalis </italic>is a well adapted commensal, carrying the genes necessary to survive and to colonize the gut, and that a subset, particularly MLST CC2 and CC9 [##REF##16880002##58##], predominates among hospital acquired infections. It may be that these clonal complexes are not more virulent <italic>per se</italic>, as defined in the assays described here, but rather are better able to survive and/or colonize hospitalized patients, taking advantage of factors that predispose to nosocomial infections such as urinary or venous catheters, or mucositis, among others.</p>", "<p>Sequencing of more <italic>E. faecalis </italic>strains may facilitate our understanding of the path from commensalism to pathogenicity, a crucial prerequisite for designing therapeutic interventions directed to control an organism that is already resistant to a large spectrum of antibiotics.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A comparison of two strains of the hospital pathogen <italic>Enterococcus faecalis</italic> suggests that mediators of virulence differ between strains and that virulence does not depend on mobile gene elements</p>", "<title>Background</title>", "<p><italic>Enterococcus faecalis </italic>has emerged as a major hospital pathogen. To explore its diversity, we sequenced <italic>E. faecalis </italic>strain OG1RF, which is commonly used for molecular manipulation and virulence studies.</p>", "<title>Results</title>", "<p>The 2,739,625 base pair chromosome of OG1RF was found to contain approximately 232 kilobases unique to this strain compared to V583, the only publicly available sequenced strain. Almost no mobile genetic elements were found in OG1RF. The 64 areas of divergence were classified into three categories. First, OG1RF carries 39 unique regions, including 2 CRISPR loci and a new WxL locus. Second, we found nine replacements where a sequence specific to V583 was substituted by a sequence specific to OG1RF. For example, the <italic>iol </italic>operon of OG1RF replaces a possible prophage and the <italic>vanB </italic>transposon in V583. Finally, we found 16 regions that were present in V583 but missing from OG1RF, including the proposed pathogenicity island, several probable prophages, and the <italic>cpsCDEFGHIJK </italic>capsular polysaccharide operon. OG1RF was more rapidly but less frequently lethal than V583 in the mouse peritonitis model and considerably outcompeted V583 in a murine model of urinary tract infections.</p>", "<title>Conclusion</title>", "<p><italic>E. faecalis </italic>OG1RF carries a number of unique loci compared to V583, but the almost complete lack of mobile genetic elements demonstrates that this is not a defining feature of the species. Additionally, OG1RF's effects in experimental models suggest that mediators of virulence may be diverse between different <italic>E. faecalis </italic>strains and that virulence is not dependent on the presence of mobile genetic elements.</p>" ]
[ "<title>Abbreviations</title>", "<p>ATCC, American type culture collection; BHI, brain heart infusion; CC, clonal complex; CFU, colony forming units; CRISPR, comprised of regularly interspaced short palindromic repeats; CSP, competence-stimulating peptide; HK-RR, histidine kinase-response regulator; MGE, mobile genetic element; MSCRAMM, microbial surface component recognizing adhesive matrix molecules; MLST, multilocus sequence typing; ORF, open reading frame; PAI, pathogenicity island.</p>", "<title>Authors' contributions</title>", "<p>GMW, DAG, and BEM designed the study. AB performed much of the post-annotation analysis and non-animal experiments, and wrote the draft of the manuscript. KVS performed the animal experiments. AB, DAG, XQ, JS, SY, AM, KAF, JG, CAA, YS, SRN, MZ, VPP, SC, and SKH annotated the genome. XQ and HJ contributed bioinformatics support. YD, SD-R, CB, HS, GC, GW, DM, LC, and RAG composed the sequencing and finishing team. DAG, BEM, SKH, and GMW assisted in critical review of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data files are available with the online version of this paper. Additional data file ##SUPPL##0##1## is a list of the ORFs unique to OG1RF compared to V583 with their OG1RF locus tag, location in the genome, and definition. Additional data file ##SUPPL##1##2## is a list of genes encoding proteins with a WxL domain in OG1RF and/or V583. Additional data file ##SUPPL##2##3## shows the results of the mouse peritonitis model using OG1RF and V583, with the statistical analysis. Additional data file ##SUPPL##3##4## is a list of the significant primers used in this study.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We would like to express our sincere thanks to J Hernandez, S Wang, Z Li, D Ngo, and L Hemphill for their help during the sequencing process. We also would like to thank JM Urbach, Massachusetts General Hospital, Boston, MA for helping localize the transposon insertions in the OG1RF unique sequences. This research was supported by grant R21 AI064470 from the National Institutes of Health to GMW and by NIH grant R37 AI47923 from the Division of Microbiology and Infectious Diseases, NIAID, to BEM.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Map of the OG1RF chromosome. The following features are displayed (from the inside out): restriction maps using <italic>Sfi</italic>I, <italic>Asc</italic>I, and <italic>Not</italic>I (black) from Murray <italic>et al</italic>. [##REF##8349561##10##] overlaid with the digestion profile predicted from the sequence (red); G+C content in percentage in green; the total OG1RF-unique genes are shown in purple with those in (+) orientation labeled in blue, and those in (-) orientation labeled in red.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Dot plot of OG1RF versus V583 generated by BLASTN. The dot plot was generated by aligning the OR1RF genome against the V583 genome using BLASTN (e-value 1e-10). The alignment pairs were plotted according to their genome coordinates. The visible areas of divergences are labeled using 'Δ ' to indicate a sequence absent in OG1RF and '∇ ' to indicate a sequence unique to OG1RF (locus tag OG1_xxxx) when compared with V583 (locus tag EFxxxx). Phages 1, 3, 4, 5, 6, 7 of V583 (φ1 to 7; see [##REF##17611618##31##]) and the PAI locations, all of which are missing from OG1RF, are also indicated.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>The two CRISPR loci of OG1RF. <bold>(a)</bold> The CRISPR1 locus. The CRISPR1 element is represented with a hatched box while the CRISPR1 associated genes are represented in orange; the white arrows indicate ORFs present in both OG1RF and V583. The black diamonds represent the 37 bp repeat sequences, while the open boxes with a number indicate the 29 bp unique sequences. <bold>(b)</bold> The CRISPR2 locus containing only a CRISPR element. <bold>(c)</bold> CRISPR consensus and unique sequences. The underlined bases indicate mismatches at these locations. The sequences numbered 1 to 14 represent the unique sequences located in the CRISPR1 and CRISPR2 elements.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Two-component systems unique to OG1RF. <bold>(a)</bold> Two-component system with homology to the Van<sub>G </sub>system. <bold>(b) </bold>Two-component system with homology to the <italic>comCD </italic>genes of <italic>S. pneumoniae</italic>. The two-component system (OG1RF_0198 and OG1RF_0199) is indicated in light blue; the two ORFs encoding potential transporter proteins (OG1RF_0200 and OG1RF_0201) are represented in pink. In green are indicated two small ORFs encoding polypeptides of less than 51 amino acids. The white arrows indicate ORFs also present in V583.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>The <italic>iol </italic>operon. The <italic>iol </italic>genes are labeled based on the homology/conserved motif of their encoded proteins with known enzymes necessary for myo-inositol degradation. For all strains, the described or probable regulator is represented in blue. <italic>E. faecalis </italic>OG1RF: the <italic>iol </italic>operon is represented in yellow, OG1RF_0166 (green arrow) located downstream of the <italic>iol </italic>operon encodes a probable PTS IIC component, while the white arrows indicate ORFs also present in V583. For <italic>B. subtilis </italic>168, <italic>C. perfringens </italic>strain 13, and <italic>L. casei </italic>BL23, the <italic>iol </italic>genes are represented in green, orange and purple, respectively. <italic>C. perfringens iol </italic>mRNA transcript includes five other genes encoding proteins whose functions do not appear to be related to myo-inositol degradation; these genes are represented in gray.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>The OG1RF competence operon and its similarity with the competence operon of <italic>S. mutans</italic>. The ORFs essential for natural competence in <italic>S. mutans </italic>are shown in green as well as their homologues in OG1RF and V583. The ORF corresponding to the homologue of ComYD was not described in V583 [##REF##12663927##4##], due to the presence of a probable prophage (EF1986-EF2043). The premature stop codon in EF1984 in V583 is indicated with an asterisk. <italic>ackA/</italic>EF1983 is represented in orange. The proteins encoded by the ORFs represented in white do not share any features of the known competence proteins or homology between <italic>S. mutans </italic>and <italic>E. faecalis</italic>; in <italic>S. mutans</italic>, <italic>ackA </italic>and <italic>ytxK </italic>are co-transcribed with the <italic>comY </italic>genes [##REF##15632435##47##].</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>Comparison of OG1RF and V583 in a mouse urinary tract infection model. <bold>(a)</bold> Mixed infection by wild-type <italic>E. faecalis </italic>strains OG1RF and V583 in the kidneys and urinary bladders of mice (n = 21; competition assay). Data are expressed as the log<sub>10</sub>(CFU)/gm for OG1RF or V583; the log<sub>10</sub>(CFU)/gm for both kidneys were combined and averaged from two independent experiments. Black solid diamonds and triangles represent <italic>E. faecalis </italic>strains OG1RF and V583, respectively, for kidney homogenates, and empty diamonds and triangles represent OG1RF and V583, respectively, for urinary bladder homogenates. Horizontal bars represent geometric means. Log<sub>10</sub>(CFU) were compared for statistical significance by the paired <italic>t</italic>-test. The minimum detection limit in these experiments was 10<sup>1 </sup>and 10<sup>2 </sup>CFU/gm of kidney and urinary bladder homogenates, respectively. <bold>(b) </bold>Mono-infection using <italic>E. faecalis </italic>strains OG1RF or V583 in the kidneys of mice (10<sup>3 </sup>CFU per mice, n = 9). Data are expressed as log<sub>10</sub>(CFU)/gm for OG1RF recovered from kidney homogenates 48 h after infection; the log<sub>10</sub>(CFU)/gm for a kidney pair were combined and averaged. Black and white triangles represent OG1RF and V583, respectively. Horizontal bars represent geometric means. The CFU of V583 recovered from kidneys was significantly reduced compared to the CFU of OG1RF, as determined by the unpaired <italic>t</italic>-test.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>General features of OG1RF compared to V583</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">V583</td><td/><td align=\"left\">OG1RF</td></tr></thead><tbody><tr><td align=\"left\">General features</td><td/><td/><td/></tr><tr><td align=\"left\">Size (base pairs)</td><td align=\"left\">3,218,031</td><td/><td align=\"left\">2,739,633</td></tr><tr><td align=\"left\">G+C content (%)</td><td align=\"left\">37.5</td><td/><td align=\"left\">37.8</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">rRNA genes</td><td align=\"left\">12</td><td/><td align=\"left\">12</td></tr><tr><td align=\"left\">tRNA genes</td><td align=\"left\">68</td><td/><td align=\"left\">58</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Genes common to both strains</td><td/><td align=\"left\">2,474*</td><td/></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Genes unique to OG1RF</td><td/><td/><td/></tr><tr><td align=\"left\">Similar to known proteins</td><td/><td/><td align=\"left\">114<sup>†</sup></td></tr><tr><td align=\"left\">Conserved hypotheticals</td><td/><td/><td align=\"left\">50</td></tr><tr><td align=\"left\">No database match</td><td/><td/><td align=\"left\">63</td></tr><tr><td align=\"left\">Total</td><td/><td/><td align=\"left\">227</td></tr><tr><td/><td/><td/><td/></tr><tr><td align=\"left\">Total number of ORFs</td><td align=\"left\">3,113</td><td/><td align=\"left\">2,701<sup>‡</sup></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Frequency of the CRISPR locus among <italic>E. faecalis</italic></p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Name</td><td align=\"left\">Other</td><td align=\"left\">Origin</td><td align=\"left\">Source/reference</td><td align=\"left\">MLST</td><td align=\"left\">ErmR*</td><td align=\"left\">VanR<sup>†</sup></td><td align=\"left\">cas<sup>‡</sup></td><td align=\"left\">EF0672-3<sup>§</sup></td></tr></thead><tbody><tr><td align=\"left\">TX4002</td><td align=\"left\">OG1RF</td><td align=\"left\">Human</td><td align=\"left\">[##REF##98769##8##,##REF##122512##9##]</td><td align=\"left\">1</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX2708</td><td align=\"left\">V583</td><td align=\"left\">Clinical isolate</td><td align=\"left\">[##REF##2554802##6##]</td><td align=\"left\">6<sup>¶</sup></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2144</td><td align=\"left\">E1840</td><td align=\"left\">Clinical isolate</td><td align=\"left\">Ruiz-Garbajosa P.<sup>#</sup></td><td align=\"left\">40</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX2135</td><td align=\"left\">E1795</td><td align=\"left\">Hospital survey</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">44</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2137</td><td align=\"left\">E1798</td><td align=\"left\">Hospital survey</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">16</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX2141</td><td align=\"left\">E1825</td><td align=\"left\">Clinical isolate</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">25</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2140</td><td align=\"left\">E1803</td><td align=\"left\">Hospital survey</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">38</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2138</td><td align=\"left\">E1801</td><td align=\"left\">Hospital survey</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">48</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2146</td><td align=\"left\">E1844</td><td align=\"left\">Clinical isolate</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">61</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX2139</td><td align=\"left\">E1802</td><td align=\"left\">Hospital survey</td><td align=\"left\">Ruiz-Garbajosa P.</td><td align=\"left\">35</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX4240</td><td align=\"left\">A0826</td><td align=\"left\">Pig</td><td align=\"left\">Jensen L.</td><td align=\"left\">98</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX4247</td><td align=\"left\">E1876</td><td align=\"left\">Pig</td><td align=\"left\">Gaastra W.</td><td align=\"left\">20</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX4245</td><td align=\"left\">E1872</td><td align=\"left\">Dog</td><td align=\"left\">Gaastra W.</td><td align=\"left\">16</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td><td align=\"left\">-</td></tr><tr><td align=\"left\">TX4243</td><td align=\"left\">E0252</td><td align=\"left\">Calf</td><td align=\"left\">Mevius D.</td><td align=\"left\">23</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX4255</td><td align=\"left\">A0808</td><td align=\"left\">Clinical isolate</td><td align=\"left\">Kawalec M.</td><td align=\"left\">88</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr><tr><td align=\"left\">TX4259</td><td align=\"left\">A1006</td><td align=\"left\">Clinical isolate</td><td align=\"left\">Kawalec M.</td><td align=\"left\">135</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>OG1RF-unique regulators</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">OG1RF</td><td align=\"left\">Description</td><td align=\"left\">Best hit</td><td align=\"left\">Size*</td><td align=\"left\">Comments</td></tr></thead><tbody><tr><td align=\"left\">OG1RF_0070</td><td align=\"left\">Transcriptional regulator</td><td align=\"left\">116512576</td><td align=\"left\">102</td><td align=\"left\">-</td></tr><tr><td align=\"left\">OG1RF_0073</td><td align=\"left\">LytR family response regulator</td><td align=\"left\">81428169</td><td align=\"left\">151</td><td align=\"left\">-</td></tr><tr><td align=\"left\">OG1RF_0120</td><td align=\"left\">BglG family transcriptional antiterminator</td><td align=\"left\">47095712</td><td align=\"left\">494</td><td align=\"left\">Probable regulator of the downstream PTS system</td></tr><tr><td align=\"left\">OG1RF_0138</td><td align=\"left\">Transcriptional regulator</td><td align=\"left\">116493423</td><td align=\"left\">219</td><td align=\"left\">Probable transcriptional regulator of the downstream ABC superfamily transporter</td></tr><tr><td align=\"left\">OG1RF_0143</td><td align=\"left\">GntR family transcriptional regulator</td><td align=\"left\">82745913</td><td align=\"left\">236</td><td align=\"left\">Probable regulator of the downstream PTS system</td></tr><tr><td align=\"left\">OG1RF_0175</td><td align=\"left\">DNA binding protein</td><td align=\"left\">15890504</td><td align=\"left\">293</td><td align=\"left\">Probable regulator of the <italic>iol </italic>operon</td></tr><tr><td align=\"left\">OG1RF_0192</td><td align=\"left\">Sensor histidine kinase VanS<sub>G</sub></td><td align=\"left\">119635646</td><td align=\"left\">371</td><td align=\"left\">Best homology with Van<sub>G </sub>and</td></tr><tr><td align=\"left\">OG1RF_0193</td><td align=\"left\">Response regulator VanR<sub>G</sub></td><td align=\"left\">119635645</td><td align=\"left\">235</td><td align=\"left\">Van<sub>G2 </sub>two-component systems.</td></tr><tr><td/><td/><td/><td/><td align=\"left\">OG1RF_0192 and OG1RF_0193 appear cotranscribed with a gene encoding a M15 family muramoylpentapeptide carboxypeptidase</td></tr><tr><td align=\"left\">OG1RF_0198</td><td align=\"left\">Response regulator</td><td align=\"left\">47567135</td><td align=\"left\">240</td><td align=\"left\">Best homology with AgrA from <italic>Bacillus cereus </italic>G9241. However, no presence of AgrB or AgrD homologues in the vicinity. Also similar to ComE of <italic>S. pneumoniae </italic>(52% similarity)</td></tr><tr><td align=\"left\">OG1RF_0199</td><td align=\"left\">Sensor histidine kinase</td><td align=\"left\">47567134</td><td align=\"left\">443</td><td align=\"left\">Best homology with AgrC from <italic>Bacillus cereus </italic>G9241. Also similar to ComD of <italic>Streptococcus pneumoniae </italic>(48% similarity)</td></tr><tr><td align=\"left\">OG1RF_0220</td><td align=\"left\">Probable endoribonuclease MazF</td><td align=\"left\">69244828</td><td align=\"left\">114</td><td align=\"left\">Toxin-antitoxin described in <italic>E.</italic></td></tr><tr><td align=\"left\">OG1EF_0221</td><td align=\"left\">Probable antitoxin MazE</td><td align=\"left\">69244829</td><td align=\"left\">77</td><td align=\"left\"><italic>coli </italic>and recently on an <italic>E. faecium </italic>plasmid</td></tr></tbody></table></table-wrap>" ]
[]
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[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>OG1RF locus tag, location in the genome, and definition are given.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Genes encoding proteins with a WxL domain in OG1RF and/or V583.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Results of the mouse peritonitis model using OG1RF and V583, with the statistical analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>The significant primers used in this study.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*The assessment of the genes common to both strains is based on the homology at the DNA level with the ORFs described for V583 (source TIGR [##UREF##2##70##]). The BLASTN cutoff e-value was 1e-5. <sup>†</sup>This number includes the proteins with domain polymorphism (see text for details). <sup>‡</sup>Estimated number of ORFs calculated by adding the OG1RF-unique ORFs to the number of ORFs shared with V583.</p></table-wrap-foot>", "<table-wrap-foot><p>*Erythromycin resistance was tested at 5 μg/ml. <sup>†</sup>Vancomycin resistance was tested at 10 μg/ml. <sup>‡</sup>Two sets of primers were used to detect the <italic>cas </italic>genes (<italic>cas1 </italic>and <italic>csn1</italic>). <sup>§</sup>This set of primers amplifies the junction between EF0672 and EF0673 where the CRISPR1 locus is inserted in OG1RF. <sup>¶</sup>CC2. <sup># </sup>Ruiz-Garbajosa P. (Spain), Jensen L. (Denmark), Gaastra W. and Mevius D. (Netherland), and Kawalec M. (Poland).</p></table-wrap-foot>", "<table-wrap-foot><p>*Amino acids</p></table-wrap-foot>" ]
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[{"article-title": ["Genboree"]}, {"surname": ["Reed", "Muench"], "given-names": ["L", "H"], "article-title": ["A simple method of estimating fifty per cent end points."], "source": ["Am J Hygiene"], "year": ["1938"], "volume": ["27"], "fpage": ["493"], "lpage": ["497"]}, {"article-title": ["TIGR"]}]
{ "acronym": [], "definition": [] }
70
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 8; 9(7):R110
oa_package/8e/cc/PMC2530867.tar.gz
PMC2530868
18613964
[ "<title>Background</title>", "<p>Glaucoma comprises a group of diseases that are characterized by optic neuropathy associated with optic disc cupping and loss of visual field and, in many patients, with elevated intraocular pressure (IOP) [##REF##15158634##1##]. There are several types of glaucoma, including juvenile and adult-onset types, primary open angle glaucoma (POAG), narrow-angle glaucoma, and secondary glaucoma, with different pathogenic mechanisms. POAG is more prevalent in Black Americans of African American (AA) ancestry than in Caucasian American (CA) populations of European ancestry (CA), with reported frequencies of 3-4% in the AA population over the age of 40 years, as compared with approximately 1% in CA populations [##REF##15078671##2##]. The disease is particularly frequent in Afro-Caribbean persons, with a prevalence of 7% in Barbados and 8.8% in St Lucia [##REF##11562932##3##]. On average, African Americans have the longest duration [##REF##9008633##4##] and higher progression of disease [##REF##18172076##5##] compared to other populations. In addition to racial differences, a positive family history of POAG is a major risk factor for the disease in African Americans [##REF##17629563##6##]. The Advanced Glaucoma Intervention Study (AGIS), which compared the glaucoma outcomes in AA and CA patients, concluded that after failure of medical therapy, surgical trabeculectomy delayed progression of glaucoma more effectively in CA than in AA patients [##REF##12698047##7##,##REF##15051195##8##].</p>", "<p>Abnormally elevated IOP elicits a complex sequence of putative neurodestructive and neuroprotective cellular responses in the optic nerve head (ONH) [##REF##9076213##9##]. Previous studies demonstrated that gene expression in astrocytes of the glaucomatous ONH serve as the basis for these responses [##REF##11921203##10##]. Here we present evidence that primary cultures of AA and CA astrocytes derived from POAG donors exhibit differential gene expression of genes that relate to reactive astrocytes and to pathological changes that occur in the glaucomatous ONH. Validations of changes in expression of selected genes were done by quantitative real-time RT-PCR, western blots, enzyme-linked immunosorbent assay (ELISA) and various functional assays. Network analysis of gene product interactions focused our findings on specific functional pathways. Our data indicate that both normal and glaucomatous astrocytes from AA donors exhibit differential expression in genes that regulate signal transduction, cell migration, intracellular trafficking and secretory pathways.</p>" ]
[ "<title>Materials and methods</title>", "<title>Human eyes</title>", "<p>Thirteen eyes from eleven CA donors (age 73 ± 9 years) with POAG (referred to as CAG) and six eyes from three AA donors (age 62 ± 13 years) with POAG (referred to as AAG) were used to generate ONH astrocyte cultures as described (Additional data file 1). Myelinated optic nerves were fixed in 4% paraformaldehyde, post-fixed in osmium, embedded in epoxy resin and stained with paraphenylendiamine to detect axon degeneration as described earlier to confirm glaucoma and to assess optic nerve damage (Additional data file 1). Normal eyes were from 12 CA donors (age 60 + 11 years) and 12 AA donors (age 58 + 12 years) with no history of eye disease, diabetes, or chronic central nervous system disease (Additional data file 2).</p>", "<title>Astrocyte cultures</title>", "<p>Primary cultures of human ONH astrocytes were generated as described previously [##REF##14613807##11##]. Briefly, four explants from each lamina cribrosa were dissected and placed into 25 cm<sup>2 </sup>Primaria tissue culture flasks (Falcon, Lincoln Park, NJ, USA). Explants were maintained in DMEM-F12 supplemented with 10% fetal bovine serum (Biowhittaker, Walkerswille, MD, USA) and 10 μl/ml of PSFM (10,000 U/ml penicillin, 10,000 μg/ml streptomycin and 25 μg/ml amphotericin B; Gibco/BRL, Gaithersburg, MD, USA). Cells were kept in a 37°C, 5% CO<sub>2 </sub>incubator. Primary cultures were purified by using an immunopanning procedure [##REF##14613807##11##]. Purified cells were expanded after characterization by immunostaining for astrocyte markers GFAP and NCAM (Neural cell adhesion molecule) as described [##REF##14613807##11##]. Second passage cell cultures were stored in RPMI 1640 with 10% DMSO in liquid nitrogen until use. For each set of experiments, cells were thawed and cultured so that sufficient cells from the same batch were available for multiple experiments.</p>", "<title>Antibodies</title>", "<p>An affinity purified rabbit polyclonal antibody to MYLK was a gift from Dr Linda van Eldik (Northwestern University). It was used in western blotting (1:10,000) and immunohistochemistry (1:50). Another MYLK antibody (M7905) is a mouse monoclonal antibody (Sigma-Aldrich, St Louis, MO, USA) and it was used in western blotting (1:10,000) and immunohistochemistry(1:50). TGFBRII (L-21) is a rabbit polyclonal antibody (Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA). It was used in western blotting (1: 200) and immunohistochemistry (1:50). VCAN is a goat polyclonal antibody (R&amp;D Systems, Minneapolis, MN, USA). It was used in immunohistochemistry (1:20).</p>", "<title>Oligonucleotide microarray analysis</title>", "<p>Total RNA was extracted using Qiagen RNeasy mini kits (Qiagen, Valencia, CA, USA). RNA was then purified and quantified by measuring absorbance at 260 nm. Quality and intactness of the RNA was assessed by capillary electrophoresis analysis using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). cDNA was synthesized from 2-5 μg purified RNA by using Superscript Choice system (Gibco BRL Life Technologies, Gaithersburg, MD, USA) and T7-(dT)24 primer (GENSET, La Jolla, CA, USA). Using Bioassay High Yield RNA Transcript Labeling Kit (Enzo Diagnostics, Farmingdale, NY, USA), <italic>in vitro </italic>transcription was carried out with the cleaned double-stranded cDNA as a template in the presence of biotinylated UTP and CTP. Purified biotin-labeled cRNA was fragmented before the hybridization. Hybridization of the labeled cRNA to Human Genome U95Av2, U133A, U133A 2.0 chips (Affymetrix, Santa Clara, CA, USA) was carried out by using Genechip Instrument System (Affymetrix) at the Genechip Core Facility of Washington University School of Medicine. The arrays were washed and stained with streptavidin-phycoerythrin (Molecular Probes, Eugene, OR, USA) followed by scanning on an Agilent GeneArray Scanner G2500A (Agilent Technologies, Palo Alto, CA, USA).</p>", "<title>Data analysis</title>", "<title>Pretreatment of data</title>", "<p>The first step in the analysis of the microarray data was to determine which genes to consider 'present' or 'absent'. We estimated the probe-set present/absent calls by using the Wilcoxon signed rank-based algorithm. In order to reduce false positives, we removed the probe-sets with all samples as 'absent' (Additional data file 3).</p>", "<title>Comparison between glaucomatous ONH astrocytes from AA and CA normal donors</title>", "<p>As the experiments were done at different times, two types of Affymetrix microarrays (Human Genome U95Av2 array and Human Genome U133A 2.0 array) were used. Samples from eight CAG donors, seven CA normal donors and six AAG samples were measured using a Human Genome U95Av2 array. Eighteen CA samples, eighteen AA samples and six AAG samples were measured using a Human Genome U133A 2.0 array. The data measured by two types of arrays were normalized separately by RMA normalization [##REF##12582260##74##,##REF##12538238##75##]. We defined common glaucoma-related genes as genes differentially expressed in both CAG versus CA and AAG versus AA, and did comparisons of CAG versus CA and AAG versus AA separately. The differentially expressed genes were identified by the empirical Bayesian shrinkage moderated t-statistics in the <italic>limma </italic>Bioconductor package [##REF##15461798##76##]. A mixed effects model was used to account for the effect of technical replicates. Genes exhibiting a fold-change &gt;1.5 and <italic>p</italic>-value &lt; 0.01 were considered significant. To reduce false positives because the AAG has only three biological replicates, we applied the Benjamini and Hochberg false discovery rate multiple testing correction with a false discovery rate of 0.05 (AAG versus CAG and AAG versus AA).</p>", "<p>To compare the significant gene list based on two types of microarray platforms, the Affymetrix probeset IDs were transferred as Entrez IDs based on the Bioconductor library. Genes whose Entrez IDs appear in both the differentially expressed gene list from CAG versus CA (using the Human Genome U95Av2 array) and AAG versus AA comparisons (using the Human Genome U133A 2.0 array) and change in the same direction were considered as common glaucoma-related genes. Genes that are differentially expressed for AAG versus CAG (using the Human Genome U95Av2 array), but without significant changes for AA versus CA (using Human Genome U133A 2.0 array), were considered as the glaucoma race-related genes. Here we considered a <italic>p</italic>-value &gt; 0.05 as indicative of changes that were not significant.</p>", "<p>GO analysis of differential expression in glaucomatous astrocytes was done with GoMiner [##REF##12702209##14##]. Briefly, gene lists of up- and downregulated genes (<italic>p </italic>&lt; 0.01 as described above) were normalized to 1 and -1, respectively, for genes that exhibited at least a 1.5-fold change in either direction. These lists were then loaded into GoMiner using local GO databases accessed using the 'Derby' module. GoMiner output was analyzed with a significance cutoff of <italic>p </italic>&lt; 0.01 and at least four genes per category.</p>", "<title>Network construction</title>", "<p>Initially, we scanned the differentially expressed gene lists for AAG-CAG, AAG-AA, and CAG-CA comparisons for groups of genes that were either in common GO categories, or were highly over- or underexpressed (&gt;1.5-fold, <italic>p </italic>&lt; 0.01). These short lists were then used as a source of nodes for each network group. Networks of interacting proteins were constructed using the BioGRID database [##REF##16381927##77##]. BioGRID is a freely accessible database of physical and genetic interactions. BioGRID release version 2.0 includes more than 116,000 interactions from <italic>Saccharomyces cerevisiae</italic>, <italic>Caenorhabditis elegans</italic>, <italic>Drosophila melanogaster </italic>and <italic>Homo sapiens</italic>. Graphs with embedded protein, gene and interaction attributes were constructed with a visualization program, Osprey [##REF##12620107##78##], that is dynamically linked to the BioGRID. Each network was begun using a single gene or node. Then more interactions were added using The BioGrid Database lookup function. These were curated to simplify the graphs, and non-expanded nodes were minimized. In general, nodes that were not differentially expressed required at least two connections or edges to remain in the network. Expression of genes depicted in the networks were checked for a 'present call' in the microarray data or otherwise validated by quantitative real time RT-PCR.</p>", "<title>Real-time qRT-PCR</title>", "<p>Real time qRT-PCR was done as previously described [##REF##12650974##60##]. To compare expression of specific genes amongst the four groups included in this study (AA, CA, AAG and CAG), we used 12 ONH astrocyte cultures from normal CA and 12 cultures from normal AA donors. cDNA of eight eyes from eight CAG donors and of six eyes from three AAG donors were used. Individual samples were processed simultaneously under the same conditions and the data were analyzed for significance using a two-tailed <italic>t</italic>-test on sample pairs (Prism 3.0 GraphPad software, San Diego, CA, USA). Primers used in this study are listed in Additional data file 4.</p>", "<title>Western blotting</title>", "<p>Protein lysates from four samples of each group of ONH astrocytes were processed together in the appropriate combinations: four AAG and four AA; four CA and four CAG. Western blots were run in triplicate to accommodate all available samples. Protein lysates containing 3-10 μg were used depending on the specific antibody. β-Actin was used as a loading control. Films of blots were scanned using a flatbed scanner in 8-bit gray scale mode. ImageJ (National Institutes of Health) was used to quantify band intensities on the blots.</p>", "<title>Detection of TGFβ1 and TGFβ2 by ELISA</title>", "<p>TGFβ1 and TGFβ2 were measured in cell culture supernatants using ELISA kits (R &amp; D Systems) specific for each protein. Briefly, astrocytes (70-80% confluent) were incubated for 24 h in 6 ml of cell culture medium without serum. Media was harvested and divided into 1 ml aliquots and frozen at -80°C until analysis. For each sample, cell counts were made and recorded. Media samples were thawed on ice and 200 μl aliquots activated by incubating with 40 μl of 0.1 N HCl at room temperature for 40 minutes. The reactions were quenched by adding 40 μl of 0.1 N NaOH in 0.5 M HEPES and mixed. Samples were diluted with the appropriate ELISA assay buffer to 400 μl. Aliquots of these solutions (50 μl TGFβ1:100 μl TGFβ2) were then assayed according to the manufacturers' protocol. Experiments were performed in duplicate and each astrocyte cell culture (n = 5-7 samples per each group) was assayed at least twice. Expressed protein values in picograms of TGFβ1/2 per ml were normalized to 10<sup>6 </sup>cells using the cell counts obtained at harvest. The means of the content were considered significantly different if <italic>p </italic>&lt; 0.05 (two-tailed <italic>t</italic>-test; Prism 3.0 GraphPad software.).</p>", "<title>Cyclic AMP assay</title>", "<p>Primary ONH astrocyte cultures obtained from six normal AA, six normal CA, eight CAG and three AAG were grown in 60 mm dishes until 80% confluence. Growth media was replaced with serum free media and the cells incubated for an additional 24 h. After washing with ice-cold phosphate-buffered saline (PBS), cells were lysed in 95% chilled ethanol for 1 h and then centrifuged at 2000 × g for 15 minutes at 4°C. The supernatant was evaporated using a Speed Vac concentrator and resuspended in 100 μl of the assay buffer and analyzed as described in the cAMP Biotrak Enzyme Immunoassay Kit (Amersham Bioscience RPN225, Piscataway, NJ, USA). cAMP concentration per well was expressed as pmol/mg of protein. Each value represents the mean cAMP level (± standard deviation) of independent experiments using primary astrocyte cultures from each donor and performed in triplicate. Sample pairs were analyzed by two-tailed <italic>t</italic>-test (Prism 3.0 GraphPad software) for significance (<italic>p </italic>&lt; 0.05).</p>", "<title>Migration assay</title>", "<p>CytoSelect™ 24-well cell migration assay (Cell BioLabs, San Diego, CA USA) was used to measure the migratory properties of cells. The assay was performed according to the manufacture's protocol. Briefly, media with 10% fetal bovine serum was placed in the lower wells followed by 50,000 cells in 300 μl of serum free media in each of the well inserts. After incubation at 37°C in a 5% CO<sub>2 </sub>atmosphere for 24 h, the media was removed from the inserts. Cells that did not migrate were removed from the inserts using a cotton swab. The inserts were stained with 400 μl of cell staining solution and washed three times with water. Cells were treated with 200 μl of extraction solution and the solution transferred to individual wells of a new plate. The absorbance of the extracted samples was measured at 560 nm by a Thermo Multiskan Spectrum plate reader. Six astrocyte cultures from each group (AA, CA, AAG and CAG) were used in the assay and data were analyzed for significance with ANOVA (Prism 3.0 GraphPad software).</p>", "<title>Rho activation assay</title>", "<p>Rho activation assay kit (Upstate Biotechnology Billerica, MA, USA) was used to detect activated Rho in cell lysates. Unstimulated cells were cultured in 60 mm dishes until 85-90% confluence and then harvested in ice cold 1 × Mg<sup>2+ </sup>Lysis/Wash (MLB) buffer (according to the manufacturer's protocol). Protein concentration was determined by the Bradford method. Protein lysate (200 μg) were mixed with 32 μl of Rho assay reagent slurry containing GST-Rhotekin-RBD fusion protein, and incubated for 45 minutes at 4°C with gentle agitation. After pelleting and washing three times with 1 × MLB, the beads were resuspended in 2 × NuPage LDS sample buffer (Invitrogen Carlsbad, CA, USA) supplemented with 0.075 M DTT and boiled at 95°C for 5 minutes. Samples were subjected to western blot analysis. An anti-Rho antibody that recognizes Rho-A, Rho-B and Rho-C was used for detection. Four cultures from each group (AA, CA, AAG and CAG) were used in the assay. Western blots were performed in duplicate. Representative blots are shown in the results and the mean optical density was used in density analysis. Statistical significance was based upon two-tailed <italic>t</italic>-test (Prism 3.0 GraphPad software) and <italic>p</italic>-value &lt; 0.05</p>", "<title>Immunohistochemistry</title>", "<p>Six eyes from normal CA donors, six eyes from normal AA donors, six eyes from CAG donors and four eyes from AAG donors were used. All donors were age matched. Tissues were fixed with 4% paraformaldehyde in 0.1 M phosphate-buffered saline pH 7.4 and processed for paraffin embedding. Two slides were stained per donor containing at least two 6 μm optic nerve sections each. In double labeling experiments we used monoclonal or polyclonal antibodies against human glial acidic fibrillar protein (GFAP) as an astrocyte marker. Secondary antibodies labeled with Alexa 488 and Alexa 568 (1:800) were from Molecular Probes. For negative controls, the primary antibody was replaced with non-immune serum. Serial sections used in comparisons (AAG versus CAG) were stained simultaneously to control for variations in immunostaining. Slides were examined in a Nikon Eclipse 80 <italic>i </italic>microscope (Tokyo, Japan) equipped with epifluorescent illumination and digital cameras (CoolSnap ES and CF, Photometrics). The images were processed using MetaMorph software (Molecular Devices Sunnyvale, CA, USA).</p>" ]
[ "<title>Results and discussion</title>", "<title>Primary cultures of ONH astrocytes from normal and glaucomatous donors</title>", "<title>Demographics and clinical history</title>", "<p>Demographic characteristics of the normal AA and CA donors used in this study are detailed in Additional data file 2. Demographic and clinical data for AA donors with glaucoma (AAGs) and CA donors with glaucoma (CAGs) included in the microarray analyses and other assays are detailed in Additional data file 1. Twelve eyes from ten CAG donors and six eyes from AAG donors were used in this study. Glaucoma drug treatment history was available for some POAG donors. None of the drug treatments are known to affect astrocytes in the ONH. The degree of glaucomatous damage in donors with POAG was assessed using histories when available and by evaluating axon degeneration in cross-sections of the myelinated optic nerve (Additional data file 1). A limitation of this study is that only six eyes from three AAG donors were available due to the extreme rarity of these samples. Consequently, we used all six eyes to generate primary cultures for all experiments in our study. Primary cultures of samples from AAG and CAG donors were fully characterized as ONH astrocytes as described in detail earlier [##REF##14613807##11##].</p>", "<title>Identification of differentially expressed genes in ONH astrocytes from AA and CA donors with POAG</title>", "<title>Comparisons</title>", "<p>For the comparisons amongst the four groups, our primary focus was to establish the differentially expressed genes between AAG and CAG donors (Additional data file 7); our secondary focus was the comparison between normal and glaucomatous astrocytes and our tertiary focus was to identify differentially expressed genes within each population: AAG versus AA and CAG versus CA.</p>", "<p>The comparisons allowed us to identify the unique gene expression profile in AAG astrocytes compared to CAG astrocytes and AAG compared to AA (Additional data file 8). In addition, we identified a common group of genes that exhibit a similar gene expression pattern in both AAG and CAG compared to normal AA and CA astrocytes, which we named common glaucoma-related genes (Tables ##TAB##0##1## and ##TAB##1##2##).</p>", "<p>Eight eyes from six CAG donors were used to generate astrocytes for eight Hu95v2 chips. Six eyes from three AAG were used to generate astrocytes for six Hu95Av2 chips and six Hu133A 2.0 chips. Eighteen Hu133 2.0 chips from nine normal AA and nine normal CA donors, and seven Hu95v2 chips from six normal CA donors were used for comparisons within the appropriate platform. All microarray data have been deposited in the NCBI GEO database under the series accession number GSE9963.</p>", "<p>The data measured by the two types of chips were normalized separately by RMA normalization as described in Materials and methods. Differentially expressed genes required an up or down fold-change of more than 1.5-fold (<italic>p </italic>&lt; 0.01, false discovery rate &lt; 0.05). A total of 618 genes were differentially expressed in AAG-CAG comparisons, 484 upregulated and 134 downregulated (Additional data file 7); 509 genes were differentially expressed in AAG compared to normal AA astrocytes, 167 upregulated and 342 downregulated (Additional data file 5); and 195 genes were differentially expressed in the CAG-CA comparison, 132 upregulated and 63 downregulated (Additional data file 6). We used empirical Bayesian methods to identify differentially expressed genes; both our results (not shown) and previous studies [##REF##15889452##12##,##REF##17720982##13##] have suggested that the empirical Bayesian method has performance similar to statistical analysis of microarrays (SAM). To reduce batch effects, we added fold-change criteria because genes with larger fold-change are less likely to be affected by such effects.</p>", "<title>Gene Ontology</title>", "<p>Gene Ontology (GO) analysis of differential expression in glaucomatous astrocytes was done with GoMiner [##REF##12702209##14##]. There were 33 significant categories for CAG-CA, 80 for AAG-AA, and 67 for AAG-CAG comparisons (<italic>p </italic>&lt; 0.01). The significant genes in selected categories were mined using GOstats in Bioconductor (Additional data file 9). The phosphorylation category (GoID: 16310) was significant in the three datasets. The percent distribution of the genes common to all of the datasets in this category was determined (Additional data file 10). For example, the genes encoding myosin light chain kinase (<italic>MYLK</italic>) and calcium/calmodulin-dependent serine protein kinase (<italic>CASK1</italic>) were found in all three glaucoma comparisons. Those encoding the regulatory subunit of phosphatidylinositol-3-kinase (<italic>PIK3R1</italic>), transforming growth factor (TGF)β-receptor 2 (<italic>TGFBR2</italic>), ERBB2, and Ephrin receptor A5 were some of the genes found in two datasets (AAG-CAG and AAG-AA). Similarly, another category with overlaps between the datasets was cell-cell signaling (Additional data file 10). Some of the genes in this category include those encoding latent transforming growth factor beta binding protein 4 (<italic>LTBP4</italic>), the glutamate receptor subunit (<italic>GRIK2</italic>), and parathyroid hormone-like protein (<italic>PTHLH</italic>). As we show below, expansion of these and other GO categories using network-protein interaction software yielded three networks that include differentially expressed GTPases, protein kinases, transmembrane receptors, and proteins involved in trafficking at cellular membranes. Altogether, the GO analysis suggests that alterations in the signaling networks that regulate cell motility, polarity, adhesion, and trafficking are present in glaucomatous astrocytes. Moreover, the overlap among the datasets in multiple categories suggests that there is a spectrum of changes in gene expression in glaucoma.</p>", "<title>Network analysis</title>", "<p>Three detailed network maps were constructed from the differential gene expression data. We focused mainly on the differences between AAG and CAG as this difference represents the maximal differential expression group (Additional data file 7). The networks include regulation of myosin, actin, TGFβ signaling and protein trafficking. For the myosin network, the initial node was myosin light chain kinase (MYLK) (Figure ##FIG##0##1b##). The actin regulatory networks were initiated using the TGFβ receptors (Figure ##FIG##1##2a##), and the protein trafficking networks were initiated using GOLGA3, catenin beta1 (CTNNB1) and RAB4A as nodes (Figure ##FIG##2##3a##). These were expanded using the BioGrid database for protein-protein interactions. In each network graph, the differentially expressed genes are shown by large nodes and font (red for increased, blue for decreased expression), while the connecting genes that are not differentially expressed are shown by black smaller nodes and font. Expression data for network nodes that are differentially expressed in the AAG-CAG comparison (Additional data file 7) are included in Table ##TAB##2##3##. Some network nodes were also selected from differentially expressed genes in AAG-AA (Additional data file 5) and in common AAG-AA and CAG-CA comparisons (Tables ##TAB##0##1## and ##TAB##1##2##). In the description of each network, we present selected experimental data that verify changes in gene expression and effects on function.</p>", "<title>Cellular motility and migration in AAG astrocytes</title>", "<p>Migration of reactive astrocytes is an important component in the remodeling of the ONH in glaucoma [##REF##9327349##15##,##REF##11008212##16##]. In glaucoma, reactive astrocytes migrate from the cribriform plates into the nerve bundles [##REF##9076213##9##,##REF##9986739##17##] and synthesize neurotoxic mediators such as nitric oxide and tumor necrosis factor (TNF)α, which may be released near the axons, causing neuronal damage [##REF##10719359##18##,##REF##10975909##19##]. Previous work in our laboratory demonstrated that human ONH astrocytes <italic>in vitro </italic>respond to elevated pressure predominantly with an increase in cell migration that may be relevant to axonal degeneration and tissue remodeling in glaucomatous optic neuropathy [##REF##14966868##20##].</p>", "<p>Here we provide <italic>in vitro </italic>data of differential astrocyte migration in astrocytes from AAG donors using a standardized migration assay. As shown in Figure ##FIG##0##1a##, migration of AAG astrocytes is significantly increased compared to CAG astrocytes and migration is faster in AA compared to CA astrocytes. Because multiple cellular processes impact cell motility and migration, we divided our analysis between two interacting networks that regulate myosin and actin.</p>", "<title>Myosin-dependent astrocyte migration</title>", "<p>From the microarray and quantitative RT-PCR (qRT-PCR) data, the following genes related to myosin regulation were differentially expressed in AAG: <italic>MYLK</italic>, <italic>MYPT1</italic>, <italic>RAC2</italic>, <italic>CALM1</italic>, <italic>RPS6KA3</italic>, <italic>MYH10</italic>, and <italic>PIK3R1</italic>. Shown in Figure ##FIG##0##1b## is the network of proteins associated with the phosphorylation of the regulatory light chain of myosin II and activation of myosin-ATPase (<italic>MYH10</italic>). Two network nodes are critical for the regulation of myosin. These include MYLK, a calmodulin-activated protein kinase that phosphorylates Ser19 on the myosin regulatory light chain and MYPT1, the regulatory subunit of myosin-light chain phosphatase, which dephosphorylates the myosin light chain. We found that both genes were expressed in AAG astrocytes at significantly higher levels than in CAG astrocytes (Table ##TAB##2##3##). Similarly, calmodulin (<italic>CALM1</italic>), the activator of MYLK is also upregulated in AAG astrocytes (Table ##TAB##2##3##)</p>", "<p>The upregulation of <italic>MYLK </italic>suggests that the myosin regulatory system may exhibit increased responsiveness towards modulation by various cellular second messenger signaling systems such as Ca<sup>2+</sup>, diacylglycerol, and cyclic nucleotides [##REF##15345524##21##]. Similarly, changes in expression of <italic>RAC2 </italic>indicate that other members of the Rho-family signaling network are altered in AAG astrocytes (Figure ##FIG##0##1c##). These changes allow us to predict that the myosin-regulated motility may be sensitized to signals from Ca2<sup>+</sup>, Rho GTPase, and growth/trophic factors coupled to the activation of phosphoinositides. Within the phosphoinositide pathway, <italic>PIK3R1 </italic>is upregulated in AAG astrocytes (Figure ##FIG##0##1c##). The PIK3R1 pathway is important for the motility of ONH astrocytes [##REF##11329180##22##] and their responses to increased hydrostatic pressure [##REF##14966868##20##]. PIK3R1 is the regulatory subunit of the lipid kinase that transforms phosphoinositide (4,5) biphosphate (PIP2) into the triphosphate (PIP3). PIP3 in turn mediates activation of several of the Rho GTPases as well as selected protein kinases. Thus, in AAG astrocytes, lipid-activated pathways that modulate astrocyte motility are altered.</p>", "<p>ERK1 potentiates MYLK activity through phosphorylation [##REF##10402467##23##] and interacts with PEA15 (Phosphoprotein enriched in astrocytes) [##REF##17658892##24##]. The increased expression of the S6-family kinase (RPS6KA3) may compete with ERK1 for binding to the phosphoprotein PEA15 [##REF##12796492##25##], potentially increasing the pool of active ERK1. Consistent with this finding, we have shown that ERK1 is activated in normal CA ONH astrocytes, under increased hydrostatic pressure and in experimental glaucoma in primates [##REF##16081055##26##]. Thus, myosin-based motility may be influenced by changes in MYLK expression and potentiation through ERK1 activation under hydrostatic pressure.</p>", "<p>Co-localization of MYLK and glial acidic fibrillar protein (GFAP) by immunohistochemistry indicates that ONH astrocytes in tissue sections in the lamina cribrosa of normal AA and AAG expressed visibly higher levels of MYLK protein <italic>in situ </italic>(Figure ##FIG##3##4a##).</p>", "<p>The <italic>MYLK </italic>gene has multiple genes within its locus [##REF##9706877##27##]. In some tissues up to three transcripts are expressed, including for long and short forms of the kinase and a protein identical to the carboxyl-terminal sequence [##REF##9706877##27##]. ONH astrocytes express both the 130 kDa (MYLK-130) and 210 kDa (MYLK-210) kinase isoforms and we quantified changes in both using standard densitometry measurements. Western blots (Figure ##FIG##3##4b##) show that the fraction of MYLK-210 in ONH astrocytes is higher in AAG and CAG compared to normal astrocytes, while the fraction of the MYLK-130 isoform decreases (Figure ##FIG##3##4b##). These differences were quantified using densitometry (Figure ##FIG##3##4c, d##). Thus, in glaucoma there appears to be MYLK isoform switching towards the larger protein. The difference between the two proteins is the presence of an amino-terminal extension in the 210 kDa species that contains additional actin binding domains. Other studies have shown that MYLK-210 displays enhanced interaction with the actin cytoskeleton compared to the 130 kDa isoform [##REF##12110694##28##,##REF##15265689##29##]. These results are consistent with the enhanced migration of ONH astrocytes mediated in part by increased expression of MYLK-210.</p>", "<p>MYLK variants have been found to confer risk of lung injury [##REF##16399953##30##], asthma or sepsis [##REF##17472811##31##], particularly in African Americans [##REF##17266121##32##]. Some of the common polymorphisms in <italic>MYLK </italic>affect its expression [##REF##17472811##31##]. Therefore, in some populations, it is possible that the effects of increased expression of <italic>MYLK </italic>may be further modified by genetic polymorphisms.</p>", "<title>Actin-dependent astrocyte migration</title>", "<p>From the microarray and qRT-PCR data the following genes were differentially expressed in AAG: <italic>TGFBR2</italic>, <italic>TGFBR1</italic>, <italic>SMAD3</italic>, <italic>NCK1</italic>, <italic>PTPN11</italic>, <italic>ARHGEF7</italic>, <italic>PDLIM1</italic>, <italic>LM04</italic>, and <italic>PLEC1</italic>. Figure ##FIG##1##2a## shows several signal transduction networks that participate in the regulation of actin. Remodeling or redistribution of actin at cellular edges is an essential part of establishing cell polarity [##REF##15950966##33##] and the formation of processes in astrocytes [##REF##10233170##34##]. Actomyosin interactions and actin polymerization are regulated by intracellular proteins such as α-actinin (ACTN4) and the ARP protein complex (ACTR2, WASP: Figure ##FIG##1##2a##). These networks involve the Rho GTPase signaling pathway. Therefore, we used a pull-down Rho activation assay to measure activated Rho in cell lysates. ONH astrocytes from CAG and AAG donors exhibited significantly higher Rho activity compared to those from normal AA and CA donors (Figure ##FIG##1##2b, c##), consistent with the differential expression of Rho regulatory components. Rho activity was also increased in astrocytes exposed to elevated hydrostatic pressure [##REF##14747662##35##]. Thus, increased Rho activity is another contributor towards increased migration of AAG astrocytes. We suspect that Rho activity may be altered by changes in the signaling proteins included in these networks. For example, RAC2 and ARGEF7 are upregulated in AAG. The Rho-family GTPase, RAC2, is downstream of TGFβ signaling [##REF##16705092##36##] and ARHGEF7 stimulates guanine nucleotide exchange on Rho family GTP-binding proteins. We further elaborated changes in TGFβ signaling as a driver to changes in Rho activity.</p>", "<title>TGFβ signaling in AAG astrocytes</title>", "<p>TGFβ1 and TGFβ2 act via TGFBR1 and TGFBR2 receptors. Using qRT-PCR we confirmed that TGFBR2 and the downstream signaling protein SMAD3 are up-regulated in AAG astrocytes, suggesting increased responsiveness (Figure ##FIG##4##5a##). TGFBR1 is down-regulated in AAG compared to CAG (Figure ##FIG##4##5a##). SMAD proteins not only function as transcriptional regulators in ONH astrocytes [##REF##15671284##37##] and other cells in the central nervous system [##REF##9460794##38##], but also participate in the regulation of cell polarity. SMAD3 was also upregulated in ONH astrocytes exposed to hydrostatic pressure <italic>in vitro</italic>, suggesting that pressure activates the TGFβ pathway [##REF##14747662##35##]. In addition, LM04, a LIM domain protein that modulates SMAD3 transcriptional activity [##REF##16331278##39##], is upregulated in glaucomatous astrocytes in both populations (Table ##TAB##0##1##). One path that limits SMAD3 signaling is ubiquitin-linked degradation by SMURF2. Although SMURF2 expression is not altered in glaucomatous astrocytes, SMURF2 is downregulated by an increase in hydrostatic pressure [##REF##14747662##35##]. Thus, there may be additional potentiation of TGFβ signaling in AAG astrocytes with changes in intraocular pressure, which may be a susceptibility factor to glaucomatous changes in the AA population.</p>", "<p>TGFβ regulates cellular motility through two components. One is through the expression of extracellular matrix (ECM) proteins, which will be discussed in detail below. Contractile forces are transmitted to the ECM through actin-based stress fibers via focal adhesions, which are assemblies of ECM proteins, transmembrane receptors, and cytoplasmic structural and signaling proteins, such as integrins. TGFβ modulates integrin-mediated cellular migration, where FYN is one of the primary signal transducing proteins. A second component of TGFβ signaling is the regulation of cell polarity. For example, PARD3 and PARD6 are part of a multi-component polarity complex that controls polarized cell migration [##REF##12650946##40##]. These complexes involve the Rho, CDC42, and RAC signaling pathways, which provide the means to remodel actin during migration [##REF##15950966##33##,##REF##11525734##41##]</p>", "<p>As shown in Figure ##FIG##1##2a##, NCK1 was upregulated in AAG (Table ##TAB##2##3##). The Nck1 SH2/SH3 adaptor couples phosphotyrosine signals to the actin cytoskeleton and receptor signaling to the regulatory machinery of the cytoskeleton [##REF##16636066##42##]. The enigma family member PDLIM1 was upregulated in AAG astrocytes (Table ##TAB##2##3##) and functions by allowing interactions among cytoskeletal proteins through PDZ and amino LIM domains [##REF##11110697##43##,##REF##10861853##44##]. Downregulation of other actin binding proteins such as PLEC1 (Table ##TAB##2##3##) may alter actin dynamics with respect to cytoskeletal changes induced by Rho-GTPase, phospholipids, and tyrosine kinase (Src) mediated signaling [##REF##9808630##45##].</p>", "<title>TGFBR2 receptors in optic nerve head astrocytes</title>", "<p>Figure ##FIG##4##5b## illustrates immunohistochemistry of the TGFBR2 on astrocytes in normal and glaucomatous ONH tissue. GFAP positive astrocytes in the lamina cribrosa of AAG exhibit higher expression of TGFBR2 compared with astrocytes in normal ONH tissue. Consistent with these findings, western blots of lysates of ONH astrocytes from AAG indicate higher levels of TGFBR2 protein compared to the normal tissue and CAG (Figure ##FIG##4##5c##).</p>", "<p>To further investigate alterations in TGFβ signaling in ONH astrocytes, we examined the production of TGFβ1 and TGFβ2. As seen in Figure ##FIG##4##5d##, TGFβ2 is the primary form of TGFβ produced by ONH astrocytes [##REF##10396201##46##]. There are significantly increased levels of secreted TGFβ1 in AA compared to CA astrocyte supernatants but the increases in AAG and CAG astrocytes were not significant compared to normal astrocytes. These data suggest that most of the changes in TGFβ signaling are due to alterations at the level of TGFβ receptors in astrocytes from AAG.</p>", "<p>Mutations in TGFBR2 are associated with Marfan syndrome type 2 [##REF##17652900##47##, ####REF##16799921##48##, ##REF##15235604##49####15235604##49##]. Ocular abnormalities, including glaucoma, are associated with Marfan syndrome type 1 in which there are mutations in the gene for fibrillin (<italic>FBN1</italic>) [##REF##1494814##50##]. However, it has not been established that mutations of TGFBR2 are associated with ocular problems in Marfan syndrome type 2 [##REF##16799921##48##,##REF##15235604##49##].</p>", "<title>Intracellular trafficking and the endoplasmic reticulum/Golgi compartments</title>", "<p>From the microarray and quantitative RT-PCR data the following genes were differentially expressed in AAG. Endosome group, <italic>RAB4A</italic>, <italic>RAB5B</italic>, <italic>RAB9P40</italic>, <italic>RAB9A</italic>; plasma membrane group, <italic>PRSS3</italic>, <italic>APPB1</italic>, <italic>CTNND1</italic>, <italic>CTNNB1</italic>, <italic>CDH2</italic>, <italic>VCAN</italic>, <italic>HAPLN1</italic>, <italic>CCL5</italic>, <italic>COL4A4</italic>, <italic>TGM2</italic>, <italic>SLIT2</italic>, <italic>GPC1</italic>; Golgi group, <italic>GOLGA1</italic>, <italic>GOLGA3</italic>, <italic>GOLGA2</italic>, <italic>RAB1A</italic>, <italic>RABGGTB </italic>(Figure ##FIG##2##3a##). Six Rab family signaling genes involved in intracellular transport of organelles were differentially regulated (Table ##TAB##2##3##). Three small GTPases, RAB4A, RAB5B, and RAB9A, were upregulated (Table ##TAB##2##3##, Figure ##FIG##2##3b##), suggesting increased endosomal transport and processing. RAB4A and RAB5B selectively regulate intracellular trafficking and signaling of G protein-coupled receptors, such as the angiotensin receptor and adrenergic receptors (β2-AR and α2B-AR) from the cell surface [##REF##14711821##51##,##REF##14607250##52##]. RAB9A participates in late endosomal events leading to fusion with the lysosomal compartment [##REF##14607250##52##].</p>", "<p>In AAG astrocytes there was a predominant increase in transcription of Golgi-resident protein transcripts (Additional data file 7). These include <italic>RAB1A</italic>, and three members of the golgin family, <italic>GOLGA1</italic>, <italic>GOLGA2 </italic>and <italic>GOLGA3 </italic>(Table ##TAB##2##3##), which may function in the stacking of Golgi cisternae and in vesicular transport [##REF##15979510##53##]. GOLGA3 promotes cell surface expression of the beta adrenergic receptors [##REF##17118120##54##]. Thus, the increased expression of Golgi proteins may further enhance adrenergic receptor signaling. Note that the RAB proteins upregulated in the endosomal pathway (above) also affect trafficking of these receptors.</p>", "<p>Included in the protein trafficking network are plasma membrane associated proteins involved in cell-cell communication from the junctional matrix (Figure ##FIG##2##3a##). Catenins (CTNNB1, CTNND1) form membrane trafficking complexes that integrate other cadherins (CDH2), and members of the amyloid precursor protein complex (presenilin, APPBP1, PRSS3). In particular, CTNND1 functions to regulate membrane trafficking either through blocking cadherin interactions, or through Rho-GTPases such as Rho A, Rac and CDC42 [##REF##16949165##55##]. As with the myosin and actin motility networks, the change in expression of GTPase regulatory proteins will likely impact plasma membrane trafficking. The upregulation of chondroitin sulfate proteoglycan 2 (versican; <italic>VCAN</italic>), transglutaminase 2 (<italic>TGM2</italic>), and hyaluronan and proteoglycan link protein 1 (<italic>HAPLN1</italic>) are significant modifiers of the ECM [##REF##14724283##56##]. Both <italic>HAPLN1 </italic>and <italic>VCAN </italic>mRNA levels were upregulated in AAG compared to CAG astrocytes by qRT-PCR (Figure ##FIG##2##3b##). VCAN immunoreactivity was observed in the ECM of the cribriform plates, the perivascular matrix and a few astrocytes in the lamina cribrosa of normal AA and CA donors (Figure ##FIG##2##3c##). In glaucomatous tissues there was a marked increase in VCAN staining in astrocytes in the cribriform plates and hypertrophied reactive astrocytes in the nerve bundles in both populations (Figure ##FIG##2##3c##). TGFβ2 signaling upregulates VCAN [##REF##17453002##57##] in astrocyte cell types and expression of collagen type 4 and transglutaminase 2 in ONH astrocytes [##REF##15671284##37##]. Our data on changes in TGFβ receptor expression and ECM proteins are similar to those found in microarray profiling of ONH tissue from a rat model of glaucoma [##REF##17591886##58##]. Expression of ECM proteins is also modulated by TGFβ in GFAP-negative lamina cribrosa cells in culture [##REF##16078232##59##].</p>", "<p>There is substantial evidence that ONH astrocytes are responsible for the normal maintenance of the ECM in normal tissue and that reactive astrocytes remodel the ECM in response to elevated IOP in human and experimental glaucoma [##REF##11921203##10##,##REF##12650974##60##,##REF##10749379##61##]. Reactive astrocytes in the ONH express abnormal ECM in glaucoma, leading to loss of resiliency and deformability in response to elevated IOP. Alterations in TGFβ2 levels and TGFβ receptors and abnormal synthesis of ECM in AAG may convey connective tissue components of susceptibility to elevated IOP to this population.</p>", "<title>cAMP signaling in glaucomatous ONH astrocytes</title>", "<p>Earlier work in our laboratory indicated upregulation of two adenylyl cyclases (ADYC3 and ADYC9) in normal AA compared to CA astrocytes, suggesting changes in cyclic AMP (cAMP) levels in this population (L Chen, MR Hernandez, ARVO (Association for Research in Vision and Ophthamology) 2007 abstract 3265). To test whether glaucomatous ONH astrocytes exhibit differential basal levels in cAMP, we conducted a standard cAMP assay in normal AA and CA astrocytes and in AAG and CAG astrocytes. Under unstimulated conditions, normal AA and CA astrocytes exhibit no difference in basal levels of cAMP, whereas AAG and CAG astrocytes have significantly higher basal levels of cAMP compared with the normal counterparts (Figure ##FIG##5##6a##). Cyclic AMP is a key intracellular second messenger in astrocytes. The cAMP signaling cascade opposes pro-inflammatory cytokines such as IL1β and TNFα and maintains astrocytes in a quiescent (non-activated) state [##REF##10818485##62##]. Thus, the higher basal levels of cAMP in astrocytes from glaucomatous donors may be a response to pro-inflammatory cytokines such as TNFα in the glaucomatous ONH [##REF##10975909##19##].</p>", "<p>We searched the expression data for differentially expressed genes that might explain the difference in basal cAMP levels between glaucomatous and normal astrocytes. One potential candidate for increasing basal cAMP is PTHLH, a parathyroid hormone-like protein that is upregulated in glaucomatous astrocytes (Figure ##FIG##5##6b##). This protein binds to ubiquitous PTH receptors that are coupled to stimulation of adenylate cyclase and elevated cyclic AMP [##REF##15282196##63##]. Thus, upregulation of PTHLH provides an autocrine pathway leading to increased basal cyclic AMP levels in glaucomatous astrocytes. Another gene that might also contribute to the activity of adenylate cyclases is CAP2 [##REF##7962207##64##]. However, we found that CAP2 was not differentially expressed in glaucomatous ONH astrocytes by qRT-PCR (Figure ##FIG##5##6b##).</p>", "<title>Other disease-associated genes differentially regulated in glaucomatous OHN astrocytes</title>", "<title>Cell-cell communication</title>", "<p>The secondary and tertiary comparisons identified genes that were differentially expressed in AAG compared to AA and in CAG compared to CA, including <italic>BMP1</italic>, <italic>LTBP1</italic>, <italic>AMIGO2</italic>, <italic>SLIT2</italic>, <italic>GPC1</italic>, and <italic>OLR1 </italic>(Tables ##TAB##0##1## and ##TAB##1##2##). Selected genes were confirmed by qRT-PCR (Figure ##FIG##6##7##).</p>", "<p>In this list we found that specific cell-surface-associated proteins are downregulated in glaucoma. These include BMP1, which activates cleavage of LTBP1 proteins that release nascent TGFβ1 [##REF##17015622##65##], and AMIGO2, a type I transmembrane protein that regulates axon extension [##REF##12629050##66##]. Down-regulation of BMP1 may reduce the levels of free TGFβ1 and thus unbalance signaling between TGFβ isoforms. A decrease in AMIGO2 might negatively impact axon survival.</p>", "<p>Two differentially expressed genes that are involved in reactive astrocyte responses to neuronal injury are <italic>SLIT2 </italic>and <italic>GPC1 </italic>(glypican-1). SLIT2 serves as a chemorepellant for multiple types of axons [##REF##12655597##67##], while GPC1 is a proteoglycan that binds SLIT2 [##REF##12655597##67##]. Upregulation of expression of SLIT2 and a reduction of GPC1 by glaucomatous astrocytes suggest an inhibitory microenvironment for RGC axons in the ONH. These data are consistent with the idea that the enhanced migratory properties of glaucomatous astrocytes coupled with the release of factors that negatively impact upon axon survival are part of the pathophysiology of the disease.</p>", "<p>Finally, lectin-like oxidized-LDL receptor (OLR1; also known as LOX-1) is highly upregulated (Additional data files 5 and 11) in AAG astrocytes. OLR1 expression is induced by TGFβ1 signaling and is known to be a component of the fluid shear stress response of endothelial cells in early atherosclerotic lesions [##REF##10833418##68##]. These data are further confirmation of enhanced TGFβ signaling in AAG astrocytes as suggested by the differential receptor expression described earlier.</p>", "<title>Intracellular calcium signaling/transport systems in ONH astrocytes</title>", "<p>Two genes directly involved in Ca<sup>2+ </sup>homeostasis are differentially regulated in ONH astrocytes of AAG (Additional data files 5 and 7). <italic>CACNB4 </italic>encodes a beta subunit of the voltage-dependent calcium channel complex. CACNB4 plays an important role in calcium channel function by modulating G protein inhibition, increasing peak calcium current, controlling the alpha-1 subunit targeting to the membrane and shifting the voltage dependence of activation and inactivation. The second gene, <italic>ATP2C1 </italic>(Additional data file 7), encodes a protein that belongs to the family of P-type primary ion transport ATPases, which pump Ca<sup>2+ </sup>into the endoplasmic reticulum.</p>", "<p>Transcripts encoding the calcium/calmodulin-related signaling proteins calmodulin 1 (CALM1) and Ca<sup>2+</sup>/calmodulin-dependent membrane-associated kinase (CASK1) are differentially expressed in one or more glaucoma groups. CALM1 was increased in AAG compared to AA donors (Additional data file 5), while CASK1 was increased in glaucomatous astrocytes from both AA and CA donors (Table ##TAB##0##1##, Figure ##FIG##6##7##). Calmodulin is the Ca<sup>2+ </sup>sensor of key signaling molecules, such as adenylyl cyclase, CAMKII, CAMKIV, and MYLK discussed above. CASK1 is a member of the membrane-associated guanylate kinase proteins (MAGUKs), a prominent family of scaffolding molecules associated with intercellular junctions. CASK1 targets Ca<sup>2+ </sup>and K<sup>+ </sup>channels [##REF##14960569##69##] and/or the Ca<sup>2+ </sup>pump 4b/CI [##REF##12511555##70##] to the plasma membrane, interacts with liprins [##REF##16186258##71##] and regulates transcription by interacting with transcription factors in the nucleus [##REF##10749215##72##]. Interestingly, <italic>CASK </italic>is a candidate gene for X-linked optic atrophy [##REF##9722958##73##]. The differential expression of genes in Ca<sup>2+ </sup>signaling pathways could be a common theme in glaucomatous astrocytes that may have a higher impact in optic nerves from AA donors due to increased sensitivity to elevated IOP in these donors.</p>" ]
[ "<title>Results and discussion</title>", "<title>Primary cultures of ONH astrocytes from normal and glaucomatous donors</title>", "<title>Demographics and clinical history</title>", "<p>Demographic characteristics of the normal AA and CA donors used in this study are detailed in Additional data file 2. Demographic and clinical data for AA donors with glaucoma (AAGs) and CA donors with glaucoma (CAGs) included in the microarray analyses and other assays are detailed in Additional data file 1. Twelve eyes from ten CAG donors and six eyes from AAG donors were used in this study. Glaucoma drug treatment history was available for some POAG donors. None of the drug treatments are known to affect astrocytes in the ONH. The degree of glaucomatous damage in donors with POAG was assessed using histories when available and by evaluating axon degeneration in cross-sections of the myelinated optic nerve (Additional data file 1). A limitation of this study is that only six eyes from three AAG donors were available due to the extreme rarity of these samples. Consequently, we used all six eyes to generate primary cultures for all experiments in our study. Primary cultures of samples from AAG and CAG donors were fully characterized as ONH astrocytes as described in detail earlier [##REF##14613807##11##].</p>", "<title>Identification of differentially expressed genes in ONH astrocytes from AA and CA donors with POAG</title>", "<title>Comparisons</title>", "<p>For the comparisons amongst the four groups, our primary focus was to establish the differentially expressed genes between AAG and CAG donors (Additional data file 7); our secondary focus was the comparison between normal and glaucomatous astrocytes and our tertiary focus was to identify differentially expressed genes within each population: AAG versus AA and CAG versus CA.</p>", "<p>The comparisons allowed us to identify the unique gene expression profile in AAG astrocytes compared to CAG astrocytes and AAG compared to AA (Additional data file 8). In addition, we identified a common group of genes that exhibit a similar gene expression pattern in both AAG and CAG compared to normal AA and CA astrocytes, which we named common glaucoma-related genes (Tables ##TAB##0##1## and ##TAB##1##2##).</p>", "<p>Eight eyes from six CAG donors were used to generate astrocytes for eight Hu95v2 chips. Six eyes from three AAG were used to generate astrocytes for six Hu95Av2 chips and six Hu133A 2.0 chips. Eighteen Hu133 2.0 chips from nine normal AA and nine normal CA donors, and seven Hu95v2 chips from six normal CA donors were used for comparisons within the appropriate platform. All microarray data have been deposited in the NCBI GEO database under the series accession number GSE9963.</p>", "<p>The data measured by the two types of chips were normalized separately by RMA normalization as described in Materials and methods. Differentially expressed genes required an up or down fold-change of more than 1.5-fold (<italic>p </italic>&lt; 0.01, false discovery rate &lt; 0.05). A total of 618 genes were differentially expressed in AAG-CAG comparisons, 484 upregulated and 134 downregulated (Additional data file 7); 509 genes were differentially expressed in AAG compared to normal AA astrocytes, 167 upregulated and 342 downregulated (Additional data file 5); and 195 genes were differentially expressed in the CAG-CA comparison, 132 upregulated and 63 downregulated (Additional data file 6). We used empirical Bayesian methods to identify differentially expressed genes; both our results (not shown) and previous studies [##REF##15889452##12##,##REF##17720982##13##] have suggested that the empirical Bayesian method has performance similar to statistical analysis of microarrays (SAM). To reduce batch effects, we added fold-change criteria because genes with larger fold-change are less likely to be affected by such effects.</p>", "<title>Gene Ontology</title>", "<p>Gene Ontology (GO) analysis of differential expression in glaucomatous astrocytes was done with GoMiner [##REF##12702209##14##]. There were 33 significant categories for CAG-CA, 80 for AAG-AA, and 67 for AAG-CAG comparisons (<italic>p </italic>&lt; 0.01). The significant genes in selected categories were mined using GOstats in Bioconductor (Additional data file 9). The phosphorylation category (GoID: 16310) was significant in the three datasets. The percent distribution of the genes common to all of the datasets in this category was determined (Additional data file 10). For example, the genes encoding myosin light chain kinase (<italic>MYLK</italic>) and calcium/calmodulin-dependent serine protein kinase (<italic>CASK1</italic>) were found in all three glaucoma comparisons. Those encoding the regulatory subunit of phosphatidylinositol-3-kinase (<italic>PIK3R1</italic>), transforming growth factor (TGF)β-receptor 2 (<italic>TGFBR2</italic>), ERBB2, and Ephrin receptor A5 were some of the genes found in two datasets (AAG-CAG and AAG-AA). Similarly, another category with overlaps between the datasets was cell-cell signaling (Additional data file 10). Some of the genes in this category include those encoding latent transforming growth factor beta binding protein 4 (<italic>LTBP4</italic>), the glutamate receptor subunit (<italic>GRIK2</italic>), and parathyroid hormone-like protein (<italic>PTHLH</italic>). As we show below, expansion of these and other GO categories using network-protein interaction software yielded three networks that include differentially expressed GTPases, protein kinases, transmembrane receptors, and proteins involved in trafficking at cellular membranes. Altogether, the GO analysis suggests that alterations in the signaling networks that regulate cell motility, polarity, adhesion, and trafficking are present in glaucomatous astrocytes. Moreover, the overlap among the datasets in multiple categories suggests that there is a spectrum of changes in gene expression in glaucoma.</p>", "<title>Network analysis</title>", "<p>Three detailed network maps were constructed from the differential gene expression data. We focused mainly on the differences between AAG and CAG as this difference represents the maximal differential expression group (Additional data file 7). The networks include regulation of myosin, actin, TGFβ signaling and protein trafficking. For the myosin network, the initial node was myosin light chain kinase (MYLK) (Figure ##FIG##0##1b##). The actin regulatory networks were initiated using the TGFβ receptors (Figure ##FIG##1##2a##), and the protein trafficking networks were initiated using GOLGA3, catenin beta1 (CTNNB1) and RAB4A as nodes (Figure ##FIG##2##3a##). These were expanded using the BioGrid database for protein-protein interactions. In each network graph, the differentially expressed genes are shown by large nodes and font (red for increased, blue for decreased expression), while the connecting genes that are not differentially expressed are shown by black smaller nodes and font. Expression data for network nodes that are differentially expressed in the AAG-CAG comparison (Additional data file 7) are included in Table ##TAB##2##3##. Some network nodes were also selected from differentially expressed genes in AAG-AA (Additional data file 5) and in common AAG-AA and CAG-CA comparisons (Tables ##TAB##0##1## and ##TAB##1##2##). In the description of each network, we present selected experimental data that verify changes in gene expression and effects on function.</p>", "<title>Cellular motility and migration in AAG astrocytes</title>", "<p>Migration of reactive astrocytes is an important component in the remodeling of the ONH in glaucoma [##REF##9327349##15##,##REF##11008212##16##]. In glaucoma, reactive astrocytes migrate from the cribriform plates into the nerve bundles [##REF##9076213##9##,##REF##9986739##17##] and synthesize neurotoxic mediators such as nitric oxide and tumor necrosis factor (TNF)α, which may be released near the axons, causing neuronal damage [##REF##10719359##18##,##REF##10975909##19##]. Previous work in our laboratory demonstrated that human ONH astrocytes <italic>in vitro </italic>respond to elevated pressure predominantly with an increase in cell migration that may be relevant to axonal degeneration and tissue remodeling in glaucomatous optic neuropathy [##REF##14966868##20##].</p>", "<p>Here we provide <italic>in vitro </italic>data of differential astrocyte migration in astrocytes from AAG donors using a standardized migration assay. As shown in Figure ##FIG##0##1a##, migration of AAG astrocytes is significantly increased compared to CAG astrocytes and migration is faster in AA compared to CA astrocytes. Because multiple cellular processes impact cell motility and migration, we divided our analysis between two interacting networks that regulate myosin and actin.</p>", "<title>Myosin-dependent astrocyte migration</title>", "<p>From the microarray and quantitative RT-PCR (qRT-PCR) data, the following genes related to myosin regulation were differentially expressed in AAG: <italic>MYLK</italic>, <italic>MYPT1</italic>, <italic>RAC2</italic>, <italic>CALM1</italic>, <italic>RPS6KA3</italic>, <italic>MYH10</italic>, and <italic>PIK3R1</italic>. Shown in Figure ##FIG##0##1b## is the network of proteins associated with the phosphorylation of the regulatory light chain of myosin II and activation of myosin-ATPase (<italic>MYH10</italic>). Two network nodes are critical for the regulation of myosin. These include MYLK, a calmodulin-activated protein kinase that phosphorylates Ser19 on the myosin regulatory light chain and MYPT1, the regulatory subunit of myosin-light chain phosphatase, which dephosphorylates the myosin light chain. We found that both genes were expressed in AAG astrocytes at significantly higher levels than in CAG astrocytes (Table ##TAB##2##3##). Similarly, calmodulin (<italic>CALM1</italic>), the activator of MYLK is also upregulated in AAG astrocytes (Table ##TAB##2##3##)</p>", "<p>The upregulation of <italic>MYLK </italic>suggests that the myosin regulatory system may exhibit increased responsiveness towards modulation by various cellular second messenger signaling systems such as Ca<sup>2+</sup>, diacylglycerol, and cyclic nucleotides [##REF##15345524##21##]. Similarly, changes in expression of <italic>RAC2 </italic>indicate that other members of the Rho-family signaling network are altered in AAG astrocytes (Figure ##FIG##0##1c##). These changes allow us to predict that the myosin-regulated motility may be sensitized to signals from Ca2<sup>+</sup>, Rho GTPase, and growth/trophic factors coupled to the activation of phosphoinositides. Within the phosphoinositide pathway, <italic>PIK3R1 </italic>is upregulated in AAG astrocytes (Figure ##FIG##0##1c##). The PIK3R1 pathway is important for the motility of ONH astrocytes [##REF##11329180##22##] and their responses to increased hydrostatic pressure [##REF##14966868##20##]. PIK3R1 is the regulatory subunit of the lipid kinase that transforms phosphoinositide (4,5) biphosphate (PIP2) into the triphosphate (PIP3). PIP3 in turn mediates activation of several of the Rho GTPases as well as selected protein kinases. Thus, in AAG astrocytes, lipid-activated pathways that modulate astrocyte motility are altered.</p>", "<p>ERK1 potentiates MYLK activity through phosphorylation [##REF##10402467##23##] and interacts with PEA15 (Phosphoprotein enriched in astrocytes) [##REF##17658892##24##]. The increased expression of the S6-family kinase (RPS6KA3) may compete with ERK1 for binding to the phosphoprotein PEA15 [##REF##12796492##25##], potentially increasing the pool of active ERK1. Consistent with this finding, we have shown that ERK1 is activated in normal CA ONH astrocytes, under increased hydrostatic pressure and in experimental glaucoma in primates [##REF##16081055##26##]. Thus, myosin-based motility may be influenced by changes in MYLK expression and potentiation through ERK1 activation under hydrostatic pressure.</p>", "<p>Co-localization of MYLK and glial acidic fibrillar protein (GFAP) by immunohistochemistry indicates that ONH astrocytes in tissue sections in the lamina cribrosa of normal AA and AAG expressed visibly higher levels of MYLK protein <italic>in situ </italic>(Figure ##FIG##3##4a##).</p>", "<p>The <italic>MYLK </italic>gene has multiple genes within its locus [##REF##9706877##27##]. In some tissues up to three transcripts are expressed, including for long and short forms of the kinase and a protein identical to the carboxyl-terminal sequence [##REF##9706877##27##]. ONH astrocytes express both the 130 kDa (MYLK-130) and 210 kDa (MYLK-210) kinase isoforms and we quantified changes in both using standard densitometry measurements. Western blots (Figure ##FIG##3##4b##) show that the fraction of MYLK-210 in ONH astrocytes is higher in AAG and CAG compared to normal astrocytes, while the fraction of the MYLK-130 isoform decreases (Figure ##FIG##3##4b##). These differences were quantified using densitometry (Figure ##FIG##3##4c, d##). Thus, in glaucoma there appears to be MYLK isoform switching towards the larger protein. The difference between the two proteins is the presence of an amino-terminal extension in the 210 kDa species that contains additional actin binding domains. Other studies have shown that MYLK-210 displays enhanced interaction with the actin cytoskeleton compared to the 130 kDa isoform [##REF##12110694##28##,##REF##15265689##29##]. These results are consistent with the enhanced migration of ONH astrocytes mediated in part by increased expression of MYLK-210.</p>", "<p>MYLK variants have been found to confer risk of lung injury [##REF##16399953##30##], asthma or sepsis [##REF##17472811##31##], particularly in African Americans [##REF##17266121##32##]. Some of the common polymorphisms in <italic>MYLK </italic>affect its expression [##REF##17472811##31##]. Therefore, in some populations, it is possible that the effects of increased expression of <italic>MYLK </italic>may be further modified by genetic polymorphisms.</p>", "<title>Actin-dependent astrocyte migration</title>", "<p>From the microarray and qRT-PCR data the following genes were differentially expressed in AAG: <italic>TGFBR2</italic>, <italic>TGFBR1</italic>, <italic>SMAD3</italic>, <italic>NCK1</italic>, <italic>PTPN11</italic>, <italic>ARHGEF7</italic>, <italic>PDLIM1</italic>, <italic>LM04</italic>, and <italic>PLEC1</italic>. Figure ##FIG##1##2a## shows several signal transduction networks that participate in the regulation of actin. Remodeling or redistribution of actin at cellular edges is an essential part of establishing cell polarity [##REF##15950966##33##] and the formation of processes in astrocytes [##REF##10233170##34##]. Actomyosin interactions and actin polymerization are regulated by intracellular proteins such as α-actinin (ACTN4) and the ARP protein complex (ACTR2, WASP: Figure ##FIG##1##2a##). These networks involve the Rho GTPase signaling pathway. Therefore, we used a pull-down Rho activation assay to measure activated Rho in cell lysates. ONH astrocytes from CAG and AAG donors exhibited significantly higher Rho activity compared to those from normal AA and CA donors (Figure ##FIG##1##2b, c##), consistent with the differential expression of Rho regulatory components. Rho activity was also increased in astrocytes exposed to elevated hydrostatic pressure [##REF##14747662##35##]. Thus, increased Rho activity is another contributor towards increased migration of AAG astrocytes. We suspect that Rho activity may be altered by changes in the signaling proteins included in these networks. For example, RAC2 and ARGEF7 are upregulated in AAG. The Rho-family GTPase, RAC2, is downstream of TGFβ signaling [##REF##16705092##36##] and ARHGEF7 stimulates guanine nucleotide exchange on Rho family GTP-binding proteins. We further elaborated changes in TGFβ signaling as a driver to changes in Rho activity.</p>", "<title>TGFβ signaling in AAG astrocytes</title>", "<p>TGFβ1 and TGFβ2 act via TGFBR1 and TGFBR2 receptors. Using qRT-PCR we confirmed that TGFBR2 and the downstream signaling protein SMAD3 are up-regulated in AAG astrocytes, suggesting increased responsiveness (Figure ##FIG##4##5a##). TGFBR1 is down-regulated in AAG compared to CAG (Figure ##FIG##4##5a##). SMAD proteins not only function as transcriptional regulators in ONH astrocytes [##REF##15671284##37##] and other cells in the central nervous system [##REF##9460794##38##], but also participate in the regulation of cell polarity. SMAD3 was also upregulated in ONH astrocytes exposed to hydrostatic pressure <italic>in vitro</italic>, suggesting that pressure activates the TGFβ pathway [##REF##14747662##35##]. In addition, LM04, a LIM domain protein that modulates SMAD3 transcriptional activity [##REF##16331278##39##], is upregulated in glaucomatous astrocytes in both populations (Table ##TAB##0##1##). One path that limits SMAD3 signaling is ubiquitin-linked degradation by SMURF2. Although SMURF2 expression is not altered in glaucomatous astrocytes, SMURF2 is downregulated by an increase in hydrostatic pressure [##REF##14747662##35##]. Thus, there may be additional potentiation of TGFβ signaling in AAG astrocytes with changes in intraocular pressure, which may be a susceptibility factor to glaucomatous changes in the AA population.</p>", "<p>TGFβ regulates cellular motility through two components. One is through the expression of extracellular matrix (ECM) proteins, which will be discussed in detail below. Contractile forces are transmitted to the ECM through actin-based stress fibers via focal adhesions, which are assemblies of ECM proteins, transmembrane receptors, and cytoplasmic structural and signaling proteins, such as integrins. TGFβ modulates integrin-mediated cellular migration, where FYN is one of the primary signal transducing proteins. A second component of TGFβ signaling is the regulation of cell polarity. For example, PARD3 and PARD6 are part of a multi-component polarity complex that controls polarized cell migration [##REF##12650946##40##]. These complexes involve the Rho, CDC42, and RAC signaling pathways, which provide the means to remodel actin during migration [##REF##15950966##33##,##REF##11525734##41##]</p>", "<p>As shown in Figure ##FIG##1##2a##, NCK1 was upregulated in AAG (Table ##TAB##2##3##). The Nck1 SH2/SH3 adaptor couples phosphotyrosine signals to the actin cytoskeleton and receptor signaling to the regulatory machinery of the cytoskeleton [##REF##16636066##42##]. The enigma family member PDLIM1 was upregulated in AAG astrocytes (Table ##TAB##2##3##) and functions by allowing interactions among cytoskeletal proteins through PDZ and amino LIM domains [##REF##11110697##43##,##REF##10861853##44##]. Downregulation of other actin binding proteins such as PLEC1 (Table ##TAB##2##3##) may alter actin dynamics with respect to cytoskeletal changes induced by Rho-GTPase, phospholipids, and tyrosine kinase (Src) mediated signaling [##REF##9808630##45##].</p>", "<title>TGFBR2 receptors in optic nerve head astrocytes</title>", "<p>Figure ##FIG##4##5b## illustrates immunohistochemistry of the TGFBR2 on astrocytes in normal and glaucomatous ONH tissue. GFAP positive astrocytes in the lamina cribrosa of AAG exhibit higher expression of TGFBR2 compared with astrocytes in normal ONH tissue. Consistent with these findings, western blots of lysates of ONH astrocytes from AAG indicate higher levels of TGFBR2 protein compared to the normal tissue and CAG (Figure ##FIG##4##5c##).</p>", "<p>To further investigate alterations in TGFβ signaling in ONH astrocytes, we examined the production of TGFβ1 and TGFβ2. As seen in Figure ##FIG##4##5d##, TGFβ2 is the primary form of TGFβ produced by ONH astrocytes [##REF##10396201##46##]. There are significantly increased levels of secreted TGFβ1 in AA compared to CA astrocyte supernatants but the increases in AAG and CAG astrocytes were not significant compared to normal astrocytes. These data suggest that most of the changes in TGFβ signaling are due to alterations at the level of TGFβ receptors in astrocytes from AAG.</p>", "<p>Mutations in TGFBR2 are associated with Marfan syndrome type 2 [##REF##17652900##47##, ####REF##16799921##48##, ##REF##15235604##49####15235604##49##]. Ocular abnormalities, including glaucoma, are associated with Marfan syndrome type 1 in which there are mutations in the gene for fibrillin (<italic>FBN1</italic>) [##REF##1494814##50##]. However, it has not been established that mutations of TGFBR2 are associated with ocular problems in Marfan syndrome type 2 [##REF##16799921##48##,##REF##15235604##49##].</p>", "<title>Intracellular trafficking and the endoplasmic reticulum/Golgi compartments</title>", "<p>From the microarray and quantitative RT-PCR data the following genes were differentially expressed in AAG. Endosome group, <italic>RAB4A</italic>, <italic>RAB5B</italic>, <italic>RAB9P40</italic>, <italic>RAB9A</italic>; plasma membrane group, <italic>PRSS3</italic>, <italic>APPB1</italic>, <italic>CTNND1</italic>, <italic>CTNNB1</italic>, <italic>CDH2</italic>, <italic>VCAN</italic>, <italic>HAPLN1</italic>, <italic>CCL5</italic>, <italic>COL4A4</italic>, <italic>TGM2</italic>, <italic>SLIT2</italic>, <italic>GPC1</italic>; Golgi group, <italic>GOLGA1</italic>, <italic>GOLGA3</italic>, <italic>GOLGA2</italic>, <italic>RAB1A</italic>, <italic>RABGGTB </italic>(Figure ##FIG##2##3a##). Six Rab family signaling genes involved in intracellular transport of organelles were differentially regulated (Table ##TAB##2##3##). Three small GTPases, RAB4A, RAB5B, and RAB9A, were upregulated (Table ##TAB##2##3##, Figure ##FIG##2##3b##), suggesting increased endosomal transport and processing. RAB4A and RAB5B selectively regulate intracellular trafficking and signaling of G protein-coupled receptors, such as the angiotensin receptor and adrenergic receptors (β2-AR and α2B-AR) from the cell surface [##REF##14711821##51##,##REF##14607250##52##]. RAB9A participates in late endosomal events leading to fusion with the lysosomal compartment [##REF##14607250##52##].</p>", "<p>In AAG astrocytes there was a predominant increase in transcription of Golgi-resident protein transcripts (Additional data file 7). These include <italic>RAB1A</italic>, and three members of the golgin family, <italic>GOLGA1</italic>, <italic>GOLGA2 </italic>and <italic>GOLGA3 </italic>(Table ##TAB##2##3##), which may function in the stacking of Golgi cisternae and in vesicular transport [##REF##15979510##53##]. GOLGA3 promotes cell surface expression of the beta adrenergic receptors [##REF##17118120##54##]. Thus, the increased expression of Golgi proteins may further enhance adrenergic receptor signaling. Note that the RAB proteins upregulated in the endosomal pathway (above) also affect trafficking of these receptors.</p>", "<p>Included in the protein trafficking network are plasma membrane associated proteins involved in cell-cell communication from the junctional matrix (Figure ##FIG##2##3a##). Catenins (CTNNB1, CTNND1) form membrane trafficking complexes that integrate other cadherins (CDH2), and members of the amyloid precursor protein complex (presenilin, APPBP1, PRSS3). In particular, CTNND1 functions to regulate membrane trafficking either through blocking cadherin interactions, or through Rho-GTPases such as Rho A, Rac and CDC42 [##REF##16949165##55##]. As with the myosin and actin motility networks, the change in expression of GTPase regulatory proteins will likely impact plasma membrane trafficking. The upregulation of chondroitin sulfate proteoglycan 2 (versican; <italic>VCAN</italic>), transglutaminase 2 (<italic>TGM2</italic>), and hyaluronan and proteoglycan link protein 1 (<italic>HAPLN1</italic>) are significant modifiers of the ECM [##REF##14724283##56##]. Both <italic>HAPLN1 </italic>and <italic>VCAN </italic>mRNA levels were upregulated in AAG compared to CAG astrocytes by qRT-PCR (Figure ##FIG##2##3b##). VCAN immunoreactivity was observed in the ECM of the cribriform plates, the perivascular matrix and a few astrocytes in the lamina cribrosa of normal AA and CA donors (Figure ##FIG##2##3c##). In glaucomatous tissues there was a marked increase in VCAN staining in astrocytes in the cribriform plates and hypertrophied reactive astrocytes in the nerve bundles in both populations (Figure ##FIG##2##3c##). TGFβ2 signaling upregulates VCAN [##REF##17453002##57##] in astrocyte cell types and expression of collagen type 4 and transglutaminase 2 in ONH astrocytes [##REF##15671284##37##]. Our data on changes in TGFβ receptor expression and ECM proteins are similar to those found in microarray profiling of ONH tissue from a rat model of glaucoma [##REF##17591886##58##]. Expression of ECM proteins is also modulated by TGFβ in GFAP-negative lamina cribrosa cells in culture [##REF##16078232##59##].</p>", "<p>There is substantial evidence that ONH astrocytes are responsible for the normal maintenance of the ECM in normal tissue and that reactive astrocytes remodel the ECM in response to elevated IOP in human and experimental glaucoma [##REF##11921203##10##,##REF##12650974##60##,##REF##10749379##61##]. Reactive astrocytes in the ONH express abnormal ECM in glaucoma, leading to loss of resiliency and deformability in response to elevated IOP. Alterations in TGFβ2 levels and TGFβ receptors and abnormal synthesis of ECM in AAG may convey connective tissue components of susceptibility to elevated IOP to this population.</p>", "<title>cAMP signaling in glaucomatous ONH astrocytes</title>", "<p>Earlier work in our laboratory indicated upregulation of two adenylyl cyclases (ADYC3 and ADYC9) in normal AA compared to CA astrocytes, suggesting changes in cyclic AMP (cAMP) levels in this population (L Chen, MR Hernandez, ARVO (Association for Research in Vision and Ophthamology) 2007 abstract 3265). To test whether glaucomatous ONH astrocytes exhibit differential basal levels in cAMP, we conducted a standard cAMP assay in normal AA and CA astrocytes and in AAG and CAG astrocytes. Under unstimulated conditions, normal AA and CA astrocytes exhibit no difference in basal levels of cAMP, whereas AAG and CAG astrocytes have significantly higher basal levels of cAMP compared with the normal counterparts (Figure ##FIG##5##6a##). Cyclic AMP is a key intracellular second messenger in astrocytes. The cAMP signaling cascade opposes pro-inflammatory cytokines such as IL1β and TNFα and maintains astrocytes in a quiescent (non-activated) state [##REF##10818485##62##]. Thus, the higher basal levels of cAMP in astrocytes from glaucomatous donors may be a response to pro-inflammatory cytokines such as TNFα in the glaucomatous ONH [##REF##10975909##19##].</p>", "<p>We searched the expression data for differentially expressed genes that might explain the difference in basal cAMP levels between glaucomatous and normal astrocytes. One potential candidate for increasing basal cAMP is PTHLH, a parathyroid hormone-like protein that is upregulated in glaucomatous astrocytes (Figure ##FIG##5##6b##). This protein binds to ubiquitous PTH receptors that are coupled to stimulation of adenylate cyclase and elevated cyclic AMP [##REF##15282196##63##]. Thus, upregulation of PTHLH provides an autocrine pathway leading to increased basal cyclic AMP levels in glaucomatous astrocytes. Another gene that might also contribute to the activity of adenylate cyclases is CAP2 [##REF##7962207##64##]. However, we found that CAP2 was not differentially expressed in glaucomatous ONH astrocytes by qRT-PCR (Figure ##FIG##5##6b##).</p>", "<title>Other disease-associated genes differentially regulated in glaucomatous OHN astrocytes</title>", "<title>Cell-cell communication</title>", "<p>The secondary and tertiary comparisons identified genes that were differentially expressed in AAG compared to AA and in CAG compared to CA, including <italic>BMP1</italic>, <italic>LTBP1</italic>, <italic>AMIGO2</italic>, <italic>SLIT2</italic>, <italic>GPC1</italic>, and <italic>OLR1 </italic>(Tables ##TAB##0##1## and ##TAB##1##2##). Selected genes were confirmed by qRT-PCR (Figure ##FIG##6##7##).</p>", "<p>In this list we found that specific cell-surface-associated proteins are downregulated in glaucoma. These include BMP1, which activates cleavage of LTBP1 proteins that release nascent TGFβ1 [##REF##17015622##65##], and AMIGO2, a type I transmembrane protein that regulates axon extension [##REF##12629050##66##]. Down-regulation of BMP1 may reduce the levels of free TGFβ1 and thus unbalance signaling between TGFβ isoforms. A decrease in AMIGO2 might negatively impact axon survival.</p>", "<p>Two differentially expressed genes that are involved in reactive astrocyte responses to neuronal injury are <italic>SLIT2 </italic>and <italic>GPC1 </italic>(glypican-1). SLIT2 serves as a chemorepellant for multiple types of axons [##REF##12655597##67##], while GPC1 is a proteoglycan that binds SLIT2 [##REF##12655597##67##]. Upregulation of expression of SLIT2 and a reduction of GPC1 by glaucomatous astrocytes suggest an inhibitory microenvironment for RGC axons in the ONH. These data are consistent with the idea that the enhanced migratory properties of glaucomatous astrocytes coupled with the release of factors that negatively impact upon axon survival are part of the pathophysiology of the disease.</p>", "<p>Finally, lectin-like oxidized-LDL receptor (OLR1; also known as LOX-1) is highly upregulated (Additional data files 5 and 11) in AAG astrocytes. OLR1 expression is induced by TGFβ1 signaling and is known to be a component of the fluid shear stress response of endothelial cells in early atherosclerotic lesions [##REF##10833418##68##]. These data are further confirmation of enhanced TGFβ signaling in AAG astrocytes as suggested by the differential receptor expression described earlier.</p>", "<title>Intracellular calcium signaling/transport systems in ONH astrocytes</title>", "<p>Two genes directly involved in Ca<sup>2+ </sup>homeostasis are differentially regulated in ONH astrocytes of AAG (Additional data files 5 and 7). <italic>CACNB4 </italic>encodes a beta subunit of the voltage-dependent calcium channel complex. CACNB4 plays an important role in calcium channel function by modulating G protein inhibition, increasing peak calcium current, controlling the alpha-1 subunit targeting to the membrane and shifting the voltage dependence of activation and inactivation. The second gene, <italic>ATP2C1 </italic>(Additional data file 7), encodes a protein that belongs to the family of P-type primary ion transport ATPases, which pump Ca<sup>2+ </sup>into the endoplasmic reticulum.</p>", "<p>Transcripts encoding the calcium/calmodulin-related signaling proteins calmodulin 1 (CALM1) and Ca<sup>2+</sup>/calmodulin-dependent membrane-associated kinase (CASK1) are differentially expressed in one or more glaucoma groups. CALM1 was increased in AAG compared to AA donors (Additional data file 5), while CASK1 was increased in glaucomatous astrocytes from both AA and CA donors (Table ##TAB##0##1##, Figure ##FIG##6##7##). Calmodulin is the Ca<sup>2+ </sup>sensor of key signaling molecules, such as adenylyl cyclase, CAMKII, CAMKIV, and MYLK discussed above. CASK1 is a member of the membrane-associated guanylate kinase proteins (MAGUKs), a prominent family of scaffolding molecules associated with intercellular junctions. CASK1 targets Ca<sup>2+ </sup>and K<sup>+ </sup>channels [##REF##14960569##69##] and/or the Ca<sup>2+ </sup>pump 4b/CI [##REF##12511555##70##] to the plasma membrane, interacts with liprins [##REF##16186258##71##] and regulates transcription by interacting with transcription factors in the nucleus [##REF##10749215##72##]. Interestingly, <italic>CASK </italic>is a candidate gene for X-linked optic atrophy [##REF##9722958##73##]. The differential expression of genes in Ca<sup>2+ </sup>signaling pathways could be a common theme in glaucomatous astrocytes that may have a higher impact in optic nerves from AA donors due to increased sensitivity to elevated IOP in these donors.</p>" ]
[ "<title>Conclusion</title>", "<p>Glaucomatous ONH astrocytes share many characteristics of reactive astrocytes in the central nervous system; however, certain properties may be specific to the pathophysiology of glaucoma. The current work and previous studies demonstrate that cultured glaucomatous ONH astrocytes exhibit differential expression of genes that promote cell motility and migration, downregulate cell adhesion, are associated with structural tissue changes, and contribute to neural degeneration. Our data further strengthen the idea that reprogramming of transcription in glaucomatous astrocytes shifts signaling towards TGFβ, Rho GTPase and Ca<sup>2+ </sup>systems, which impact the multiple networks described earlier.</p>", "<p>Our demonstration of this wide variety of genes that remain differentially expressed after weeks in culture suggests that glaucomatous ONH astrocytes have an altered phenotype. In the current study, using microarray analysis, we identified a number of genes (for example, <italic>MYLK</italic>, <italic>TGFBR2</italic>, <italic>VCAN</italic>, and <italic>RAC2</italic>) whose expression may underlie higher susceptibility of astrocytes of AA individuals to elevated IOP and that may be relevant to reactive astrocyte responses in glaucoma. Some limitations of our approach should be noted. First, ONH astrocyte derived from human glaucomatous eyes during the disease process does not allow assessment of changes or the identification of early mechanisms of disease that might be available from animal models. In addition, the difficulty to obtain and include more AA glaucomatous eyes limited our ability to identify differentially expressed genes in this group. However, stringent filters allowed the selection of a group of genes with functional significance. For each comparison, selected genes were validated by qRT-PCR and relevant gene products were confirmed by western blots in the four groups.</p>", "<p>We propose that part of the increased susceptibility to elevated IOP in AAG relates directly to astrocyte functions in the ONH. Astrocytes in AAG, which are reactive astrocytes, may have increased responsiveness to TGFβ signaling and enhanced migratory abilities, which may impact the remodeling of the ECM, inhibit axon survival, and alter vascular permeability in the glaucomatous ONH. Any one of these changes may represent a susceptibility risk factor in the AA population to withstand abnormally elevated IOP.</p>", "<p>This study provides an initial survey of the molecular differences of ONH astrocytes from AA and CA donors with glaucoma. Genes encoding many potential therapeutic targets, such as motility genes, ion channels, adhesion molecules, and signaling pathways, are selectively expressed in glaucomatous astrocytes, making them interesting as potential targets for astrocyte-specific therapeutics. Additional applications of these data include identification and characterization of signaling pathways involved in astrocyte function and further exploration of the role of selected identified genes in experimental animal and in <italic>vitro </italic>models of glaucoma.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Comparison of gene expression in normal and glaucomatous eyes from Caucasian American and African American donors reveals differences that might reflect different susceptibility to glaucoma.</p>", "<title>Background</title>", "<p>Epidemiological and genetic studies indicate that ethnic/genetic background plays an important role in susceptibility to primary open angle glaucoma (POAG). POAG is more prevalent among the African-descent population compared to the Caucasian population. Damage in POAG occurs at the level of the optic nerve head (ONH) and is mediated by astrocytes. Here we investigated differences in gene expression in primary cultures of ONH astrocytes obtained from age-matched normal and glaucomatous donors of Caucasian American (CA) and African American (AA) populations using oligonucleotide microarrays.</p>", "<title>Results</title>", "<p>Gene expression data were obtained from cultured astrocytes representing 12 normal CA and 12 normal AA eyes, 6 AA eyes with POAG and 8 CA eyes with POAG. Data were normalized and significant differential gene expression levels detected by using empirical Bayesian shrinkage moderated t-statistics. Gene Ontology analysis and networks of interacting proteins were constructed using the BioGRID database. Network maps included regulation of myosin, actin, and protein trafficking. Real-time RT-PCR, western blots, ELISA, and functional assays validated genes in the networks.</p>", "<title>Conclusion</title>", "<p>Cultured AA and CA glaucomatous astrocytes retain differential expression of genes that promote cell motility and migration, regulate cell adhesion, and are associated with structural tissue changes that collectively contribute to neural degeneration. Key upregulated genes include those encoding myosin light chain kinase (<italic>MYLK</italic>), transforming growth factor-β receptor 2 (<italic>TGFBR2</italic>), rho-family GTPase-2 (<italic>RAC2</italic>), and versican (<italic>VCAN</italic>). These genes along with other differentially expressed components of integrated networks may reflect functional susceptibility to chronic elevated intraocular pressure that is enhanced in the optic nerve head of African Americans.</p>" ]
[ "<title>Abbreviations</title>", "<p>AA, African American; AAG, AA donor with glaucoma; CA, Caucasian American; CAG, CA donor with glaucoma; cAMP, cyclic AMP; ECM, extracellular matrix; ELISA, enzyme-linked immunosorbent assay; GFAP, glial acidic fibrillar protein; GO, Gene Ontology; IOP, intraocular pressure; MYLK, myosin light chain kinase; ONH, optic nerve head; POAG, primary open angle glaucoma; qRT-PCR, quantitative RT-PCR; TGF, transforming growth factor; TNF, tumor necrosis factor.</p>", "<title>Authors' contributions</title>", "<p>MRH conceived the study, directed individual efforts, and wrote drafts of the manuscript. TJL performed network analysis, data mining, and wrote drafts of the manuscript. HM coordinated molecular biology studies, cultured cell preparations, and contributed sections of the manuscript. LC and WL performed molecular biology and biochemical analyses. SMR, AMC, and AW performed migration assays and cell/tissue immunohistochemistry experiments. SNL and PD performed statistical analysis and bioinformatics on the microarray data. All authors viewed and approved the manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data files are available in the online version of the paper. Additional data file ##SUPPL##0##1## is a table listing clinical information about CAG and AAG eyes used to generate primary cultures of ONH astrocytes. Additional data file ##SUPPL##1##2## is a table listing demographic information of CA and AA normal donor eyes used to generate primary cultures of ONH astrocytes. Additional data file ##SUPPL##2##3## is a table that summarizes the number of probe-sets on the chip and used in analysis. Additional data file ##SUPPL##3##4## is spreadsheet listing the primers used for qRT-PCR. Additional data file ##SUPPL##4##5## is a spreadsheet listing genes differentially expressed in glaucomatous ONH astrocytes and including the comparison between AAG versus normal AA. Additional data file ##SUPPL##5##6## is a spreadsheet listing differentially expressed genes between CAG and normal CA. Additional data file ##SUPPL##6##7## is a spreadsheet listing differentially expressed genes between AAG and CAG. Additional data file ##SUPPL##7##8## is a spreadsheet listing genes differentially expressed in ONH astrocytes from AAG compared to both normal AA and CAG. Additional data file ##SUPPL##6##7## is a spreadsheet summarizing Gene Ontology for the comparisons between AAG and AA data. Additional data file ##SUPPL##7##8## is a spreadsheet with Gene ontology comparisons for CAG and CA. Additional data file ##SUPPL##8##9## is a spreadsheet with GO comparisons for AAG versus CAG expression sets. Additional data file ##SUPPL##9##10## is a figure showing the distribution of genes in two GO categories. Additional data file ##SUPPL##10##11## is a figure showing qRT-PCR data that confirm additional differentially expressed genes from the CAG-CA comparison.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Jose Bongolan for immunohistochemistry staining and Ping Yang and Marina Vracar-Grabar for generating cell cultures. This work was supported in part by NIH grant EY 06416 and an unrestricted grant from Research to Prevent Blindness.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Astrocyte migration and the myosin regulatory network in glaucoma astrocytes. <bold>(a) </bold>Cell migration assay shows that AA and AAG astrocytes migrate significantly faster than CA and CAG astrocytes. The assay was performed as described in the Materials and methods. Values represent mean optical density (OD) ± standard deviation of triplicate experiments using primary astrocyte cultures of six AA, five AAG, five CA and five CAG donors. Asterisk indicates <italic>p</italic>-value &lt; 0.05. <bold>(b) </bold>Schematic representation of the myosin regulatory network. Upregulated mRNAs have large red nodes and font while downregulated mRNAs have large blue nodes and font. Small black nodes and font show genes have 'present calls' without differential expression. <bold>(c) </bold>Confirmation of three differentially expressed genes from myosin network by qRT-PCR in human ONH astrocytes: MYLK, RAC2 and PIK3R1. Genes were normalized to 18S RNA. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test). Asterisk indicates <italic>p </italic>&lt; 0.05).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Actin regulatory network and TGFβ signaling in AAG astrocytes. <bold>(a) </bold>Schematic representation of the actin and TGFβ regulatory network. Upregulated mRNAs have large red nodes and capital font, while downregulated mRNAs are shown with large blue nodes and capital font. Small black nodes and capital font indicate genes that have 'present calls' without differential expression. The RhoA GTPase is in bold in black because of higher activity in glaucoma astrocytes. <bold>(b) </bold>Representative western blot of the pull-down Rho activation assay demonstrated that both AAG and CAG astrocytes exhibit significantly higher Rho activity than normal astrocytes under unstimulated conditions. <bold>(c) </bold>Densitometry analysis of the blots from Rho activation assay. Bars show mean fold difference in density ± standard error of two independent experiments. (Asterisk indicates <italic>p </italic>&lt; 0.05)</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Intracellular trafficking networks associated with golgi, plasma membrane, and endosomes that have differentially expressed genes in glaucoma astrocytes. <bold>(a) </bold>Schematic representation of the intracellular trafficking network. Upregulated mRNAs have a large red node and font, while downregulated genes have a large blue node and font. Small black nodes and font indicate genes that have 'present calls' without differential expression. <bold>(b) </bold>Confirmation of four differentially expressed genes from the trafficking network by qRT-PCR in human ONH astrocytes: <italic>RAB4A</italic>, <italic>RAB5B</italic>, <italic>HAPLN </italic>and <italic>VCAN</italic>. Genes were normalized to 18S RNA. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test). Asterisk indicates <italic>p </italic>&lt; 0.05. <bold>(c) </bold>Representative double immunofluorescent staining of versican (VCAN; red) and astrocyte marker GFAP (green) in sections of human ONH from an AA donor (51 year old female), AAG donor (70 year old male), CA donor (70 year old male) and CAG donor (76 year old male). Nuclei (blue) are stained with DAPI. Note staining of VCAN (red) in the cribriform plates and surrounding the blood vessels (arrowheads). Arrows indicate versican co-localized with GFAP in astrocytes in the cribriform plates of the lamina cribrosa. VCAN staining is stronger in astrocytes of the glaucomatous lamina cribrosa. V, blood vessel; NB, nerve bundle. Scale bar 35 μm.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>MYLK isoform expression in ONH astrocytes. <bold>(a) </bold>Representative double immunofluorescent staining of MYLK (red) and astrocyte marker GFAP (green) in sections of human ONH from an AA donor (51 year old male), AAG donor (70 year old male), CA donor (56 year old female) and CAG donor (76 year old male). Nuclei (blue) are stained with DAPI. Note strong granular staining of MYLK in astrocytes (arrows) in the cribriform plates of the lamina cribrosa of AA and AAG donors compared to CA and CAG donors. V, blood vessel; NB, nerve bundle. Scale bar 35 μm. <bold>(b) </bold>Representative western blots of astrocyte cell lysates with MYLK antibody. β-Actin was used as a loading control. Note that AAG1-4 donors express more MYLK-210 and less MYLK-130 than CAG1-4 donors. <bold>(c) </bold>Graph of MYLK-210 expressed as the fraction of MYLK-210 in the four groups. <bold>(d) </bold>Graph of the fraction of MYLK-130 expressed in the four groups. These results represent densitometry analysis of western blots using seven AA, five AAG, eight CA and eight CAG donor samples.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>TGFβ and its receptors in ONH astrocytes. <bold>(a) </bold>Confirmation of three differentially expressed genes from the TGFβ-actin network (Figure 3a) by qRT-PCR in human ONH astrocytes: <italic>TGFBR2</italic>, <italic>SMAD3 </italic>and <italic>TGFBR1</italic>. Genes were normalized to 18S. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test was used. Asterisk indicates <italic>p </italic>&lt; 0.05). <bold>(b) </bold>Representative double immunofluorescent staining of TGFBR2 (red) and astrocyte marker GFAP (green) in sections of human ONH from an AA donor (51 year old male), AAG donor (70 year old male), CA donor (54 year old male) and CAG donor (76 year old male). Nuclei (blue) are stained with DAPI. Note granular staining of TGFBR2 in astrocytes (arrows) in the cribriform plates of the lamina cribrosa in AAG and CAG donors. Fewer astrocytes stain for TGFBR2 in the lamina cribrosa of CA donors. V, blood vessel; NB, nerve bundle. Scale bar 35 μm. <bold>(c) </bold>Representative western blots of astrocyte cell lysates with TGFBR2 antibody. β-Actin was used as a loading control. Note that AAG donors express more TGFBR2 than CAG donors. Normal AA and CA express lessTGFBR2 than glaucomatous donors. <bold>(d) </bold>Secreted TGFβ1 and TGFβ2 detected by ELISA. TGFβ2 is the primary form of TGFβ produced by ONH astrocytes. Secreted TGFβ1 is significantly higher in AA astrocytes compared to CA astrocytes (Asterisk indicates <italic>p </italic>&lt; 0.05, two-tailed <italic>t</italic>-test); however, the increase in glaucomatous astrocytes compared to normal astrocytes is not significant. Secreted TGFβ2 levels are elevated significantly from normal AA astrocytes compared to all other donors (n = 24; asterisk indicates <italic>p </italic>&lt; 0.05, two-tailed <italic>t</italic>-test).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>cAMP signaling in glaucomatous astrocytes. <bold>(a) </bold>cAMP levels in unstimulated ONH astrocytes were determined as described in the Materials and methods. The basal cAMP level was significantly higher in glaucomatous astrocytes compared to their normal counterparts. Values are the mean ± standard deviation of cAMP expressed in pmol/mg of protein. Eight AA, four AAG, nine CA and four CAG individual samples were used in this study. <bold>(b) </bold>Confirmation of <italic>PTHLH </italic>and <italic>CAP2 </italic>expression by qRT-PCR in human ONH astrocytes. Genes were normalized to 18S. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test). Asterisk indicates <italic>p </italic>&lt; 0.05).</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>Glaucoma disease-associated genes differentially regulated in glaucomatous OHN astrocytes. Differential expression of six glaucoma disease associated genes (<italic>BMP1</italic>, <italic>AMIGO2</italic>, <italic>DMPK</italic>, <italic>SLIT2</italic>, <italic>RBP-1 </italic>and <italic>CASK</italic>) was validated by qRT-PCR in human ONH astrocytes. Genes were normalized to 18S. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test). Asterisk indicates <italic>p </italic>&lt; 0.05).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Common genes significantly decreased in glaucomatous ONH astrocytes compared to their normal counterparts</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td/><td align=\"center\" colspan=\"2\">AAG-AA (U133Av2)</td><td align=\"center\" colspan=\"2\">CAG-CA (U95Av2)</td></tr><tr><td/><td/><td/><td colspan=\"2\"><hr/></td><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\">Symbol</td><td align=\"left\">Description</td><td align=\"center\">CL</td><td align=\"center\">FC</td><td align=\"center\"><italic>p</italic>-value</td><td align=\"center\">FC</td><td align=\"center\"><italic>p</italic>-value</td></tr></thead><tbody><tr><td align=\"left\"><italic>AMIGO2</italic></td><td align=\"left\">Adhesion molecule with Ig-like domain 2</td><td align=\"center\">12q13.11</td><td align=\"center\">-1.52</td><td align=\"center\">0.0498</td><td align=\"center\">-2.01</td><td align=\"center\">0.0011</td></tr><tr><td align=\"left\"><italic>BMP1</italic></td><td align=\"left\">Bone morphogenetic protein 1</td><td align=\"center\">8p21</td><td align=\"center\">-1.92</td><td align=\"center\">0.0005</td><td align=\"center\">-2.08</td><td align=\"center\">0.0001</td></tr><tr><td align=\"left\"><italic>CD97</italic></td><td align=\"left\">CD97 molecule</td><td align=\"center\">19p13</td><td align=\"center\">-1.65</td><td align=\"center\">0.0015</td><td align=\"center\">-1.36</td><td align=\"center\">0.0008</td></tr><tr><td align=\"left\"><italic>CRIP2</italic></td><td align=\"left\">Cysteine-rich protein 2</td><td align=\"center\">14q32.3</td><td align=\"center\">-2.58</td><td align=\"center\">0</td><td align=\"center\">-1.44</td><td align=\"center\">0.0034</td></tr><tr><td align=\"left\"><italic>DGKA</italic></td><td align=\"left\">Diacylglycerol kinase, alpha 80 kDa</td><td align=\"center\">12q13.3</td><td align=\"center\">-1.54</td><td align=\"center\">0.0034</td><td align=\"center\">-1.28</td><td align=\"center\">0.0001</td></tr><tr><td align=\"left\"><italic>DMPK</italic></td><td align=\"left\">Dystrophia myotonica-protein kinase</td><td align=\"center\">19q13.3</td><td align=\"center\">-2.45</td><td align=\"center\">0</td><td align=\"center\">-1.62</td><td align=\"center\">0.0021</td></tr><tr><td align=\"left\"><italic>EFHD1</italic></td><td align=\"left\">EF-hand domain family, member D1</td><td align=\"center\">2q37.1</td><td align=\"center\">-4</td><td align=\"center\">0</td><td align=\"center\">-2.01</td><td align=\"center\">0.0011</td></tr><tr><td align=\"left\"><italic>GPC1</italic></td><td align=\"left\">glypican 1</td><td align=\"center\">2q35-q37</td><td align=\"center\">-1.61</td><td align=\"center\">0.0032</td><td align=\"center\">-1.31</td><td align=\"center\">0.0026</td></tr><tr><td align=\"left\"><italic>MGLL</italic></td><td align=\"left\">Monoglyceride lipase</td><td align=\"center\">3q21.3</td><td align=\"center\">-1.52</td><td align=\"center\">0.0083</td><td align=\"center\">-1.75</td><td align=\"center\">0.0005</td></tr><tr><td align=\"left\"><italic>MICAL2</italic></td><td align=\"left\">Microtubule associated monoxygenase, calponin and LIM domain containing 2</td><td align=\"center\">11p15.3</td><td align=\"center\">-1.62</td><td align=\"center\">0.0186</td><td align=\"center\">-2.02</td><td align=\"center\">0.0013</td></tr><tr><td align=\"left\"><italic>NPAL3</italic></td><td align=\"left\">NIPA-like domain containing 3</td><td align=\"center\">1p36.12-p35.1</td><td align=\"center\">-1.54</td><td align=\"center\">0.0034</td><td align=\"center\">-1.51</td><td align=\"center\">0.0079</td></tr><tr><td align=\"left\"><italic>PDGFA</italic></td><td align=\"left\">Platelet-derived growth factor alpha polypeptide</td><td align=\"center\">7p22</td><td align=\"center\">-1.65</td><td align=\"center\">0.0076</td><td align=\"center\">-2.21</td><td align=\"center\">0.0004</td></tr><tr><td align=\"left\"><italic>SLC12A2</italic></td><td align=\"left\">Solute carrier family 12, member 2</td><td align=\"center\">5q23.3</td><td align=\"center\">-1.61</td><td align=\"center\">0.0032</td><td align=\"center\">-1.51</td><td align=\"center\">0.0001</td></tr><tr><td align=\"left\"><italic>SLC12A4</italic></td><td align=\"left\">Solute carrier family 12, member 4</td><td align=\"center\">16q22.1</td><td align=\"center\">-2.42</td><td align=\"center\">0.0007</td><td align=\"center\">-1.19</td><td align=\"center\">0.0046</td></tr><tr><td align=\"left\"><italic>SMTN</italic></td><td align=\"left\">Smoothelin</td><td align=\"center\">22q12.2</td><td align=\"center\">-1.79</td><td align=\"center\">0.0162</td><td align=\"center\">-1.99</td><td align=\"center\">0.001</td></tr><tr><td align=\"left\"><italic>WWP2</italic></td><td align=\"left\">WW domain containing E3 ubiquitin protein ligase 2</td><td align=\"center\">16q22.1</td><td align=\"center\">-1.87</td><td align=\"center\">0.0006</td><td align=\"center\">-1.39</td><td align=\"center\">0.0029</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Differentially expressed genes in glaucomatous astrocytes*</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Gene</td><td align=\"left\">Description</td><td align=\"center\">FC</td><td align=\"center\"><italic>p</italic>-value</td><td align=\"center\">CL</td></tr></thead><tbody><tr><td align=\"left\"><bold>Genes associated with myosin regulation</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"><italic>CALM1</italic></td><td align=\"left\">Calmodulin 1</td><td align=\"center\">2.23<sup>†</sup></td><td align=\"center\">0.00121</td><td align=\"center\">14q24-q31</td></tr><tr><td align=\"left\"><italic>MYH10</italic></td><td align=\"left\">Myosin, heavy chain 2</td><td align=\"center\">1.64</td><td align=\"center\">0.00588</td><td align=\"center\">17p13.1</td></tr><tr><td align=\"left\"><italic>MYLK</italic></td><td align=\"left\">Myosin, light polypeptide kinase</td><td align=\"center\">2.89</td><td align=\"center\">0.000133</td><td align=\"center\">3q21</td></tr><tr><td align=\"left\"><italic>PIK3R1</italic></td><td align=\"left\">Phosphoinositide-3-kinase, subunit (p85-alpha)</td><td align=\"center\">1.62</td><td align=\"center\">0.00201</td><td align=\"center\">5q13.1</td></tr><tr><td align=\"left\"><italic>MYPT1</italic></td><td align=\"left\">Protein phosphatase 1, regulator subunit 12A (PPP1R12A)</td><td align=\"center\">1.51</td><td align=\"center\">0.000775</td><td align=\"center\">12q15-q21</td></tr><tr><td align=\"left\"><italic>RAC2</italic></td><td align=\"left\">Ras-related 2 (Rho family, Rac2)</td><td align=\"center\">2.34</td><td align=\"center\">0.001059</td><td align=\"center\">22q13.1</td></tr><tr><td align=\"left\"><italic>RPS6KA3</italic></td><td align=\"left\">Ribosomal protein S6 kinase, 90 kDa, polypeptide 3</td><td align=\"center\">1.5</td><td align=\"center\">0.000061</td><td align=\"center\">Xp22.2-p22.1</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Genes associated with actin regulation</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"><italic>ARHGEF7</italic></td><td align=\"left\">Rho guanine nucleotide exchange factor (GEF) 7</td><td align=\"center\">1.71</td><td align=\"center\">0.000064</td><td align=\"center\">13q34</td></tr><tr><td align=\"left\"><italic>NCK1</italic></td><td align=\"left\">NCK adaptor protein 1</td><td align=\"center\">1.64<sup>†</sup></td><td align=\"center\">0.000015</td><td align=\"center\">3q21</td></tr><tr><td align=\"left\"><italic>PDLIM1</italic></td><td align=\"left\">PDZ and LIM domain 1 (elfin, CLP36)</td><td align=\"center\">1.61</td><td align=\"center\">0.00106</td><td align=\"center\">10q22-q26.3</td></tr><tr><td align=\"left\"><italic>PIK3R1</italic></td><td align=\"left\">Phosphoinositide-3-kinase, regulatory subunit 1</td><td align=\"center\">1.61</td><td align=\"center\">0.002012</td><td align=\"center\">5q13.1</td></tr><tr><td align=\"left\"><italic>PLEC1</italic></td><td align=\"left\">Plectin 1, intermediate filament binding protein</td><td align=\"center\">-1.82</td><td align=\"center\">0.00199</td><td align=\"center\">8q24</td></tr><tr><td align=\"left\"><italic>PTPN11</italic></td><td align=\"left\">Protein tyrosine phosphatase, non-receptor type 11</td><td align=\"center\">-1.9</td><td align=\"center\">0.000005</td><td align=\"center\">12q24</td></tr><tr><td align=\"left\"><italic>RAC2</italic></td><td align=\"left\">Ras-related 2 (Rho family, Rac2)</td><td align=\"center\">2.34</td><td align=\"center\">0.001059</td><td align=\"center\">22q13.1</td></tr><tr><td align=\"left\"><italic>SMAD3</italic></td><td align=\"left\">SMAD, mothers against DPP homolog 3</td><td align=\"center\">1.9</td><td align=\"center\">0.000488</td><td align=\"center\">15q22.33</td></tr><tr><td align=\"left\"><italic>TGFBR1</italic></td><td align=\"left\">Transforming growth factor, beta receptor I</td><td align=\"center\">-1.57</td><td align=\"center\">0.000038</td><td align=\"center\">9q22</td></tr><tr><td align=\"left\"><italic>TGFBR2</italic></td><td align=\"left\">Transforming growth factor, beta receptor II</td><td align=\"center\">2.11</td><td align=\"center\">0.007253</td><td align=\"center\">3p22</td></tr><tr><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Genes associated with protein trafficking</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"><italic>APPBP1</italic></td><td align=\"left\">Amyloid beta precursor protein binding protein 1</td><td align=\"center\">1.62</td><td align=\"center\">0.001688</td><td align=\"center\">16q22</td></tr><tr><td align=\"left\"><italic>CCL5</italic></td><td align=\"left\">Chemokine (C-C motif) ligand 5</td><td align=\"center\">-1.74</td><td align=\"center\">0.002283</td><td align=\"center\">17q11.2-q12</td></tr><tr><td align=\"left\"><italic>CDH2</italic></td><td align=\"left\">Cadherin 2, type 1, N-cadherin (neuronal)</td><td align=\"center\">1.55</td><td align=\"center\">0.003173</td><td align=\"center\">18q11.2</td></tr><tr><td align=\"left\"><italic>COL4A4</italic></td><td align=\"left\">Collagen, type IV, alpha 4</td><td align=\"center\">1.59</td><td align=\"center\">0.002335</td><td align=\"center\">2q35-q37</td></tr><tr><td align=\"left\"><italic>CTNNB1</italic></td><td align=\"left\">Catenin (cadherin-associated protein), beta 1, 88 kDa</td><td align=\"center\">2.14</td><td align=\"center\">0.005445</td><td align=\"center\">3p21</td></tr><tr><td align=\"left\"><italic>CTNND1</italic></td><td align=\"left\">Catenin (cadherin-associated protein), delta 1</td><td align=\"center\">1.68</td><td align=\"center\">0.000025</td><td align=\"center\">11q11</td></tr><tr><td align=\"left\"><italic>GOLGA1</italic></td><td align=\"left\">Golgi autoantigen, golgin subfamily a, 1</td><td align=\"center\">1.51</td><td align=\"center\">0.00002</td><td align=\"center\">9q33.3</td></tr><tr><td align=\"left\"><italic>GOLGA2</italic></td><td align=\"left\">Golgi autoantigen, golgin subfamily a, 2</td><td align=\"center\">1.77</td><td align=\"center\">0.000002</td><td align=\"center\">9q34.11</td></tr><tr><td align=\"left\"><italic>GOLGA3</italic></td><td align=\"left\">Golgi autoantigen, golgin subfamily a, 3</td><td align=\"center\">1.97</td><td align=\"center\">0.000128</td><td align=\"center\">12q24.33</td></tr><tr><td align=\"left\"><italic>HAPLN1</italic></td><td align=\"left\">Hyaluronan and proteoglycan link protein 1</td><td align=\"center\">8.04</td><td align=\"center\">0.001193</td><td align=\"center\">5q14.3</td></tr><tr><td align=\"left\"><italic>PRSS3</italic></td><td align=\"left\">Protease, serine, 3 (mesotrypsin)</td><td align=\"center\">2.53</td><td align=\"center\">0.005135</td><td align=\"center\">9p11.2</td></tr><tr><td align=\"left\"><italic>RAB1A</italic></td><td align=\"left\">RAB1A, member RAS oncogene family</td><td align=\"center\">1.51</td><td align=\"center\">0.000274</td><td align=\"center\">2p14</td></tr><tr><td align=\"left\"><italic>RAB4A</italic></td><td align=\"left\">RAB4A, member RAS oncogene family</td><td align=\"center\">1.52</td><td align=\"center\">0.00035</td><td align=\"center\">1q42-q43</td></tr><tr><td align=\"left\"><italic>RAB5B</italic></td><td align=\"left\">RAB5B, member RAS oncogene family</td><td align=\"center\">1.5<sup>‡</sup></td><td align=\"center\">0.0081</td><td align=\"center\">12q13</td></tr><tr><td align=\"left\"><italic>RAB9A</italic></td><td align=\"left\">RAB9A, member RAS oncogene family</td><td align=\"center\">1.64</td><td align=\"center\">0.000256</td><td align=\"center\">Xp22.2</td></tr><tr><td align=\"left\"><italic>RAB9P40</italic></td><td align=\"left\">RAB9 effector protein with kelch motifs</td><td align=\"center\">1.84</td><td align=\"center\">0.000002</td><td align=\"center\">9q33.3</td></tr><tr><td align=\"left\"><italic>RABGGTB</italic></td><td align=\"left\">Rab geranylgeranyltransferase, beta subunit</td><td align=\"center\">1.76</td><td align=\"center\">0.000375</td><td align=\"center\">1p31</td></tr><tr><td align=\"left\"><italic>TGM2</italic></td><td align=\"left\">Transglutaminase 2</td><td align=\"center\">2.75</td><td align=\"center\">0.008289</td><td align=\"center\">20q12</td></tr><tr><td align=\"left\"><italic>VCAN</italic></td><td align=\"left\">Versican (chondroitin sulfate proteoglycan 2, CSPG2)</td><td align=\"center\">2.94</td><td align=\"center\">0.000265</td><td align=\"center\">5q14.3</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Common genes significantly increased in glaucomatous ONH astrocytes compared to their normal counterparts</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td/><td align=\"center\" colspan=\"2\">AAG-AA (U133Av2)</td><td align=\"center\" colspan=\"2\">CAG-CA (U95Av2)</td></tr><tr><td/><td/><td/><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Symbol</td><td align=\"left\">Description</td><td align=\"center\">CL</td><td align=\"center\">FC</td><td align=\"center\"><italic>p</italic>-value</td><td align=\"center\">FC</td><td align=\"center\"><italic>p</italic>-value</td></tr></thead><tbody><tr><td align=\"left\"><italic>ABCA8</italic></td><td align=\"left\">ATP-binding cassette, sub-family A, member 8</td><td align=\"center\">17q24</td><td align=\"center\">2.34</td><td align=\"center\">0.0291</td><td align=\"center\">2.53</td><td align=\"center\">9.43E-05</td></tr><tr><td align=\"left\"><italic>C5orf30</italic></td><td align=\"left\">Chromosome 5 open reading frame 30</td><td align=\"center\">5q21.1</td><td align=\"center\">1.57</td><td align=\"center\">0.0028</td><td align=\"center\">1.48</td><td align=\"center\">0.0042</td></tr><tr><td align=\"left\"><italic>CASK</italic></td><td align=\"left\">Calcium/calmodulin-dependent serine protein kinase</td><td align=\"center\">Xp11.4</td><td align=\"center\">1.99</td><td align=\"center\">0.0064</td><td align=\"center\">1.31</td><td align=\"center\">0.002</td></tr><tr><td align=\"left\"><italic>CASP4</italic></td><td align=\"left\">Caspase 4, apoptosis-related cysteine peptidase</td><td align=\"center\">11q22.2-q22.3</td><td align=\"center\">1.59</td><td align=\"center\">0.0007</td><td align=\"center\">1.9</td><td align=\"center\">0.0026</td></tr><tr><td align=\"left\"><italic>GSTA4</italic></td><td align=\"left\">Glutathione S-transferase A4</td><td align=\"center\">6p12.1</td><td align=\"center\">1.25</td><td align=\"center\">0.005</td><td align=\"center\">1.85</td><td align=\"center\">5.21E-05</td></tr><tr><td align=\"left\"><italic>GULP1</italic></td><td align=\"left\">GULP, engulfment adaptor PTB domain containing 1</td><td align=\"center\">2q32.3-q33</td><td align=\"center\">1.89</td><td align=\"center\">0.0023</td><td align=\"center\">1.38</td><td align=\"center\">0.0075</td></tr><tr><td align=\"left\"><italic>HEPH</italic></td><td align=\"left\">Hephaestin</td><td align=\"center\">Xq11-q12</td><td align=\"center\">4.15</td><td align=\"center\">0.0021</td><td align=\"center\">1.88</td><td align=\"center\">0.0021</td></tr><tr><td align=\"left\"><italic>HOXB2</italic></td><td align=\"left\">Homeobox B2</td><td align=\"center\">17q21-q22</td><td align=\"center\">1.59</td><td align=\"center\">0.0133</td><td align=\"center\">1.86</td><td align=\"center\">0.0014</td></tr><tr><td align=\"left\"><italic>KCNK2</italic></td><td align=\"left\">Potassium channel, subfamily K, member 2</td><td align=\"center\">1q41</td><td align=\"center\">1.55</td><td align=\"center\">0.0489</td><td align=\"center\">1.52</td><td align=\"center\">0.0024</td></tr><tr><td align=\"left\"><italic>KIAA1199</italic></td><td align=\"left\">KIAA1199</td><td align=\"center\">15q24</td><td align=\"center\">1.68</td><td align=\"center\">0.0152</td><td align=\"center\">1.94</td><td align=\"center\">0.0026</td></tr><tr><td align=\"left\"><italic>LMO4</italic></td><td align=\"left\">LIM domain only 4</td><td align=\"center\">1p22.3</td><td align=\"center\">1.7</td><td align=\"center\">0.0034</td><td align=\"center\">1.83</td><td align=\"center\">0.0052</td></tr><tr><td align=\"left\"><italic>MYH10</italic></td><td align=\"left\">Myosin, heavy polypeptide 10, non-muscle</td><td align=\"center\">17p13</td><td align=\"center\">1.64</td><td align=\"center\">0.0012</td><td align=\"center\">1.57</td><td align=\"center\">0.0017</td></tr><tr><td align=\"left\"><italic>PYGL</italic></td><td align=\"left\">Phosphorylase, glycogen; liver</td><td align=\"center\">14q21-q22</td><td align=\"center\">1.47</td><td align=\"center\">0.0141</td><td align=\"center\">2.2</td><td align=\"center\">0.0025</td></tr><tr><td align=\"left\"><italic>RBP1</italic></td><td align=\"left\">Retinol binding protein 1, cellular</td><td align=\"center\">3q23</td><td align=\"center\">2.2</td><td align=\"center\">0.0007</td><td align=\"center\">2.32</td><td align=\"center\">0.00073</td></tr><tr><td align=\"left\"><italic>SERPING1</italic></td><td align=\"left\">Serpin peptidase inhibitor, clade G, member 1</td><td align=\"center\">11q12-q13.1</td><td align=\"center\">2.3</td><td align=\"center\">0.0064</td><td align=\"center\">1.86</td><td align=\"center\">0.0014</td></tr><tr><td align=\"left\"><italic>SH3BP5</italic></td><td align=\"left\">SH3-domain binding protein 5</td><td align=\"center\">3p24.3</td><td align=\"center\">1.65</td><td align=\"center\">0.0407</td><td align=\"center\">2.74</td><td align=\"center\">4.87E-05</td></tr><tr><td align=\"left\"><italic>SLIT2</italic></td><td align=\"left\">Slit homolog 2</td><td align=\"center\">4p15.2</td><td align=\"center\">1.6</td><td align=\"center\">0.0077</td><td align=\"center\">1.42</td><td align=\"center\">0.0027</td></tr><tr><td align=\"left\"><italic>TINP1</italic></td><td align=\"left\">TGF beta-inducible nuclear protein 1</td><td align=\"center\">5q13.3</td><td align=\"center\">1.53</td><td align=\"center\">7.93E-05</td><td align=\"center\">1.36</td><td align=\"center\">0.0055</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Clinical information about CAG and AAG eyes used to generate primary cultures of ONH astrocytes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Demographic information of CA and AA normal donor eyes used to generate primary cultures of ONH astrocytes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Probe-sets on the chip and used in analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Primers used for qRT-PCR.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Includes the comparison between AAG versus normal AA.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Differentially expressed genes between CAG and normal CA.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Differentially expressed genes between AAG and CAG.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>Differentially expressed genes in ONH astrocytes from AAG compared to both normal AA and CAG.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>GO comparisons for AAG versus CAG expression sets.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional data file 10</title><p>Common genes were selected from the GO lists (Additional data files 7-9) for each dataset (AAG-CAG, AAG-AA, and CAG-CA comparisons). The fraction of common genes (y-axis) for the GO terms 'phosphorylation' (grey bar) and 'cell-cell signaling' categories (black bar) are shown.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S11\"><caption><title>Additional data file 11</title><p><bold>(a-e) </bold>CAG-CA and comparison: CALM (a), CAPG (b), GJA1 (c), GPNMB (d) and SOD2 (e). <bold>(f-j) </bold>AAG-AA comparison: GSTA4 (f), LOXL2 (g), MYH10 (h), PDLIM7 (i) and OLR1 (j). Genes were normalized to 18S. Graphical representation of the relative mRNA levels in normal and glaucomatous AA and normal and glaucomatous CA astrocytes (n = 6, two-tailed <italic>t</italic>-test was used. Asterisk indicates <italic>p </italic>&lt; 0.05).</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>CL, chromosome location; FC, fold change.</p></table-wrap-foot>", "<table-wrap-foot><p>*Genes differentially expressed in AAG compared to CAG (Additional data file 7) except where noted. <sup>†</sup>From Additional data file 5. <sup>‡</sup>From qRT-PCR data (Figure 3b). FC, fold change; CL, chromosome location.</p></table-wrap-foot>", "<table-wrap-foot><p>CL, chromosome location; FC, fold change.</p></table-wrap-foot>" ]
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{ "acronym": [], "definition": [] }
78
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 9; 9(7):R111
oa_package/73/af/PMC2530868.tar.gz
PMC2530869
18613972
[ "<title>Background</title>", "<p>The generation of genome-wide gene expression data for the reference plant <italic>Arabidopsis thaliana </italic>yielded important insights into transcriptional control of development, with genome-wide expression maps having become an indispensable tool for the research community. Specific gene expression profiles for various plant organs, developmental stages, growth conditions, treatments, mutants, or even single cell types are available (for example [##REF##14671301##1##, ####REF##18192438##2##, ##REF##17376166##3##, ##REF##15937229##4##, ##REF##15806101##5##, ##REF##17189330##6##, ##REF##15710687##7####15710687##7##]). These data have helped to elucidate transcriptional networks and attending promoter motifs, to uncover gene functions, and to reveal molecular explanations for mutant phenotypes (for review [##REF##17291825##8##]).</p>", "<p>The most widely used platform for <italic>Arabidopsis </italic>is the Affymetrix ATH1 array [##REF##15086809##9##,##REF##15375207##10##]. Its design used prior information in the form of experimentally confirmed transcripts and gene predictions, and was intended to provide information on most known transcripts. Although the ATH1 array includes more than 22,500 probe sets, it lacks almost one-third of the 32,041 genes found in the most recent TAIR7 annotation [##REF##17986450##11##]. All users of ATH1 arrays are confronted with a problem; as the number of newly discovered genes is rising, expression analysis becomes more and more restricted.</p>", "<p>More unbiased detection of transcriptional activity can be achieved by sequencing techniques such as massively parallel signature sequencing and serial analysis of gene expression or, alternatively, by microarrays that interrogate the entire genomic sequence, so called 'whole genome tiling arrays' [##REF##10835600##12##, ####REF##11050322##13##, ##REF##14593172##14####14593172##14##]. In contrast to arrays that are focused on gene expression, which contain only probes complementary to annotated genes, whole-genome tiling arrays are designed irrespectively of gene annotations and contain probes that are regularly spaced throughout the nonrepetitive portion of the genome [##REF##15607417##15##]. This includes intergenic and intronic regions, and whole-genome tiling arrays can therefore measure transcription from annotated genes, identify new splice and transcript variants of known genes, and even lead to the discovery of entirely new transcripts.</p>", "<p>Outside the context of plants, tiling arrays have been used to detect transcriptional activity in the genome of several organisms, including baker's yeast, <italic>Caenorhabtidis elegans</italic>, <italic>Drosophila melanogaster</italic>, and humans [##REF##16569694##16##, ####REF##16537507##17##, ##REF##16951679##18##, ##REF##17785534##19##, ##REF##15790807##20##, ##REF##15998911##21##, ##REF##15539566##22####15539566##22##]. Apart from the discovery of new transcripts, tiling arrays are useful for mapping the 5' and 3' ends of transcripts, and for the identification of introns (for example [##REF##17244705##23##]). Perhaps most importantly, these studies have expanded our understanding of genome organization. Apparently, genomes give rise to many more transcripts than was previously assumed. Most of these are noncoding RNAs emerging from intergenic regions, a large portion of which had previously been underrated as 'junk' DNA [##REF##15661355##24##]. Although the functional relevance of the majority of these transcripts remains unclear, their abundance and the fact that they have escaped <italic>ab initio </italic>gene predictions highlight the advantages of whole-genome tiling arrays. Another group of transcripts that has frequently been ignored in the past are nonpolyadenylated transcripts. Up to 50% of distinct transcripts in human and <italic>C. elegans </italic>lack polyA tails; this phenomenon is neglected by most gene expression studies, which typically use polyA(+) RNA as starting material or oligo-dT-primers for reverse transcription [##REF##17785534##19##,##REF##15790807##20##].</p>", "<p>The first tiling array analyses of <italic>Arabidopsis </italic>and rice combined with sequencing of full-length cDNAs delivered important information about gene content, gene structure, and genome organization [##REF##14593172##14##,##REF##15863518##25##, ####REF##17372628##26##, ##REF##16369532##27##, ##REF##15960804##28##, ##REF##17395691##29##, ##REF##15755812##30####15755812##30##]. Furthermore, gene expression profiling with tiling arrays of <italic>Arabidopsis </italic>mutants led to the identification of hundreds of noncoding transcripts that are normally silenced or removed by the exosome [##REF##16949657##31##,##REF##18160042##32##].</p>", "<p>In line with findings in yeast and animals, Yamada and colleagues [##REF##14593172##14##] reported that many <italic>Arabidopsis </italic>genes are also transcribed in anti-sense orientation, implicating anti-sense transcription in gene regulation. More recent studies in yeast and mammals suggested that at least some of the signals may be due to artifacts of reverse transcription methods used to generate the probes for array hybridization [##REF##17897965##33##,##REF##18173853##34##].</p>", "<p>Here, we use the Affymetrix GeneChip<sup>® </sup>Tiling 1.0R Array (Affymetrix Inc., Santa Clara, CA, USA) to provide an initial whole-genome expression atlas for <italic>A. thaliana</italic>, dubbed '<italic>Arabidopsis thaliana </italic>Tiling Array Express' (At-TAX), using RNA samples from 11 different tissues collected at various stages of plant development. We directly compare the performance of the tiling array, which contains one 25-base probe in each nonrepetitive 35 base pair (bp) window of the reference genome, with that of the 'gold standard' ATH1 array. We also report on the expression profile of over 9,000 annotated genes that are not represented on the ATH1 array. Applying a recently developed computational method for transcript identification to the tiling array data allowed us to identify regions not previously annotated as transcribed [##REF##18229713##35##]. Our data also suggest that most <italic>Arabidopsis </italic>transcripts expressed at detectable levels are polyadenylated. To benefit the <italic>Arabidopsis </italic>research community, we provide an online tool for visualization of gene expression estimates, along with a customized genome browser [##UREF##0##36##].</p>" ]
[ "<title>Materials and methods</title>", "<title>Plant material and RNA isolation</title>", "<p>Wild-type Col-0 and <italic>clv3</italic>-7 plants [##REF##10082464##37##] were grown on soil or on solid MS medium under continuous light at 23°C. Additional data file 1 describes each sample. Tissue samples were frozen in liquid nitrogen and total RNA was isolated using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany). RNA integrity was determined on a Bioanalyzer with the RNA 6000 Series II Nano kit (Agilent, Santa Clara, CA, USA).</p>", "<title>Probe preparation and array hybridization</title>", "<p>For synthesis of probes (targets) for ATH1 and tiling arrays, 1 μg of total RNA was used as template for generation of cRNA using the MessageAmp II-Biotin Enhanced Kit (Ambion, Austin, TX, USA). We followed the manufacturer's instructions with one exception; for tiling arrays, biotinylated NTPs were replaced by unmodified NTPs (stock solution 25 mmol/l each). Sixteen micrograms of biotinylated cRNA (for ATH1 arrays) was fragmented using 5× Fragmentation Buffer. Seven micrograms of unmodified cRNA (for tiling arrays) was converted into dsDNA (GeneChip<sup>® </sup>WT Double-Stranded cDNA Synthesis Kit; Affymetrix Inc.) and dsDNA was purified using the MinElute Reaction Cleanup Kit (Qiagen). A total of 7.5 μg dsDNA was fragmented and labeled using the GeneChip<sup>® </sup>WT Double-Stranded DNA Terminal Labeling Kit (Affymetrix Inc.). Targets were hybridized to ATH1 and <italic>Arabidopsis </italic>Tiling 1.0R arrays for 14 hours at 42°C, washed (Fluidics Station 450, wash protocol EukGE-WS2_V4 for ATH1 arrays or wash protocol FS450_0001 for tiling arrays) and scanned using a GeneChip<sup>® </sup>Scanner 3000 7 G.</p>", "<p>For comparison of polyA(+) and polyA(±), rRNA was depleted from 10 μg total RNA using RiboMinus™ Yeast Transcriptome Isolation Kit (Invitrogen) and an <italic>Arabidopsis </italic>specific RiboMinus™ LNA oligonucleotide mix kindly provided by Invitrogen, Carlsbad, CA, USA. rRNA depleted RNA was precipitated and resuspended in 12 μl water, from which 11 μl were used for reverse transcription using MessageAmp II-Biotin Enhanced Kit (Ambion) with an oligo-dT-T7 primer (MessageAmp II-Biotin Enhanced Kit) or a random-T7 primer (included in the GeneChip<sup>® </sup>WT Amplified Double-Stranded cDNA Synthesis Kit; Affymetrix Inc.). All subsequent steps were performed exactly as described above.</p>" ]
[ "<title>Results</title>", "<title>A tiling array based expression atlas of polyadenylated transcripts</title>", "<p>We isolated RNA from ten tissues and different developmental stages, ranging from young seedlings to senescing leaves, and roots to fruits of the <italic>A. thaliana </italic>Col-0 referenced strain. In addition, we made use of inflorescence apices from the <italic>clavata3 </italic>(<italic>clv3</italic>) mutant [##REF##10082464##37##] to enrich for shoot and floral meristems (Additional data file 1). We used both GeneChip<sup>® </sup>Tiling 1.0R and ATH1 gene expression arrays to obtain triplicate expression estimates from all samples. Because our priority was to detect transcribed regions, we decided to use double-stranded DNA (dsDNA) as hybridization targets for the tiling arrays. Consequently, we did not obtain information about the strand from which a signal originates. However, several recent reports have raised the question of how reliable the detection of antisense transcripts on tiling arrays is [##REF##17897965##33##,##REF##18173853##34##]. Another advantage is that DNA targets exhibit higher specificity than RNA targets [##REF##16964210##38##].</p>", "<p>To profile the expression of annotated genes on tiling arrays, we extracted probe information for all genes that can be analyzed in a robust manner (see Materials and methods [below] for details). Consequently, we ignored small transcription units such as tRNA genes, which are represented by an insufficient number of probes. Having each gene represented by a set of probes allowed us to apply a standard algorithm, robust multichip analysis (RMA), to both microarray platforms, thereby minimizing differences resulting from different analytical procedures [##REF##12925520##39##]. A total of 20,583 genes were represented on both platforms; an additional 136 and 9,645 genes were exclusively represented on ATH1 and the tiling array, respectively. Resulting RMA log2 expression values for tiling and ATH1 arrays spanned 11 to 12 log2 units in both cases.</p>", "<p>To compare the expression values derived from ATH1 array and tiling array, we generated scatter plots and calculated pair-wise Pearson correlation coefficients (PCCs) for all samples (Figure ##FIG##0##1a,b## and Table ##TAB##0##1##). Expression values for all genes in a given sample were well correlated across platforms, with PCCs ranging from 0.854 to 0.882 (<italic>P </italic>&lt; 10<sup>-15</sup>), indicating that both produce comparable results. Transcripts with expression estimates close to background correlate the least between platforms, as a result of higher variance of tiling array estimates (Figure ##FIG##0##1a,b##).</p>", "<p>We were particularly interested in the power of the tiling array to detect differential gene expression. To this end, we compared two samples, roots and inflorescences, which are known to have very different expression profiles [##REF##15806101##5##]. Applying the RankProduct method (RankProd) [##REF##16278953##40##,##REF##16982708##41##], we detected 2,484 and 2,294 differentially expressed genes (<italic>P </italic>&lt; 0.05) on ATH1 and tiling arrays, respectively, with 1,780 genes in common. A PCC of 0.92 (<italic>P </italic>&lt; 10<sup>-15</sup>) indicated a good agreement for detecting expression differences of individual genes across platforms (Figure ##FIG##0##1c##). In addition, we generated a 'correspondence at the top' (CAT) plot using <italic>P </italic>values to rank the genes (Figure ##FIG##0##1d##) [##REF##15846361##42##]. In the top 200 and 1,500 lists, 150 and 1,308 genes, respectively, were found in common, further supporting high concordance between the two types of arrays.</p>", "<p>Comparing the platforms across all samples, we found that more than 70% of all genes showed a correlation of 0.8 or greater (Figure ##FIG##1##2a##). Genes with low correlation between platforms tend to be those that are represented by a comparably small number of tiling probes (Figure ##FIG##1##2b##). Qualitatively, the same is true for genes that, because of the improved annotation, are represented by only a limited number of probes on the ATH1 array (Additional data file 4) or by strongly overlapping probes on ATH1 (Figure ##FIG##1##2b##). These results indicate that gene expression estimates based on ten or more tiling array probes are highly robust. More than 27,000 annotated genes fulfill this requirement for the Affymetrix Arabidopsis 1.0R tiling array, making it a powerful tool for gene expression studies.</p>", "<title>Expression of annotated genes not represented on the ATH1 array</title>", "<p>The tiling array allows the analysis of 9,645 genes, corresponding to 31.9% of all annotated genes, that are not represented on the ATH1 array. The average expression levels of these genes across all 11 samples are clearly lower than of those that are also present on the ATH1 array. Although only 15% of genes represented on both the tiling and ATH1 array platform have average expression level of less than six log<sub>2 </sub>units, this applies to more than 50% of the genes found only on the tiling array (Figure ##FIG##2##3a##). This is consistent with priority during the ATH1 design being given to genes with prior expression evidence [##REF##15086809##9##]. Nevertheless, many genes absent from ATH1 are expressed more highly in at least one sample (Figure ##FIG##2##3b##).</p>", "<p>Of the 9,645 genes, 1,065 genes had z scores exceeding 2.5 across the 11 samples, making them good candidates for having tissue-specific or stage-specific expression patterns (Additional data file 9, Table ##TAB##0##1##, and Figure ##FIG##2##3c##). The number of easily detectable transcripts was higher in roots or senescing leaves than in young leaves or seedlings, which is in agreement with previous observations [##REF##15806101##5##].</p>", "<title>Identification of new transcripts across different developmental stages</title>", "<p>To identify transcripts that are not present in the current genome annotation, we adopted a computational method, margin-based segmentation of tiling array data (mSTAD), for the segmentation of tiling array data into exonic, intronic, and intergenic regions [##REF##18229713##35##]. Extending a segmentation method developed for yeast tiling arrays [##REF##16787969##43##], we modeled spliced transcripts with ten discrete expression levels and incorporated a more flexible error model. Moreover, mSTAD is a supervised machine-learning algorithm with internal parameters that are estimated on hybridization data together with information on the location of annotated genes. After training, it can make predictions based on hybridization data alone.</p>", "<p>When comparing a genome-wide sample of all mSTAD exon predictions with annotated genes, we found that the predictions were generally accurate for the more highly expressed half of genes (Figure ##FIG##3##4a##; see Materials and methods [below] for details). For each sample, we further analyzed a set of high-confidence exon predictions (Figure ##FIG##3##4b## and Additional data file 5). These contained a minimum number of four probes, had predicted discrete expression level between 6 and 10, and had at most 25% repetitive probes. From these high-confidence exon predictions, which make up 37% to 50% of the total length of all predictions depending on the tissue analyzed, more than 97% overlap at least 25 bp with annotated exons (Figure ##FIG##3##4c##). Between 26% and 36% of the remainder overlap with cDNAs and expressed sequence tags (ESTs) but not with annotated transcripts.</p>", "<p>In summary there are between 1,107 and 1,947 predicted high-confidence exons per sample, for a total length of 242 to 406 kilobases (kb), that are neither included in the current annotation nor covered by sequenced cDNA clones. A complete list of all high-confidence exons with chromosome start and end position can be downloaded from the At-TAX homepage [##UREF##0##36##]. Among the unannotated high-confidence predictions, 14% to 31% are specifically detected in a single sample, with inflorescences and senescing leaves showing the highest proportion (Figure ##FIG##3##4d##). Whether these predictions indeed correspond to expressed transcripts was tested for some of these by RT-PCR. From high-confidence predictions that do not overlap with known cDNAs or ESTs, a subset of 47 segments was selected so that different lengths as well as different predicted expression strengths were covered. We could confirm by RT-PCR that more than three-quarters (37) of these 47 predicted segments as transcribed (Figure ##FIG##3##4e## and Additional data file 6).</p>", "<title>Analysis of nonpolyadenylated transcripts</title>", "<p>Previous analyses with whole-genome tiling arrays have focused on the polyadenylated portion of the <italic>Arabidopsis </italic>transcriptome [##REF##14593172##14##,##REF##15755812##30##, ####REF##16949657##31##, ##REF##18160042##32####18160042##32##]. However, studies conducted in several other organisms have suggested that there is a large fraction of nonpolyadenylated RNAs (for example [##REF##17785534##19##,##REF##15790807##20##]). In order to revisit this question in <italic>Arabidopsis</italic>, we isolated total RNA from two different tissues, whole seedlings and inflorescences, and depleted it for rRNA using a mix of locked nucleic acid (LNA) oligonucleotides. This RNA preparation was used for reverse transcription with either an oligo-dT primer (which targets only polyA [+] RNA) or random primers (which target both polyA [+]and polyA [-] RNAs). After conversion to dsDNA, samples were hybridized to tiling arrays. For both tissues analyzed, there was a good correlation between polyA(+) samples and polyA(±)samples (PCC = 0.84; <italic>P </italic>&lt; 10<sup>-15</sup>; Figure ##FIG##4##5a##). Nevertheless, we found many transcripts that were more easily detected in polyA(+) samples than in polyA(±) samples. This probably reflects the fact that mean signal intensities are for unknown reasons generally lower toward the 3' end after random priming (Additional data file 7). Hence, expression values of short transcripts in particular may be underestimated with random-primed hybridization targets.</p>", "<p>Only a small proportion of annotated genes produced a much higher polyA(±) signal compared with the polyA(+) fraction (Table ##TAB##1##2##). Large differences were detected for two structural RNAs: a U12 small nuclear RNA and an H/ACA-box small nucleolar RNA (Table ##TAB##1##2##). The majority of snRNAs undergo 3' end processing that is very distinct from polyadenylation [##REF##12067654##44##,##REF##16246719##45##], indicating that our method appears suitable for detecting nonpolyadenylated transcripts. Most other transcripts that were much more abundant in polyA(±) than in polyA(+) samples emanate from transposons and pseudogenes (Table ##TAB##1##2##). These results suggest that in <italic>Arabidopsis </italic>the overwhelming majority of known protein coding transcripts possess a polyA tail.</p>", "<p>We also applied the above described mSTAD algorithm to the two polyA(±) samples, to detect transcription from unannotated regions. When we subtracted high-confidence segments found in at least one polyA(+) sample from the segments found in both polyA(±) samples, segments totaling less than 100 kb were identified as potential polyA(-) transcripts (Figure ##FIG##4##5b##). These regions represent less than 0.1% of the entire genome, which appears to be very low compared with results reported for <italic>C. elegans </italic>tiling array studies using the transfrag method [##REF##17785534##19##]. To rule out the possibility that this discrepancy is a computational artifact, we applied the transfrag method to our tiling array data also [##REF##14993201##46##]. This method led to similar estimates of polyA(±) specific transcribed fragments (transfrags), with a combined length of about 250 kb, or 0.2% of the genome (Figure ##FIG##4##5b##). These results imply that nonpolyadenylated transcripts are much less abundant in <italic>Arabidopsis </italic>than in <italic>C. elegans </italic>and humans [##REF##15790807##20##,##REF##17785534##47##].</p>", "<title>Online resources for visualization of <italic>Arabidopsis </italic>tiling array data</title>", "<p>To make our results easily accessible to the research community, we created an online resource that consists of two parts: a web-tool that reports expression values for user-specified genes, and a customized generic genome browser [##REF##12368253##48##].</p>", "<p>The At-TAX gene expression visualization tool can be fed with TAIR (The <italic>Arabidopsis </italic>Information Resource) locus IDs [##UREF##1##49##]. Expression estimates for input gene(s) are displayed in all analyzed samples and on both ATH1 and tiling arrays, where available (Figure ##FIG##5##6a##). This not only provides a convenient means of analyzing genes not represented on the ATH1 array, but also allows simple cross-platform comparison. The generic genome browser displays transcriptional active regions as predicted by mSTAD across the genome, as well as all raw expression values for each probe in all analyzed samples [##UREF##2##50##] (Figure ##FIG##5##6b##).</p>" ]
[ "<title>Discussion</title>", "<p>In this study, we present an RNA expression atlas, At-TAX, of the <italic>A. thaliana </italic>reference strain Col-0 based on the GeneChip<sup>® </sup><italic>Arabidopsis </italic>Tiling 1.0R Array. Expression data have been collected across a series of tissues and developmental stages for the vast majority of annotated genes, including more than 9,000 genes that are not represented on the older ATH1 gene expression array. Moreover, our systematic comparison of the performance of the two arrays should provide valuable information for anybody considering experiments on either one of these two platforms.</p>", "<title>Gene expression profiling with whole genome tiling arrays</title>", "<p>Tiling arrays have several advantages compared with focused gene expression arrays such as the ATH1 platform, because tiling arrays allow detection of all transcripts irrespective of their annotation status as well as different splice forms. However, because probes have not been optimized in a similar manner, especially for uniform isothermal hybridization behavior, it has been unclear how broadly suitable they are for routine expression analysis. To address this issue, we used both array types to analyze 11 different samples covering different tissues and developmental stages. The resulting gene expression estimates on both array platforms are highly correlated, including measures of expression changes between tissues. We conclude that whole genome tiling arrays are indeed an appropriate tool for standard gene expression analyses. However, expression estimates derived from the two different platforms can differ for various reasons, indicating that expression data must be interpreted carefully. Discrepancies are often due to the selection of probes on the ATH1 arrays, which are biased towards the 3' end of transcripts and sometimes overlap, thus violating assumptions of independence. Conversely, expression analysis with tiling arrays can be inaccurate for small genes represented by very few probes, especially if these have unfavorable hybridization properties. Uncertainty in gene annotations is another source of error, because expression may erroneously be measured from intronic probes.</p>", "<p>Compared with the ATH1 array, a disproportionately high number of genes that are represented only on the tiling array produced very low hybridization signals. This is not unexpected because the genes selected for the ATH1 array were supported by cDNAs and ESTs, whereas the tiling array includes hypothetical genes that lack any experimental evidence of expression. In addition, the number of annotated pseudogenes in <italic>A. thaliana </italic>has been increasing dramatically. The first annotation released in 2001 (TIGR1) contained 1,274 pseudogenes, whereas the recent TAIR7 annotation includes 3,889 pseudogenes [##REF##17986450##11##].</p>", "<title>The dark matter of the <italic>Arabidopsis </italic>genome</title>", "<p>Identification of unannotated transcribed regions is a major motivation for tiling array experiments. That our segmentation algorithm generated highly reliable predictions is evident from the observation that there was very good overlap with annotated genes as well as high success rates for RT-PCR validation experiments. Despite extensive cDNA cloning and previous use of tiling arrays (for example, [##REF##14593172##14##]), we could detect more than 1,000 additional transcripts. We found that exonic regions in the different tissues comprise on average about one-third of the genome. Despite the finding of unannotated transcripts, the ratio of annotated exons to polyA(+) transcripts detectable on tiling arrays appears to be much higher in <italic>Arabidposis </italic>than in some other organisms [##REF##17510325##51##]. Interestingly, tiling array analysis of <italic>Arabidopsis </italic>mutants impaired in DNA methylation or RNA quality control has revealed more than 200 noncoding transcripts that are normally transcriptionally silenced, indicating that the <italic>Arabidopsis </italic>genome has at least the potential to generate a large number of transcripts from intergenic regions [##REF##16949657##31##,##REF##18160042##32##].</p>", "<title>The nonpolyadenylated <italic>Arabidopsis </italic>transcriptome</title>", "<p>Tiling array studies of human and <italic>C. elegans </italic>indicated that about half of all transcripts are not polyadenylated [##REF##15790807##20##]. In contrast, our data suggest that nonpolyadenylated RNAs make a more limited contribution to the <italic>Arabidopsis </italic>transcriptome. It is already known that specific classes of plants transcripts are generated in a different manner than in animals. For example, some human microRNA precursors are transcribed by RNA polymerase III and hence are not polyadenylated, whereas <italic>Arabidopsis </italic>microRNA precursors feature characteristics of RNA polymerase II generated transcripts [##REF##17099701##52##,##REF##16040653##53##]. Another reason might be differences in 3' end processing. For example, histone mRNAs in land plants are polyadenylated, which is in contrast to histone mRNAs in animals, which are subject to a unique form of 3' end processing resulting in a hairpin that protects the 3' end from RNA degrading enzymes [##REF##2905689##54##, ####REF##8268253##55##, ##REF##2831497##56##, ##REF##17531405##57##, ##REF##2471147##58####2471147##58##].</p>", "<p>We found that many nonpolyadenylated RNAs in <italic>Arabidopsis </italic>are derived from pseudogenes and transposons. Several examples of actively transcribed pseudogenes have been reported [##REF##14616058##59##], and many pseudogenes become transcriptionally activated in methylation-deficient mutants [##REF##16949657##31##]. Known mechanisms for the transcriptional silencing of pseudogenes involve small RNAs that are generated through the RNA-dependent-RNA-polymerase (RDR)2/DICER-LIKE 3 biogenesis pathway [##REF##17298187##60##,##REF##16954541##61##]. Interestingly, improperly terminated, nonpolyadenylated RNAs derived from transgenes can be subject to a silencing pathway that involves another RNA-dependent-RNA-polymerase, namely RDR6, which can use both polyadenylated and nonpolyadenylated transcripts as a substrate [##REF##17384170##62##,##REF##18063577##63##]. Therefore, our observation that RNAs corresponding to a subset of pseudogenes are much more abundant in the polyA(±) fraction is compatible with a scenario in which these pseudogenes are transcribed into polyA(-) RNAs that subsequently serve as template for RDR-dependent amplification. However, transcripts from some pseudogenes are also detectable in polyA(+) samples. These pseudogenes might either be transcribed into both polyA(+) RNAs and polyA(-) RNA or, alternatively, polyA(-) RNAs derived from polyA(+) RNAs accumulate during RNA amplification and processing steps.</p>", "<title>Outlook</title>", "<p>We have demonstrated that the use of the GeneChip<sup>® </sup>Arabidopsis Tiling 1.0R Array for routine expression analyses does not have any apparent disadvantages compared with the ATH1 array. Rather, it has many advantages, including the ability to provide information on genes that are not represented on ATH1, as well as the ability to analyze additional aspects of gene expression, such as alternative transcript initiation and 3' end formation or splicing, all of which might be under physiological or developmental control [##REF##17222076##64##,##REF##16980712##65##]. Tiling arrays might be the platform of choice to further resolve transcriptional activity over developmental stages and cell types, especially when combined with techniques for the isolation of specific cells by laser microdissection or cell sorting (for review [##REF##16669770##66##]).</p>" ]
[]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A developmental expression atlas, At-TAX, based on whole-genome tiling arrays, is presented along with associated analysis methods.</p>", "<p>Gene expression maps for model organisms, including <italic>Arabidopsis thaliana</italic>, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, <italic>Arabidopsis thaliana </italic>Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage.</p>" ]
[ "<title>Repetitive probe annotation</title>", "<p>To assess the potential of each 25 mer oligonucleotide probe on the tiling array to crosshybridize to transcripts from different locations, we determined whether its sequence occurred more than once in the <italic>A. thaliana </italic>genome. To this end we applied a method proposed previously [##REF##17641193##67##], which annotates 25 mers occurring as exact duplicates elsewhere in the genome, those which align with identity at the innermost 21 nucleotides, and those that have a single mismatch in the 25 mer alignment. Probes with exact 25 mer matches were excluded from tiling array expression measurements, and all types of repetitive probes were used to annotate and filter exon segments predicted by mSTAD and transfrags.</p>", "<title>Probe set definitions</title>", "<p>In order to analyze annotated genes, we mapped tiling probes to <italic>Arabidopsis </italic>gene models as per TAIR7 annotation [##REF##12519987##68##]. Probe sets for individual genes were defined as follows. From all probes mapped to exons (either coding or untranslated region) in their entire length, we retained only those for expression analysis that correspond to constitutive exons in all annotated splice forms of the same gene. We further excluded probes that mapped to more than one (overlapping) gene model, and in order to reduce cross-hybridization artifacts we also removed repetitive probes whose 25 mer sequence occurred multiple times in the genome. For expression measurements from tiling arrays we only considered the set of 30,228 annotated genes that are represented by at least three probes.</p>", "<p>For the ATH1 array, probe sets were defined according to the <italic>A. thaliana </italic>CDF version 10 provided by the Microarray Lab at the Molecular and Behavioral Neurobiology Institute of the University of Michigan [##REF##16284200##69##].</p>", "<title>Expression estimates</title>", "<p>In order to minimize artificial expression level differences between platforms only resulting from differences in the computational analyses procedures, the RMA method was applied to hybridization data from both platforms [##REF##12925520##39##]. RMA proceeds in three steps. First, background correction and quantile normalization were applied before gene expression levels were calculated with the median polish method. Data from ATH1 arrays were analyzed using the RMA implementation in the Bioconductor package affy [##REF##14960456##70##, ####REF##15461798##71##, ##UREF##3##72####3##72##]. For the analysis of tiling arrays, we constructed a pipeline that combined the same background and quantile normalization methods from Bioconductor (BufferedMatrixMethods package by BM Bolstad), with the median polish routine extracted from Bioconductor sources (preprocessCore package by BM Bolstad) and adopted for the analysis of custom probe sets.</p>", "<title>Detection of differentially expressed genes and CAT plot analysis</title>", "<p>We applied the Rank Product method (Bioconductor package RankProduct) [##REF##16278953##40##,##REF##18204063##73##] to identify significantly expressed genes at a cut-off of <italic>P </italic>&lt; 0.05. The <italic>P </italic>values were also used to assess platform concordance by CAT analysis, in which gene lists ordered by <italic>P </italic>value were compared between platforms. The proportion of most significant genes in common between platforms was plotted as a function of list sizes increasing in steps of ten [##REF##15846361##42##]. As a measure of tissue-specific expression, z scores were calculated as described by Schmid and coworkers [##REF##15806101##5##].</p>", "<title>Segmentation of tiling array data</title>", "<p>We preprocessed the hybridization signal to reduce a bias due to divergent probe sequences using a transcript normalization method [##REF##18229713##35##,##REF##17387113##74##] and subsequently applied a modified version of the mSTAD algorithm [##REF##18229713##35##].</p>", "<p>For each sample, we trained mSTAD separately on mean intensities across replicates and used the trained instance only for prediction of array data from the same sample. To obtain unbiased whole-genome predictions we employed cross-validation. After splitting the genome between pairs of neighboring genes, one instance of mSTAD was trained on 500 of these genic regions and hyper-parameters were tuned on another 500 genic regions. We trained and tuned a second instance of mSTAD on two further disjoint sets of 500 genes each. For region-wise whole-genome predictions, we chose the mSTAD instance that had not seen the particular region during training and hyperparameter tuning (or a random instance if neither of them had). From the predicted labeling of tiling probes we extracted exon segments by assigning the genomic coordinates corresponding to the start of the first and the end of the last probe of a run of consecutive exon labels. The resulting segmentations are available as gff files and visualized in the At-TAX Generic Genome Browser.</p>", "<p>Prediction accuracy was determined on genomic regions that had not been used for training or parameter tuning of the mSTAD instance evaluated. Sensitivity and specificity were assessed in comparison to annotated genes on a per-probe level as well as for the overlap between annotated and predicted exons. Figure ##FIG##3##4a## shows mean performance across 1,000 genic test regions (with at least five probes annotated as exonic and at least ten probes in total) chosen randomly for each of the mSTAD instances used to make whole-genome predictions for root data. Accuracy on probe level was also calculated for whole-genome (test) predictions for all other samples (see Additional data file 2).</p>", "<p>To determine overlap with annotated regions, we used the TAIR7 annotation [##REF##17986450##11##] and direct alignments with EST and cDNA sequences (downloaded from TAIR on 15 August 2007) [##UREF##4##75##]. Sample-specific segments were obtained as residual after computing the overlap between predicted exon segments in the tissue of interest to those from all other tissues (Figure ##FIG##3##4d##). Similarly, we obtained predictions specifically made for polyA(±) conditions as exon segments that were predicted for both polyA(±) samples (ones that overlapped between samples), but did not overlap to predictions for any polyA(+) sample (Figure ##FIG##4##5c##).</p>", "<title>RT-PCR validation</title>", "<p>One microgram of RNA from seedlings and young leaves was treated with DNaseI (MBI Fermentas, St. Leon-Rot, Germany) and converted into cDNA using the RevertAid™ First Strand cDNA Synthesis Kit (MBI Fermentas). One microliter of the resulting cDNA solution was used as a template in a PCR reaction with primers lying within the predicted transcribed region. The sizes of PCR products ranged from about 150 to 300 bp. A complete list of all used primers is available in Additional data file 3.</p>", "<title>Computation of transcribed fragments (transfrags)</title>", "<p>As an independent method to compare transcriptional activity between polyA(+) and polyA(±) samples, we computed transfrags as described previously [##REF##14993201##76##] and implemented in the Affymetrix Tiling Analysis Software version 1.1 build 2. In order to select optimal parameters, we evaluated transfrags generated for root tissues for 900 different combinations of parameters in comparison with annotated genes (bandwidth in steps of 25 between 50 and 150, signal threshold between 5 and 13, minimum run in steps of 20 between 20 and 100, and maximum gap in steps of 20 between 40 and 100). As optimal setting for all transfrag computations we chose the one with maximal sensitivity at a precision similar to mSTAD predictions (bandwidth 100, signal threshold 6, minimum run 100, maximum gap 40; see Additional data file 8). Among nonrepetitive transfrags (at most 25% repetitive probes) comprising at least four probes and without overlap to annotated transcripts, the ones specific to polyA(+) or polyA(±) samples were computed the same way as for high-confidence mSTAD predictions (Figure ##FIG##4##5d##).</p>", "<title>Abbreviations</title>", "<p>At-TAX, Arabidopsis thaliana Tiling Array Express; bp, base pair; CAT, correspondence at the top; dsDNA, double-stranded DNA; EST, expressed sequence tag; kb, kilobases; LNA, locked nucleic acid; mSTAD, margin-based segmentation of tiling array data; PCC, Pearson correlation coefficient; RDR, RNA-dependent-RNA-polymerase; RMA, robust multichip analysis; RT-PCR, reverse transcription polymerase chain reaction; TAIR, The Arabidopsis Information Resource.</p>", "<title>Authors' contributions</title>", "<p>SL, GZ, MV, BS, GR, and DW designed the study. SL carried out target preparation and array hybridization. GZ, SRH, TS and NN developed tools for tiling array analysis. GZ, SRH, SL, and TS analyzed the data. TS and CKW developed online visualization tools. SL, GZ, GR, and DW wrote the manuscript. All authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data file ##SUPPL##0##1## lists all analyzed samples, including growth conditions and plant age. Additional data file ##SUPPL##1##2## shows segmentation accuracy of mSTAD. Additional data file ##SUPPL##2##3## lists oligonucleotide primers that were used for RT-PCR validation of new transcripts. Additional data file ##SUPPL##3##4## shows correlation between platform concordances and probe numbers on the ATH1 array. Additional data file ##SUPPL##4##5## shows segmentation accuracy achieved by mSTAD along the five <italic>Arabidopsis </italic>chromosomes. Additional data file ##SUPPL##5##6## shows the results of all RT-PCR validation experiments. Additional data file ##SUPPL##6##7## shows a comparison of mean hybridization intensities in random-primed and oligo-dT-primed samples. Additional data file ##SUPPL##7##8## shows a comparison of segmentation accuracy for mSTAD and the transfrag method. Additional data file ##SUPPL##8##9## contains gene identifiers with corresponding expression values and z-scores in all samples we analyzed.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We are grateful to Jan Lohmann and Markus Schmid for discussion, and Wolfgang Busch, Joffrey Fitz, Stephan Ossowski, Korbinian Schneeberger, and Norman Warthmann for helpful suggestions and critical reading of the manuscript. Funded by FP6 IP AGRON-OMICS (contract LSHG-CT-2006-037704) and the Max Planck Society.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Comparison of expression estimates on tiling and ATH1 array platforms. Scatter plot of expression estimates in <bold>(a) </bold>roots and <bold>(b) </bold>inflorescences. <bold>(c) </bold>Correlation between expression changes between roots and inflorescences. <bold>(d) </bold>CAT (correspondence at the top) plot for genes identified differentially expressed in roots and inflorescences. Proportion of genes in common is shown as a function of increasing size of subsets containing the <italic>n </italic>genes with the highest <italic>P </italic>values.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Platform concordance and factors affecting it for genes represented on both ATH1 and tiling arrays. <bold>(a) </bold>Pearson correlation coefficients (PCCs) of expression estimates. <bold>(b) </bold>Box plots showing expression correlation for genes that were either categorized by the number of probes on tiling arrays or categorized by the total length of nonredundant sequence spanned by ATH1 probes. The boxes have lines at the lower quartile, median, and upper quartile values. Whiskers extend to the most extreme value within 1.5 times the interquartile range from the ends of the corresponding box. Box plots are based on genes represented on both the ATH1 and the tiling array, with the total number of genes per category on the respective platform indicated at the top.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Analysis of genes represented only on tiling arrays. <bold>(a) </bold>Average or <bold>(b) </bold>maximum expression levels for all genes across all samples. <bold>(c) </bold>Expression values of genes with an apparent tissue-specific or stage-specific expression pattern across all samples. Twenty genes with the highest z scores and maximum expression in root, senescing leaf, inflorescence, or flowers are shown.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><italic>De novo </italic>segmentation of tiling array data. <bold>(a) </bold>Segmentation accuracy for roots across ten discrete expression levels (see inset). Sensitivity is defined as the proportion of exonic probes contained in predicted segments relative to all annotated exonic probes, or the proportion of identified exon segments to all annotated exons. Specificity indicates how many predicted expressed probes or predicted exons are annotated as such. <bold>(b) </bold>Sensitivity and specificity of predicted exon segments for roots in comparison with annotated exons, plotted in a sliding window across 2,000 exons along chromosome 4 together with information on repetitive probes (window of 5,000 probes; see inset). The heterochromatic knob, the centromere and peri-centromeres are depicted below the x-axis (for other chromosomes, see Additional data file 5). <bold>(c) </bold>Proportion of predicted exon segments, high-confidence exon segments (see text for definition), and unannotated exon segments (high-confidence predictions that do not overlap with any annotated exon by at least 25 base pairs). Numbers are based on combined length of each class. <bold>(d) </bold>Proportion of sample-specific exon segments among all unannotated high-confidence predictions. <bold>(e) </bold>Examples of RT-PCR validation of predicted novel transcripts.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Non-polyadenylated transcripts. <bold>(a) </bold>Correlation between expression levels for polyA(+) and polyA(±) samples. <bold>(b) </bold>Proportion of unannotated transcripts found in common or exclusively in either polyA(+) samples and polyA(±) samples, respectively, as determined with two independent methods.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>At-TAX online resources for gene expression analysis. <bold>(a) </bold>At-TAX gene expression estimates derived from tiling (right) and ATH1 arrays across all analyzed samples in TileViz. Included in this example is a gene not represented on the ATH1 array (red line). <bold>(b) </bold>Display of predicted expressed segments (middle) and raw hybridization signals (bottom) along the chromosome (top) in a generic genome browser.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Correlation of ATH1 and tiling arrays expression values across the analyzed samples</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Sample</td><td align=\"left\">Description</td><td align=\"left\">PCC</td><td align=\"left\">Potential tissue-specific transcripts</td></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Roots</td><td align=\"left\">0.86</td><td align=\"left\">378</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Seedlings</td><td align=\"left\">0.88</td><td align=\"left\">5</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Expanding leaves</td><td align=\"left\">0.87</td><td align=\"left\">13</td></tr><tr><td align=\"left\">4</td><td align=\"left\">Senescing leaves</td><td align=\"left\">0.87</td><td align=\"left\">301</td></tr><tr><td align=\"left\">5</td><td align=\"left\">Stem</td><td align=\"left\">0.87</td><td align=\"left\">34</td></tr><tr><td align=\"left\">6</td><td align=\"left\">Vegetative shoot meristem</td><td align=\"left\">0.86</td><td align=\"left\">19</td></tr><tr><td align=\"left\">7</td><td align=\"left\">Inflorescence shoot meristem</td><td align=\"left\">0.87</td><td align=\"left\">14</td></tr><tr><td align=\"left\">8</td><td align=\"left\">Whole inflorescences</td><td align=\"left\">0.85</td><td align=\"left\">152</td></tr><tr><td align=\"left\">9</td><td align=\"left\">Whole inflorescences (<italic>clv3-7</italic>)</td><td align=\"left\">0.86</td><td/></tr><tr><td align=\"left\">10</td><td align=\"left\">Flowers</td><td align=\"left\">0.88</td><td align=\"left\">51</td></tr><tr><td align=\"left\">11</td><td align=\"left\">Fruits</td><td align=\"left\">0.86</td><td align=\"left\">98</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Transcripts that are more abundant in polyA(±) samples than in polyA(+) samples</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"left\">TAIR7 annotation</td><td align=\"left\">PolyA(+) (log2)</td><td align=\"left\">PolyA(±) (log2)</td></tr></thead><tbody><tr><td align=\"left\">AT1G12013</td><td align=\"left\">H/ACA-box snoRNA</td><td align=\"left\">9.07</td><td align=\"left\">13.51</td></tr><tr><td align=\"left\">AT1G15405</td><td align=\"left\">Unknown gene</td><td align=\"left\">11.07</td><td align=\"left\">14.59</td></tr><tr><td align=\"left\">AT1G31960</td><td align=\"left\">Unknown protein</td><td align=\"left\">5.34</td><td align=\"left\">8.74</td></tr><tr><td align=\"left\">AT1G33860</td><td align=\"left\">Unknown protein</td><td align=\"left\">8.10</td><td align=\"left\">11.78</td></tr><tr><td align=\"left\">AT1G34700</td><td align=\"left\">Mutator-like transposase family</td><td align=\"left\">4.69</td><td align=\"left\">8.14</td></tr><tr><td align=\"left\">AT1G35080</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">3.70</td><td align=\"left\">7.03</td></tr><tr><td align=\"left\">AT1G35640</td><td align=\"left\">Unknown protein</td><td align=\"left\">5.91</td><td align=\"left\">9.29</td></tr><tr><td align=\"left\">AT1G41726</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.73</td><td align=\"left\">10.30</td></tr><tr><td align=\"left\">AT1G61275</td><td align=\"left\">U12 snRNA</td><td align=\"left\">7.11</td><td align=\"left\">12.45</td></tr><tr><td align=\"left\">AT2G01022</td><td align=\"left\">Gypsy-like retrotransposon family</td><td align=\"left\">5.72</td><td align=\"left\">9.43</td></tr><tr><td align=\"left\">AT2G05567</td><td align=\"left\">Pseudogene</td><td align=\"left\">4.62</td><td align=\"left\">8.59</td></tr><tr><td align=\"left\">AT2G06250</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.45</td><td align=\"left\">9.87</td></tr><tr><td align=\"left\">AT2G06370</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.36</td><td align=\"left\">9.71</td></tr><tr><td align=\"left\">AT2G07709</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.40</td><td align=\"left\">11.28</td></tr><tr><td align=\"left\">AT2G07711</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.05</td><td align=\"left\">10.42</td></tr><tr><td align=\"left\">AT2G07712</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.90</td><td align=\"left\">10.87</td></tr><tr><td align=\"left\">AT2G07717</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.72</td><td align=\"left\">11.22</td></tr><tr><td align=\"left\">AT2G08986</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">6.64</td><td align=\"left\">10.15</td></tr><tr><td align=\"left\">AT2G10285</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">6.16</td><td align=\"left\">9.85</td></tr><tr><td align=\"left\">AT2G10720</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.15</td><td align=\"left\">10.67</td></tr><tr><td align=\"left\">AT2G10790</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.03</td><td align=\"left\">10.86</td></tr><tr><td align=\"left\">AT2G12240</td><td align=\"left\">CACTA-like transposase family</td><td align=\"left\">5.30</td><td align=\"left\">9.98</td></tr><tr><td align=\"left\">AT2G12320</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">6.56</td><td align=\"left\">10.05</td></tr><tr><td align=\"left\">AT2G12750</td><td align=\"left\">Gypsy-like retrotransposon family</td><td align=\"left\">7.20</td><td align=\"left\">10.71</td></tr><tr><td align=\"left\">AT2G13860</td><td align=\"left\">Gypsy-like retrotransposon</td><td align=\"left\">6.88</td><td align=\"left\">10.29</td></tr><tr><td align=\"left\">AT2G25255</td><td align=\"left\">Encodes a defensin-like (DEFL) family protein</td><td align=\"left\">5.65</td><td align=\"left\">9.04</td></tr><tr><td align=\"left\">AT3G24370</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">5.06</td><td align=\"left\">9.58</td></tr><tr><td align=\"left\">AT3G29570</td><td align=\"left\">Similar to ATEXT3</td><td align=\"left\">5.41</td><td align=\"left\">9.60</td></tr><tr><td align=\"left\">AT3G30846</td><td align=\"left\">Gypsy-like retrotransposon family</td><td align=\"left\">6.78</td><td align=\"left\">10.21</td></tr><tr><td align=\"left\">AT3G32010</td><td align=\"left\">Gypsy-like retrotransposon family (Athila)</td><td align=\"left\">5.37</td><td align=\"left\">9.41</td></tr><tr><td align=\"left\">AT3G32880</td><td align=\"left\">Gypsy-like retrotransposon family (Athila)</td><td align=\"left\">6.37</td><td align=\"left\">10.60</td></tr><tr><td align=\"left\">AT3G42251</td><td align=\"left\">Pseudogene</td><td align=\"left\">5.82</td><td align=\"left\">9.24</td></tr><tr><td align=\"left\">AT3G42750</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">4.44</td><td align=\"left\">7.85</td></tr><tr><td align=\"left\">AT3G43154</td><td align=\"left\">Pseudogene</td><td align=\"left\">5.21</td><td align=\"left\">9.22</td></tr><tr><td align=\"left\">AT3G43160</td><td align=\"left\">MEE38</td><td align=\"left\">7.42</td><td align=\"left\">11.95</td></tr><tr><td align=\"left\">AT3G43862</td><td align=\"left\">Athila retroelement ORF2-related</td><td align=\"left\">6.07</td><td align=\"left\">10.44</td></tr><tr><td align=\"left\">AT4G05290</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">5.39</td><td align=\"left\">10.08</td></tr><tr><td align=\"left\">AT4G06531</td><td align=\"left\">Pseudogene</td><td align=\"left\">4.21</td><td align=\"left\">7.93</td></tr><tr><td align=\"left\">AT4G06573</td><td align=\"left\">Athila retroelement ORF1 protein</td><td align=\"left\">7.25</td><td align=\"left\">11.01</td></tr><tr><td align=\"left\">AT4G06710</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.53</td><td align=\"left\">11.72</td></tr><tr><td align=\"left\">AT4G06736</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.27</td><td align=\"left\">9.75</td></tr><tr><td align=\"left\">AT4G08080</td><td align=\"left\">Gypsy-like retrotransposon family (Athila)</td><td align=\"left\">6.84</td><td align=\"left\">10.74</td></tr><tr><td align=\"left\">AT5G32400</td><td align=\"left\">Hypothetical protein</td><td align=\"left\">6.92</td><td align=\"left\">10.32</td></tr><tr><td align=\"left\">AT5G32404</td><td align=\"left\">Pseudogene</td><td align=\"left\">4.90</td><td align=\"left\">9.12</td></tr><tr><td align=\"left\">AT5G32475</td><td align=\"left\">Athila retroelement ORF2-related</td><td align=\"left\">5.75</td><td align=\"left\">9.37</td></tr><tr><td align=\"left\">AT5G32483</td><td align=\"left\">Pseudogene</td><td align=\"left\">6.41</td><td align=\"left\">9.89</td></tr><tr><td align=\"left\">AT5G32495</td><td align=\"left\">Pseudogene</td><td align=\"left\">5.74</td><td align=\"left\">9.44</td></tr><tr><td align=\"left\">AT5G32517</td><td align=\"left\">Pseudogene</td><td align=\"left\">5.91</td><td align=\"left\">9.34</td></tr><tr><td align=\"left\">AT5G33150</td><td align=\"left\">Pseudogene</td><td align=\"left\">7.33</td><td align=\"left\">10.75</td></tr><tr><td align=\"left\">AT5G34970</td><td align=\"left\">Similar to unknown protein</td><td align=\"left\">5.16</td><td align=\"left\">8.63</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Listed are all analyzed samples, including growth conditions and plant age.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Shown is the segmentation accuracy of mSTAD.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Shown are the oligonucleotide primers that were used for RT-PCR validation of new transcripts.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Shown is the correlation between platform concordances and probe numbers on the ATH1 array.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Shown is the segmentation accuracy achieved by mSTAD along the five <italic>Arabidopsis </italic>chromosomes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Presented are the results of all RT-PCR validation experiments.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Presented is a comparison of mean hybridization intensities in random-primed and oligo-dT-primed samples.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>Presented is a comparison of segmentation accuracy for mSTAD and the transfrag method.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>Presented are gene identifiers with corresponding expression values and z-scores in all samples we analyzed.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>Presented are the correlations for gene expression estimates between ATH1 and tiling array platform, and number of candidates for tissue-specific genes (z score &gt; 2.5 across all samples and most abundant in this tissue) detected in each sample. PCC, Pearson correlation coefficient.</p></table-wrap-foot>" ]
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[{"article-title": ["At-TAX homepage"]}, {"article-title": ["At-TAX TileViz"]}, {"article-title": ["At-TAX Gbrowse"]}, {"article-title": ["Bioconducter"]}, {"article-title": ["The "], "italic": ["Arabidopsis "]}]
{ "acronym": [], "definition": [] }
76
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 9; 9(7):R112
oa_package/24/f0/PMC2530869.tar.gz
PMC2530870
18616806
[ "<title>Background</title>", "<p>Cichlid fishes from the East African Rift lakes Victoria, Tanganyika, and Malawi represent a preeminent example of replicated and rapid evolutionary radiation [##REF##15131652##1##]. This group of fishes is a significant model of the evolutionary process and the coding of genotype to phenotype, largely because tremendous diversity has evolved in a short period of time among lineages with similar genomes [##REF##15851665##2##, ####REF##16461358##3##, ##REF##17519189##4####17519189##4##]. Recently evolved cichlid species segregate ancestral polymorphism [##UREF##0##5##,##REF##9826684##6##] and may exchange genes [##REF##12919487##7##,##REF##16701254##8##]. Numerous genomic resources have been developed for East African cichlids (many of which are summarized by the Cichlid Genome Consortium [##UREF##1##9##]). These include the following: genetic linkage maps for tilapia [##REF##12704237##10##, ####REF##9539437##11##, ##REF##12047227##12####12047227##12##] and Lake Malawi species [##REF##12704237##10##,##REF##16555305##13##]; fingerprinted bacterial artificial chromosome libraries [##UREF##2##14##]; expressed sequence tag sequences for Lake Tanganyika and Lake Victoria cichlids [##UREF##3##15##]; and first-generation microarrays [##REF##15858202##16##,##REF##15238158##17##]. Many studies have used these resources to study cichlid population genetics, molecular ecology, and phylogeny (for review [##UREF##4##18##,##UREF##5##19##]). Recent reports have capitalized on the diversity among East African cichlids to study the evolution and genetic basis of many traits, including behavior [##REF##17391260##20##], olfaction [##REF##17710134##21##], pigmentation [##REF##12919484##22##, ####REF##14614144##23##, ##REF##15716505##24####15716505##24##], vision [##REF##15772376##25##,##REF##16213819##26##], sex determination [##REF##15716505##24##,##REF##15100706##27##], the brain [##REF##9288416##28##], and craniofacial development [##REF##12704237##10##,##REF##16555305##13##,##REF##16251275##29##].</p>", "<p>In 2006, under the auspices of the Community Sequencing Program, the Joint Genome Institute (JGI) completed low coverage survey sequencing of the genomes of five Lake Malawi species. Species were chosen to maximize the morphological, behavioral, and genetic diversity among the Malawi species flock. This represents a novel genome project. Low coverage sequencing is now a routine strategy to uncover functional or 'constrained' genomic elements [##REF##18347593##30##]. The rationale is as follows; one compares genome sequences of distantly related organisms (for example, shark, diverse mammals) with that of a reference (for instance, human, mouse), and outliers of similarity will be observed against the background expectation of divergence [##REF##14512627##31##, ####REF##17407382##32##, ##REF##17975172##33##, ##REF##15778292##34####15778292##34##]. Our interests in diversity suggest a conceptually similar but logically reversed research objective. When the background expectation is similarity, how does one use low coverage genome sequencing to detect that which makes organisms distinct?</p>", "<p>Here, we report computational and comparative analyses of survey sequence data to address the question of diversity. We had four major goals: to produce a low coverage assembly for each of the five Lake Malawi species; to identify orthologs of vertebrate genes in these data; to predict single nucleotide polymorphisms (SNPs) segregating between species; and to use SNPs to evaluate the degree of genomic polymorphism and divergence at different evolutionary scales. Consequently, we produced assemblies for the five species ranging in aggregate length from 68 to 79 megabases (Mb), identified putative orthologs for more than 12,000 human genes, and predicted more than 32,000 cross-species segregating sites (with about 2,700 located in genic regions). We genotyped a set of these SNPs within and between Lake Malawi cichlid lineages and demonstrate signatures of differentiation on the background of similarity and polymorphism. Our work should facilitate further understanding of evolutionary processes in the species flocks of East African cichlids. Moreover, the approach we outline should be broadly applicable in other lineages where phenotypic and behavioral diversity has evolved in a short window of evolutionary time.</p>" ]
[ "<title>Materials and methods</title>", "<title>Samples</title>", "<p>Individuals of <italic>Mchenga conophorus </italic>(MC), <italic>Labeotropheus fuelleborni </italic>(LF), <italic>Melanochromis auratus </italic>(MA), <italic>Maylandia zebra </italic>(MZ), and <italic>Rhamphochromis esox </italic>(RE) were sampled from the wild during an expedition to Malawi in 2005. Specimens prepared for survey sequencing by the JGI were collected from Mazinzi Reef (MZ), Domwe Island (LF and MA), and Otter Point (MC and RE), all of which are locales in the southeastern portion of the lake. High-quality DNA was extracted and prepared in the laboratory of TDK.</p>", "<title>Trace sequences</title>", "<p>Trace sequences generated by the JGI for MC, LF, MA, MZ, and RE, together with their sequence quality scores, were downloaded (6 May 2007) from the National Center for Biotechnology Information (NCBI) Trace Archive. The dataset for each species consisted of an average of about 152,000 individual trace reads with total read lengths ranging from 137 to 185 million bases. Detailed sequence statistics for each species are provided in Additional data file 1.</p>", "<title>Sequence preprocessing and assembly</title>", "<p>The trace and quality sequences were first pre-processed for assembly by masking out all possible vector sequences available from the NCBI UniVec vector sequence database (downloaded 6 May 2007). The vector masking was performed using the cross_match.pl perl script provided by the Phred-Phrap package [##REF##9521921##66##]. In order to reduce the computational complexity and time required for the final assembly, repeat sequences were masked before assembly using RepeatMasker version 3.1.8 (Smit AFA, Hubley R and Green P, unpublished data) in conjunction with the latest repeatmasker libraries from RepBase Update [##REF##16093699##67##]. Bases with sequencing quality score of less than 20 were also masked. The actual assembly of each species' trace sequences into contiguous sequences (contigs) was then performed using the Phrap version 0.990329 assembly program from the Phred-Phrap package. Contigs with more than 80% low quality bases (defined as &lt;20 assembly quality score) were removed from the assembly. This whole genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the project accessions ABPJ00000000 (MC), ABPK00000000 (LF), ABPL00000000 (MA), ABPM00000000 (MZ), and ABPN00000000 (RE). The versions described in this paper are the first versions: ABPJ01000000, ABPK01000000, ABPL01000000, ABPM01000000, and ABPN01000000.</p>", "<title>Similarity search and alignment</title>", "<p>Orthologous genomic contig pairs were first identified using reciprocal BLASTN similarity searches with a strict E-value cutoff of 10<sup>-100</sup>, performed across the sequence contigs of all possible species pairs. To reduce spurious ortholog assignments, putative ortholog contig pairs were only retained if their regions of high sequence similarity formed good end-to-end overlaps (defined as within 100 bases of the 5' end or 30 bases from the 3' end of a sequence) or overlap more than 80% of the shorter contig. Although some of the filtered regions could represent biologically relevant loci where recombination or translocations might have occurred, we decided to remove them from this analysis. Contig pair assignments were then passed to an algorithm that created clusters of contigs whereby each contig within the cluster must be related to all other contigs in the cluster through one or more putatively orthologous relations.</p>", "<p>Each cluster of contigs was then individually aligned using Phrap, resulting in a continuous alignment tiling path where each alignment position may consist of a base from any one or up to all five cichlid species (Figure ##FIG##0##1##). Segregating sites were then identified from alignment positions with high quality bases (&gt;20 score) from two or more species. A PQS was defined, corresponding to the first digit of the lowest Phrap quality score among the nucleotides of the different species present at the polymorphic site (for example, a polymorphic site between four species with base quality scores of 34, 45, 46, and 50 would be assigned a PQS of 3). To compare the extent of nucleotide diversity among the five cichlid species, we calculated Watterson's theta (θ<sub>w </sub>[##REF##1145509##68##]). This measure takes into account the number of variable positions and the sample size analyzed. Our data violate the assumption of an infinite, interbreeding population, but we chose this metric to in order to make direct comparisons to similar measures from study of other genomes (for example, zebrafish).</p>", "<title>Protein-coding sequence identification</title>", "<p>Cichlid protein coding sequences were inferred based on similarity searches to known protein databases of fishes and humans. BLASTX searches with E-value cutoff of 10<sup>-10 </sup>were performed for the each cichlid genomic assembly as well as the overall consensus sequence of the cluster alignments, against a protein database made up of all GenBank <italic>Actinopterygii </italic>(ray-finned fishes) sequences (downloaded 2 June 2007; 163,471 entries) and all human RefSeq proteins (downloaded 25 June 2007; 34,180 sequences). The alignment with the highest scoring hit for each genomic locus was then used as a reference to determine the coding strand and phase of the protein-coding cichlid locus.</p>", "<title>Evolutionary sequence divergence among JGI species</title>", "<p>All cluster alignment segments with contributing bases from two or more species were split into pairwise alignments (each two, three, four, or five species alignment position can be split into one, three, six, or ten pair-wise alignments respectively). Pair-wise alignments within each of the ten possible species pair combinations (MC-LF, MC-MA, MC-MZ, MC-RE, LF-MA, LF-MZ, LF-RE, MA-MZ, MA-RE, and MZ-RE) were then concatenated and the number of substitutions counted. Jukes-Cantor correction for multiple substitutions was applied to these direct distance measurements [##UREF##11##69##]. Pair-wise alignments consisting of only genic sequences were obtained from multi-species cluster alignment segments in a manner similar to that described above. The DNAStatistics package of Bioperl [##UREF##12##70##] was then used to calculate the K<sub>a</sub>/K<sub>s </sub>values of pair-wise alignments.</p>", "<title>Genotyping and validation of SNPs</title>", "<p>We genotyped 96 SNPs in 364 diverse Lake Malawi cichlid samples. These SNPs included 13 positive controls, 59 loci from the automated procedure described in this report, and an additional 24 loci chosen manually by BLAST of individual traces to the <italic>Tetraodon </italic>genome (see main text for further description). The GenomeLab SNPstream Genotyping System Software Suite v2.3 (Beckman Coulter, Inc.) was used for experimental setup, data uploading, image analysis, genotype calling and QC review, at Emory University's Center for Medical Genomics. In brief, marker panel data (multiplexed SNP panel designed by SNPstream's Primer Design Engine website [##UREF##13##71##]) were first uploaded to the SNPstream database using the PlateExplorer application software. Also uploaded was the Process Group Data containing all test sample information generated through a Laboratory Information Management System (Nautilus 2002; Thermo Fisher Scientific, Waltham, MA, USA). An on-board CCD camera of the SNPstream Imager took two snapshot images of each well of the 384-well tag array, one under a blue excitation laser and the other under a green excitation laser. Image application software was used to analyze the captured images to detect spots, overlay an alignment grid, and determine spot intensity. The fluorescent pixel intensity data for each SNP under the two channels, representing the relative abundance of the two alleles, were uploaded to the database. The GetGenos application software was used to calculate and generate a Log(B+G) versus B/(B+G) plot, where B and G were the pixel intensities under the blue and green channels, respectively, for each sample and each SNP. Next, automated genotype calling was accomplished using the QCReview application software based on a number of criteria (for instance, signal baseline, clustering pattern of the three genotypes, and Hardy-Weinberg score). A genotype summary was generated using the Report application software.</p>", "<title>Genetic differentiation within and among lineages</title>", "<p>Locus-specific F<sub>ST </sub>[##UREF##14##72##] was calculated using FSTAT version 2.9.3.2 [##UREF##15##73##] for three evolutionary scales: within LF and MZ; between LF and MZ; and between mbuna and non-mbuna. We determined that a SNP locus was a statistical outlier using the empirical distribution of F<sub>ST </sub>values. F<sub>ST </sub>outliers exceed the sum of the upper quartile value and 1.5 times the interquartile range.</p>", "<title>Genomic assignment</title>", "<p>We used a Bayesian method (STRUCTURE v.2.2 [##REF##10835412##46##]) to determine how well our SNP genotypes assigned individuals to evolutionary lineages. We chose to define the number of K genetic clusters in accord with previous research showing about three major evolutionary groups of Lake Malawi cichlids [##REF##16461358##3##, ####REF##17519189##4##, ##UREF##0##5####0##5##,##REF##8747298##47##]. Note that we do not intend this to mean that three is the best supported estimate of K in these data; our rationale is rather to demonstrate how individual genomes are composites (or not) of the major evolutionary lineages found in the lake. Thus, we used the admixture model to estimate q, the proportion of each genome derived from each of K genetic clusters. For comparison, we also ran analyses with K set to two, four, or five (not shown). Each run of the program included 50,000 cycles of burn-in and run length of 50,000 steps. Multiple runs were conducted to ensure reliability and consistency of results.</p>" ]
[ "<title>Results</title>", "<title>Sequence assembly</title>", "<p>Trace sequences of five Lake Malawi cichlid species, namely <italic>Mchenga conophorus </italic>(MC; formerly genus <italic>Copadichromis</italic>), <italic>Labeotropheus fuelleborni </italic>(LF), <italic>Melanochromis auratus </italic>(MA), <italic>Maylandia zebra </italic>(MZ; formerly genus <italic>Metriaclima</italic>) and <italic>Rhamphochromis esox </italic>(RE), were downloaded from the GenBank Trace Archive and assembled into contiguous (contig) sequences. The average cichlid genome is 1.1 × 10<sup>9 </sup>bases [##REF##17090588##35##], so the traces represent a sequence coverage of 12-17% for each of the five species (see Additional data file 1). Through several quality filtering and assembly steps (see Materials and methods [below]), the resultant genomic assemblies of the five cichlid species yielded an average of 60,862 contigs with a mean length of 1,193 bases per contig. The total first-pass assembly sequence length for each species ranged from 68,238,634 bases (MA) to 79,168,277 bases (MZ), or about 7% of an average cichlid genome. Assembly statistics are shown in Table ##TAB##0##1##.</p>", "<p>We noted that these first-pass assemblies were 'over-assembled' by roughly a factor of 2 when compared with theoretical expectations [##REF##3294162##36##]. Theory suggests that random shotgun sequencing of single copy DNA, at 15% coverage of a 1.1 gigabase genome, will result in an assembly length of about 153 Mb. We reasoned that our assemblies might be shorter than expected because multicopy elements were grouped as if they were single copy sequence. Given the theoretical expectation (again for 15% coverage of a 1.1 gigabase genome) that individual bases should only be sequenced a maximum of four to five times, we examined whether contigs were built from five or more trace sequences contributing overlapping bases. We observed that about 10 Mb of each first-pass assembly were derived from such contigs, and excluded these data from subsequent analyses (for example SNP prediction [see below]). Notably, individual sequences contributing to these 'high trace number' contigs were not identified by RepeatMasker but did sometimes have Basic Local Alignment Search Tool (BLAST) matches to putative repetitive elements (for example, pol polyprotein, reverse transcriptase). Because of the keen interest in repetitive DNA families in cichlids [##REF##12140242##37##] and other organisms [##REF##12547512##38##], we have retained alignments of these 'high trace number' contigs and have marked them as such (see Additional data files 3 and 4).</p>", "<title>Gene content and coverage</title>", "<p>To establish the extent of gene content and coverage present in each assembly, we carried out BLASTX similarity searches (10<sup>-10 </sup>E value cutoff) for each of the five assemblies against a reference human proteome (RefSeq proteins). The average proportion of putative genic sequence amounted to 3.9% of the available genomes. The MZ assembly contained the highest gene coverage, possessing genic loci that were significantly similar to approximately 5,240 unique human proteins. The remaining four species yielded approximately similar numbers ranging from 5,020 to 5,170 genes. It must be noted, however, that most of these genes are highly fragmented and incomplete, because of low coverage of the assembly. In all, a total of 36% (12,211 genes out of 34,180; see Additional data file 2) of the reference human proteome could be identified in one or more of the cichlid species.</p>", "<title>Clustering and alignment</title>", "<p>We obtained 25,458 clusters of putatively orthologous sequences, which were individually assembled into multi-species alignments for subsequent comparative analyses. Genic regions, as identified by similarity searches to known human and fish genes, were marked onto each alignment. Figure ##FIG##0##1## illustrates a typical example of one such alignment.</p>", "<p>Roughly 1% of the alignments (294 alignments) showed percentages of variable sites above 2% (about tenfold higher than the average). It is impossible to know, given the low coverage of the sequenced genomes, whether these represent orthologous but divergent regions of cichlid genomes or the alignment of paralogous sequence. We therefore retained these alignments, and included a calculation of polymorphism for each alignment (see Additional data file 3), for the consideration of researchers using these data. For example, alignment 108,866 contains sequence with similarity to asteroid homolog 1, with 8% of sites variable and a majority of replacement polymorphism. Given the lack of functional information about this novel signaling protein (first described in <italic>Drosophila </italic>[##REF##9644838##39##]), this alignment provides useful information even if (and perhaps because) it includes paralogous loci. Another 12% of the alignments (2,119 total) contained individual species contigs that had consensus base positions derived from five or more trace sequences (see above).</p>", "<p>For all subsequent analyses, we excluded 2,413 alignments that exhibited a high percentage of variable sites and/or higher than expected coverage. More than 11.6 million bases of multiple species alignments remain, of which roughly 1.06 Mb were inferred as genic. This included 10,902,011 (986,506 genic) bases of two-species alignments, 721,049 (75,371 genic) bases of three-species alignments, 27,951 (2,898 genic) bases of four-species alignments, and 877 (193 genic) bases of alignments containing all five species.</p>", "<title>Segregating sites</title>", "<p>Further analysis of these 11.6 million bases of multiple alignments identified a total of 32,417 (0.28%) cross-species SNPs. In order to classify the quality of an identified variable site, a polymorphism quality score (PQS) was defined, corresponding to the first digit of the lowest Phrap quality score among the nucleotides of the different species present at the polymorphic site (for example, a polymorphic site between four species with base quality scores of 34, 45, 46, and 50 would be assigned a PQS of 3). In total, 4,468 (13.8%) variable sites had a PQS of 5 or higher, 7,952 (24.5%) had a PQS of 4, 8,236 (25.4%) a PQS of 3, and the remaining 11,761 (36.3%) had a PQS of 2. PQS for each variable site are provided on the alignments described in Additional data file 3 (also available online [##UREF##6##40##]). Nucleotide diversity (Watterson's θ<sub>w</sub>) averaged over two-, three-, and four-species alignments was 0.00257. Roughly 8% of all polymorphic sites (2,709) were located within the putative genic regions identified earlier. Alignments with fish and human proteins provided us with the phase information required to further classify these into 1,066 synonymous and 1,643 nonsynonymous SNPs. Summaries of all alignments containing genic and nongenic polymorphisms are provided in Additional data files 3 and 4.</p>", "<p>In order to investigate the pair-wise differences between any two of the five species, all sequence alignment segments with two or more species were broken up into all possible pair-wise alignments; this resulted in 1.06 to 1.55 Mb of alignment per pair. We then calculated the Jukes-Cantor distance between species pairs. The three shortest distances were between LF and MZ (0.229%), followed by MA/MZ (0.232%) and LF/MA (0.241%), and the greatest was between LF and RE (0.288%). These genetic distances include both within-species polymorphism and the fixed differences between species. Currently, there is no exhaustive estimate of within-species polymorphism for Malawi cichlids. Unpublished data from our own group (Streelman JT) indicates that for LF and MZ, within-species diversity (π) may be as high as 0.2%. Thus, the percentage of fixed genetic differences is likely to be extremely small in this assemblage (see following sections).</p>", "<p>Finally, we calculated the ratio of replacement to synonymous substitutions (K<sub>a</sub>/K<sub>s</sub>) for concatenated genic alignments among all pairs of species. We used concatenated sequences because each segment represented only a small fraction of a gene, with only few nonsynonymous and synonymous sites. K<sub>a</sub>/K<sub>s </sub>ranged from 0.380 in MC/LF to 0.562 in LF/MA. These numbers are greater than the ratios found between <italic>Fugu </italic>and <italic>Tetraodon </italic>(0.127 to 0.144 [##REF##15496914##41##]). Such high K<sub>a</sub>/K<sub>s </sub>values may indicate that positive selection, driven by adaptive radiation, is prevalent in cichlid fishes. However, given the expectation of few fixed differences between groups, this topic should be revisited with more data on the levels of segregating and fixed nucleotide substitutions among lineages.</p>", "<title>Validation and generality of SNPs</title>", "<p>We genotyped 96 SNPs in 384 Lake Malawi cichlid samples using Beckman Coulter SNPstream™ technology (Beckman Coulter, Inc., Fullerton, CA). The SNPs were partitioned into three categories to help us evaluate the comparative success rate of automated SNP prediction. First, we included 13 positive controls: genes previously sequenced by others [##REF##16461358##3##,##REF##15772376##25##] and by us (Streelman JT, unpublished data), with expected variation in Malawi cichlids. Positive controls included genes involved in morphogenesis (<italic>otx1</italic>, <italic>otx2</italic>, and <italic>pax9</italic>), pigmentation (<italic>mitf</italic>, <italic>ednrb</italic>, and <italic>aim1</italic>), and visual sensitivity (opsins <italic>rh1</italic>, <italic>sws1</italic>, <italic>lws</italic>, <italic>sws2a</italic>, and <italic>sws2b</italic>). Next, we genotyped 59 SNPs identified using the automated procedure described in this report. We selected these SNPs to represent a range of PQS (from 2 to 5) and a variety of sequence types (genic, nongenic with a BLAST match &lt; e<sup>-100 </sup>to <italic>Tetraodon</italic>, and nongenic with no BLAST match). Finally, we wished to compare our automated SNP selection to a manual approach. Therefore, we included an additional 24 SNPs identified by manual inspection of BLAST matches between single JGI traces and <italic>Tetraodon </italic>chromosome 11; we have previously shown <italic>Tetraodon </italic>11 to share orthologs with cichlid chromosome 5 [##REF##16555305##13##]. Note that these SNPs were most often not discovered by our automated procedure because they originated in single traces that did not meet percentage quality cutoffs and/or they did not align into comparative contigs because of overlap cutoffs.</p>", "<p>Our validation strategy sought to document the general use and segregation of these markers among Lake Malawi cichlids. Given recent divergence times among species (some as recent as 1,000 years [##REF##15851665##2##]), we expected that SNPs might segregate throughout the assemblage. Therefore, Malawi samples comprised about ten individuals from each of ten populations of MZ and LF, as well as one to five individuals of 77 additional species (25 of which were rock-dwelling mbuna). Taxa were included to represent the morphological, functional, and behavioral diversity of the Malawi lineage, which may contain more than 800 species [##REF##11298988##42##].</p>", "<p>Ten out of 13 (about 77%) positive controls gave reliable genotypes and were variable across the dataset. For the 59 SNPs predicted by our automated procedure, 11 were fixed (no variation) in all samples, indicating an error in sequencing (or genotyping), an error in prediction, or the presence of a low frequency allele in the sequenced samples. Six predicted SNPs did not produce data reliable enough for genotype calls. The remaining 42 loci from automated predictions (about 71%) were polymorphic across the dataset. For 24 SNPs predicted using manual similarity searches, four were fixed and four failed reliability for genotype calls, with the remaining 16 loci (about 67%) showing polymorphism (Table ##TAB##1##2##). Twelve out of 20 (60%) predicted SNPs with PQS of 3 or less were successful, whereas 30 out of 39 (76%) predictions with PQS of at least 4 yielded polymorphisms (Table ##TAB##2##3##). There is evidence of ascertainment bias in our genotypic data (see Additional data file 5). For example, three SNP loci (Aln100674, Aln114498, and Aln102321) exhibit alleles unique to <italic>Rhamphochromis</italic>. Similarly, SNPs predicted from comparisons of RE and mbuna (LF, MA, and MZ) are sometimes fixed in mbuna. Polymorphisms predicted from comparisons of mbuna taxa are more likely to vary within LF and MZ populations and across mbuna species.</p>", "<title>Genetic polymorphism and divergence at multiple scales</title>", "<p>Strikingly, among all 68 loci showing polymorphism, no SNP locus was alternately fixed between LF and MZ, or between rock-dwelling mbuna and non-mbuna. We thus sought to investigate the degree of polymorphism versus divergence at multiple evolutionary scales.</p>", "<p>The data (Additional data file 5) support the previously reported population structures in MZ [##REF##11108599##43##,##UREF##7##44##] and LF [##UREF##8##45##], as well as the genetic distinction between these species (MC Mims, unpublished data). For example, mean genetic differentiation (F<sub>ST</sub>) in MZ is 0.148 and in LF is 0.271. Mean F<sub>ST </sub>between LF and MZ was 0.215, and between mbuna (25 species) and non-mbuna (52 species) it was 0.224, demonstrating that most genetic variation segregates within and not between lineages, regardless of evolutionary scale. Nevertheless, these distributions of F<sub>ST </sub>yielded statistical outliers, which exhibit greater than average genetic differentiation (Figure ##FIG##1##2##). Four loci were found to be statistical outliers for F<sub>ST </sub>among MZ and LF populations. In MZ the opsin loci <italic>lws </italic>(F<sub>ST </sub>= 0.514), <italic>sws1 </italic>(0.572) and <italic>rh1 </italic>(0.733), and in LF the opsin locus <italic>rh1 </italic>(0.853) exhibit differentiation between populations. Between LF and MZ, three loci were identified as outliers: a nonsynonymous polymorphism in <italic>csrp1 </italic>(F<sub>ST </sub>= 0.893), a synonymous polymorphism in <italic>β-catenin </italic>(Aln101106_1089; F<sub>ST </sub>= 0.904), and an intronic polymorphism in <italic>ptc2 </italic>(Aln100281_1741; F<sub>ST </sub>= 0.863). Two statistical outliers were identified for F<sub>ST </sub>between rock-dwelling mbuna and non-mbuna groups: a nonsynonymous polymorphism in <italic>irx1 </italic>(Aln102504_1609; F<sub>ST </sub>= 0.984), and a nongenic polymorphism (Aln103534_280; F<sub>ST </sub>= 0.919) in sequence with similarity to pufferfish and stickleback genomes between <italic>contactin 3 </italic>and <italic>ncam L1</italic>.</p>", "<title>Genetic clustering and ancestry</title>", "<p>To further visualize the segregation of SNPs across the Malawi cichlid flock, we utilized a Bayesian approach that assigns individuals to a predefined number of genetic clusters [##REF##10835412##46##]. Specifically, we were interested in how species would be assigned to major Malawi cichlid lineages identified in previous studies [##REF##16461358##3##,##REF##17519189##4##,##REF##8747298##47##]. There are three such groups supported by the majority of molecular data: the rock-dwelling mbuna; pelagic and sand-dwelling species; and a group comprised of <italic>Rhamphochromis</italic>, <italic>Diplotaxodon</italic>, and other deep-water taxa. Analysis of 68 SNP loci accurately classifies species to respective lineages (Figure ##FIG##2##3##). For instance, all species considered mbuna (blue) cluster with other mbuna, to the exclusion of other groups; species thought to represent the earliest divergence within the species flock (<italic>Rhamphochromis</italic>) clustered together as a separate group (green); all remaining non-mbuna species formed the third group (red). Notably, deepwater genera <italic>Diplotaxodon </italic>and <italic>Pallidochromis </italic>contain individuals with mosaic genomes (red and green) and <italic>Astatotilapia calliptera</italic>, a nonendemic species and possible Malawi ancestor [##REF##12590750##48##] combines mbuna and non-mbuna genomes.</p>", "<p>For comparison, additional analyses were performed setting the predefined number of genetic clusters to from two to five. When set to two genetic clusters, species were accurately classified as mbuna or non-mbuna. At settings of four or five, the program was unable to yield stable classification results between replicate runs. Thus, these latter three sets of analyses (data not shown) did not provide any further insights into the genetic lineages of Malawi cichlids.</p>" ]
[ "<title>Discussion</title>", "<p>African cichlid fishes are important models of evolutionary diversification in form and function [##UREF##7##44##]. They are singularly remarkable for the extent of phenotypic and behavioral diversity on a backdrop of genomic similarity. Lake Malawi is home to the most species-rich assemblage of African cichlids; as many as 800 to 1,000 species are thought to have evolved from a common ancestor during the past 500,000 to 1 million years ago [##REF##11298988##42##]. These recently formed species segregate ancestral polymorphism and exchange genes by hybridization [##UREF##0##5##,##REF##12919487##7##,##REF##15245419##49##]. Such circumstances present both opportunities and challenges for understanding evolutionary history and biological diversity. Opportunistically, researchers have used molecular markers across studies to interrogate the genetic basis of phenotypic differentiation [##REF##16555305##13##,##REF##12919484##22##,##REF##15716505##24##,##REF##16251275##29##]. This approach views Malawi cichlid species as natural mutants screened for function by natural selection, with essentially identical ancestral genomes honed by contrasting historical processes. By contrast, the task of reconstructing a phylogeny of species has been hindered by the very same phenomena of genomic similarity and mosaicism [##REF##15851665##2##,##REF##16461358##3##]; even the promising approach of Amplified Fragment Length Polymorphism (AFLP) does not provide strong resolution of the relationships among genera [##REF##14614144##23##,##REF##12590750##48##,##REF##10220426##50##,##REF##16448413##51##]. The data we present here should provide new resources and perspectives for cichlid evolutionary genomics.</p>", "<title>Cichlid species exhibit genomic polymorphism</title>", "<p>Lake Malawi cichlid species sequenced by the JGI embody the phylogenetic, morphological, and behavioral diversity found within the assemblage. <italic>Rhamphochromis esox </italic>(RE) is a large (about 0.5 m) pelagic predator that represents one of the basal lineages of the species flock [##REF##16461358##3##,##REF##17519189##4##,##REF##8747298##47##]. <italic>Mchenga conophorus </italic>(MC) is a sand-dwelling species that breeds on leks, where males construct 'bowers' to attract females. <italic>Melanochromis auratus </italic>(MA), <italic>Maylandia zebra </italic>(MZ), and <italic>Labeotropheus fuelleborni </italic>(LF) are rock-dwelling (mbuna) species that differ in color pattern, trophic ecology, body shape, and craniofacial morphology (pictures of these and others are available online [##UREF##9##52##]).</p>", "<p>Our data confirm the conclusions from previous genetic analyses on a smaller scale; Lake Malawi species are genetically similar. Nucleotide diversity observed among the five cichlid species (Watterson's θ<sub>w </sub>= 0.26%) is less than that found among laboratory strains of the zebrafish <italic>Danio rerio </italic>(Watterson's θ<sub>w </sub>= 0.48% [##REF##16533913##53##]). Although overall nucleotide diversity is less than that observed in <italic>Danio</italic>, the ratio of replacement to silent change is nearly fivefold higher in the Lake Malawi genomes. Such a result might suggest that East African cichlid evolution is characterized by adaptive molecular evolution, as has been indicated in a few instances [##REF##15772376##25##,##REF##12200490##54##], or a relaxation of purifying selection attributable to small effective population size. However, we should view this estimate of K<sub>a</sub>/K<sub>s </sub>with caution because of one of the remarkable features of these data (see below). Variable sites identified from cross-species alignments are not substitutions fixed between species. The K<sub>a</sub>/K<sub>s </sub>approach to identifying selection may be largely inappropriate for such young species where ancestral alleles segregate as polymorphisms.</p>", "<p>The pattern of variation observed across the approximately 75 species genotyped in this study demonstrates that biallelic polymorphisms segregate widely throughout the Malawi species flock. SNPs segregate within and between MZ and LF populations, as well as within and among mbuna species and other lineages. No SNP locus surveyed is alternately fixed in LF versus MZ, nor between mbuna and non-mbuna. Remarkably, the degree of genetic differentiation (F<sub>ST</sub>) within species is roughly equivalent to that between species and to that between major lineages. Lake Malawi cichlid species are mosaics of ancestrally polymorphic genomes. Add to this a propensity of recently diverged species to exchange genes [##REF##15851665##2##], and Malawi cichlids present a case of complex and dynamic evolutionary diversification, where recombination and the sorting of ancestral polymorphism may be more important than new mutation as sources of genetic variation. Despite allele sharing, SNP frequencies contain a clear signal of ancestry for the entire flock. Rock-dwelling mbuna comprise a genetic cluster, as do pelagic and sand-dwelling species, in addition to <italic>Rhamphochromis</italic>. Notably, <italic>Astatotilapia calliptera</italic>, one of a few nonendemic haplochromines in Lake Malawi, appears to retain a reservoir of ancestral polymorphisms from which mbuna and non-mbuna genomes have emerged.</p>", "<title>Genomic polymorphism and the divergence of Malawi cichlids</title>", "<p>Our hierarchical sampling design allows us to consider whether there are loci exhibiting extreme genetic differentiation against the background of shared polymorphism within species, between species, and between major lineages. Strikingly, regardless of the evolutionary scale, statistical outliers comprise approximately 3% to 5% of loci surveyed. Opsin loci <italic>lws</italic>, <italic>rh1</italic>, and <italic>sws1 </italic>are differentiated among populations of LF and MZ, adding to reports that opsin polymorphisms are associated with population-specific color patterns or visual environments [##REF##16313597##55##].</p>", "<p>SNPs in <italic>csrp1</italic>, <italic>β-catenin</italic>, and <italic>ptc2 </italic>exhibit greater than expected differentiation between LF and MZ. <italic>Csrp1 </italic>(cysteine-rich protein) is a vertebrate LIM-domain family member acting in the noncanonical WNT pathway, expressed in gut, intestine, and cardiac mesoderm [##REF##17592114##56##]. <italic>β-catenin </italic>acts to transduce signals in the canonical WNT pathway [##REF##12130776##57##] and is expressed in developing cichlid fins, dentitions, brains, and lateral lines (Fraser GJ, Streelman JT, unpublished data). Patched is a receptor for sonic hedgehog [##REF##18284698##58##]; both areexpressed in developing cichlid dentitions, jaws, and brains (Fraser GJ, Sylvester JB, Streelman JT, unpublished data). A SNP in <italic>irx1 </italic>nearly perfectly differentiates rock-dwelling mbuna from the remainder of the Malawi species flock. <italic>Irx1 </italic>acts to position the boundary between the telencephalon and the posterior forebrain [##REF##17670791##59##]. Finally, a SNP located between <italic>contactin 3 </italic>and <italic>ncam L1 </italic>exhibits differentiation between mbuna and non-mbuna lineages; these genes are linked in other genomes and functionally interact to pattern dendritic branching in the neocortex [##REF##18046458##60##]. Taken together, differentiated loci are interesting in the context of cichlid diversification because they affect the phenotypes that vary among lineages: color and vision [##REF##15772376##25##,##REF##16213819##26##], guts [##UREF##10##61##], dentitions [##REF##16555305##13##,##REF##18625062##62##], jaws [##REF##12704237##10##,##REF##16251275##29##], and brains [##REF##9288416##28##].</p>", "<title>Discovery for evolutionary biology</title>", "<p>There are obvious challenges when attempting to extract information from low coverage genomic sequence, and also obvious payoffs [##REF##14512627##31##, ####REF##17407382##32##, ##REF##17975172##33##, ##REF##15778292##34####15778292##34##]. Most previous studies have used this information for species-specific discovery (for example, dog breeds) or broad evolutionary comparisons with respect to a reference genome (for example, dog-human, shark-human, or cat-mammal). Our goals in the present analysis stem from the unique characteristics of Lake Malawi cichlids; these are biologic species that behave genetically like a single subdivided population. Therefore, our biggest challenge was to devise a strategy that retains information from these low coverage survey sequences (75% genomic coverage spread over five closely related species), but minimizes error and bias in assembly and cross-species alignment for SNP identification. For example, we excluded many contigs because they appeared to be over-assembled, and we excluded multi-species alignments if they exceeded a polymorphism threshold. The over-assembly problem limits the coverage of these genomes in relation to expectation; this phenomenon, observed in the cat genome and in simulation, has complex and varying causes and has yet to be fully resolved [##REF##17975171##63##]. It is likely to be mitigated to some degree by comparison with a higher coverage reference sequence. The power of the data we present comes from the broad utility of the genic sequences and SNPs we have identified for many questions in genomic evolutionary biology.</p>", "<p>Our analyses identified about 12,000 Lake Malawi cichlid sequences with similarity to human and fish proteins. This is a significant advance in our understanding of cichlid genomic content. To put this in context, approximately 13,500 unique expressed sequence tags, from three different East African cichlids, represent the sum total of such publicly released sequences [##UREF##3##15##]. Our contribution roughly doubles the available data.</p>", "<p>The approximately 32,000 (2,700 genic) SNPs we identified should provide a wealth of molecular markers for studies of population genetics and molecular ecology, linkage and quantitative trait locus mapping, association mapping, and phylogeny. We convert about 70% of predicted SNPs to polymorphic markers; this percentage is comparable to that of other studies from white spruce (74% to 85%, depending on quality cutoffs [##REF##16824208##64##]), zebrafish (65% [##REF##16533913##53##]), and cow (43% [##REF##17244488##65##]). We have shown these biallelic markers to be of general use, many segregating across the major cichlid lineages of Lake Malawi. We used the SNPs to assign Malawi species to ancestral genetic clusters, and this approach should hold promise for similar questions of genetic structure that span the population versus species continuum. It is important to note that early runs of this analysis, with fewer SNP loci, resulted in stable results with more individuals showing mosaic genomes. This suggests that careful consideration should be given to the number of polymorphic loci necessary to yield confidence in evolutionary interpretation. As more SNP loci (with known genome coordinates) are assayed, it will be possible to compute and compare ancestry proportions across scales (for example, genome versus chromosome versus gene cluster).</p>", "<p>Notably, we have used the background level of genomic similarity and polymorphism to identify loci that may have experienced a history of selection within species, between species and between major lineages. Because SNP markers are co-dominant, easy to genotype, reliable and reproducible from laboratory to laboratory, and readily mapped in silico (NHGRI will sequence a related cichlid, the tilapia, to 7-fold draft assembly coverage in 2008), they are likely to complement microsatellites and AFLP for most applications in cichlid evolutionary genomics. Given the unique mosaic structure of Lake Malawl cichlid genomes, it is exciting to envision experiments employing SNPs to identity genotype-phenotype associations, using the entire species flock as a mapping panel. Finally, as sequencing costs continue to drop, the approach we outline here should prove applicable to those studying evolutionary and phenotypic diversity among closely related species [##UREF##7##44##].</p>" ]
[]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Low coverage survey sequencing shows that although Lake Malawi cichlids are phenotypically and behaviorally diverse, they appear genetically like a subdivided population.</p>", "<title>Background</title>", "<p>Cichlid fish from East Africa are remarkable for phenotypic and behavioral diversity on a backdrop of genomic similarity. In 2006, the Joint Genome Institute completed low coverage survey sequencing of the genomes of five phenotypically and ecologically diverse Lake Malawi species. We report a computational and comparative analysis of these data that provides insight into the mechanisms that make closely related species different from one another.</p>", "<title>Results</title>", "<p>We produced assemblies for the five species ranging in aggregate length from 68 to 79 megabase pairs, identified putative orthologs for more than 12,000 human genes, and predicted more than 32,000 cross-species single nucleotide polymorphisms (SNPs). Nucleotide diversity was lower than that found among laboratory strains of the zebrafish. We collected around 36,000 genotypes to validate a subset of SNPs within and among populations and across multiple individuals of about 75 Lake Malawi species. Notably, there were no fixed differences observed between focal species nor between major lineages. Roughly 3% to 5% of loci surveyed are statistical outliers for genetic differentiation (F<sub>ST</sub>) within species, between species, and between major lineages. Outliers for F<sub>ST </sub>are candidate genes that may have experienced a history of natural selection in the Malawi lineage.</p>", "<title>Conclusion</title>", "<p>We present a novel genome sequencing strategy, which is useful when evolutionary diversity is the question of interest. Lake Malawi cichlids are phenotypically and behaviorally diverse, but they appear genetically like a subdivided population. The unique structure of Lake Malawl cichlid genomes should facilitate conceptually new experiments, employing SNPs to identity genotype-phenotype association, using the entire species flock as a mapping panel.</p>" ]
[ "<title>Abbreviations</title>", "<p>BLAST, Basic Local Alignment Search Tool; F<sub>ST</sub>, genetic differentiation; JGI, Joint Genome Institute; K<sub>a</sub>/K<sub>s</sub>, ratio of replacement to synonymous substitutions; LF, <italic>Labeotropheus fuelleborni</italic>; MA, <italic>Melanochromis auratus</italic>; Mb, megabases; MC, <italic>Mchenga conophorus</italic>; MZ, <italic>Maylandia zebra</italic>; NCBI, National Center for Biotechnology Information; PQS, polymorphism quality score; RE, <italic>Rhamphochromis esox</italic>; SNP, single nucleotide polymorphism.</p>", "<title>Authors' contributions</title>", "<p>YHL, JTS, SVY, and TDK conceived the idea and designed the study. YHL, LSK, and MCM performed the research. YHL and JTS analyzed the data and drafted the manuscript. All authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data file ##SUPPL##0##1## is a table of trace sequence statistics of five Lake Malawi cichlid species. Additional data file ##SUPPL##1##2## is a list of human gene homologs found in the five cichlid species. Additional data file ##SUPPL##2##3## is a list of alignments and polymorphic sites. Additional data file ##SUPPL##3##4## is a list of alignments with BLAST hits to fish and humans. Additional data file ##SUPPL##4##5## is a table of major allele frequencies for biallelic SNPs surveyed across Lake Malawi cichlid populations and species.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We thank members of the Streelman laboratory, Karen Carleton, and two anonymous reviewers for comments on previous drafts of the manuscript. The research is supported by grants from the NSF (IOS 0546423), NIH (R21 DE017182), and Alfred P Sloan Foundation (BR-4499) to JTS. Drs Karen Carleton and Federica DiPalma extracted high-quality DNA from the five species of Malawi cichlid. Library construction and sequencing was performed by the JGI under the auspices of the US Department of Energy's Office of Science, Biological and Environmental Research Program and by the University of California, Lawrence Livermore National Laboratory under contract number W-7405-Eng-48, Lawrence Berkeley National Laboratory under contract number DE-AC03-76SF00098 and Los Alamos National Laboratory under contract number W-7405-ENG-36.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Alignment of a typical cluster of orthologous sequences. <bold>(a) </bold>Overall alignment of assembly contigs from three different cichlid species with alignment positions indicated. <bold>(b) </bold>Expanded detail of nucleotide alignment. Filled pink block shows the expanded alignment corresponding to dotted red box in panel a. Filled blue block shows the alignment of corresponding species' traces that made up the assembly sequences. Lower case nucleotides have base quality scores under 20. Dashes '-' represent sequence unavailability. Asterisks '*' represent gaps inserted into the sequences. Dots '·' represent identity in alignment. Cap '^' represents segregating site. Alignment positions shown after consensus sequence. Polymorphism quality score shown below A-G single nucleotide polymorphism site.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Box-and-whisker plots of F<sub>ST </sub>values. F<sub>ST </sub>values were calculated for the following: within MZ, within LF, LF versus MZ, and Mbuna versus non-Mbuna. Upper and lower box bounds represent 75th and 25th percentiles, respectively. The solid lines within boxes represent the median value. Whiskers mark the furthest points from the median that are not classified as outliers. Unfilled circles represent outliers that are more than 1.5 times the interquartile range higher than the upper box bound. F<sub>ST</sub>, genetic differentiation; LF, <italic>Labeotropheus fuelleborni</italic>; MA, <italic>Melanochromis auratus</italic>; Mb, megabases; MC, <italic>Mchenga conophorus</italic>; MZ, <italic>Maylandia zebra</italic>.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Bayesian assignment of Lake Malawi cichlids to different evolutionary lineages. We show the contribution to each individual genome (q, which ranges from 0% to 100%) from each of K = 3 predefined genetic clusters (blue, red, and green), for data derived from single nucleotide polymorphisms (SNPs) in Tables 2 and 3. Note that this method predefines the number but not the identity of genetic clusters. Species names are written once; multiple individuals from species are grouped together (for example, four individuals of <italic>Pseudotropheus crabro</italic>). Species considered mbuna (blue) cluster with other mbuna, to the exclusion of other groups; species thought to represent the earliest divergence within the species flock (<italic>Rhamphochromis</italic>) clustered together as a separate group (green); and all remaining non-mbuna species formed the third group (red).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>First-pass genomic assembly statistics for five Lake Malawi cichlid species</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">MC</td><td align=\"left\">LF</td><td align=\"left\">MA</td><td align=\"left\">MZ</td><td align=\"left\">RE</td></tr></thead><tbody><tr><td align=\"left\">Total number of contigs in assembly</td><td align=\"left\">61,923</td><td align=\"left\">58,245</td><td align=\"left\">63,297</td><td align=\"left\">65,094</td><td align=\"left\">55,751</td></tr><tr><td align=\"left\">Total length (bases)</td><td align=\"left\">73,425,564</td><td align=\"left\">70,858,381</td><td align=\"left\">68,238,634</td><td align=\"left\">79,168,277</td><td align=\"left\">71,295,074</td></tr><tr><td align=\"left\">Genome coverage<sup>a </sup>(%)</td><td align=\"left\">6.68</td><td align=\"left\">6.44</td><td align=\"left\">6.20</td><td align=\"left\">7.20</td><td align=\"left\">6.48</td></tr><tr><td align=\"left\">Mean trace length (bases)</td><td align=\"left\">1,055</td><td align=\"left\">1,092</td><td align=\"left\">991</td><td align=\"left\">1,145</td><td align=\"left\">1,153</td></tr><tr><td align=\"left\">Shortest contig length (bases)</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">50</td></tr><tr><td align=\"left\">Longest contig length (bases)</td><td align=\"left\">19,632</td><td align=\"left\">17,437</td><td align=\"left\">21,601</td><td align=\"left\">15,371</td><td align=\"left\">21,351</td></tr><tr><td align=\"left\">Mean contig length (bases)</td><td align=\"left\">1,186</td><td align=\"left\">1,217</td><td align=\"left\">1,078</td><td align=\"left\">1,216</td><td align=\"left\">1,279</td></tr><tr><td align=\"left\">Q25 contig length (bases)</td><td align=\"left\">759</td><td align=\"left\">846</td><td align=\"left\">783</td><td align=\"left\">805</td><td align=\"left\">934</td></tr><tr><td align=\"left\">Q50 (median) contig length (bases)</td><td align=\"left\">966</td><td align=\"left\">1,063</td><td align=\"left\">949</td><td align=\"left\">1,163</td><td align=\"left\">1,113</td></tr><tr><td align=\"left\">Q75 contig length (bases)</td><td align=\"left\">1,403</td><td align=\"left\">1,355</td><td align=\"left\">1,102</td><td align=\"left\">1,417</td><td align=\"left\">1,407</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"left\">Total genic length (bases)</td><td align=\"left\">2,863,110 (3.9%)</td><td align=\"left\">2,841,933 (4.0%)</td><td align=\"left\">2,761,941 (4.0%)</td><td align=\"left\">2,851,968 (3.6%)</td><td align=\"left\">2,797,548 (3.9%)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>SNP genotyping success categorized by detection method</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">SNP detection method</td><td align=\"left\">Control genes</td><td align=\"left\">Automated</td><td align=\"left\">Manual BLAST</td></tr></thead><tbody><tr><td align=\"left\">Number of genotyped loci</td><td align=\"left\">13</td><td align=\"left\">59</td><td align=\"left\">24</td></tr><tr><td align=\"left\">Number of polymorphic loci</td><td align=\"left\">10</td><td align=\"left\">42</td><td align=\"left\">16</td></tr><tr><td align=\"left\">Number of fixed loci</td><td align=\"left\">3</td><td align=\"left\">11</td><td align=\"left\">4</td></tr><tr><td align=\"left\">Number of failed loci</td><td align=\"left\">0</td><td align=\"left\">6</td><td align=\"left\">4</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Successful SNP detection (%)</td><td align=\"left\">76.9</td><td align=\"left\">71.2</td><td align=\"left\">66.7</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>SNP genotyping success categorized by polymorphic quality score</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Polymorphic quality score</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">4</td><td align=\"left\">5</td></tr></thead><tbody><tr><td align=\"left\">Number of genotyped loci</td><td align=\"left\">5</td><td align=\"left\">15</td><td align=\"left\">28</td><td align=\"left\">11</td></tr><tr><td align=\"left\">Number of polymorphic loci</td><td align=\"left\">2</td><td align=\"left\">10</td><td align=\"left\">24</td><td align=\"left\">6</td></tr><tr><td align=\"left\">Number of fixed/failed loci</td><td align=\"left\">3</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">5</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\">Successful SNP detection (%)</td><td align=\"left\">40</td><td align=\"left\">66.7</td><td align=\"left\">85.7</td><td align=\"left\">54.5</td></tr></tbody></table></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Presented is a table of trace sequence statistics of five Lake Malawi cichlid species.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Presented is a list of human gene homologs found in the five cichlid species.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Presented is a list of alignments and polymorphic sites.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Presented is a list of alignments with BLAST hits to fish and humans.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Presented is a table of major allele frequencies for biallelic SNPs surveyed across Lake Malawi cichlid populations and species.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p><sup>a</sup>Using an average cichlid genome size of 1.1 × 10<sup>9 </sup>bases. LF, <italic>Labeotropheus fuelleborni</italic>; MA, <italic>Melanochromis auratus</italic>; MC, <italic>Mchenga conophorus</italic>; MZ, <italic>Maylandia zebra</italic>; RE, <italic>Rhamphochromis esox</italic>; Q25, 25<sup>th </sup>percentile; Q50, median or 50<sup>th </sup>percentile; Q75, 75<sup>th </sup>percentile.</p></table-wrap-foot>", "<table-wrap-foot><p>BLAST, Basic Local Alignment Search Tool; SNP, single nucleotide polymorphism.</p></table-wrap-foot>", "<table-wrap-foot><p>SNP, single nucleotide polymorphism.</p></table-wrap-foot>" ]
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[{"surname": ["Moran", "Kornfield"], "given-names": ["P", "I"], "article-title": ["Retention of ancestral polymorphism in the Mbuna species flock of Lake Malawi."], "source": ["Mol Biol Evol"], "year": ["1993"], "volume": ["10"], "fpage": ["1015"], "lpage": ["1029"]}, {"article-title": ["Cichlid Genome Consortium"]}, {"surname": ["Katigiri", "Kidd", "Tomasino", "Davis", "Wishon", "Stern", "Carleton", "Howe", "Kocher"], "given-names": ["T", "CE", "E", "JT", "C", "JE", "KL", "AE", "TD"], "article-title": ["A BAC-based physical map of the Nile tilapia genome 89."], "source": ["BMC Genomics"], "year": ["2005"], "volume": ["9"], "fpage": ["89"], "pub-id": ["10.1186/1471-2164-6-89"]}, {"article-title": ["The Gene Index Project"]}, {"surname": ["Kornfield", "Smith"], "given-names": ["I", "PF"], "article-title": ["African cichlid fishes: model systems for evolutionary biology."], "source": ["Ann Rev Ecol Evol Syst"], "year": ["2000"], "volume": ["31"], "fpage": ["163"], "lpage": ["196"]}, {"surname": ["Genner", "Turner"], "given-names": ["MJ", "GF"], "article-title": ["The mbuna cichlids of Lake Malawi: a model for rapid speciation and adaptive radiation."], "source": ["Fish Fisheries"], "year": ["2005"], "volume": ["6"], "fpage": ["1"], "lpage": ["34"], "pub-id": ["10.1111/j.1467-2679.2005.00173.x"]}, {"article-title": ["Georgia Tech Streelman Lab: Online Cichlid Resources"]}, {"surname": ["Streelman", "Peichel", "Parichy"], "given-names": ["JT", "CL", "DM"], "article-title": ["Developmental genetics of adaptation in fishes: the case for novelty."], "source": ["Ann Rev Ecol Evol Syst"], "year": ["2007"], "volume": ["38"], "fpage": ["655"], "lpage": ["681"], "pub-id": ["10.1146/annurev.ecolsys.38.091206.095537"]}, {"surname": ["Arnegard", "Markert", "Danley", "Stauffer", "Ambali", "Kocher"], "given-names": ["ME", "JA", "PD", "JR", "AJ", "TD"], "article-title": ["Population structure and colour variation of the cichlid fish "], "italic": ["Labeotropheus fuelleborni "], "source": ["Proc Biol Sci"], "year": ["1999"], "volume": ["266"], "fpage": ["119"], "lpage": ["130"], "pub-id": ["10.1098/rspb.1999.0611"]}, {"article-title": ["The Cichlid Fishes of Lake Malawi, Africa"]}, {"surname": ["Reinthal"], "given-names": ["PN"], "article-title": ["The feeding habits of a group of herbivorous rock-dwelling fishes from Lake Malawi, Africa."], "source": ["Env Biol Fishes"], "year": ["1990"], "volume": ["27"], "fpage": ["215"], "lpage": ["233"], "pub-id": ["10.1007/BF00001674"]}, {"surname": ["Jukes", "Cantor", "Munro HN"], "given-names": ["TH", "CR"], "article-title": ["Evolution of protein molecules."], "source": ["Mammalian Protein Metabolism"], "year": ["1969"], "publisher-name": ["New York, NY: Academic Press"], "fpage": ["21"], "lpage": ["132"]}, {"article-title": ["Bioperl"]}, {"article-title": ["Beckman Coulter Autoprimer.com"]}, {"surname": ["Weir", "Cockerham"], "given-names": ["BS", "CC"], "article-title": ["Estimating F-statistics for the analysis of population structure."], "source": ["Evolution"], "year": ["1984"], "volume": ["38"], "fpage": ["1358"], "lpage": ["1370"], "pub-id": ["10.2307/2408641"]}, {"surname": ["Goudet"], "given-names": ["J"], "article-title": ["FSTAT (Version 1.2): A computer program to calculate F-statistics."], "source": ["J Hered"], "year": ["1995"], "volume": ["86"], "fpage": ["485"], "lpage": ["486"]}]
{ "acronym": [], "definition": [] }
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2022-01-12 14:47:27
Genome Biol. 2008 Jul 10; 9(7):R113
oa_package/bc/41/PMC2530870.tar.gz
PMC2530871
18631379
[ "<title>Background</title>", "<p>Clostridia are of major importance to human and animal health and physiology, cellulose degradation, bioremediation, and for the production of biofuels and chemicals from renewable resources [##REF##16261177##1##]. These obligate anaerobic, Gram-positive, endospore-forming firmicutes include several major human and animal pathogens, such as <italic>C. botulinum</italic>, <italic>C. perfringens</italic>, <italic>C. difficile</italic>, and <italic>C. tetani</italic>, the cellulolytic <italic>C. thermocellum </italic>and <italic>C. phytofermentans</italic>, several ethanologenic [##REF##15755956##2##], and many solventogenic (butanol, acetone and ethanol) species [##REF##7646848##3##]. Their sporulation/differentiation program is critical for understanding important cellular functions or programs, yet it remains largely unknown. We have recently examined the similarity of the clostridia and bacilli sporulation programs using information from sequenced clostridial genomes [##REF##16261177##1##]. We concluded that, based on genomic information alone, the two programs are substantially different, reflecting the different evolutionary age and roles of these two genera. We have also argued that <italic>C. acetobutylicum </italic>is a good model organism for all clostridia [##REF##16261177##1##]. Transcriptional or functional genomic information is, however, necessary for detailing these differences and for understanding clostridial differentiation and physiology. Key issues awaiting resolution include: the identification of the mid to late sigma and sporulation factors and their regulons; the orchestration and timing of their action; the set of genes employed by the cells in the mid and late stages of spore maturation; identification of candidate histidine kinases that might be capable of phosphorylating the master regulator (Spo0A) of sporulation; and some functional assessment of the roles of several sigma factors of unknown function encoded by the <italic>C. acetobutylicum </italic>genome. Furthermore, an understanding of the transcriptional basis of the complex physiology of this organism will go a long way to improve our ability to metabolically engineer, for practical applications, its complex sporulation and metabolic programs. Such information generates tremendous new opportunities for further exploration of this complex anaerobe and its clostridial relatives, and constitutes a firm basis for future detailed genetic and functional studies.</p>", "<p>Using a limited in scope and resolution transcriptional study, we have previously shown that it is possible to use DNA-microarray-based transcriptional analysis to generate valuable functional information related to stress response [##REF##15028679##4##,##REF##15028684##5##], initiation of sporulation [##REF##15640230##6##] and the early sporulation program of <italic>C. acetobutylicum </italic>[##REF##16199581##7##]. In order to be able to accurately study the transcriptional orchestration underlying the complete sporulation program of the cells, it was necessary to develop a more sensitive and accurate microarray platform, a better mRNA isolation protocol (in order to isolate RNA from the mid and late stationary phases), as well as to use a much higher frequency of observation and sampling. We also aimed to employ more sophisticated bioinformatic tools in order to globally interrogate any desirable cellular program and relate it to the characteristic phenotypic metabolism and sporulation of this organism. The results of this extensive study are presented here as a single, undivided story, which offers unprecedented insights and a tremendous wealth of information for further explorations. Furthermore, it serves as a paradigm of what can be effectively accomplished with the now highly accurate DNA-microarray analysis in generating a robust transcriptional roadmap and in illuminating the physiology of a lesser understood organism.</p>" ]
[ "<title>Materials and methods</title>", "<title>Fermentation analysis</title>", "<p>Two cultures of <italic>C. acetobutylicum </italic>ATCC 824 were grown in pH controlled (pH &gt;5) bioreactors (Bioflow II and 110, New Brunswick Scientific, Edison, NJ, USA) [##REF##16199581##7##]. Cell density, substrate and product concentrations were analyzed as described [##REF##12618456##56##].</p>", "<title>RNA isolation and cDNA labeling</title>", "<p>Samples were collected by centrifuging 3-10 ml of culture at 5,000×g for 10 minutes, 4°C and storing the cell pellets at -85°C. Prior to RNA isolation, cells were washed in 1 ml SET buffer (25% sucrose, 50 mM EDTA [pH 8.0], and 50 mM Tris-HCl [pH 8.0]) and centrifuged at 5,000×g for 10 minutes, 4°C. Pellets were processed similarly to [##REF##16199581##7##] but with the noted modifications. Cells were lysed by resuspending in 220 μl SET buffer with 20 mg/ml lysozyme (Sigma, St. Louis, MO, USA) and 4.55 U/ml proteinase K (Roche, Indianapolis, IN, USA) and incubated at room temperature for 6 minutes. Following incubation, 40 mg of acid-washed glass beads (≤106 μm; Sigma) were added to the solution, and the mixture was continuously vortexed for 4 minutes at room temperature. Immediately afterwards, 1 ml of ice cold TRIzol (Invitrogen, Carlsbad, CA, USA) was added; 500 μl of sample was diluted with an equal volume of ice cold TRIzol and purified. Following dilution, 200 μl of ice cold chloroform was added to each sample, mixed vigorously for 15 s, and incubated at room temperature for 3 minutes. Samples were then centrifuged at 12,000 rpm in a tabletop microcentrifuge for 15 minutes at 4°C. The upper phase was saved and diluted by adding 500 μl of 70% ethanol. Samples were then applied to the RNeasy Mini Kit (Qiagen, Valencia, CA, USA), following the manufacturer's instructions. To minimize genomic DNA contamination, samples were incubated with the RW1 buffer at room temperature for 4 minutes. The method disrupted all cell types equally, as evidenced by microscopy (data not shown). cDNA was generated and labeled as described [##REF##16199581##7##]. The reference RNA pool contained 25 μg of RNA from samples taken from the same culture at 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 44, 48, 54, 58, and 66 h.</p>", "<title>Microarray analysis</title>", "<p>Agilent technology 22k arrays, (GEO accession number GPL4412) as described in [##REF##17526797##63##], were hybridized, washed, and scanned per Agilent's recommendations. Spot quantification employed Agilent's eXtended Dynamic Range technique with gains of 100% and 10% (Agilent's Feature Extraction software (v. 9.1)). Normalization and slide averaging was carried out as described [##REF##16199581##7##,##REF##17526797##63##]. A minimum intensity of 50 intensity units was used as described [##REF##17526797##63##]. Microarray data have been deposited in the Gene Expression Omnibus database under accession number GSE6094. To gain a qualitative measure of the abundance of an mRNA transcript, the averaged normalized log mean intensity values were ranked on a scale of 1 (lowest intensity value) to 100 (highest intensity value). Genes were clustered using TIGR's MEV program [##REF##12613259##64##].</p>", "<title>Quantitative RT-PCR</title>", "<p>Q-RT-PCR was performed as described [##UREF##2##48##]. Specific primer sequences are included in Additional data file 9; CAC3571 was used as the housekeeping gene.</p>", "<title>Microscopy</title>", "<p>For light microscopy, samples were stored at -85°C after 15% glycerol was added to the sampled culture. Samples were then pelleted, washed twice with 1% w/v NaCl and fixed using 50 μl of 0.05% HCl/0.5% NaCl solution to a final count of 10<sup>6 </sup>cells/μl. Slides were imaged using a Leica widefield microscope with either phase contrast or Syto-9 and PI dyes (Invitrogen LIVE/DEAD <italic>Bac</italic>Light Kit) to distinguish cell morphology.</p>", "<p>For electron microscopy, samples were fixed by addition of 16% paraformaldehyde and 8% glutaraldehyde to the culture medium for a final concentration of 2% paraformaldehyde and 2% glutaraldehyde. For cultures grown on plates, colonies were scraped from the agar and suspended in 2% paraformaldehyde and 2% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). Cultures were fixed for 1 h at room temperature, pelleted and resuspended in buffer.</p>", "<p>For transmission electron microscopy, bacteria were pelleted, embedded in 4% agar and cut into 1 mm × 1 mm cubes. The samples were washed three times for 15 minutes in 0.1 M sodium cacodylate buffer (pH 7.4), fixed in 1% osmium tetroxide in buffer for 2 h, and then washed extensively with buffer and double de-ionized water. Following dehydration in an ascending series of ethanol (25, 50, 75, 95, 100, 100%; 15 minutes each), the samples were infiltrated with Embed-812 resin in 100% ethanol (1:3, 1:2, 1:1, 2:1, 3:1; 1 h each) and then several changes in 100% resin. After an overnight infiltration in 100% resin, the samples were embedded in BEEM capsules and polymerized at 65°C for 48 h. Blocks were sectioned on a Reichert-Jung UltracutE ultramicrotome and ultrathin sections were collected onto formvar-carbon coated copper grids. Sections were stained with methanolic uranyl acetate and Reynolds' lead citrate [##REF##13986422##65##] and viewed on a Zeiss CEM 902 transmission electron microscope at 80 kV. Images were recorded with an Olympus Soft Imaging System GmbH Megaview II digital camera. Brightness levels were adjusted in the images so that the background between images appeared similar.</p>", "<p>For scanning electron microscopy, fixed samples were incubated on poly-L-lysine coated silica wafers for 1 h and then rinsed three times for 15 minutes in 0.1 M sodium cacodylate buffer (pH 7.4). The samples were fixed with 1% osmium tetroxide in buffer for 2 h, washed in buffer and double de-ionized water, and then dehydrated in ethanol (25, 50, 75, 95, 100, 100%; 15 minutes each). The wafers were critical point dried in an Autosamdri 815B critical point drier and mounted onto aluminum stubs with silver paint. The samples were coated with Au/Pd with a Denton Bench Top Turbo III sputter-coater and viewed with a Hitachi 4700 FESEM at 3.0 kV.</p>", "<title>Phylogenetic tree generation</title>", "<p>Based on the genome annotations available at NCBI, we considered any sigma factor that was annotated as σ<sup>70 </sup>or unannotated. A second filter was applied by requiring that all the sequences should contain a Region 2, the most conserved region of the σ<sup>70 </sup>protein. All members of this class of sigma factor contain Region 2, and it was modeled with the HMM pfam04542. This criterion removed CAC0550, CAC1766 and CAP0157, but they were added to the list again despite their lack of a Region 2. The alignment was made using ClustalW 1.83 using the default settings and visualized as a radial tree as created by Phylodraw v. 0.8 from Pusan National University.</p>", "<title>Generation and characterization of antisense strains</title>", "<p>Oligonucleotides were designed to produce asRNA complementary to the upstream 20 bp and first 30-40 bp of the targeted genes' transcripts (Additional data file 7). The constructs were cloned into pSOS95del under the control of a thiolase (<italic>thl</italic>) promoter and confirmed by restriction digest. Plasmids were then methylated and transformed into <italic>C. acetobutylicum </italic>ATCC 824, as previously described [##REF##15812030##33##,##REF##10049845##55##,##REF##12618456##56##]. Strains were grown in 10 ml cultures and characterized using microscopy and HPLC to analyze final product concentrations [##REF##12618456##56##].</p>" ]
[ "<title>Results and discussion</title>", "<title>Metabolism and differentiation of <italic>C. acetobutylicum</italic>: identification of a new cell type?</title>", "<p>We aimed to relate the metabolic and morphological characteristics of the cells in a typical batch culture, whereby cells underwent a full differentiation program, to the transcriptional profile of the cell population [##REF##16346038##8##]. The metabolism of solventogenic clostridia is characterized by an initial acidogenic phase followed by acid re-assimilation and solvent production [##REF##16199581##7##]. As shown in Figure ##FIG##0##1a##, the peak of butyrate concentration, around 16 hours after the start of the culture, coincided with the initiation of butanol production. Around this time, the culture transitioned from exponential growth to stationary phase and initiated solventogenesis and sporulation. This period is called the transitional phase and is indicated by the gray bar in Figure ##FIG##0##1a## and all following figures. The butanol concentration increased to over 150 mM until hour 45, after which no substantial change in solvent or acid concentration took place. Nevertheless, cells continued to display morphological changes well past hour 60. Solventogenic clostridia display a series of morphological forms over this differentiation program: vegetative, clostridial, forespore, endospore, and free-spore forms [##REF##16346276##9##]. In addition to phase-contrast microscopy, we found that by using Syto-9 (a green dye assumed to stain live cells) and propidium iodide (PI; a red dye assumed to stain dead cells) [##REF##11872123##10##] we could microscopically distinguish these morphologies and identify new cell subtypes. Staining by these two dyes did not follow typical expectations. During exponential growth, vegetative cells, characterized by a thin-rod morphology, were visibly motile under the microscope, which is consistent with the finding that chemotaxis and motility genes were highly expressed during this time [##REF##16199581##7##]. When double stained with Syto-9 and PI dyes, these vegetative cells took on a predominantly red color, indicating the uptake of more PI than Syto-9 (Figure ##FIG##0##1b, I, II##). At the onset of butanol production, swollen, cigar-shaped clostridial-form cells began to appear (Figure ##FIG##0##1b, III##). These clostridial forms (confirmed by phase-contrast microscopy; data not shown), generally assumed to be the cells that produce solvents [##REF##16346038##8##], were far less motile than exponential-phase cells and stained almost equally with both dyes, taking on an orange color. Clostridial forms persisted until solvent production decreased, after which forespore forms (cells with one end swollen, which is indicative of a spore forming) and endospore forms (cells with the middle swollen, which is indicative of a developing spore) became visible [##REF##16346276##9##]. These cells stained almost exclusively green, indicating an uptake of more Syto-9 than PI (Figure ##FIG##0##1b, IV-VI##). The sporulation process is completed when the mother cell undergoes autolysis to release the mature spore. Mature free spores could be seen as early as hour 44 (Figure ##FIG##0##1b, V##). Later, around hour 58 (Figure ##FIG##0##1b, VI##), a portion of the cells became motile again. Though these cells appear like vegetative cells, they stained predominantly green, instead of red, and did not produce appreciable amounts of acid. We hypothesize that this staining change reflects modifications in membrane composition due to different environmental conditions (presence of solvents and other metabolites) rather than cell viability and assume that this newly identified cell type has different transcriptional characteristics, which we tested next.</p>", "<title>The transcriptional program of clostridial differentiation</title>", "<p>To ensure that important transcriptional, physiological, and morphological changes were captured [##REF##16199581##7##,##REF##16346038##8##], RNA samples were taken every hour during exponential phase and every two hours after that until late stationary phase when sampling frequency decreased. mRNA from 25 timepoints (Figure ##FIG##0##1a##) were selected for transcriptional analysis by hybridizing pairs of 22k oligonucleotide microarrays on a dye swap configuration using an mRNA pool as reference. There were 814 genes, or 21% of the genome, that surpassed the threshold of expression in at least 20 of the 25 microarray timepoints and had two or more timepoints differentially expressed at a 95% confidence level [##REF##12529501##11##]; these genes were classified as having a temporal differential expression profile. We chose these strict selection criteria in order to robustly identify the key expression patterns of the differentiation process. We relaxed these criteria in subsequent gene ontology-driven analyses. Expression data were extensively validated by, first, quantitative reverse transcription PCR (Q-RT-PCR) analysis (focusing on key sporulation factors) from a biological replicate culture (Figure ##FIG##1##2##), and, second, by systematic comparison to our published (but limited in scope and duration) microarray study (see Additional data file 1 for Figure S1 and discussion).</p>", "<p>Six distinct clusters of temporal expression patterns were selected (Figure ##FIG##0##1c,d##) by K-means to achieve a balance between inter- and intra-cluster variability. To examine transcriptional changes in larger functional groups (for example, transcription, motility, translation), each cluster was analyzed according to the Cluster of Orthologous Groups of proteins (COG) classification [##REF##10592175##12##] and the functional genome annotation [##REF##11466286##13##]. To determine if a COG functional group was overrepresented in any of the K-means clusters, first the percentage of each group in the genome was determined, and then the percentage of each group was determined in each of the K-means clusters. By comparing the percentage in the K-means clusters to the genome percentage, we could identify overrepresented groups (Additional data file 2).</p>", "<title>Exponential phase: motility, chemotaxis, nucleotide and primary metabolism</title>", "<p>The first cluster contains 134 genes highly expressed during exponential growth (hours 6 to 10; see Additional data file 2 for a list of the genes). This cluster characterizes highly motile vegetative cells (Figure ##FIG##0##1b, I##) and, given the minimal amount of knowledge on the genes responsible for motility and chemotaxis in clostridia, our analysis offers the possibility of identifying these genes at the genome scale [##UREF##0##14##]. This cluster includes the flagella structural components flagellin and <italic>flbD</italic>, the main chemotaxis response regulator, <italic>cheY </italic>(CAC0122; responsible for flagellar rotation in <italic>B. subtilis</italic> \n[##REF##8068685##15##]), as well as several methyl-accepting chemotaxis receptor genes (CAC0432, CAC0443, CAC0542, CAC1600, CAP0048). COG analysis showed that genes related to cell motility (COG class N) and nucleotide transport and metabolism (COG class F) were overrepresented in this cluster (Additional data file 2). In order to investigate cell motility further, all genes that fell within this COG class were hierarchically clustered according to their expression profiles (see Additional data file 3 for Figure S2 and discussion). Interestingly, the two main cell motility gene clusters, the first including most of the flagellar assembly and motor proteins and the second containing most of the known chemotaxis proteins, clustered together and displayed a bimodal expression pattern (Figure S2). The genes were not only expressed during exponential phase but also during late stationary phase, around hour 38, which is consistent with the observation that a motile cell population was again observed in late stationary phase. Included in the category of nucleotide transport and metabolism are several purine and pyrimidine biosynthesis genes: a set of five consecutive genes, <italic>purECFMN</italic>, the bi-functional <italic>purQ/L </italic>gene, <italic>purA</italic>, <italic>pyrPR</italic>, <italic>pyrD</italic>, and <italic>pyrI</italic>. Two other purine synthesis genes (<italic>purH</italic>, <italic>purD</italic>) showed very similar profiles but were not classified within this cluster by the clustering algorithm. Vegetative cells, which correspond to this cluster, produce ATP through acidogenesis, whereby the cells uptake glucose and convert it to acetic and butyric acid. Because glucose is the main energy source, multiple genes for glucose transport were included within this cluster, including the glucose-specific phosphotransferase gene, <italic>ptsG</italic>, the glucose kinase <italic>glcK </italic>and CAP0131, the gene most similar to <italic>B. subtilis </italic>glucose permease <italic>glcP</italic>. The genes required for the metabolism of glucose to pyruvate did not show temporal regulation, suggesting that expression of these genes is constitutive-like (see Additional data file 3 for Figure S3 and discussion). Acetic acid production genes <italic>pta </italic>and <italic>ack </italic>were not temporally expressed, but butyrate production genes <italic>ptb </italic>and <italic>buk </italic>were. Though expressed throughout exponential phase, the expression of both <italic>ptb </italic>and <italic>buk </italic>slightly peaked during late exponential phase, as previously seen [##REF##16199581##7##], and thus fall in the transitional (second) cluster. Analysis of the expression patterns of all the genes involved in acidogenesis, not just the differentially expressed genes discussed here, is included in Figure S3 in Additional data file 3. Finally, the expression patterns of the two classes of hydrogenases (iron only and nickel-iron) were investigated (Figure S3 in Additional data file 3). <italic>hydA</italic>, the iron only hydrogenase that catalyzes the production of molecular hydrogen, was expressed only during exponential phase, whereas the iron-nickel hydrogenase, <italic>mbhS </italic>and <italic>mbhL</italic>, was expressed throughout stationary phase.</p>", "<title>Initiation of sporulation: abrB, sinR, lipid and iron metabolism</title>", "<p>The transitional phase is captured by 139 genes in the second cluster (Figure ##FIG##0##1c,d##; Additional data file 2). It is made up of genes that show elevated expression between hours 10 and 18 and is when solvent formation was initiated. This cluster characterizes the shift from vegetative cells to cells committing to sporulation and thus includes two important regulators of sporulation, <italic>abrB </italic>(CAC0310) and <italic>sinR </italic>(CAC0549), which are discussed in more detail below. Also characteristic of this shift from vegetative growth to sporulation was the overrepresentation of genes related to energy production and conversion (COG class C), since sporulation is an energy intensive process. Solvent production began in the transitional phase, though the genes responsible for solvent production fall in the next (third) cluster; the third cluster partially overlaps with this second cluster but is distinguished by a sustained expression pattern. In response to these solvents, <italic>C. acetobutylicum </italic>undergoes a change in its membrane composition and fluidity, generally decreasing the ratio between unsaturated to saturated fatty acids [##REF##16347502##16##, ####UREF##1##17##, ##REF##6696415##18####6696415##18##]. Consistent with this change, genes related to lipid metabolism (COG class I) were overrepresented in this cluster. To further investigate this COG class, all genes identified as COG class I were hierarchically clustered (see Additional data file 3 for Figure S4 and discussion). Seven genes that were upregulated just before the onset of sporulation fall within the same operon and are related to fatty acid synthesis. In contrast, many of the most characterized genes involved in fatty acid synthesis (<italic>accBC</italic>, <italic>fabDFZ</italic>, and <italic>acp</italic>) maintain a fairly flat profile throughout the timecourse (Figure S4 in Additional data file 3). Also within this cluster is the gene responsible for cyclopropane fatty acid synthesis (<italic>cfa</italic>), though classified in COG class M (cell envelope biogenesis) and not COG class I. Importantly, the ratio of cyclopropane fatty acids in the outer membrane has been shown to increase as cells enter stationary phase [##REF##6696415##18##,##REF##12732555##19##], but the overexpression of this gene alone was unable to produce a solvent tolerant strain [##REF##12732555##19##]. Though not overrepresented in this cluster, all the genes within COG class M were also hierarchically clustered (see Additional data file 3 for Figure S5 and discussion). The transitional cluster also included several genes related to iron transport and regulation like the <italic>fur </italic>family iron uptake regulator CAC2634, the iron permease CAC0788, <italic>feoA</italic>, <italic>feoB</italic>, <italic>fhuC</italic>, and two iron-regulated transporters (CAC3288, CAC3290), which is consistent with the earlier, more limited data [##REF##16199581##7##]. Significantly, iron-limitation has been found to promote solventogenesis [##REF##16534918##20##].</p>", "<title>Solventogenesis, clostridial form, stress proteins, and early sigma factors</title>", "<p>The third cluster (Figure ##FIG##0##1c,d##; Additional data file 2) of 175 upregulated genes represents the solventogenic/stationary phase as it contains all key solventogenic genes. This cluster characterizes the transcriptional pattern of clostridial cells, the unique developmental stage in clostridia and first recognizable cell type of the sporulation cascade, and exhibited a longer upregulation of gene expression than the previous two clusters. Indeed, its range overlapped the previous (second) and the next two (fourth and fifth) clusters. The clostridial form is generally recognized to be the form responsible for solvent production [##REF##16346038##8##,##REF##3540574##21##] and is distinguished morphologically as swollen cell forms with phase bright granulose within the cell [##REF##3540574##21##]. This cluster captures both of these characteristics with the inclusion of the solventogenic genes and several granulose formation genes. The solventogenic genes <italic>adhE1</italic>-<italic>ctfA</italic>-<italic>ctfB</italic>, <italic>adc</italic>, and <italic>bdhB </italic>were initially induced during transitional phase, the second cluster, but were expressed throughout stationary phase and were thus placed within this cluster. Two granulose formation genes, <italic>glgC </italic>(CAC2237) and CAC2240, and a granulose degradation gene, <italic>glgP </italic>(CAC1664), were included within this cluster. The other two granulose formation genes, <italic>glgD </italic>(CAC2238) and <italic>glgA </italic>(CAC2239), though not included in this cluster, displayed a similar expression profile to <italic>glgC </italic>and CAC2240. The concomitant requirement of NADH during butanol production drove the expression of three genes involved in NAD formation: <italic>nadABC</italic>. Expression of the stress-response gene <italic>hsp18</italic>, a heat-shock related chaperone, and the <italic>ctsR</italic>-<italic>yacH</italic>-<italic>yacI</italic>-<italic>clpC </italic>operon, containing the molecular chaperone <italic>clpC </italic>and the stress-gene repressor <italic>ctsR</italic>, also fell in this cluster and paralleled the expression of the solventogenic genes (see Additional data file 3 for Figure S6). Other important stress-response genes, <italic>groEL</italic>-<italic>groES </italic>(CAC2703-04) and <italic>hrcA</italic>-<italic>grpE</italic>-<italic>dnaK</italic>-<italic>dnaJ </italic>(CAC1280-83), mirrored this expression pattern, though were not differentially expressed according to the strict criteria employed for selecting the genes of Figure ##FIG##1##2c,d## (Figure S6 in Additional data file 3). Although genes encoded on the pSOL1 megaplasmid [##REF##9286999##22##] represent less than 5% of the genome, they constitute 15% of genes in this cluster. pSOL1 harbors all essential solvent-formation genes and, importantly, some unknown gene(s) essential for sporulation [##REF##9286999##22##]. Besides the genes listed in this cluster, the vast majority of the genes located on pSOL1 were expressed throughout stationary phase, with most being upregulated at the onset of solventogenesis (see Additional data file 3 for Figure S7). Several key sporulation-specific sigma factors (σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>) and the σ<sup>F</sup>-associated anti-sigma factors in the form of the tricistronic <italic>spoIIA </italic>operon (CAC2308-06) belong to this cluster along with one of the two paralogs of <italic>spoVS </italic>(CAC1750) and one of three <italic>spoVD </italic>paralogs (CAP0150). The second <italic>spoVS </italic>paralog (CAC1817) did not meet the threshold of expression in 12 of the 25 timepoints; the other two paralogs of <italic>spoVD </italic>(CAC0329, CAC2130) were above the expression cutoff but did not show significant temporal regulation. Of unknown significance was the expression of a large cluster of genes involved in the biosynthesis of the branched-chain amino acids valine, leucine and isoleucine (CAC3169-74) coinciding with the onset of solventogenesis, as shown before [##REF##16199581##7##,##REF##11824611##23##], as well as the upregulation of several glycosyltranferases (see Additional data file 3 for Figure S8). The upregulation of valine, leucine, and isoleucine synthesis genes could be indicative of a membrane fluidity adaptation [##REF##16199581##7##]. In <italic>B. subtilis</italic>, these branched-chain amino acids can be converted into branched-chain fatty acids and change the membrane fluidity [##REF##15466018##24##], and under cold shock stress, <italic>B. subtilis </italic>downregulates a number of genes related to valine, leucine, and isoleucine synthesis [##REF##12427936##25##]. Therefore, this upregulation may be another mechanism to change membrane fluidity, though the ratio of unbranched and branched fatty acids has not been reported in studies investigating membrane composition [##REF##16347502##16##, ####UREF##1##17##, ##REF##6696415##18####6696415##18##,##REF##6886674##26##].</p>", "<title>Stationary phase carbohydrate (beyond glucose) and amino acid metabolism</title>", "<p>The fourth cluster (Figure ##FIG##0##1c,d##; Additional data file 2) of 84 genes represents a sharp induction of expression between 18 and 24 hours (early stationary phase). This cluster falls within the stationary (third) cluster described above. This is a compact group, with 70% belonging to one of three COG categories: carbohydrate transport and metabolism, transport and metabolism of amino acids, and inorganic ion transport and metabolism. A number of different carbohydrate substrate pathways, from monosaccharides (fructose, galactose, mannose, and xylose) to disaccharides (lactose, maltose, and sucrose) to complex carbohydrates (cellulose, glycogen, starch, and xylan), were investigated, and many exhibited upregulation during stationary phase, though only a few are highly expressed (see Additional data file 3 for Figure S9). The significance of this upregulation of non-glucose pathways is unknown, because sufficient glucose remains in the media (approximately 200 mM or about 44% of the initial glucose level). Of particular interest was the upregulation of several genes related to starch and xylan degradation (Figure S9 in Additional data file 3). The two annotated α-amylases (CAP0098 and CAP0168) along with the less characterized glucosidases and glucoamylase were all upregulated throughout stationary phase and a number were highly expressed, like CAC2810 and CAP0098. Also upregulated were the predicted xylanases CAC2383, CAP0054, and CAC1037, with CAP0054 and CAC1037 being highly expressed during stationary phase. Mirroring this pattern were CAC1086, a xylose associated transcriptional regulator, and the highly expressed CAC2612, a xylulose kinase. The genes related to glycogen metabolism are believed to be involved in granulose formation, as discussed earlier. Several genes for arginine biosynthesis (<italic>argF</italic>, <italic>argGH</italic>, <italic>argDB</italic>, <italic>argCJ</italic>, <italic>carB</italic>) were induced during this time, probably as a result of its depletion in the culture medium.</p>", "<title>Genes underlying the activation of the sporulation machinery and the genes for tryptophan and histidine biosynthesis</title>", "<p>The fifth cluster (Figure ##FIG##0##1c,d##; Additional data file 2), representing the middle stationary phase, contains 120 genes mainly expressed between hours 24 and 36, and again falls within the stationary (third) cluster described above. Most of the genes in this cluster activate the sporulation-related sigma factors (σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>) or are putatively regulated by them. These include <italic>spoIIE</italic>, the phosphatase that dephosphorylates SpoIIAA and results in the activation of σ<sup>F</sup>, and the σ<sup>E</sup>-dependent operons <italic>spoVR </italic>(involved in cortex synthesis), <italic>spoIIIAA</italic>-<italic>AH </italic>(required for the activation of σ<sup>G</sup>), and <italic>spoIVA </italic>(involved in cortex formation and spore coat assembly). The σ<sup>G</sup>-dependent <italic>spoVT </italic>gene has two paralogs in <italic>C. acetobutylicum </italic>(CAC3214, CAC3649); the transcriptional pattern suggests that CAC3214, included in this cluster, is the real <italic>spoVT</italic>. Sporulation-related genes included in this cluster are three <italic>cotF </italic>genes, one <italic>cotJ </italic>gene, one <italic>cotS </italic>gene, the spore maturation protein B, a small acid soluble protein (CAC2365), and two spore lytic enzymes (CAC0686, CAC3244). Though several sporulation-related genes are included in the next (sixth) cluster as well, most, beyond those listed here, are upregulated in mid-stationary phase (see Additional data file 3 for Figure S10 and discussion). Seven genes of the putative operon (CAC3157-63) encoding genes for tryptophan synthesis from chorismate and ten genes for histidine synthesis (CAC0935-43, CAC3031) were also included here.</p>", "<title>Spore maturation and late-stationary phase vegetative cells</title>", "<p>The sixth cluster, representative of the late stationary phase, includes 162 genes mainly expressed after hour 36 (Figure ##FIG##0##1c,d##; Additional data file 2). This cluster captured the expression profiles of the forespore and endospore forms, free spores, and late-stage vegetative-like cells. The endospore form represents the last stage before mature spores are released, and therefore fewer sporulation-related genes are within this cluster than previous ones. The sporulation-related genes included in this cluster are two small acid-soluble proteins (CAC1522 and CAC2372), a spore germination protein (CAC3302), a spore coat biosynthesis protein (CAC2190) and a spore protease (CAC1275). Also within this cluster are the two phosphotransferase genes, CAC2958 (a galactitol-specific transporter) and CAC2965 (a lactose-specific transporter), another annotated <italic>cheY </italic>(CAC2218), various enzymes related to different sugar pathways (CAC2180, CAC2250, CAC2954), and two glycosyltransferases (CAC2172, CAC3049). Expression of these genes may be reflective of the late-stage vegetative-like cells observed during microscopy and demonstrate they have a different genetic profile compared to the early vegetative cells. Interestingly, this cluster is enriched in defense mechanism genes (COG class V) like a phospholipase (CAC3026) and multidrug transporters that may play a role in resistance to a variety of environmental toxins.</p>", "<title>General processes: cell division and ribosomal proteins</title>", "<p>Two additional gene classes (cell division and ribosomal proteins), though not overrepresented in any of the six clusters described above, were investigated because of their importance in cellular processes and interesting expression patterns. COG class D (cell division and chromosome partitioning), besides important genes for vegetative symmetric division, includes <italic>ftsAZ</italic>, important for both symmetric and asymmetric cell division, and <italic>soj </italic>(a regulator of <italic>spo0J</italic>) and <italic>spoIIIE</italic>, important for proper chromosomal partitioning between the mother cell and prespore. These genes, along with several uncharacterized genes, were upregulated at the beginning of sporulation (see Additional data file 3 for Figure S11). Almost all the ribosomal proteins were downregulated as the culture entered stationary phase, and interestingly, about half of those downregulated genes were again upregulated in mid-stationary phase and remained upregulated until late-stationary phase (see Additional data file 3 for Figure S12). This upregulation is likely related to the late-stage vegetative-like cells seen.</p>", "<title>Expression and activity patterns of sporulation-related sigma factors and related genes</title>", "<title>Expression of sporulation transcription factors</title>", "<p>Sporulation in bacilli is initiated by a multi-component phosphorelay [##REF##1846779##27##], which is absent in clostridia, but the master regulator of sporulation, Spo0A, is conserved [##REF##16261177##1##,##REF##11466286##13##]. Briefly, in <italic>B. subtilis</italic>, phosphorylated Spo0A promotes the expression of prespore-specific sigma factor σ<sup>F </sup>and mother cell-specific sigma factor σ<sup>E </sup>[##REF##8982457##28##]. σ<sup>F </sup>is followed by σ<sup>G</sup>, which is controlled by both σ<sup>F </sup>and σ<sup>E</sup>, and σ<sup>E </sup>is followed by σ<sup>K</sup>, which is controlled by σ<sup>E </sup>and SpoIIID [##REF##8982457##28##]. <italic>sigH </italic>expression, in bacilli, is induced before the onset of sporulation and aids <italic>spo0A </italic>transcription [##REF##8982457##28##]. Here, <italic>sigH </italic>expression underwent a modest two-fold induction, relative to the first timepoint, during the onset of sporulation but never increased beyond three-fold, in contrast to all other sporulation factors (Figure ##FIG##2##3a##). <italic>spo0A </italic>expression also peaked during the onset of sporulation at over 12-fold and maintained a minimum of 3-fold induction until hour 36 (Figure ##FIG##2##3a,b##). Once phosphorylated, in bacilli and likely in <italic>C. acetobutylicum </italic>[##REF##12057953##29##], Spo0A regulates the expression of the operons encoding <italic>sigF</italic>, <italic>sigE</italic>, and <italic>spoIIE </italic>[##REF##14651647##30##], the latter of which acts as an activator of σ<sup>F</sup>. <italic>sigF </italic>and <italic>sigE </italic>exhibited an initial 16- and 8-fold induction, respectively, at hour 12, the timing of peak <italic>spo0A </italic>expression, but a second higher level of induction, 46- and 66-fold, respectively, was reached later at hour 24 (Figure ##FIG##2##3c##) and confirmed with Q-RT-PCR (Figure ##FIG##1##2##). The plateau or decrease in expression of <italic>spo0A</italic>, <italic>sigF</italic>, and <italic>sigE </italic>coincided with the peak expression of two known repressors, <italic>abrB </italic>and <italic>sinR</italic>, of sporulation genes in <italic>B. subtilis </italic>(Figure ##FIG##2##3b##), the former repressing the expression of <italic>spo0A </italic>promoters and the latter directly binding to the promoter sequences of the <italic>spo0A</italic>, <italic>sigF</italic>, and <italic>sigE </italic>operons [##REF##9685500##31##,##REF##7642487##32##]. <italic>C. acetobutylicum </italic>contains three paralogs of <italic>abrB</italic>, among which CAC0310 exhibited the highest promoter activity and, when downregulated, causes delayed sporulation and decreased solvent formation [##REF##15812030##33##]. <italic>sinR </italic>(CAC0549) expression in <italic>C. acetobutylicum </italic>was previously reported [##REF##15812030##33##] to be weak, but our data show a significant amount of expression and suggest a similar role as that in <italic>B. subtilis</italic>. In <italic>B. subtilis</italic>, Spo0A either indirectly (<italic>sinR</italic>) or directly (<italic>abrB</italic>) represses the genes of these two repressors [##REF##7642487##32##,##REF##3145384##34##]. The expression patterns of both genes did decrease after peak Spo0A~P deduced activity (Figure ##FIG##3##4b##; see below), indicating a similar regulatory network may be involved in <italic>C. acetobutylicum</italic>. <italic>sigF</italic>, <italic>sigE </italic>and <italic>sigG </italic>have very similar expression patterns (Figure ##FIG##2##3c##). Both <italic>sigF </italic>and <italic>sigE </italic>are activated by Spo0A~P, so similar expression profiles were expected. In <italic>B. subtilis</italic>, a <italic>sigG </italic>transcript is also detected early, but this transcript is read-through from <italic>sigE</italic>, located immediately upstream of <italic>sigG</italic>, and is not translated [##REF##16166546##35##,##REF##1902213##36##]. Translation of <italic>sigG </italic>occurs when the gene is expressed as a single cistron from a σ<sup>F</sup>-dependent promoter located between <italic>sigE </italic>and <italic>sigG </italic>[##REF##16166546##35##,##REF##1902213##36##]. In <italic>C. acetobutylicum</italic>, <italic>sigE </italic>and <italic>sigG </italic>are also located adjacent to each other, but a σ<sup>F </sup>promoter was not predicted between the two genes [##REF##15060177##37##]. Thus, it was predicted that <italic>sigG </italic>is only expressed as part of the <italic>sigE </italic>operon (consisting of <italic>spoIIGA</italic>, the processing enzyme for σ<sup>E</sup>, and <italic>sigE</italic>). Our transcriptional data seem to support this prediction because all three genes, <italic>spoIIGA</italic>, <italic>sigE</italic>, and <italic>sigG</italic>, have very similar transcriptional patterns (Figure ##FIG##2##3f##), suggesting they are expressed as a single transcript, like the <italic>spoIIAA</italic>-<italic>spoIIAB</italic>-<italic>sigF </italic>operon (Figure ##FIG##2##3e##). However, from Northern blots probing against <italic>sigE</italic>-<italic>sigG</italic>, three separate transcripts were seen: one for <italic>spoIIGA</italic>-<italic>sigE</italic>-<italic>sigG</italic>, one for <italic>spoIIGA</italic>-<italic>sigE</italic>, and one for <italic>sigG </italic>[##REF##12057953##29##]. Unfortunately, the current data cannot resolve this issue definitively, since the microarrays only detect if a transcript is present or not.</p>", "<title>Deduced activity profiles of sporulation factors</title>", "<p>We also desired to estimate the activity profiles for the key sporulation factors (σ<sup>H</sup>, Spo0A, σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G</sup>; Figure ##FIG##3##4##). We did so by averaging the expression profiles of known or robustly identifiable canonical genes of their regulons [##REF##16261177##1##]. To adjust for differences in relative expression levels, expression profiles were standardized before averaging [##REF##16199581##7##]. This is a surrogate reporter assay, which we believe is as accurate as most reporter assays. For a detailed discussion of the genes used to construct the plots, see Additional data file 4. For all of the plots (Figure ##FIG##3##4##), peak activity took place after peak expression, as expected. Of all the factors, σ<sup>H </sup>activity peaked first, during early transitional phase, and this was followed by a decrease in activity until stationary phase, when activity increased again (Figure ##FIG##3##4a,f##). Spo0A~P activity was the next to peak, during late transitional phase, and stayed fairly constant throughout the rest of the timecourse (Figure ##FIG##3##4b,f##). σ<sup>F </sup>activity had an initial induction during transitional phase, but then stayed constant until 24 hours (Figure ##FIG##3##4c,f##). After 24 hours, the activity increased again and stayed fairly constant at this higher activity level for the rest of the culture. σ<sup>E </sup>activity increased slightly during late transitional phase, but its major increase occurred after 24 hours during mid-stationary phase (Figure ##FIG##3##4d,f##). Like the previous sigma factors, σ<sup>G </sup>activity increased throughout early stationary phase and early mid-stationary phase, but the major increase occurred after hour 30 (Figure ##FIG##3##4e,f##). The activity of all of the factors, except for Spo0A and σ<sup>F</sup>, decreased during late stationary phase at hour 38. σ<sup>G </sup>activity began to increase slightly again at hour 48 but did not peak again. Considering only major peaks in activity, the <italic>Bacillus </italic>model of sporulation is generally true with the peaks progressing from σ<sup>H </sup>to Spo0A~P to σ<sup>F </sup>to σ<sup>E </sup>and finally to σ<sup>G </sup>(Figure ##FIG##3##4f##).</p>", "<title>Can we deduce the activation and processing of σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G </sup>from transcriptional data?</title>", "<p>In <italic>B. subtilis</italic>, the sigma factors downstream of Spo0A (σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G</sup>) are all regulated by a complex network of interactions [##REF##16261177##1##]. We desired to examine if our transcriptional data could be used to do a first test to determine whether the mechanisms employed in the <italic>B. subtilis </italic>model are valid for <italic>C. acetobutylicum</italic>. In <italic>B. subtilis</italic>, σ<sup>F </sup>is held inactive in the pre-divisional cell by the anti-σ<sup>F </sup>factor SpoIIAB. σ<sup>F </sup>is released when the anti-anti-σ<sup>F </sup>factor SpoIIAA is dephosphorylated by SpoIIE, resulting in SpoIIAA binding to SpoIIAB, which then releases σ<sup>F</sup>. In <italic>C. acetobutylicum</italic>, <italic>spoIIAB </italic>(CAC2307) and <italic>spoIIAA </italic>(CAC2308) are transcribed on the same operon as <italic>sigF </italic>(Figure ##FIG##2##3e##), but <italic>spoIIE </italic>(CAC3205) is transcribed separately. The initial increase in σ<sup>F </sup>activity during the transitional phase was not accompanied by an increase in <italic>spoIIE </italic>expression, but the peak in σ<sup>F </sup>activity did occur after <italic>spoIIE </italic>upregulation (Figure ##FIG##3##4c##). Despite the sustained level of σ<sup>F </sup>activity, <italic>sigF </italic>and <italic>spoIIE </italic>decreased in expression, though <italic>spoIIE </italic>expression did increase slightly again after 48 hours (Figure ##FIG##3##4c##). In <italic>B. subtilis</italic>, the pro-σ<sup>E </sup>translated from the <italic>sigE </italic>gene undergoes processing from SpoIIGA, which must interact with SpoIIR in order to accomplish the σ<sup>E </sup>activation. In <italic>C. acetobutylicum</italic>, SpoIIGA (CAC1694) is transcribed on the same operon as <italic>sigE </italic>(Figure ##FIG##2##3f##), and SpoIIR is coded by CAC2898. σ<sup>E </sup>activity increased with the induction of <italic>spoIIR </italic>(Figure ##FIG##3##4d##), suggesting a similar mechanism as in <italic>B. subtilis</italic>. Finally, σ<sup>G </sup>activation in <italic>B. subtilis</italic> is dependent upon the eight genes within the <italic>spoIIIA </italic>operon. Here, the second and larger increase in σ<sup>G </sup>activity followed peak expression of the <italic>spoIIIA </italic>operon, but the early increase in σ<sup>G </sup>activity was not characterized by a large induction of <italic>spoIIIA </italic>expression (Figure ##FIG##3##4e##). We tentatively conclude that the <italic>B. subtilis </italic>processing and activation model does generally hold true in <italic>C. acetobutylicum</italic>, but further investigation is needed to determine the exact timing and interaction of the various factors and their activators.</p>", "<title>Is there a functional <italic>sigK</italic>?</title>", "<p>In <italic>B. subtilis</italic>, σ<sup>K </sup>is formed by splicing together two genes (<italic>spoIVCB </italic>and <italic>spoIIIC</italic>), both under the control of σ<sup>E </sup>and SpoIIID [##REF##2492118##38##], separated by a <italic>skin </italic>element [##REF##2536191##39##]. In contrast, a single gene encoding σ<sup>K </sup>has been annotated in <italic>C. acetobutylicum </italic>[##REF##11466286##13##]. The gene was initially identified using a PCR-approach [##REF##7961408##40##] and was later detected by primer extension in a phosphate-limited, continuous culture of <italic>C. acetobutylicum </italic>DSM 1731 [##REF##9561744##41##]. <italic>spoIIID</italic>, which controls <italic>sigK </italic>expression with σ<sup>E </sup>in <italic>B. subtilis</italic>, reached peak expression at hour 30, which is consistent with it being under σ<sup>E </sup>control (Figure ##FIG##2##3d##) [##REF##1744038##42##]. However, at no timepoint in this study did <italic>sigK </italic>exceed the cutoff expression criterion. Q-RT-PCR also showed a significantly lower <italic>sigK </italic>induction compared to the other sigma factors and suggests the transcript, if expressed, is at much lower levels than any other gene analyzed (Figure ##FIG##1##2##). The putative main σ<sup>K </sup>processing enzyme, SpoIVFB (CAC1253), also did not exceed the cutoff criterion. To help determine if there is an active σ<sup>K</sup>, we investigated two genes controlled by σ<sup>K </sup>in <italic>B. subtilis</italic>. <italic>yabG </italic>(CAC2905), which encodes a protein involved in spore coat assembly, was upregulated mid-stationary phase and peaked at hour 30 (Figure ##FIG##2##3d##), and <italic>spsF </italic>(CAC2190), involved in spore coat synthesis, was not upregulated until late stationary phase, at hour 38 (Figure ##FIG##2##3d##). From these two genes, it is difficult to determine whether a functional <italic>sigK </italic>gene exists or not. Clearly they are both transcribed, but based on its expression pattern, <italic>yabG </italic>could fall under the control of σ<sup>E </sup>instead of σ<sup>K</sup>. <italic>spsF </italic>upregulation is late enough to possibly indicate σ<sup>K </sup>regulation though. Ideally, more genes need to be investigated to draw firmer conclusions, but because few σ<sup>K </sup>regulon homologs exist in <italic>C. acetobutylicum</italic>, we cannot currently determine if there is σ<sup>K </sup>activity or not.</p>", "<title>Distinct profiles of sensory histidine kinases: which for Spo0A?</title>", "<title>Revisiting the orphan kinases</title>", "<p>As discussed, phosphorylated Spo0A is responsible for initiating sporulation in both bacilli and clostridia along with solvent formation in <italic>C. acetobutylicum</italic>. In bacilli, Spo0A is phosphorylated via a multi-component phosphorelay [##REF##15556029##43##], initiated by five orphan histidine kinases, KinA-E (kinases that lack an adjacent response regulator); this phosphorelay system is absent in all sequenced clostridia [##REF##16261177##1##]. Alternatively, Spo0A in clostridia may be directly phosphorylated by a histidine kinase, orphan or not, as was hypothesized in [##REF##16261177##1##,##REF##16199581##7##]. This alternative was demonstrated in <italic>C. botulinum</italic>, where the orphan kinase CBO1120 was able to phosphorylate Spo0A [##REF##16420367##44##]. In <italic>C. acetobutylicum</italic>, five true orphan kinases have been identified with a sixth orphan, CAC2220, identified as CheA, which has a known response regulator [##REF##16261177##1##].</p>", "<p>A kinase that could directly phosphorylate Spo0A is expected to have a peak in expression before or during the activation of Spo0A, as the orphan kinases in <italic>B. subtilis </italic>do [##REF##11069677##45##, ####REF##8576055##46##, ##REF##8002614##47####8002614##47##]. As a measure of Spo0A activity, the expression of the <italic>sol </italic>operon (CAP0162-64) was used, as before [##REF##16199581##7##], because it is induced by Spo0A~P. The initial induction of the <italic>sol </italic>operon, almost 100-fold, occured at hour 10 (before <italic>spo0A </italic>reached it maximum expression), with detectable levels of butanol appearing before the second induction of the <italic>sol </italic>operon. This second induction, of another 10-fold, followed the peak in <italic>spo0A </italic>expression (Figure ##FIG##4##5a##). It is clear that some level of phosphorylated Spo0A exists at 10 hours; therefore, kinase candidates must display an increase in expression before 10 hours. Of the five orphan kinases (Figure ##FIG##4##5b,c##), CAC2730 displayed the earliest peak followed by CAC0437, CAC0903, and CAC3319. CAC0323 never displayed a prominent peak in expression either before or after <italic>sol </italic>operon induction (Figure ##FIG##4##5b##) and likely does not play a role in phosphorylating Spo0A. Of the remaining four, CAC0437 and CAC2730 peaked only once before the initial <italic>sol </italic>operon induction, while CAC0903 peaked before each induction of the <italic>sol </italic>operon (Figure ##FIG##4##5b,c##). CAC3319 expression slightly mirrored that of the <italic>sol </italic>operon, with an increase before initial induction followed by a plateau, and an increase in expression again until it peaked just after the <italic>sol </italic>operon peaked (Figure ##FIG##4##5c##). The proteins encoded by CAC0437 and CA0903 displayed the most similarity to the protein encoded by CBO1120, the orphan kinase in <italic>C. botulinum </italic>shown to phosphorylate Spo0A [##REF##16420367##44##].</p>", "<title>Non-orphan kinase expression</title>", "<p>Though primarily interested in orphan kinases because of the similarity to the <italic>B. subtilis </italic>model, a two-component response system could also be responsible for the phosphorylation of Spo0A. The remaining 30 annotated histidine kinases were also investigated to determine if any displayed a peak in expression before the initial induction of the <italic>sol </italic>operon (Additional data file 5). Six kinases (Figure ##FIG##4##5d,e##) were found to have a peak in expression at 8 hours. CAC0290 and CAC3430 subsequently decreased in expression while CAC0225 and CAC0863 maintained expression at initial levels. Despite a dip in expression at hour 9, CAC1582 maintained an increased expression level from 8 hours on. CAC2434 peaked at hour 8, dropped back to initial levels, but then steadily increased with the second induction of the <italic>sol </italic>operon.</p>", "<title>Sigma factors of unknown function: a first assessment of their functional roles</title>", "<p>Seventeen sigma factors are annotated on the <italic>C. acetobutylicum </italic>genome, including two on pSOL1. Two, <italic>sigK </italic>(CAC1689) and CAC1770 (a <italic>sigK</italic>-like sigma factor), are expressed at very low levels and two others, CAC1509 (annotated 'specialized sigma subunit of RNA polymerase') and CAC1226 (one of two annotated <italic>sigA</italic>s), are only above the expression cutoff in 8 out of 25 timepoints, and these timepoints are not consecutively expressed. Among the expressed sigma factors, six, CAP0157, CAP0167, CAC3267, CAC1766, CAC2052, and CAC0550, are of unknown function, while the remaining seven expressed sigma factors (σ<sup>H</sup>, σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>, σ<sup>A</sup>, σ<sup>D</sup>, and σ<sup>54</sup>/rpoN) are of predicted known function. To assess the potential role of the remaining six sigma factors of unknown function, we examined the transcriptional profiles (Figure ##FIG##5##6a,b##) and probed the binding motifs in their promoter regions for predicted Spo0A, σ<sup>A</sup>, σ<sup>E</sup>, and σ<sup>F</sup>/σ<sup>G </sup>binding motifs [##REF##15060177##37##].</p>", "<title>Transcriptional analysis of the sigma factors of unknown function</title>", "<p>Loss of pSOL1 impairs sporulation at the level of <italic>spo0A </italic>expression [##REF##16199581##7##,##UREF##2##48##], thus generating increased interest for sigma factors located on the pSOL1 plasmid as these may play a role in the regulation of sporulation. Two sigma factors, CAP0157 and CAP0167, are located on pSOL1 and are annotated as 'special sigma factor (σ<sup>F</sup>/σ<sup>E</sup>/σ<sup>G </sup>family)' and 'specialized sigma factor (σ<sup>F</sup>/σ<sup>E </sup>family)', respectively. It was predicted that CAP0167 is putatively co-transcribed with CAP0166 from a promoter of the σ<sup>F</sup>/σ<sup>G </sup>family [##REF##15060177##37##] and it displayed an expression pattern similar to that of <italic>spo0A</italic>, consistent with the computational prediction of an 0A box [##REF##12057953##29##] and two reverse 0A boxes in its promoter region (Figure ##FIG##5##6a##). CAP0157 was expressed from an unidentified promoter late in the timecourse (40+ hours) and thus may be involved in late-stage sporulation, despite its low level of expression at hour 20 (Figure ##FIG##5##6a##). CAC3267, putatively the fourth gene in an operon starting with CAC3270 and ending with CAC3264 [##REF##15060177##37##], was mainly expressed during early exponential growth (Figure ##FIG##5##6a##), then decreased, and peaked again around 14 hours, after which expression decreased again. This pattern of expression suggests that it plays a role in vegetative growth and possibly early sporulation. CAC0550, putatively transcribed from a σ<sup>A </sup>promoter as a single cistron [##REF##15060177##37##], was mainly transcribed early with its expression ending after 20-24 hours (Figure ##FIG##5##6b##), suggesting that it is not involved in sporulation. CAC1766, expressed from an unknown promoter, displayed a unique pattern with a progressive buildup starting around hours 8-12 and a distinct peak around hour 22 (Figure ##FIG##5##6b##). CAC2052 is annotated as 'DNA-dependent RNA polymerase σ-subunit' and was putatively expressed together with CAC2053, a hypothetical protein, from a σ<sup>A </sup>and/or a σ<sup>F</sup>/σ<sup>G </sup>promoter [##REF##15060177##37##]. Our data suggest that it is unlikely to be transcribed from a σ<sup>F</sup>/σ<sup>G </sup>promoter without any other effectors, as their transcription peaked at hour 16, when there was very little (if any) σ<sup>F </sup>or σ<sup>G </sup>activity (Figure ##FIG##5##6b##).</p>", "<title>Phylogenetic tree comparison</title>", "<p>To help determine a possible function for these sigma factors, a phylogenetic tree was constructed of σ<sup>70 </sup>sigma factors from ten species, including <italic>B. subtilis </italic>and all sequenced clostridial species. The resulting tree (Additional data file 6) contains eleven major branches, and of these, seven can be definitively classified based on known sigma factors within the branch. These categories are extracytoplasmic function (ECF), sporulation factors (<italic>sigF</italic>, <italic>sigE</italic>, and <italic>sigG</italic>), <italic>sigH</italic>, <italic>sigA </italic>(a basal sigma factor), <italic>sigD </italic>(regulates chemotaxis and motility), and <italic>sigB </italic>(a general response sigma factor). Two factors, CAC3267 and CAC1766, fell within ECF branches. CAC3267 fell within an ECF branch close to the <italic>B. subtilis</italic> σ<sup>V</sup>, a sigma factor of unknown function, and σ<sup>M</sup>, a sigma factor essential for growth and survival in high salt concentrations. CAC1766 fell within a different ECF branch close to <italic>B. subtilis</italic> σ<sup>Z</sup>, a sigma factor of unknown function, and CAC1509, a sigma factor expressed for less than eight consecutive timepoints. The remaining four factors fell within clusters with other clostridial sigma factors of unknown function, though several could have possible ECF function.</p>", "<title>Antisense RNA knock-down of four sigma factors: 'fat' clostridial forms and enhanced glucose metabolism</title>", "<p>Of the six expressed sigma factors of unknown function, CAP0157, CAP0167, CAC2052, and CAC1766 were chosen for further study because the timing and shape of their expression patterns suggested potential involvement in sporulation and/or solventogenesis. Since the two processes are coupled, phenotypic changes in differentiation may affect solvent production, as has been previously observed [##REF##15028679##4##,##REF##15640230##6##,##REF##12057953##29##,##REF##15812030##33##,##REF##15743939##49##]. Antisense RNA (asRNA) knock-down was chosen over knocking out the genes, because knockouts are still extremely difficult to produce in this and all other clostridia. Indeed, to date, only a handful of knockouts have been created [##REF##12057953##29##,##REF##8760920##50##, ####REF##15063491##51##, ##REF##16759397##52##, ##REF##10476029##53####10476029##53##], and these have only been achieved after screening thousands of transformants [##REF##15063491##51##, ####REF##16759397##52##, ##REF##10476029##53####10476029##53##]. Recently, a group II intron system has been developed for clostridia [##REF##17658189##54##], but this system was not yet available when these experiments were carried out. In contrast, asRNA is relatively quick, has been shown to reduce gene expression by up to 90% [##REF##15812030##33##,##REF##10049845##55##,##REF##12618456##56##] and has been used to knock-down a large number of genes with a high level of specificity [##REF##15812030##33##,##REF##15743939##49##,##REF##10049845##55##, ####REF##12618456##56##, ##REF##14756797##57##, ##REF##17259355##58##, ##REF##12775702##59####12775702##59##]. asRNA constructs (see Additional data file 7 for specific sequences used) were designed against CAP0157, CAP0167, CAC2052, and CAC1766 along with CAC2053 and CAP0166, the first genes in the operons predicted to contain CAC2052 and CAP0167, respectively [##REF##15060177##37##]. Cultures of these strains were examined and compared against the wild type (WT) and plasmid control strain 824(pSOS95del) for cell morphology differences and metabolic changes.</p>", "<p>Microscopy results from the asRNA-strain cultures revealed both novel morphologies and apparently altered differentiation (Figure ##FIG##5##6d##). Most notable were changes in strains asCAP0166, asCAP0167 and asCAC1766. Typical WT cultures display a predominately vegetative, symmetrically dividing population through 72 hours as evidenced by the thin, rod-shaped, phase dark cells (Figure ##FIG##5##6d, I##). By 72 hours, WT cultures exhibited only a small percentage of swollen, cigar-shaped clostridial forms and then a proportional population of free spores by 96 hours.</p>", "<p>pSOS95del cultures exhibited clostridial forms by 48 hours, suggesting an accelerated differentiation compared to WT, as has been seen before in our laboratory (Figure ##FIG##5##6d, II##). Moreover, a greater percentage of clostridial forms and free spores compared to WT were observed at 72 and 96 hours, respectively. asCAP0166 cultures generated a large percentage of clostridial forms and endospores/free spores by hours 48 and 72, respectively (Figure ##FIG##5##6d, III##). This differentiation is accelerated in comparison to pSOS95del. By hour 96, asCAP0166 cultures exhibited predominately vegetative cells apparently derived from germinated spores (data not shown). asCAP0167 cultures also exhibited accelerated differentiation and displayed a novel (to our knowledge) form of cellular morphology that was most profoundly observable at 72 hours (Figure ##FIG##5##6d, IV##). This novel morphology has qualities of an excessively swollen clostridial cigar-form (which makes them look much shorter than normal clostridial forms), with what appears to be endospore formation occurring, but without the associated phase bright characteristics seen in the 72 hour asCAP0166 cultures. The asCAP0166 culture displayed cells in this novel morphological state as well, but to a lesser extent, although it is possible that because of its faster sporulation, such cell forms appeared prior to 72 hours. The asCAC1766 cultures also exhibited altered differentiation; most importantly, at 72 hours the majority of the cells exhibited a very swollen clostridial-form morphology similar to that in the asCAP0167 cultures at 72 hours, but slightly more elongated (Figure ##FIG##5##6d, V##).</p>", "<p>To further characterize this novel cell form, transmission electron microscopy (TEM) and scanning electron microscopy images of cells were taken for strains asCAP0167 and asCAC1766. To determine morphological differences involved in differentiation, the TEM images were compared against cell images taken from the plasmid control strain (Figure ##FIG##6##7##). For both asRNA strains, the very swollen cell forms observed can be documented as approximately 2.5-4 μm long, and 1.1-1.3 μm in diameter, and should be compared to control or WT swollen clostridial forms, which are 3.5-6 μm long and 0.8-1 μm in diameter. Forespore and endospore forms of both asCAP0167 (Figure ##FIG##6##7c,d##) and asCAC1766 (Figure ##FIG##6##7e,f##) displayed a pinched end not seen in the plasmid control (Figure ##FIG##6##7b##). A slight pinching is seen in the clostridial forms of the plasmid control strain (Figure ##FIG##6##7a##), but this is probably indicative that an asymmetric division is about to occur. Rather, the pinched ends seen in the antisense strains occur after asymmetric division and while the spore is developing within the mother cell. These pinched ends are also noticeable in the scanning electron microscopy images (Figure ##FIG##7##8##). Though granulose is distinguishable in most of the TEM images (Figure ##FIG##6##7c,d,f##), it is not the characteristic electron translucent seen in typical clostridial, forespore, and endospore forms (Figure ##FIG##6##7a,b##). These differences were seen throughout the culture and additional TEM images of both the plasmid control and the antisense strains are included in Additional data file 8.</p>", "<p>Glucose, acetone, and butanol concentrations from two to four biological replicates for each strain were averaged together, and the results are shown in Table ##TAB##0##1##. We averaged data from cultures that displayed similar characteristics; most cultures did so despite the fact that each culture was inoculated from a different colony for each strain. Acetone and butanol levels were typical for WT and control cultures, with the WT producing 90 mM of acetone and 150 mM of butanol and the plasmid-control strain producing 80 mM of acetone and 160 mM of butanol [##REF##12902291##60##]. By 192 hours, all strains had either produced comparable amounts of butanol to the WT and the plasmid control strain or had somewhat outperformed these two strains. The most significant differences were that all asRNA strains consumed higher levels of glucose and also had a delayed metabolism in terms of product formation. These metabolic changes, although preliminary, are consistent with and support the large changes in the kinetics of sporulation observed by microscopy.</p>" ]
[ "<title>Results and discussion</title>", "<title>Metabolism and differentiation of <italic>C. acetobutylicum</italic>: identification of a new cell type?</title>", "<p>We aimed to relate the metabolic and morphological characteristics of the cells in a typical batch culture, whereby cells underwent a full differentiation program, to the transcriptional profile of the cell population [##REF##16346038##8##]. The metabolism of solventogenic clostridia is characterized by an initial acidogenic phase followed by acid re-assimilation and solvent production [##REF##16199581##7##]. As shown in Figure ##FIG##0##1a##, the peak of butyrate concentration, around 16 hours after the start of the culture, coincided with the initiation of butanol production. Around this time, the culture transitioned from exponential growth to stationary phase and initiated solventogenesis and sporulation. This period is called the transitional phase and is indicated by the gray bar in Figure ##FIG##0##1a## and all following figures. The butanol concentration increased to over 150 mM until hour 45, after which no substantial change in solvent or acid concentration took place. Nevertheless, cells continued to display morphological changes well past hour 60. Solventogenic clostridia display a series of morphological forms over this differentiation program: vegetative, clostridial, forespore, endospore, and free-spore forms [##REF##16346276##9##]. In addition to phase-contrast microscopy, we found that by using Syto-9 (a green dye assumed to stain live cells) and propidium iodide (PI; a red dye assumed to stain dead cells) [##REF##11872123##10##] we could microscopically distinguish these morphologies and identify new cell subtypes. Staining by these two dyes did not follow typical expectations. During exponential growth, vegetative cells, characterized by a thin-rod morphology, were visibly motile under the microscope, which is consistent with the finding that chemotaxis and motility genes were highly expressed during this time [##REF##16199581##7##]. When double stained with Syto-9 and PI dyes, these vegetative cells took on a predominantly red color, indicating the uptake of more PI than Syto-9 (Figure ##FIG##0##1b, I, II##). At the onset of butanol production, swollen, cigar-shaped clostridial-form cells began to appear (Figure ##FIG##0##1b, III##). These clostridial forms (confirmed by phase-contrast microscopy; data not shown), generally assumed to be the cells that produce solvents [##REF##16346038##8##], were far less motile than exponential-phase cells and stained almost equally with both dyes, taking on an orange color. Clostridial forms persisted until solvent production decreased, after which forespore forms (cells with one end swollen, which is indicative of a spore forming) and endospore forms (cells with the middle swollen, which is indicative of a developing spore) became visible [##REF##16346276##9##]. These cells stained almost exclusively green, indicating an uptake of more Syto-9 than PI (Figure ##FIG##0##1b, IV-VI##). The sporulation process is completed when the mother cell undergoes autolysis to release the mature spore. Mature free spores could be seen as early as hour 44 (Figure ##FIG##0##1b, V##). Later, around hour 58 (Figure ##FIG##0##1b, VI##), a portion of the cells became motile again. Though these cells appear like vegetative cells, they stained predominantly green, instead of red, and did not produce appreciable amounts of acid. We hypothesize that this staining change reflects modifications in membrane composition due to different environmental conditions (presence of solvents and other metabolites) rather than cell viability and assume that this newly identified cell type has different transcriptional characteristics, which we tested next.</p>", "<title>The transcriptional program of clostridial differentiation</title>", "<p>To ensure that important transcriptional, physiological, and morphological changes were captured [##REF##16199581##7##,##REF##16346038##8##], RNA samples were taken every hour during exponential phase and every two hours after that until late stationary phase when sampling frequency decreased. mRNA from 25 timepoints (Figure ##FIG##0##1a##) were selected for transcriptional analysis by hybridizing pairs of 22k oligonucleotide microarrays on a dye swap configuration using an mRNA pool as reference. There were 814 genes, or 21% of the genome, that surpassed the threshold of expression in at least 20 of the 25 microarray timepoints and had two or more timepoints differentially expressed at a 95% confidence level [##REF##12529501##11##]; these genes were classified as having a temporal differential expression profile. We chose these strict selection criteria in order to robustly identify the key expression patterns of the differentiation process. We relaxed these criteria in subsequent gene ontology-driven analyses. Expression data were extensively validated by, first, quantitative reverse transcription PCR (Q-RT-PCR) analysis (focusing on key sporulation factors) from a biological replicate culture (Figure ##FIG##1##2##), and, second, by systematic comparison to our published (but limited in scope and duration) microarray study (see Additional data file 1 for Figure S1 and discussion).</p>", "<p>Six distinct clusters of temporal expression patterns were selected (Figure ##FIG##0##1c,d##) by K-means to achieve a balance between inter- and intra-cluster variability. To examine transcriptional changes in larger functional groups (for example, transcription, motility, translation), each cluster was analyzed according to the Cluster of Orthologous Groups of proteins (COG) classification [##REF##10592175##12##] and the functional genome annotation [##REF##11466286##13##]. To determine if a COG functional group was overrepresented in any of the K-means clusters, first the percentage of each group in the genome was determined, and then the percentage of each group was determined in each of the K-means clusters. By comparing the percentage in the K-means clusters to the genome percentage, we could identify overrepresented groups (Additional data file 2).</p>", "<title>Exponential phase: motility, chemotaxis, nucleotide and primary metabolism</title>", "<p>The first cluster contains 134 genes highly expressed during exponential growth (hours 6 to 10; see Additional data file 2 for a list of the genes). This cluster characterizes highly motile vegetative cells (Figure ##FIG##0##1b, I##) and, given the minimal amount of knowledge on the genes responsible for motility and chemotaxis in clostridia, our analysis offers the possibility of identifying these genes at the genome scale [##UREF##0##14##]. This cluster includes the flagella structural components flagellin and <italic>flbD</italic>, the main chemotaxis response regulator, <italic>cheY </italic>(CAC0122; responsible for flagellar rotation in <italic>B. subtilis</italic> \n[##REF##8068685##15##]), as well as several methyl-accepting chemotaxis receptor genes (CAC0432, CAC0443, CAC0542, CAC1600, CAP0048). COG analysis showed that genes related to cell motility (COG class N) and nucleotide transport and metabolism (COG class F) were overrepresented in this cluster (Additional data file 2). In order to investigate cell motility further, all genes that fell within this COG class were hierarchically clustered according to their expression profiles (see Additional data file 3 for Figure S2 and discussion). Interestingly, the two main cell motility gene clusters, the first including most of the flagellar assembly and motor proteins and the second containing most of the known chemotaxis proteins, clustered together and displayed a bimodal expression pattern (Figure S2). The genes were not only expressed during exponential phase but also during late stationary phase, around hour 38, which is consistent with the observation that a motile cell population was again observed in late stationary phase. Included in the category of nucleotide transport and metabolism are several purine and pyrimidine biosynthesis genes: a set of five consecutive genes, <italic>purECFMN</italic>, the bi-functional <italic>purQ/L </italic>gene, <italic>purA</italic>, <italic>pyrPR</italic>, <italic>pyrD</italic>, and <italic>pyrI</italic>. Two other purine synthesis genes (<italic>purH</italic>, <italic>purD</italic>) showed very similar profiles but were not classified within this cluster by the clustering algorithm. Vegetative cells, which correspond to this cluster, produce ATP through acidogenesis, whereby the cells uptake glucose and convert it to acetic and butyric acid. Because glucose is the main energy source, multiple genes for glucose transport were included within this cluster, including the glucose-specific phosphotransferase gene, <italic>ptsG</italic>, the glucose kinase <italic>glcK </italic>and CAP0131, the gene most similar to <italic>B. subtilis </italic>glucose permease <italic>glcP</italic>. The genes required for the metabolism of glucose to pyruvate did not show temporal regulation, suggesting that expression of these genes is constitutive-like (see Additional data file 3 for Figure S3 and discussion). Acetic acid production genes <italic>pta </italic>and <italic>ack </italic>were not temporally expressed, but butyrate production genes <italic>ptb </italic>and <italic>buk </italic>were. Though expressed throughout exponential phase, the expression of both <italic>ptb </italic>and <italic>buk </italic>slightly peaked during late exponential phase, as previously seen [##REF##16199581##7##], and thus fall in the transitional (second) cluster. Analysis of the expression patterns of all the genes involved in acidogenesis, not just the differentially expressed genes discussed here, is included in Figure S3 in Additional data file 3. Finally, the expression patterns of the two classes of hydrogenases (iron only and nickel-iron) were investigated (Figure S3 in Additional data file 3). <italic>hydA</italic>, the iron only hydrogenase that catalyzes the production of molecular hydrogen, was expressed only during exponential phase, whereas the iron-nickel hydrogenase, <italic>mbhS </italic>and <italic>mbhL</italic>, was expressed throughout stationary phase.</p>", "<title>Initiation of sporulation: abrB, sinR, lipid and iron metabolism</title>", "<p>The transitional phase is captured by 139 genes in the second cluster (Figure ##FIG##0##1c,d##; Additional data file 2). It is made up of genes that show elevated expression between hours 10 and 18 and is when solvent formation was initiated. This cluster characterizes the shift from vegetative cells to cells committing to sporulation and thus includes two important regulators of sporulation, <italic>abrB </italic>(CAC0310) and <italic>sinR </italic>(CAC0549), which are discussed in more detail below. Also characteristic of this shift from vegetative growth to sporulation was the overrepresentation of genes related to energy production and conversion (COG class C), since sporulation is an energy intensive process. Solvent production began in the transitional phase, though the genes responsible for solvent production fall in the next (third) cluster; the third cluster partially overlaps with this second cluster but is distinguished by a sustained expression pattern. In response to these solvents, <italic>C. acetobutylicum </italic>undergoes a change in its membrane composition and fluidity, generally decreasing the ratio between unsaturated to saturated fatty acids [##REF##16347502##16##, ####UREF##1##17##, ##REF##6696415##18####6696415##18##]. Consistent with this change, genes related to lipid metabolism (COG class I) were overrepresented in this cluster. To further investigate this COG class, all genes identified as COG class I were hierarchically clustered (see Additional data file 3 for Figure S4 and discussion). Seven genes that were upregulated just before the onset of sporulation fall within the same operon and are related to fatty acid synthesis. In contrast, many of the most characterized genes involved in fatty acid synthesis (<italic>accBC</italic>, <italic>fabDFZ</italic>, and <italic>acp</italic>) maintain a fairly flat profile throughout the timecourse (Figure S4 in Additional data file 3). Also within this cluster is the gene responsible for cyclopropane fatty acid synthesis (<italic>cfa</italic>), though classified in COG class M (cell envelope biogenesis) and not COG class I. Importantly, the ratio of cyclopropane fatty acids in the outer membrane has been shown to increase as cells enter stationary phase [##REF##6696415##18##,##REF##12732555##19##], but the overexpression of this gene alone was unable to produce a solvent tolerant strain [##REF##12732555##19##]. Though not overrepresented in this cluster, all the genes within COG class M were also hierarchically clustered (see Additional data file 3 for Figure S5 and discussion). The transitional cluster also included several genes related to iron transport and regulation like the <italic>fur </italic>family iron uptake regulator CAC2634, the iron permease CAC0788, <italic>feoA</italic>, <italic>feoB</italic>, <italic>fhuC</italic>, and two iron-regulated transporters (CAC3288, CAC3290), which is consistent with the earlier, more limited data [##REF##16199581##7##]. Significantly, iron-limitation has been found to promote solventogenesis [##REF##16534918##20##].</p>", "<title>Solventogenesis, clostridial form, stress proteins, and early sigma factors</title>", "<p>The third cluster (Figure ##FIG##0##1c,d##; Additional data file 2) of 175 upregulated genes represents the solventogenic/stationary phase as it contains all key solventogenic genes. This cluster characterizes the transcriptional pattern of clostridial cells, the unique developmental stage in clostridia and first recognizable cell type of the sporulation cascade, and exhibited a longer upregulation of gene expression than the previous two clusters. Indeed, its range overlapped the previous (second) and the next two (fourth and fifth) clusters. The clostridial form is generally recognized to be the form responsible for solvent production [##REF##16346038##8##,##REF##3540574##21##] and is distinguished morphologically as swollen cell forms with phase bright granulose within the cell [##REF##3540574##21##]. This cluster captures both of these characteristics with the inclusion of the solventogenic genes and several granulose formation genes. The solventogenic genes <italic>adhE1</italic>-<italic>ctfA</italic>-<italic>ctfB</italic>, <italic>adc</italic>, and <italic>bdhB </italic>were initially induced during transitional phase, the second cluster, but were expressed throughout stationary phase and were thus placed within this cluster. Two granulose formation genes, <italic>glgC </italic>(CAC2237) and CAC2240, and a granulose degradation gene, <italic>glgP </italic>(CAC1664), were included within this cluster. The other two granulose formation genes, <italic>glgD </italic>(CAC2238) and <italic>glgA </italic>(CAC2239), though not included in this cluster, displayed a similar expression profile to <italic>glgC </italic>and CAC2240. The concomitant requirement of NADH during butanol production drove the expression of three genes involved in NAD formation: <italic>nadABC</italic>. Expression of the stress-response gene <italic>hsp18</italic>, a heat-shock related chaperone, and the <italic>ctsR</italic>-<italic>yacH</italic>-<italic>yacI</italic>-<italic>clpC </italic>operon, containing the molecular chaperone <italic>clpC </italic>and the stress-gene repressor <italic>ctsR</italic>, also fell in this cluster and paralleled the expression of the solventogenic genes (see Additional data file 3 for Figure S6). Other important stress-response genes, <italic>groEL</italic>-<italic>groES </italic>(CAC2703-04) and <italic>hrcA</italic>-<italic>grpE</italic>-<italic>dnaK</italic>-<italic>dnaJ </italic>(CAC1280-83), mirrored this expression pattern, though were not differentially expressed according to the strict criteria employed for selecting the genes of Figure ##FIG##1##2c,d## (Figure S6 in Additional data file 3). Although genes encoded on the pSOL1 megaplasmid [##REF##9286999##22##] represent less than 5% of the genome, they constitute 15% of genes in this cluster. pSOL1 harbors all essential solvent-formation genes and, importantly, some unknown gene(s) essential for sporulation [##REF##9286999##22##]. Besides the genes listed in this cluster, the vast majority of the genes located on pSOL1 were expressed throughout stationary phase, with most being upregulated at the onset of solventogenesis (see Additional data file 3 for Figure S7). Several key sporulation-specific sigma factors (σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>) and the σ<sup>F</sup>-associated anti-sigma factors in the form of the tricistronic <italic>spoIIA </italic>operon (CAC2308-06) belong to this cluster along with one of the two paralogs of <italic>spoVS </italic>(CAC1750) and one of three <italic>spoVD </italic>paralogs (CAP0150). The second <italic>spoVS </italic>paralog (CAC1817) did not meet the threshold of expression in 12 of the 25 timepoints; the other two paralogs of <italic>spoVD </italic>(CAC0329, CAC2130) were above the expression cutoff but did not show significant temporal regulation. Of unknown significance was the expression of a large cluster of genes involved in the biosynthesis of the branched-chain amino acids valine, leucine and isoleucine (CAC3169-74) coinciding with the onset of solventogenesis, as shown before [##REF##16199581##7##,##REF##11824611##23##], as well as the upregulation of several glycosyltranferases (see Additional data file 3 for Figure S8). The upregulation of valine, leucine, and isoleucine synthesis genes could be indicative of a membrane fluidity adaptation [##REF##16199581##7##]. In <italic>B. subtilis</italic>, these branched-chain amino acids can be converted into branched-chain fatty acids and change the membrane fluidity [##REF##15466018##24##], and under cold shock stress, <italic>B. subtilis </italic>downregulates a number of genes related to valine, leucine, and isoleucine synthesis [##REF##12427936##25##]. Therefore, this upregulation may be another mechanism to change membrane fluidity, though the ratio of unbranched and branched fatty acids has not been reported in studies investigating membrane composition [##REF##16347502##16##, ####UREF##1##17##, ##REF##6696415##18####6696415##18##,##REF##6886674##26##].</p>", "<title>Stationary phase carbohydrate (beyond glucose) and amino acid metabolism</title>", "<p>The fourth cluster (Figure ##FIG##0##1c,d##; Additional data file 2) of 84 genes represents a sharp induction of expression between 18 and 24 hours (early stationary phase). This cluster falls within the stationary (third) cluster described above. This is a compact group, with 70% belonging to one of three COG categories: carbohydrate transport and metabolism, transport and metabolism of amino acids, and inorganic ion transport and metabolism. A number of different carbohydrate substrate pathways, from monosaccharides (fructose, galactose, mannose, and xylose) to disaccharides (lactose, maltose, and sucrose) to complex carbohydrates (cellulose, glycogen, starch, and xylan), were investigated, and many exhibited upregulation during stationary phase, though only a few are highly expressed (see Additional data file 3 for Figure S9). The significance of this upregulation of non-glucose pathways is unknown, because sufficient glucose remains in the media (approximately 200 mM or about 44% of the initial glucose level). Of particular interest was the upregulation of several genes related to starch and xylan degradation (Figure S9 in Additional data file 3). The two annotated α-amylases (CAP0098 and CAP0168) along with the less characterized glucosidases and glucoamylase were all upregulated throughout stationary phase and a number were highly expressed, like CAC2810 and CAP0098. Also upregulated were the predicted xylanases CAC2383, CAP0054, and CAC1037, with CAP0054 and CAC1037 being highly expressed during stationary phase. Mirroring this pattern were CAC1086, a xylose associated transcriptional regulator, and the highly expressed CAC2612, a xylulose kinase. The genes related to glycogen metabolism are believed to be involved in granulose formation, as discussed earlier. Several genes for arginine biosynthesis (<italic>argF</italic>, <italic>argGH</italic>, <italic>argDB</italic>, <italic>argCJ</italic>, <italic>carB</italic>) were induced during this time, probably as a result of its depletion in the culture medium.</p>", "<title>Genes underlying the activation of the sporulation machinery and the genes for tryptophan and histidine biosynthesis</title>", "<p>The fifth cluster (Figure ##FIG##0##1c,d##; Additional data file 2), representing the middle stationary phase, contains 120 genes mainly expressed between hours 24 and 36, and again falls within the stationary (third) cluster described above. Most of the genes in this cluster activate the sporulation-related sigma factors (σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>) or are putatively regulated by them. These include <italic>spoIIE</italic>, the phosphatase that dephosphorylates SpoIIAA and results in the activation of σ<sup>F</sup>, and the σ<sup>E</sup>-dependent operons <italic>spoVR </italic>(involved in cortex synthesis), <italic>spoIIIAA</italic>-<italic>AH </italic>(required for the activation of σ<sup>G</sup>), and <italic>spoIVA </italic>(involved in cortex formation and spore coat assembly). The σ<sup>G</sup>-dependent <italic>spoVT </italic>gene has two paralogs in <italic>C. acetobutylicum </italic>(CAC3214, CAC3649); the transcriptional pattern suggests that CAC3214, included in this cluster, is the real <italic>spoVT</italic>. Sporulation-related genes included in this cluster are three <italic>cotF </italic>genes, one <italic>cotJ </italic>gene, one <italic>cotS </italic>gene, the spore maturation protein B, a small acid soluble protein (CAC2365), and two spore lytic enzymes (CAC0686, CAC3244). Though several sporulation-related genes are included in the next (sixth) cluster as well, most, beyond those listed here, are upregulated in mid-stationary phase (see Additional data file 3 for Figure S10 and discussion). Seven genes of the putative operon (CAC3157-63) encoding genes for tryptophan synthesis from chorismate and ten genes for histidine synthesis (CAC0935-43, CAC3031) were also included here.</p>", "<title>Spore maturation and late-stationary phase vegetative cells</title>", "<p>The sixth cluster, representative of the late stationary phase, includes 162 genes mainly expressed after hour 36 (Figure ##FIG##0##1c,d##; Additional data file 2). This cluster captured the expression profiles of the forespore and endospore forms, free spores, and late-stage vegetative-like cells. The endospore form represents the last stage before mature spores are released, and therefore fewer sporulation-related genes are within this cluster than previous ones. The sporulation-related genes included in this cluster are two small acid-soluble proteins (CAC1522 and CAC2372), a spore germination protein (CAC3302), a spore coat biosynthesis protein (CAC2190) and a spore protease (CAC1275). Also within this cluster are the two phosphotransferase genes, CAC2958 (a galactitol-specific transporter) and CAC2965 (a lactose-specific transporter), another annotated <italic>cheY </italic>(CAC2218), various enzymes related to different sugar pathways (CAC2180, CAC2250, CAC2954), and two glycosyltransferases (CAC2172, CAC3049). Expression of these genes may be reflective of the late-stage vegetative-like cells observed during microscopy and demonstrate they have a different genetic profile compared to the early vegetative cells. Interestingly, this cluster is enriched in defense mechanism genes (COG class V) like a phospholipase (CAC3026) and multidrug transporters that may play a role in resistance to a variety of environmental toxins.</p>", "<title>General processes: cell division and ribosomal proteins</title>", "<p>Two additional gene classes (cell division and ribosomal proteins), though not overrepresented in any of the six clusters described above, were investigated because of their importance in cellular processes and interesting expression patterns. COG class D (cell division and chromosome partitioning), besides important genes for vegetative symmetric division, includes <italic>ftsAZ</italic>, important for both symmetric and asymmetric cell division, and <italic>soj </italic>(a regulator of <italic>spo0J</italic>) and <italic>spoIIIE</italic>, important for proper chromosomal partitioning between the mother cell and prespore. These genes, along with several uncharacterized genes, were upregulated at the beginning of sporulation (see Additional data file 3 for Figure S11). Almost all the ribosomal proteins were downregulated as the culture entered stationary phase, and interestingly, about half of those downregulated genes were again upregulated in mid-stationary phase and remained upregulated until late-stationary phase (see Additional data file 3 for Figure S12). This upregulation is likely related to the late-stage vegetative-like cells seen.</p>", "<title>Expression and activity patterns of sporulation-related sigma factors and related genes</title>", "<title>Expression of sporulation transcription factors</title>", "<p>Sporulation in bacilli is initiated by a multi-component phosphorelay [##REF##1846779##27##], which is absent in clostridia, but the master regulator of sporulation, Spo0A, is conserved [##REF##16261177##1##,##REF##11466286##13##]. Briefly, in <italic>B. subtilis</italic>, phosphorylated Spo0A promotes the expression of prespore-specific sigma factor σ<sup>F </sup>and mother cell-specific sigma factor σ<sup>E </sup>[##REF##8982457##28##]. σ<sup>F </sup>is followed by σ<sup>G</sup>, which is controlled by both σ<sup>F </sup>and σ<sup>E</sup>, and σ<sup>E </sup>is followed by σ<sup>K</sup>, which is controlled by σ<sup>E </sup>and SpoIIID [##REF##8982457##28##]. <italic>sigH </italic>expression, in bacilli, is induced before the onset of sporulation and aids <italic>spo0A </italic>transcription [##REF##8982457##28##]. Here, <italic>sigH </italic>expression underwent a modest two-fold induction, relative to the first timepoint, during the onset of sporulation but never increased beyond three-fold, in contrast to all other sporulation factors (Figure ##FIG##2##3a##). <italic>spo0A </italic>expression also peaked during the onset of sporulation at over 12-fold and maintained a minimum of 3-fold induction until hour 36 (Figure ##FIG##2##3a,b##). Once phosphorylated, in bacilli and likely in <italic>C. acetobutylicum </italic>[##REF##12057953##29##], Spo0A regulates the expression of the operons encoding <italic>sigF</italic>, <italic>sigE</italic>, and <italic>spoIIE </italic>[##REF##14651647##30##], the latter of which acts as an activator of σ<sup>F</sup>. <italic>sigF </italic>and <italic>sigE </italic>exhibited an initial 16- and 8-fold induction, respectively, at hour 12, the timing of peak <italic>spo0A </italic>expression, but a second higher level of induction, 46- and 66-fold, respectively, was reached later at hour 24 (Figure ##FIG##2##3c##) and confirmed with Q-RT-PCR (Figure ##FIG##1##2##). The plateau or decrease in expression of <italic>spo0A</italic>, <italic>sigF</italic>, and <italic>sigE </italic>coincided with the peak expression of two known repressors, <italic>abrB </italic>and <italic>sinR</italic>, of sporulation genes in <italic>B. subtilis </italic>(Figure ##FIG##2##3b##), the former repressing the expression of <italic>spo0A </italic>promoters and the latter directly binding to the promoter sequences of the <italic>spo0A</italic>, <italic>sigF</italic>, and <italic>sigE </italic>operons [##REF##9685500##31##,##REF##7642487##32##]. <italic>C. acetobutylicum </italic>contains three paralogs of <italic>abrB</italic>, among which CAC0310 exhibited the highest promoter activity and, when downregulated, causes delayed sporulation and decreased solvent formation [##REF##15812030##33##]. <italic>sinR </italic>(CAC0549) expression in <italic>C. acetobutylicum </italic>was previously reported [##REF##15812030##33##] to be weak, but our data show a significant amount of expression and suggest a similar role as that in <italic>B. subtilis</italic>. In <italic>B. subtilis</italic>, Spo0A either indirectly (<italic>sinR</italic>) or directly (<italic>abrB</italic>) represses the genes of these two repressors [##REF##7642487##32##,##REF##3145384##34##]. The expression patterns of both genes did decrease after peak Spo0A~P deduced activity (Figure ##FIG##3##4b##; see below), indicating a similar regulatory network may be involved in <italic>C. acetobutylicum</italic>. <italic>sigF</italic>, <italic>sigE </italic>and <italic>sigG </italic>have very similar expression patterns (Figure ##FIG##2##3c##). Both <italic>sigF </italic>and <italic>sigE </italic>are activated by Spo0A~P, so similar expression profiles were expected. In <italic>B. subtilis</italic>, a <italic>sigG </italic>transcript is also detected early, but this transcript is read-through from <italic>sigE</italic>, located immediately upstream of <italic>sigG</italic>, and is not translated [##REF##16166546##35##,##REF##1902213##36##]. Translation of <italic>sigG </italic>occurs when the gene is expressed as a single cistron from a σ<sup>F</sup>-dependent promoter located between <italic>sigE </italic>and <italic>sigG </italic>[##REF##16166546##35##,##REF##1902213##36##]. In <italic>C. acetobutylicum</italic>, <italic>sigE </italic>and <italic>sigG </italic>are also located adjacent to each other, but a σ<sup>F </sup>promoter was not predicted between the two genes [##REF##15060177##37##]. Thus, it was predicted that <italic>sigG </italic>is only expressed as part of the <italic>sigE </italic>operon (consisting of <italic>spoIIGA</italic>, the processing enzyme for σ<sup>E</sup>, and <italic>sigE</italic>). Our transcriptional data seem to support this prediction because all three genes, <italic>spoIIGA</italic>, <italic>sigE</italic>, and <italic>sigG</italic>, have very similar transcriptional patterns (Figure ##FIG##2##3f##), suggesting they are expressed as a single transcript, like the <italic>spoIIAA</italic>-<italic>spoIIAB</italic>-<italic>sigF </italic>operon (Figure ##FIG##2##3e##). However, from Northern blots probing against <italic>sigE</italic>-<italic>sigG</italic>, three separate transcripts were seen: one for <italic>spoIIGA</italic>-<italic>sigE</italic>-<italic>sigG</italic>, one for <italic>spoIIGA</italic>-<italic>sigE</italic>, and one for <italic>sigG </italic>[##REF##12057953##29##]. Unfortunately, the current data cannot resolve this issue definitively, since the microarrays only detect if a transcript is present or not.</p>", "<title>Deduced activity profiles of sporulation factors</title>", "<p>We also desired to estimate the activity profiles for the key sporulation factors (σ<sup>H</sup>, Spo0A, σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G</sup>; Figure ##FIG##3##4##). We did so by averaging the expression profiles of known or robustly identifiable canonical genes of their regulons [##REF##16261177##1##]. To adjust for differences in relative expression levels, expression profiles were standardized before averaging [##REF##16199581##7##]. This is a surrogate reporter assay, which we believe is as accurate as most reporter assays. For a detailed discussion of the genes used to construct the plots, see Additional data file 4. For all of the plots (Figure ##FIG##3##4##), peak activity took place after peak expression, as expected. Of all the factors, σ<sup>H </sup>activity peaked first, during early transitional phase, and this was followed by a decrease in activity until stationary phase, when activity increased again (Figure ##FIG##3##4a,f##). Spo0A~P activity was the next to peak, during late transitional phase, and stayed fairly constant throughout the rest of the timecourse (Figure ##FIG##3##4b,f##). σ<sup>F </sup>activity had an initial induction during transitional phase, but then stayed constant until 24 hours (Figure ##FIG##3##4c,f##). After 24 hours, the activity increased again and stayed fairly constant at this higher activity level for the rest of the culture. σ<sup>E </sup>activity increased slightly during late transitional phase, but its major increase occurred after 24 hours during mid-stationary phase (Figure ##FIG##3##4d,f##). Like the previous sigma factors, σ<sup>G </sup>activity increased throughout early stationary phase and early mid-stationary phase, but the major increase occurred after hour 30 (Figure ##FIG##3##4e,f##). The activity of all of the factors, except for Spo0A and σ<sup>F</sup>, decreased during late stationary phase at hour 38. σ<sup>G </sup>activity began to increase slightly again at hour 48 but did not peak again. Considering only major peaks in activity, the <italic>Bacillus </italic>model of sporulation is generally true with the peaks progressing from σ<sup>H </sup>to Spo0A~P to σ<sup>F </sup>to σ<sup>E </sup>and finally to σ<sup>G </sup>(Figure ##FIG##3##4f##).</p>", "<title>Can we deduce the activation and processing of σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G </sup>from transcriptional data?</title>", "<p>In <italic>B. subtilis</italic>, the sigma factors downstream of Spo0A (σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G</sup>) are all regulated by a complex network of interactions [##REF##16261177##1##]. We desired to examine if our transcriptional data could be used to do a first test to determine whether the mechanisms employed in the <italic>B. subtilis </italic>model are valid for <italic>C. acetobutylicum</italic>. In <italic>B. subtilis</italic>, σ<sup>F </sup>is held inactive in the pre-divisional cell by the anti-σ<sup>F </sup>factor SpoIIAB. σ<sup>F </sup>is released when the anti-anti-σ<sup>F </sup>factor SpoIIAA is dephosphorylated by SpoIIE, resulting in SpoIIAA binding to SpoIIAB, which then releases σ<sup>F</sup>. In <italic>C. acetobutylicum</italic>, <italic>spoIIAB </italic>(CAC2307) and <italic>spoIIAA </italic>(CAC2308) are transcribed on the same operon as <italic>sigF </italic>(Figure ##FIG##2##3e##), but <italic>spoIIE </italic>(CAC3205) is transcribed separately. The initial increase in σ<sup>F </sup>activity during the transitional phase was not accompanied by an increase in <italic>spoIIE </italic>expression, but the peak in σ<sup>F </sup>activity did occur after <italic>spoIIE </italic>upregulation (Figure ##FIG##3##4c##). Despite the sustained level of σ<sup>F </sup>activity, <italic>sigF </italic>and <italic>spoIIE </italic>decreased in expression, though <italic>spoIIE </italic>expression did increase slightly again after 48 hours (Figure ##FIG##3##4c##). In <italic>B. subtilis</italic>, the pro-σ<sup>E </sup>translated from the <italic>sigE </italic>gene undergoes processing from SpoIIGA, which must interact with SpoIIR in order to accomplish the σ<sup>E </sup>activation. In <italic>C. acetobutylicum</italic>, SpoIIGA (CAC1694) is transcribed on the same operon as <italic>sigE </italic>(Figure ##FIG##2##3f##), and SpoIIR is coded by CAC2898. σ<sup>E </sup>activity increased with the induction of <italic>spoIIR </italic>(Figure ##FIG##3##4d##), suggesting a similar mechanism as in <italic>B. subtilis</italic>. Finally, σ<sup>G </sup>activation in <italic>B. subtilis</italic> is dependent upon the eight genes within the <italic>spoIIIA </italic>operon. Here, the second and larger increase in σ<sup>G </sup>activity followed peak expression of the <italic>spoIIIA </italic>operon, but the early increase in σ<sup>G </sup>activity was not characterized by a large induction of <italic>spoIIIA </italic>expression (Figure ##FIG##3##4e##). We tentatively conclude that the <italic>B. subtilis </italic>processing and activation model does generally hold true in <italic>C. acetobutylicum</italic>, but further investigation is needed to determine the exact timing and interaction of the various factors and their activators.</p>", "<title>Is there a functional <italic>sigK</italic>?</title>", "<p>In <italic>B. subtilis</italic>, σ<sup>K </sup>is formed by splicing together two genes (<italic>spoIVCB </italic>and <italic>spoIIIC</italic>), both under the control of σ<sup>E </sup>and SpoIIID [##REF##2492118##38##], separated by a <italic>skin </italic>element [##REF##2536191##39##]. In contrast, a single gene encoding σ<sup>K </sup>has been annotated in <italic>C. acetobutylicum </italic>[##REF##11466286##13##]. The gene was initially identified using a PCR-approach [##REF##7961408##40##] and was later detected by primer extension in a phosphate-limited, continuous culture of <italic>C. acetobutylicum </italic>DSM 1731 [##REF##9561744##41##]. <italic>spoIIID</italic>, which controls <italic>sigK </italic>expression with σ<sup>E </sup>in <italic>B. subtilis</italic>, reached peak expression at hour 30, which is consistent with it being under σ<sup>E </sup>control (Figure ##FIG##2##3d##) [##REF##1744038##42##]. However, at no timepoint in this study did <italic>sigK </italic>exceed the cutoff expression criterion. Q-RT-PCR also showed a significantly lower <italic>sigK </italic>induction compared to the other sigma factors and suggests the transcript, if expressed, is at much lower levels than any other gene analyzed (Figure ##FIG##1##2##). The putative main σ<sup>K </sup>processing enzyme, SpoIVFB (CAC1253), also did not exceed the cutoff criterion. To help determine if there is an active σ<sup>K</sup>, we investigated two genes controlled by σ<sup>K </sup>in <italic>B. subtilis</italic>. <italic>yabG </italic>(CAC2905), which encodes a protein involved in spore coat assembly, was upregulated mid-stationary phase and peaked at hour 30 (Figure ##FIG##2##3d##), and <italic>spsF </italic>(CAC2190), involved in spore coat synthesis, was not upregulated until late stationary phase, at hour 38 (Figure ##FIG##2##3d##). From these two genes, it is difficult to determine whether a functional <italic>sigK </italic>gene exists or not. Clearly they are both transcribed, but based on its expression pattern, <italic>yabG </italic>could fall under the control of σ<sup>E </sup>instead of σ<sup>K</sup>. <italic>spsF </italic>upregulation is late enough to possibly indicate σ<sup>K </sup>regulation though. Ideally, more genes need to be investigated to draw firmer conclusions, but because few σ<sup>K </sup>regulon homologs exist in <italic>C. acetobutylicum</italic>, we cannot currently determine if there is σ<sup>K </sup>activity or not.</p>", "<title>Distinct profiles of sensory histidine kinases: which for Spo0A?</title>", "<title>Revisiting the orphan kinases</title>", "<p>As discussed, phosphorylated Spo0A is responsible for initiating sporulation in both bacilli and clostridia along with solvent formation in <italic>C. acetobutylicum</italic>. In bacilli, Spo0A is phosphorylated via a multi-component phosphorelay [##REF##15556029##43##], initiated by five orphan histidine kinases, KinA-E (kinases that lack an adjacent response regulator); this phosphorelay system is absent in all sequenced clostridia [##REF##16261177##1##]. Alternatively, Spo0A in clostridia may be directly phosphorylated by a histidine kinase, orphan or not, as was hypothesized in [##REF##16261177##1##,##REF##16199581##7##]. This alternative was demonstrated in <italic>C. botulinum</italic>, where the orphan kinase CBO1120 was able to phosphorylate Spo0A [##REF##16420367##44##]. In <italic>C. acetobutylicum</italic>, five true orphan kinases have been identified with a sixth orphan, CAC2220, identified as CheA, which has a known response regulator [##REF##16261177##1##].</p>", "<p>A kinase that could directly phosphorylate Spo0A is expected to have a peak in expression before or during the activation of Spo0A, as the orphan kinases in <italic>B. subtilis </italic>do [##REF##11069677##45##, ####REF##8576055##46##, ##REF##8002614##47####8002614##47##]. As a measure of Spo0A activity, the expression of the <italic>sol </italic>operon (CAP0162-64) was used, as before [##REF##16199581##7##], because it is induced by Spo0A~P. The initial induction of the <italic>sol </italic>operon, almost 100-fold, occured at hour 10 (before <italic>spo0A </italic>reached it maximum expression), with detectable levels of butanol appearing before the second induction of the <italic>sol </italic>operon. This second induction, of another 10-fold, followed the peak in <italic>spo0A </italic>expression (Figure ##FIG##4##5a##). It is clear that some level of phosphorylated Spo0A exists at 10 hours; therefore, kinase candidates must display an increase in expression before 10 hours. Of the five orphan kinases (Figure ##FIG##4##5b,c##), CAC2730 displayed the earliest peak followed by CAC0437, CAC0903, and CAC3319. CAC0323 never displayed a prominent peak in expression either before or after <italic>sol </italic>operon induction (Figure ##FIG##4##5b##) and likely does not play a role in phosphorylating Spo0A. Of the remaining four, CAC0437 and CAC2730 peaked only once before the initial <italic>sol </italic>operon induction, while CAC0903 peaked before each induction of the <italic>sol </italic>operon (Figure ##FIG##4##5b,c##). CAC3319 expression slightly mirrored that of the <italic>sol </italic>operon, with an increase before initial induction followed by a plateau, and an increase in expression again until it peaked just after the <italic>sol </italic>operon peaked (Figure ##FIG##4##5c##). The proteins encoded by CAC0437 and CA0903 displayed the most similarity to the protein encoded by CBO1120, the orphan kinase in <italic>C. botulinum </italic>shown to phosphorylate Spo0A [##REF##16420367##44##].</p>", "<title>Non-orphan kinase expression</title>", "<p>Though primarily interested in orphan kinases because of the similarity to the <italic>B. subtilis </italic>model, a two-component response system could also be responsible for the phosphorylation of Spo0A. The remaining 30 annotated histidine kinases were also investigated to determine if any displayed a peak in expression before the initial induction of the <italic>sol </italic>operon (Additional data file 5). Six kinases (Figure ##FIG##4##5d,e##) were found to have a peak in expression at 8 hours. CAC0290 and CAC3430 subsequently decreased in expression while CAC0225 and CAC0863 maintained expression at initial levels. Despite a dip in expression at hour 9, CAC1582 maintained an increased expression level from 8 hours on. CAC2434 peaked at hour 8, dropped back to initial levels, but then steadily increased with the second induction of the <italic>sol </italic>operon.</p>", "<title>Sigma factors of unknown function: a first assessment of their functional roles</title>", "<p>Seventeen sigma factors are annotated on the <italic>C. acetobutylicum </italic>genome, including two on pSOL1. Two, <italic>sigK </italic>(CAC1689) and CAC1770 (a <italic>sigK</italic>-like sigma factor), are expressed at very low levels and two others, CAC1509 (annotated 'specialized sigma subunit of RNA polymerase') and CAC1226 (one of two annotated <italic>sigA</italic>s), are only above the expression cutoff in 8 out of 25 timepoints, and these timepoints are not consecutively expressed. Among the expressed sigma factors, six, CAP0157, CAP0167, CAC3267, CAC1766, CAC2052, and CAC0550, are of unknown function, while the remaining seven expressed sigma factors (σ<sup>H</sup>, σ<sup>F</sup>, σ<sup>E</sup>, σ<sup>G</sup>, σ<sup>A</sup>, σ<sup>D</sup>, and σ<sup>54</sup>/rpoN) are of predicted known function. To assess the potential role of the remaining six sigma factors of unknown function, we examined the transcriptional profiles (Figure ##FIG##5##6a,b##) and probed the binding motifs in their promoter regions for predicted Spo0A, σ<sup>A</sup>, σ<sup>E</sup>, and σ<sup>F</sup>/σ<sup>G </sup>binding motifs [##REF##15060177##37##].</p>", "<title>Transcriptional analysis of the sigma factors of unknown function</title>", "<p>Loss of pSOL1 impairs sporulation at the level of <italic>spo0A </italic>expression [##REF##16199581##7##,##UREF##2##48##], thus generating increased interest for sigma factors located on the pSOL1 plasmid as these may play a role in the regulation of sporulation. Two sigma factors, CAP0157 and CAP0167, are located on pSOL1 and are annotated as 'special sigma factor (σ<sup>F</sup>/σ<sup>E</sup>/σ<sup>G </sup>family)' and 'specialized sigma factor (σ<sup>F</sup>/σ<sup>E </sup>family)', respectively. It was predicted that CAP0167 is putatively co-transcribed with CAP0166 from a promoter of the σ<sup>F</sup>/σ<sup>G </sup>family [##REF##15060177##37##] and it displayed an expression pattern similar to that of <italic>spo0A</italic>, consistent with the computational prediction of an 0A box [##REF##12057953##29##] and two reverse 0A boxes in its promoter region (Figure ##FIG##5##6a##). CAP0157 was expressed from an unidentified promoter late in the timecourse (40+ hours) and thus may be involved in late-stage sporulation, despite its low level of expression at hour 20 (Figure ##FIG##5##6a##). CAC3267, putatively the fourth gene in an operon starting with CAC3270 and ending with CAC3264 [##REF##15060177##37##], was mainly expressed during early exponential growth (Figure ##FIG##5##6a##), then decreased, and peaked again around 14 hours, after which expression decreased again. This pattern of expression suggests that it plays a role in vegetative growth and possibly early sporulation. CAC0550, putatively transcribed from a σ<sup>A </sup>promoter as a single cistron [##REF##15060177##37##], was mainly transcribed early with its expression ending after 20-24 hours (Figure ##FIG##5##6b##), suggesting that it is not involved in sporulation. CAC1766, expressed from an unknown promoter, displayed a unique pattern with a progressive buildup starting around hours 8-12 and a distinct peak around hour 22 (Figure ##FIG##5##6b##). CAC2052 is annotated as 'DNA-dependent RNA polymerase σ-subunit' and was putatively expressed together with CAC2053, a hypothetical protein, from a σ<sup>A </sup>and/or a σ<sup>F</sup>/σ<sup>G </sup>promoter [##REF##15060177##37##]. Our data suggest that it is unlikely to be transcribed from a σ<sup>F</sup>/σ<sup>G </sup>promoter without any other effectors, as their transcription peaked at hour 16, when there was very little (if any) σ<sup>F </sup>or σ<sup>G </sup>activity (Figure ##FIG##5##6b##).</p>", "<title>Phylogenetic tree comparison</title>", "<p>To help determine a possible function for these sigma factors, a phylogenetic tree was constructed of σ<sup>70 </sup>sigma factors from ten species, including <italic>B. subtilis </italic>and all sequenced clostridial species. The resulting tree (Additional data file 6) contains eleven major branches, and of these, seven can be definitively classified based on known sigma factors within the branch. These categories are extracytoplasmic function (ECF), sporulation factors (<italic>sigF</italic>, <italic>sigE</italic>, and <italic>sigG</italic>), <italic>sigH</italic>, <italic>sigA </italic>(a basal sigma factor), <italic>sigD </italic>(regulates chemotaxis and motility), and <italic>sigB </italic>(a general response sigma factor). Two factors, CAC3267 and CAC1766, fell within ECF branches. CAC3267 fell within an ECF branch close to the <italic>B. subtilis</italic> σ<sup>V</sup>, a sigma factor of unknown function, and σ<sup>M</sup>, a sigma factor essential for growth and survival in high salt concentrations. CAC1766 fell within a different ECF branch close to <italic>B. subtilis</italic> σ<sup>Z</sup>, a sigma factor of unknown function, and CAC1509, a sigma factor expressed for less than eight consecutive timepoints. The remaining four factors fell within clusters with other clostridial sigma factors of unknown function, though several could have possible ECF function.</p>", "<title>Antisense RNA knock-down of four sigma factors: 'fat' clostridial forms and enhanced glucose metabolism</title>", "<p>Of the six expressed sigma factors of unknown function, CAP0157, CAP0167, CAC2052, and CAC1766 were chosen for further study because the timing and shape of their expression patterns suggested potential involvement in sporulation and/or solventogenesis. Since the two processes are coupled, phenotypic changes in differentiation may affect solvent production, as has been previously observed [##REF##15028679##4##,##REF##15640230##6##,##REF##12057953##29##,##REF##15812030##33##,##REF##15743939##49##]. Antisense RNA (asRNA) knock-down was chosen over knocking out the genes, because knockouts are still extremely difficult to produce in this and all other clostridia. Indeed, to date, only a handful of knockouts have been created [##REF##12057953##29##,##REF##8760920##50##, ####REF##15063491##51##, ##REF##16759397##52##, ##REF##10476029##53####10476029##53##], and these have only been achieved after screening thousands of transformants [##REF##15063491##51##, ####REF##16759397##52##, ##REF##10476029##53####10476029##53##]. Recently, a group II intron system has been developed for clostridia [##REF##17658189##54##], but this system was not yet available when these experiments were carried out. In contrast, asRNA is relatively quick, has been shown to reduce gene expression by up to 90% [##REF##15812030##33##,##REF##10049845##55##,##REF##12618456##56##] and has been used to knock-down a large number of genes with a high level of specificity [##REF##15812030##33##,##REF##15743939##49##,##REF##10049845##55##, ####REF##12618456##56##, ##REF##14756797##57##, ##REF##17259355##58##, ##REF##12775702##59####12775702##59##]. asRNA constructs (see Additional data file 7 for specific sequences used) were designed against CAP0157, CAP0167, CAC2052, and CAC1766 along with CAC2053 and CAP0166, the first genes in the operons predicted to contain CAC2052 and CAP0167, respectively [##REF##15060177##37##]. Cultures of these strains were examined and compared against the wild type (WT) and plasmid control strain 824(pSOS95del) for cell morphology differences and metabolic changes.</p>", "<p>Microscopy results from the asRNA-strain cultures revealed both novel morphologies and apparently altered differentiation (Figure ##FIG##5##6d##). Most notable were changes in strains asCAP0166, asCAP0167 and asCAC1766. Typical WT cultures display a predominately vegetative, symmetrically dividing population through 72 hours as evidenced by the thin, rod-shaped, phase dark cells (Figure ##FIG##5##6d, I##). By 72 hours, WT cultures exhibited only a small percentage of swollen, cigar-shaped clostridial forms and then a proportional population of free spores by 96 hours.</p>", "<p>pSOS95del cultures exhibited clostridial forms by 48 hours, suggesting an accelerated differentiation compared to WT, as has been seen before in our laboratory (Figure ##FIG##5##6d, II##). Moreover, a greater percentage of clostridial forms and free spores compared to WT were observed at 72 and 96 hours, respectively. asCAP0166 cultures generated a large percentage of clostridial forms and endospores/free spores by hours 48 and 72, respectively (Figure ##FIG##5##6d, III##). This differentiation is accelerated in comparison to pSOS95del. By hour 96, asCAP0166 cultures exhibited predominately vegetative cells apparently derived from germinated spores (data not shown). asCAP0167 cultures also exhibited accelerated differentiation and displayed a novel (to our knowledge) form of cellular morphology that was most profoundly observable at 72 hours (Figure ##FIG##5##6d, IV##). This novel morphology has qualities of an excessively swollen clostridial cigar-form (which makes them look much shorter than normal clostridial forms), with what appears to be endospore formation occurring, but without the associated phase bright characteristics seen in the 72 hour asCAP0166 cultures. The asCAP0166 culture displayed cells in this novel morphological state as well, but to a lesser extent, although it is possible that because of its faster sporulation, such cell forms appeared prior to 72 hours. The asCAC1766 cultures also exhibited altered differentiation; most importantly, at 72 hours the majority of the cells exhibited a very swollen clostridial-form morphology similar to that in the asCAP0167 cultures at 72 hours, but slightly more elongated (Figure ##FIG##5##6d, V##).</p>", "<p>To further characterize this novel cell form, transmission electron microscopy (TEM) and scanning electron microscopy images of cells were taken for strains asCAP0167 and asCAC1766. To determine morphological differences involved in differentiation, the TEM images were compared against cell images taken from the plasmid control strain (Figure ##FIG##6##7##). For both asRNA strains, the very swollen cell forms observed can be documented as approximately 2.5-4 μm long, and 1.1-1.3 μm in diameter, and should be compared to control or WT swollen clostridial forms, which are 3.5-6 μm long and 0.8-1 μm in diameter. Forespore and endospore forms of both asCAP0167 (Figure ##FIG##6##7c,d##) and asCAC1766 (Figure ##FIG##6##7e,f##) displayed a pinched end not seen in the plasmid control (Figure ##FIG##6##7b##). A slight pinching is seen in the clostridial forms of the plasmid control strain (Figure ##FIG##6##7a##), but this is probably indicative that an asymmetric division is about to occur. Rather, the pinched ends seen in the antisense strains occur after asymmetric division and while the spore is developing within the mother cell. These pinched ends are also noticeable in the scanning electron microscopy images (Figure ##FIG##7##8##). Though granulose is distinguishable in most of the TEM images (Figure ##FIG##6##7c,d,f##), it is not the characteristic electron translucent seen in typical clostridial, forespore, and endospore forms (Figure ##FIG##6##7a,b##). These differences were seen throughout the culture and additional TEM images of both the plasmid control and the antisense strains are included in Additional data file 8.</p>", "<p>Glucose, acetone, and butanol concentrations from two to four biological replicates for each strain were averaged together, and the results are shown in Table ##TAB##0##1##. We averaged data from cultures that displayed similar characteristics; most cultures did so despite the fact that each culture was inoculated from a different colony for each strain. Acetone and butanol levels were typical for WT and control cultures, with the WT producing 90 mM of acetone and 150 mM of butanol and the plasmid-control strain producing 80 mM of acetone and 160 mM of butanol [##REF##12902291##60##]. By 192 hours, all strains had either produced comparable amounts of butanol to the WT and the plasmid control strain or had somewhat outperformed these two strains. The most significant differences were that all asRNA strains consumed higher levels of glucose and also had a delayed metabolism in terms of product formation. These metabolic changes, although preliminary, are consistent with and support the large changes in the kinetics of sporulation observed by microscopy.</p>" ]
[ "<title>Conclusion</title>", "<p>This detailed and previously unrevealed transcriptional roadmap has allowed for the first time a complete investigation of the genetic events associated with clostridial differentiation. We were able to link distinct and striking global transcriptional changes to previously known important morphological and physiological changes. To date, this is the most complete genetic analysis of the different morphological forms: vegetative, clostridial, and forespore/endospore. Importantly, this analysis was performed on a mixed culture, which may either dilute or produce noise in the data, but investigation of the clusters identified revealed that these clusters do capture important known processes. We were also able to identify a cell population late in the timecourse similar to vegetative cells. Visually, these late cells looked and acted like vegetative cells, and transcriptionally, they were also fairly similar. The major cell motility and chemotaxis genes were upregulated both early and late in the timecourse (Figure S2 in Additional data file 3), as were the ribosomal proteins (Figure S12 in Additional data file 3). Also, the cell division associated genes <italic>rodA</italic>, <italic>ftsE</italic>, and <italic>ftsX </italic>follow the same transcriptional pattern of both early and late expression (Figure S11 in Additional data file 3). Although, these cells stain differently from the early vegetative cells, probably due to changes in membrane structure in response to the presence of solvents and do not produce detectable levels of acids or solvents, we believe these cells are germinated cells from spores produced early in the timecourse. While the triggers for both sporulation and germination are not known [##REF##16261177##1##], the culture late in the timecourse is less acidic because of the acid reassimilation, and pH has been shown to be a trigger for sporulation [##REF##3540574##21##].</p>", "<p>This study has also allowed the first full comparison to the widely studied <italic>B. subtilis </italic>sporulation program. We have confidently identified the temporal orchestration of all known sporulation-related transcription factors and conclude the <italic>Bacillus </italic>model generally holds true with the cascade progressing in the following manner: σ<sup>H</sup>, Spo0A, σ<sup>F</sup>, σ<sup>E</sup>, and σ<sup>G </sup>(Figure ##FIG##3##4f##). In addition, we can conclude that the major activating/processing proteins involved in sigma factor activation in <italic>B. subtilis </italic>play a similar role in <italic>C. acetobutylicum</italic>, though additional investigation is needed to clarify their role. Of significance is the lack of <italic>sigK </italic>signal. The genes responsible for transcribing <italic>sigK </italic>in <italic>B. subtilis</italic>, <italic>sigE </italic>and <italic>spoIIID</italic>, were expressed, but the putative processing enzyme <italic>spoIVFB </italic>was not. Two genes under the control of σ<sup>K </sup>in <italic>B. subtilis </italic>were expressed, but their expression patterns are not consistent with each other. Based on the expression pattern of <italic>yabG</italic>, it could be controlled by σ<sup>E</sup>, while the late expression of <italic>spsF </italic>could be an indication of σ<sup>K </sup>activity.</p>", "<p>Finally, in order to determine if one of the annotated sigma factors of unknown function could be a <italic>sigK</italic>-like gene, we first investigated their transcriptional profiles. CAP0157 was a possible candidate with its upregulation late in the timecourse, as was CAC1766 since its expression was sustained throughout the stationary phase (Figure ##FIG##5##6a,b##). Neither of these genes, nor any of the other sigma factors of unknown function, clustered close to the known sporulation-related sigma factors on the phylogenetic tree (Additional data file 6), but when downregulated using asRNA, both CAC1766 and the CAP0167 operon (CAP0166 and CAP0167) displayed altered differentiation (Figures ##FIG##5##6d##, ##FIG##6##7## and ##FIG##7##8##). Though involved in differentiation, the exact role of these two sigma factors is difficult to assess because of the incomplete silencing of the genes through asRNA downregulation. Mature free spores and typical endospore forms without a pinched end are still seen (data not shown), but whether these develop from the novel cell types or from cells not affected by the antisense cannot be determined. Interestingly, both CAP0167 and CAC1766 clustered together with other clostridial sigma factors and closer to ECF sigma factors than to the major sporulation sigma factors <italic>sigF</italic>, <italic>sigE</italic>, and <italic>sigG </italic>(Additional data file 6). In <italic>B. subtilis</italic>, ECF sigma factors do not play a role in differentiation [##REF##18223082##61##,##REF##17675383##62##], though a triple mutant in <italic>sigM</italic>, <italic>sigW</italic>, and <italic>sigX </italic>did display altered phenotypes [##REF##17675383##62##]. The fact that CAC1766 and CAP0167 appear to affect the developmental process of sporulation (Figures ##FIG##6##7## and ##FIG##7##8##; Additional data file 8) suggests either that ECF factors may play a role in sporulation in clostridia or that a novel category of sigma factors exist in clostridia that play a role in sporulation.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A detailed microarray analysis of transcription during sporulation of the strict anaerobe and endospore former <italic>Clostridium acetobutylicum</italic> is presented.</p>", "<title>Background</title>", "<p>Clostridia are ancient soil organisms of major importance to human and animal health and physiology, cellulose degradation, and the production of biofuels from renewable resources. Elucidation of their sporulation program is critical for understanding important clostridial programs pertaining to their physiology and their industrial or environmental applications.</p>", "<title>Results</title>", "<p>Using a sensitive DNA-microarray platform and 25 sampling timepoints, we reveal the genome-scale transcriptional basis of the <italic>Clostridium acetobutylicum </italic>sporulation program carried deep into stationary phase. A significant fraction of the genes displayed temporal expression in six distinct clusters of expression, which were analyzed with assistance from ontological classifications in order to illuminate all known physiological observations and differentiation stages of this industrial organism. The dynamic orchestration of all known sporulation sigma factors was investigated, whereby in addition to their transcriptional profiles, both in terms of intensity and differential expression, their activity was assessed by the average transcriptional patterns of putative canonical genes of their regulon. All sigma factors of unknown function were investigated by combining transcriptional data with predicted promoter binding motifs and antisense-RNA downregulation to provide a preliminary assessment of their roles in sporulation. Downregulation of two of these sigma factors, CAC1766 and CAP0167, affected the developmental process of sporulation and are apparently novel sporulation-related sigma factors.</p>", "<title>Conclusion</title>", "<p>This is the first detailed roadmap of clostridial sporulation, the most detailed transcriptional study ever reported for a strict anaerobe and endospore former, and the first reported holistic effort to illuminate cellular physiology and differentiation of a lesser known organism.</p>" ]
[ "<title>Abbreviations</title>", "<p>asRNA, antisense RNA; COG, Cluster of Orthologous Groups; ECF, extracytoplasmic function; PI, propidium iodide; Q-RT-PCR, quantitative reverse transcription PCR; TEM, transmission electron microscopy; WT, wild type.</p>", "<title>Authors' contributions</title>", "<p>SWJ carried out the microarray experiments, helped with the electron microscopy, helped analyze the data, and drafted and finalized the manuscript. CJP designed the microarray platform used, helped with the bioinformatic tools used in the analysis, and drafted parts of the manuscript. BT carried out all the microscopy except the electron microscopy and generated the antisense RNA strains. NC carried out the microarray experiments and helped with the generation of the antisense strains. RS helped design the microarray experiments, carried out the Q-RT-PCR experiments, helped analyze the data, and drafted parts of the manuscript. RSS helped with the bioinformatic tools used in the analysis. ETP helped in the design of all the experiments, the analysis and interpretation of the data, and helped in the organization, draft and editing of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available. Additional data file ##SUPPL##0##1## is a figure comparing the present microarray study to an earlier microarray study that examined the early sporulation of <italic>C. acetobutylicum </italic>followed by a brief discussion. Additional data file ##SUPPL##1##2## contains tables detailing the COG analysis for each cluster and all the genes placed in each cluster. Additional data file ##SUPPL##2##3## contains figures of the transcriptional profiles, in terms of both intensity and differential expression, of specific gene clusters with brief discussions following several figures. Additional data file ##SUPPL##3##4## is a composite figure showing the individual expression profiles of the genes that were standardized and averaged and is followed by a brief discussion on how the genes used to construct the deduced activity plots were chosen. Additional data file ##SUPPL##4##5## is a figure showing the differential expression and intensity of all annotated histidine kinases and response regulators. Additional data file ##SUPPL##5##6## is a figure showing the phylogenetic tree resulting from the alignment of the σ<sup>70</sup>-related and unannotated sigma factors from ten bacterial species. Additional data file ##SUPPL##6##7## is a table listing the sequences for each asRNA construct. Additional data file ##SUPPL##7##8## contains figures showing additional TEM images of the plasmid control strain, asCAP0167, and asCAC1766. Additional data file ##SUPPL##8##9## is a table listing the primer sequences used in the Q-RT-PCR experiments.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We acknowledge the use of the Northwestern University Keck Biophysics Facility, the Northwestern University Biological Imaging Facility for the light microscopy, and Shannon Modla in the Delaware Biotechnology Institute Bio-Imaging Facility for the electron microscopy. Supported by NSF grant (BES-0418157) and an NIH/NIGMS Biotechnology Training grant (T32-GM08449) fellowship for Bryan Tracy.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Morphological and gene expression changes <italic>C. acetobutylicum </italic>undergoes during exponential, transitional, and stationary phases. <bold>(a) </bold>Growth and acid and solvent production curves as they relate to morphological and transcriptional changes during sporulation. The gray bar indicates the beginning of the transitional phase as determined by solvent production. A<sub>600 </sub>with microarray sample (filled squares); A<sub>600 </sub>(open squares); butyrate (filled circles); butanol (filled triangles). Roman numerals correspond with those in (b), and bars and numbers along the top correspond to the clusters in (c). <bold>(b) </bold>Morphological changes during sporulation. When stained with Syto-9 (green) and PI (red), vegetative cells take on a predominantly red color (I and II). At peak butanol production, swollen, cigar-shaped clostridial-form cells appear (arrow in III), which stain almost equally with both dyes, and persist until late stationary phase. Towards the end of solvent production (IV), endospore (arrow 1) forms are visible, and clostridial (arrow 2) forms are still present. As the culture enters late stationary phase (V and VI), cells stain almost exclusively green, regardless of morphology. All cell types are still present, including free spores (arrows in V and VI), and vegetative cells identified by their motility. <bold>(c) </bold>Average expression profiles for each K-means cluster generated using a moving average trendline with period 3. <bold>(d) </bold>Expression of the 814 genes (rows) at 25 timepoints (columns, hours 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 44, 48, 54, 58, and 66). Genes with higher expression than the reference RNA are shown in red and those with lower expression as green. Saturated expression levels: ten-fold difference.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Q-RT-PCR and microarray data comparison. RNA from a biological replicate bioreactor experiment was reverse transcribed into cDNA for the Q-RT-PCR. All expression ratios are shown relative to the first timepoint for both Q-RT-PCR (open circles) and microarray data (filled squares). Asterisks represent data below the cutoff value for microarray analysis. Samples were taken every six hours starting from hour 6 and continuing until hour 48. The genes examined were from several operons with different patterns of expression.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Investigation of the sporulation cascade in <italic>C. acetobutylicum</italic>. <bold>(a-f) </bold>Expression profiles of sporulation genes shown as ratios against the first expressed timepoint. <bold>(a) </bold>The first three sporulation factors: <italic>spo0A </italic>(red filled triangles), <italic>sigH </italic>(black filled squares), and <italic>sigF </italic>(open blue circles). <bold>(b) </bold><italic>spo0A </italic>(red filled triangles) and possible sporulation regulators: <italic>abrB </italic>(open black circles) and <italic>sinR </italic>(green filled diamonds). <bold>(c) </bold>Sporulation factors downstream of <italic>spo0A</italic>: <italic>sigF </italic>(open blue circles), <italic>sigE </italic>(black filled triangles), and <italic>sigG </italic>(open red squares). <bold>(d) </bold>Genes related to <italic>sigK </italic>expression: <italic>spoIIID </italic>(blue filled diamonds), <italic>yabG </italic>(red filled triangles), and <italic>spsF </italic>(black filled triangles). <bold>(e) </bold><italic>spoIIA </italic>operon: <italic>spoIIAA </italic>(black filled diamonds), <italic>spoIIAB </italic>(red filled triangles), and <italic>sigF </italic>(open blue circles). <bold>(f) </bold><italic>spoIIG </italic>operon and <italic>sigG</italic>: <italic>spoIIGA </italic>(green filled diamonds), <italic>sigE </italic>(black filled triangles), and <italic>sigG </italic>(open red squares). The gray bar indicates the onset of transitional phase. <bold>(g) </bold>Ranked expression intensities. White denotes a rank of 1, while dark blue denotes a rank of 100 (see scale). Gray squares indicate timepoints at which the intensity did not exceed the threshold value. Bracketed genes are predicted to be coexpressed as an operon.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Transcriptional and putative activity profiles for the major sporulation factors. The standardized expression ratios compared to the RNA reference pool of <bold>(a) </bold><italic>sigH</italic>, \n<bold>(b) </bold><italic>spo0A</italic>, \n<bold>(c) </bold><italic>sigF</italic>, \n<bold>(d) </bold><italic>sigE</italic>, and \n<bold>(e) </bold><italic>sigG</italic> are shown in black, while the activity profiles based on the averaged standardized profiles of canonical genes under their control are shown in red. Putative genes (based on the <italic>B. subtilis </italic>model) responsible for activating σ<sup>F </sup>(<italic>spoIIE</italic>), σ<sup>E </sup>(<italic>spoIIR</italic>), and σ<sup>G </sup>(<italic>spoIIIA </italic>operon) are shown as light blue diamonds. For the <italic>spoIIIA </italic>operon, the individual standardized ratios (Figure S13g in Additional data file 4) were averaged together. The gray bar indicates the onset of the transitional phase. <bold>(f) </bold>Compilation of the activity profiles for <italic>sigH </italic>(red), <italic>spo0A </italic>(blue), <italic>sigF </italic>(green), <italic>sigE </italic>(black), and <italic>sigG </italic>(purple). The numbers along the top correspond to the clusters in Figure 1c,d and the bars indicate the timing of each cluster.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Expression profiles of uncharacterized sensory histidine kinases that could phosphorylate Spo0A. Gene and operon profiles are ratios compared against the first expressed timepoint. Gray bar indicates the onset of the transitional phase. <bold>(a) </bold>Activation of Spo0A as represented through the upregulation of the <italic>sol </italic>operon (black filled diamonds; CAP0162-164) and the production of butanol (green crosses). Activation occurs before <italic>spo0A </italic>(red filled triangles) reaches peak expression. <bold>(b) </bold>Expression of the orphan kinases CAC0323 (blue filled diamonds), CAC0437 (green filled triangles), and CAC0903 (red filled circles) relative to the <italic>sol </italic>operon (black filled diamonds) (right-hand side vertical axis). <bold>(c) </bold>Expression of the orphan kinases CAC2730 (blue filled squares) and CAC3319 (open red circles) relative to the <italic>sol </italic>operon (black filled diamonds) (right-hand side vertical axis). <bold>(d) </bold>Expression of the two-component kinases CAC0225 (green filled circles), CAC0290 (red filled squares), and CAC0863 (open blue diamonds) relative to the <italic>sol </italic>operon (black filled diamonds) (right-hand side vertical axis). <bold>(e) </bold>Expression of the two-component kinases CAC1582 (green filled squares), CAC2434 (open blue circles), and CAC3430 (open red diamonds) relative to the <italic>sol </italic>operon (black filled diamonds) (right-hand side vertical axis). <bold>(f) </bold>Ranked expression intensities. White denotes a rank of 1, while dark blue denotes a rank of 100 (see scale). Plot covers the entire timecourse, whereas the previous figures only covered the first 14 hours. Gray squares indicate timepoints at which the intensity did not exceed the threshold value.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Expression profiles of sigma factors with unknown function and the effects of down-regulation. <bold>(a) </bold>Expression profiles of CAC3267 (open triangles), CAP0167 (filled squares), and CAP0157 (open circles) as ratios compared to the first expressed timepoint. Gray bar indicates the onset of transitional phase. <bold>(b) </bold>Expression profiles of CAC0550 (filled circles), CAC2052 (open squares), and CAC1766 (filled triangles) as ratios compared to the first expressed timepoint. Gray bar indicates the onset of transitional phase. <bold>(c) </bold>Ranked expression intensities of the sigma factors. White denotes a rank of 1, while dark blue denotes a rank of 100 (see scale). Gray squares indicate timepoints at which the intensity did not exceed the threshold value. <bold>(d) </bold>Microscopy time-course of asRNA strains compared to WT and plasmid control strains. Microscopy samples from WT (I) and pSOS95del (II) cultures (as controls) and three asRNA strains taken for two timepoints over a course of 72 hours. At 72 hours, WT (I) and pSOS95del (II) exhibit the typical clostridial forms (white arrows), while asCAP0166 (III) shows advanced differentiation with forespores and endospores (orange arrows) already visible. Strains asCAP0166 (III), asCAP0167 (IV), and asCAC1766 (V) show a novel, extra-swollen clostridial form (yellow arrows).</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>TEM images of the novel cell forms. <bold>(a-b) </bold>TEM images of the plasmid control strain pSOS95del: typical elongated clostridial form with electron translucent granulose (a); typical endospore form with a developing endospore at one end of the cell and electron translucent granulose still visible at the other end of the cell (b). <bold>(c-d) </bold>TEM images of the antisense strain asCAP0167. <bold>(e-f) </bold>TEM images of the antisense strain asCAC1766. Red arrows in (c-f) indicate pinched portions of the cell membrane not seen in the control strain and are characteristic of this novel cell type. Also noticeable is the electron dense granulose in the antisense strains, in contrast to the electron translucent granulose in the control samples.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p>Scanning electron microscopy (SEM) images of the novel cell forms. SEM images of the antisense strains <bold>(a-c) </bold>asCAP0167 and <bold>(d-f) </bold>asCAC1766. Red arrows in indicate pinched portions of the cell membrane not seen in the control strain and are characteristic of this novel cell type.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Concentrations of glucose, acetone, and butanol for asRNA strains</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"3\">96 hours</td><td align=\"center\" colspan=\"3\">144 hours</td><td align=\"center\" colspan=\"3\">192 hours*</td></tr><tr><td/><td colspan=\"3\"><hr/></td><td colspan=\"3\"><hr/></td><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">Sample</td><td align=\"center\">Glucose<sup>†</sup></td><td align=\"center\">Acetone<sup>†</sup></td><td align=\"center\">Butanol<sup>†</sup></td><td align=\"center\">Glucose<sup>†</sup></td><td align=\"center\">Acetone<sup>†</sup></td><td align=\"center\">Butanol<sup>†</sup></td><td align=\"center\">Glucose<sup>†</sup></td><td align=\"center\">Acetone<sup>†</sup></td><td align=\"center\">Butanol<sup>†</sup></td></tr></thead><tbody><tr><td align=\"left\">Wild type</td><td align=\"center\">165</td><td align=\"center\">91</td><td align=\"center\">157</td><td align=\"center\">143</td><td align=\"center\">74</td><td align=\"center\">157</td><td align=\"center\">120</td><td align=\"center\">61</td><td align=\"center\">162</td></tr><tr><td align=\"left\">pSOS95del<sup>‡</sup></td><td align=\"center\">264</td><td align=\"center\">57</td><td align=\"center\">97</td><td align=\"center\">136</td><td align=\"center\">83</td><td align=\"center\">169</td><td align=\"center\">125</td><td align=\"center\">57</td><td align=\"center\">158</td></tr><tr><td align=\"left\">asCAC1766</td><td align=\"center\">274</td><td align=\"center\">67</td><td align=\"center\">84</td><td align=\"center\">118</td><td align=\"center\">123</td><td align=\"center\">169</td><td align=\"center\">114</td><td align=\"center\">97</td><td align=\"center\">163</td></tr><tr><td align=\"left\">asCAC2052</td><td align=\"center\">294</td><td align=\"center\">49</td><td align=\"center\">69</td><td align=\"center\">191</td><td align=\"center\">84</td><td align=\"center\">122</td><td align=\"center\">116</td><td align=\"center\">92</td><td align=\"center\">154</td></tr><tr><td align=\"left\">asCAC2053</td><td align=\"center\">285</td><td align=\"center\">54</td><td align=\"center\">77</td><td align=\"center\">158</td><td align=\"center\">94</td><td align=\"center\">142</td><td align=\"center\">94</td><td align=\"center\">88</td><td align=\"center\">161</td></tr><tr><td align=\"left\">asCAP0157</td><td align=\"center\">314</td><td align=\"center\">49</td><td align=\"center\">63</td><td align=\"center\">198</td><td align=\"center\">91</td><td align=\"center\">122</td><td align=\"center\">96</td><td align=\"center\">111</td><td align=\"center\">174</td></tr><tr><td align=\"left\">asCAP0166</td><td align=\"center\">290</td><td align=\"center\">55</td><td align=\"center\">77</td><td align=\"center\">118</td><td align=\"center\">125</td><td align=\"center\">167</td><td align=\"center\">77</td><td align=\"center\">91</td><td align=\"center\">176</td></tr><tr><td align=\"left\">asCAP0167</td><td align=\"center\">294</td><td align=\"center\">54</td><td align=\"center\">73</td><td align=\"center\">78</td><td align=\"center\">125</td><td align=\"center\">180</td><td align=\"center\">56</td><td align=\"center\">98</td><td align=\"center\">185</td></tr></tbody></table></table-wrap>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Comparison of the present microarray study to an earlier microarray study that examined the early sporulation of <italic>C. acetobutylicum</italic>.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>COG analysis for each cluster and all the genes placed in each cluster.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Transcriptional profiles, in terms of both intensity and differential expression, of specific gene clusters.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Includes a brief discussion on how the genes used to construct the deduced activity plots were chosen.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Differential expression and intensity of all annotated histidine kinases and response regulators.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Phylogenetic tree resulting from the alignment of the σ<sup>70</sup>-related and unannotated sigma factors from ten bacterial species.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Sequences for each asRNA construct.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>TEM images of the plasmid control strain, asCAP0167, and asCAC1766.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>Primer sequences used in the Q-RT-PCR experiments.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*At 192 hours, significant amounts of acetone had evaporated along with small amounts of butanol. However, the cultures were still metabolically active, as indicated by the decreased amounts of glucose and increased amounts of butanol. <sup>†</sup>Concentrations are mM. <sup>‡</sup>pSOS95del was used as a plasmid control strain.</p></table-wrap-foot>" ]
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[{"surname": ["Lyristis", "Boynton", "Petersen", "Kan", "Bennett", "Rudolph"], "given-names": ["M", "ZL", "D", "Z", "GN", "FB"], "article-title": ["Cloning, sequencing, and characterization of the gene encoding flagellin, "], "italic": ["flaC", "Clostridium acetobutylicum "], "source": ["Anaerobe"], "year": ["2000"], "volume": ["6"], "fpage": ["69"], "lpage": ["79"], "pub-id": ["10.1006/anae.1999.0311"]}, {"surname": ["Lepage", "Fayolle", "Hermann", "Vandecasteele"], "given-names": ["C", "F", "M", "J-P"], "article-title": ["Changes in membrane lipid composition of "], "italic": ["Clostridium acetobutylicum"], "source": ["J Gen Microbiol"], "year": ["1987"], "volume": ["133"], "fpage": ["103"], "lpage": ["110"]}, {"surname": ["Alsaker", "Paredes", "Papoutsakis"], "given-names": ["KV", "CJ", "ET"], "article-title": ["Design, optimization and validation of genomic DNA microarrays for examining the "], "italic": ["Clostridium acetobutylicum "], "source": ["Biotechnol Bioprocess Eng"], "year": ["2005"], "volume": ["10"], "fpage": ["432"], "lpage": ["443"]}, {"surname": ["Driks", "Sonenshein AL, Hoch JA, Losick R"], "given-names": ["A"], "article-title": ["Proteins of the spore core and coat."], "source": ["Bacillus subtilis and its Closest Relatives: From Genes to Cells"], "year": ["2002"], "publisher-name": ["Washington, DC: American Society for Microbiology"], "fpage": ["527"], "lpage": ["535"]}]
{ "acronym": [], "definition": [] }
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CC BY
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2022-01-12 14:47:27
Genome Biol. 2008 Jul 16; 9(7):R114
oa_package/cf/53/PMC2530871.tar.gz
PMC2530872
18652698
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[ "<title>Conclusion</title>", "<p>MeV+R is a convenient platform to provide biologists with point and click GUI access to Bioconductor packages. We have demonstrated the successful integration of Bioconductor and MeV through three Bioconductor packages, RAMA, BRIDGE and iterativeBM, and that the incorporated Bioconductor packages produced superior results in the analysis of microarray data compared to existing tools in MeV. Additional Bioconductor packages are straightforward to add: the framework for moving data from MeV to R and back is generalized for code re-use, and each new package will merely require the development of a GUI for input and output.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>MeV+R provides users with point-and-click access to traditionally command-line-driven tools written in R.</p>", "<p>We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA.</p>" ]
[ "<title>Rationale</title>", "<p>While microarray technology has given biologists unprecedented access to gene expression data, reliable and effective data analysis remains a difficult problem. There are many freely or commercially available software packages, but biologists are often faced with trading off power and flexibility for usability and accessibility. In addition to the potentially prohibitive costs, researchers using commercial software tools may find themselves waiting for state-of-the-art algorithms to be implemented with the packages. The Bioconductor project [##REF##15461798##1##,##UREF##0##2##] is an open source software project that provides a wide range of statistical tools primarily based on the R programming environment and language [##UREF##1##3##,##UREF##2##4##]. Taking advantage of R's powerful statistical and graphical capabilities, developers have created and contributed numerous Bioconductor packages to solve a variety of data analysis needs. The use of these packages, however, requires a basic understanding of the R programming/command language and an understanding of the documentation accompanying each package. The primary users of R and the Bioconductor packages have been computational scientists, statisticians and the more computationally oriented biologists. However, in our experience, many biologists find themselves uncomfortable issuing command lines in a terminal. Hence, there is a need for a graphical user interface (GUI) for Bioconductor packages that will allow biologists easy access to data analytical tools without learning the command line syntax. The tcltk package in R adds GUI elements to R by allowing programmers to write GUI-driven modules by embedding Tk commands into the R language [##UREF##3##5##]. There are also GUIs developed for basic statistical analysis in R, such as the R Commander [##UREF##4##6##] and windows-based SciViews [##UREF##5##7##]. However, these GUIs are not designed for microarray analysis. There are Bioconductor packages, such as limmaGUI [##REF##15297296##8##], affylmGUI [##REF##16455752##9##] and OLINgui [##REF##15585527##10##] that are built on the R tcltk package to provide GUIs. LimmaGUI and affylmGUI provide GUIs for the analysis of designed experiments and the assessment of differential expression for two-color spotted microarrays and single-color Affymetrix data, respectively. OLINgui provides a GUI for the visualization, normalization and quality testing of two-channel microarray data. However, no such GUIs are available for the majority of Bioconductor packages. In addition, since each Bioconductor package is often written by a different research group, there is generally no uniformity in the look and feel of the GUIs available for the different packages. Hence, the end user may not be able to easily transfer experience gained with one analysis tool to the use of another.</p>", "<p>An alternative microarray data analysis tool is the MultiExperiment Viewer (MeV), a component of the TM4 suite of microarray analysis tools [##REF##12613259##11##]. MeV has a user-friendly GUI designed with the biological community in mind. MeV is an open source Java application with a simple to learn, easy to use GUI. It comes with many popular microarray analytical algorithms for clustering, visualization, classification and biological theme discovery, such as hierarchical clustering [##REF##9843981##12##] and Expression Analysis Systematic Explorer (EASE) [##REF##14519205##13##]. MeV was carefully designed to provide an application programming interface (API), thus allowing straightforward contributions by the community. MeV is hosted at SourceForge [##UREF##6##14##] in a concurrent versions system repository. As such, frequent builds of the source code are made possible, greatly reducing the lag time between version releases.</p>", "<p>In this paper, we present MeV+R, which is an effort to provide more consistent and well-integrated GUIs for Bioconductor packages by using MeV as a 'wrapper' application for Bioconductor methods. Our work brings the best of both worlds together: providing state-of-the-art statistical algorithms from Bioconductor through the open source and easy to use MeV graphical interface to the biomedical community. MeV+R has many advantages, including platform independence, a well-defined modular API, and a point and click GUI that is easy to learn and use. We demonstrate the successful integration and advantages of three Bioconductor packages (RAMA [##UREF##7##15##], BRIDGE [##REF##16542223##16##], and iterativeBMA [##REF##15713736##17##]) over existing tools in the MeV environment through case studies. The underlying framework that we used to integrate these Bioconductor packages with MeV is easily extensible to other analysis tools developed in R. The software, documentation and a tutorial are publicly available from our project home page [##UREF##8##18##].</p>", "<title>Implementation</title>", "<p>Our integration effort is composed of three separate entities (Figure ##FIG##0##1##). MeV provides the graphical user interface while Rserve serves as the communication layer and R is the language and environment in which the analysis packages run. Rserve is a TCP/IP server that allows various languages to use the facilities of R without the need to initialize R or link against an R library [##UREF##9##19##]. In other words, we use R as the back end to run Bioconductor packages through the use of Rserve. Rserve is open source, freely available [##UREF##10##20##], and licensed under GPL.</p>", "<p>As such, Java, Rserve, and R must all be installed on the user's computer, and we provide an automated installer on our project web site. Furthermore, Rserve needs to be running to be used. However, R does not need to be started. Since Rserve works through TCP/IP, it can run on the user's own machine, on an internal network or over the internet. By default, our code assumes Rserve to be running on the local host, but the user can change, add and save additional new hosts using a pull down menu. Once a connection is established, the Java code in MeV converts the user's data from the MeV data structure to the R format and loads it into R. The appropriate R libraries are loaded followed by the R commands that are necessary to initiate the analysis. Upon completion, the returned data from R are explicitly called back into MeV and presented to the user.</p>", "<p>We have incorporated three Bioconductor packages, RAMA [##UREF##7##15##], BRIDGE [##REF##16542223##16##], and iterativeBMA [##REF##15713736##17##], into MeV to illustrate the successful MeV+R integration. The Robust Analysis of MicroArray (RAMA) algorithm computes robust estimates of expression intensities from two-color microarray data, which typically consist of a few replicates and potential outliers [##UREF##7##15##]. RAMA also takes advantage of dye swap experimental designs. Bayesian Robust Inference for Differential Gene Expression (BRIDGE) is a robust algorithm that selects differentially expressed genes under different experimental conditions on both one- and two-color microarray data [##REF##16542223##16##]. Both RAMA and BRIDGE make use of a computationally intensive technique called Markov Chain Monte Carlo for parameter estimation, and it is non-trivial to re-implement these algorithms in Java. Hence, we took advantage of our previous development work by simply using MeV as an interface to the Bioconductor packages. The iterative BMA algorithm is a multivariate gene selection and classification algorithm, which considers multiple genes simultaneously and typically leads to a small number of relevant genes to classify microarray data [##REF##15713736##17##]. The iterativeBMA Bioconductor package implements the iterative BMA algorithm as previously described [##REF##15713736##17##] in R, and its implementation is part of our current integration effort. Both RAMA and BRIDGE are included in the latest release of MeV (version 4.1), and iterativeBMA will be included in future releases. The user interfaces, usage and case studies for RAMA, BRIDGE and iterativeBMA are briefly described below. Detailed documentation is included with the software distribution [##UREF##11##21##] as well as linked in the MeV application. Help pages are also available as Help Dialogs accessed via buttons on the MeV dialog boxes. Our MeV+R implementation is publicly available and runs on Windows, Mac OS X and Linux.</p>", "<title>Integrated Bioconductor packages: description and user interfaces</title>", "<title>RAMA: Robust Analysis of MicroArrays</title>", "<p>RAMA uses a Bayesian hierarchical model for the robust estimation of cDNA microarray intensities with replicates. This is highly relevant for replicated microarray experiments because even one outlying replicate (such as due to scratches or dust) can have a disastrous effect on the estimated signal intensity. Outliers are modeled explicitly using a t-distribution, which is more robust than the usual Gaussian model. Our model borrows strength from all the genes to decide if a measurement is an outlier, and hence it is better at detecting outliers based on a small number of replicate measurements than other classical robust estimators. Our algorithm uses Markov Chain Monte Carlo for parameter estimation, and addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. Please refer to [##UREF##7##15##] for a detailed description of the algorithm.</p>", "<title>User interface</title>", "<p>The user can start RAMA by clicking 'Adjust Data' - 'Replicate Analysis' - '<italic>RAMA</italic>' from the MeV main menu. The RAMA dialog box is then displayed asking the user to label the arrays that were loaded into MeV with their appropriate dye color. At this time, the user is asked to make sure that Rserve is running. On a Win32 system, double clicking Rserve.exe accomplishes this. On a UNIX or Linux or Mac OS X system, the user issues the command 'R CMD Rserve' at a prompt. By default, RAMA will look on the local machine for an Rserve server. However, since Rserve is a TCP/IP server, the Rserve server can be a remote machine. The user is allowed to adjust a few advanced parameters, though suggested values are given as defaults. If an Rserve connection is successfully made, the location of Rserve is written to the user's MeV configuration file and will be available in later sessions. After clicking 'OK', the input data are sent to R. An indeterminate progress bar is displayed while RAMA runs - unfortunately, the architecture of RServe and the R Server do not allow for an accurate indication of the time remaining in an ongoing analysis. Once completed, the user is given a dialog box to save the results. The returned results will then replace the loaded data in a new Multiple Array Viewer (MAV). The old MAV is deleted. The user can then choose to continue using MeV as if the data were loaded through the native loading modules.</p>", "<title>BRIDGE: Bayesian Robust Inference for Differential Gene Expression</title>", "<p>BRIDGE fits a robust Bayesian hierarchical model to test for differentially expressed genes on microarray data. It can be used with both two-color microarrays and single-channel Affymetrix chips. BRIDGE builds on the previous work of Gottardo <italic>et al</italic>. [##UREF##7##15##] by allowing each gene to have a different variance and the detection of differentially expressed genes under multiple (up to three in our current implementation) experimental conditions. Robust inference is accomplished by modeling outliers using a t-distribution, and hence BRIDGE is powerful even with a small number of samples (either biological or technical replicates) under each experimental condition. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo. The current implementation of BRIDGE does not handle missing values. Please refer to [##REF##16542223##16##] for a detailed description of the model.</p>", "<title>User interface</title>", "<p>BRIDGE starts when a user clicks the '<italic>BRIDGE</italic>' button in the toolbar located on top of the MeV window. The user is once again presented with a dialog box similar to that of RAMA asking for the dye labeling identity of each loaded slide. The user is offered the option to adjust the advanced parameters and to establish an Rserve connection. After clicking OK, the input data are sent to R. An indeterminate progress bar is displayed while BRIDGE runs. The results are presented to the user in three formats: heat maps, expression graphs or tables. In each format, the genes for which there is strong evidence of differential expression are identified as 'Significant Genes', defined by the posterior probability being above 0.5.</p>", "<title>IterativeBMA: Iterative Bayesian Model Averaging</title>", "<p>The iterativeBMA algorithm is a multivariate technique for gene selection and classification of microarray data. Bayesian Model Averaging (BMA) takes model uncertainty into consideration by averaging over the predicted probabilities based on multiple models, weighted by their posterior model probabilities [##UREF##12##22##]. The most commonly used BMA algorithm is limited to data in which the number of variables is greater than the number of responses, and the algorithm is inefficient for datasets containing more than 30 genes (variables). In the case of classifying samples using microarray data, there are typically thousands or tens of thousands of genes (variables) under a few dozen samples (responses). In the iterative BMA algorithm, we start by ranking the genes using the ratio of between-group to within-group sum of squares (BSS/WSS) [##UREF##13##23##]. In this initial preprocessing step, genes with large BSS/WSS ratios (that is, genes with relatively large variation between classes and relatively small variation within classes) receive high rankings. We then apply the traditional BMA algorithm to the 30 top ranked genes, and remove genes with low posterior probabilities. Genes from the rank ordered BSS/WSS ratios are then added to the set of genes to replace genes with low probabilities. These steps of gene swaps and iterative applications of BMA are repeated until all genes are subsequently considered. We have previously shown that the iterative BMA algorithm selects small numbers of relevant genes, achieves high prediction accuracy, and produces posterior probabilities for the predictions, selected genes and models [##REF##15713736##17##].</p>", "<p>The iterativeBMA Bioconductor package implements the iterative BMA algorithm described in Yeung <italic>et al</italic>. [##REF##15713736##17##] (previously implemented in Splus) when there are two classes. It is part of the original work for this publication. The user documentation (vignette) is included in the package.</p>", "<title>User interface</title>", "<p>We have integrated the iterativeBMA Bioconductor package in MeV. IterativeBMA starts after the user clicks on the 'iBMA' icon on top of the MeV window. The current implementation of the iterativeBMA Bioconductor package is limited to only two classes. After loading the data, the user is asked to label the two classes. The default labels for the two classes are 0 and 1, respectively. In the same dialog box, the user is asked to establish an Rserve connection. The user is also given the option of specifying advanced parameters for the analysis. The next dialog box asks the user to assign labels to each of the samples in the data, either by using a pull-down menu or loading an assignment file. At this point, if Rserve is not already running, the user is reminded to start the connection. Then, the data and the parameters are sent to R, and a progress bar is shown warning the user that the computation could take a long time. After the iterativeBMA Bioconductor package finishes running, the following analysis results are displayed: the predicted probability and class for each test sample; the posterior probabilities of the selected genes sorted in descending order; the posterior probabilities of the selected models sorted in descending order; and the heatmaps of the selected genes in both classes.</p>", "<title>Case studies illustrating the merits of the integrated Bioconductor packages</title>", "<p>In this section, we compare the performance of the integrated Bioconductor packages (RAMA, BRIDGE and iterativeBMA) to existing tools in MeV in order to illustrate the merits of the integrated packages. In addition, we demonstrate that our MeV+R modules can be used together with other MeV modules in the integrated analysis of microarray data, hence, extending the capabilities of MeV.</p>", "<title>RAMA: Robust Analysis of MicroArrays</title>", "<p>We compared the microarray gene intensities estimated using RAMA to that of the log ratios over intensities averaged over all the replicates on two microarray datasets and the results are summarized in Table ##TAB##0##1##. The first dataset is a subset of the HIV data [##REF##12502855##24##] consisting of the expression levels of 1,028 transcripts, including 13 positive controls and 24 negative controls, in CD4-T-cell lines at time t = 1 hour after infection with HIV virus type 1 hybridized to two-color cDNA arrays. The experimental design consists of four technical replicates and balanced dye swap in which two of the four replicates were hybridized with Cy3 for the control and Cy5 for the treatment and then the dyes were reversed on the other two replicates. The second dataset is a subset of the like and like data [##UREF##7##15##] consisting of 1,000 genes over four experiments using the same RNA preparation isolated from a HeLa cell line on four different microarray slides. Since the same RNA was used in both channels, no genes from these data should show any differential expression. Both sample datasets are available on our project web site and are included as part of our MeV+R package release.</p>", "<p>Figure ##FIG##1##2## shows the log ratios of all genes sorted in descending order after applying RAMA integrated in MeV+R to the HIV data. As shown in Figure ##FIG##1##2##, the log ratios (to base 2) computed with the robust intensities estimated using RAMA for all 13 positive controls are all greater than one. The log ratios from RAMA for all 24 negative controls are smaller than one (data not shown in Figure ##FIG##1##2##). On the contrary, computing the log ratios by simply averaging the gene intensities over the four replicates produces log ratios greater than one for three negative controls. Applying RAMA to the like and like data produces no log ratio greater than one as desired since we do not expect any differentially expressed genes. On the contrary, the average log ratio of gene intensities yields six genes with log ratios greater than one. Please refer to the supplementary material [##UREF##8##18##] for the details of our case studies. To summarize, RAMA produced the desired results on both datasets while the averaged log ratio produced three and six false positives, respectively, on these two datasets.</p>", "<title>BRIDGE: Bayesian Robust Inference for Differential Gene Expression</title>", "<p>We compared the differentially expressed genes identified using BRIDGE, <italic>t</italic>-test and SAM (Significance Analysis of Microarrays) [##REF##11309499##25##] as implemented in MeV on two datasets. Applying BRIDGE to the HIV data described in the previous section identified all 13 positive controls as 'significant' genes (Figure ##FIG##2##3##). On the other hand, applying the one-sample <italic>t</italic>-test as implemented in MeV to the same HIV data identified a total of 14 significant genes, including all 13 positive controls and one negative control using a <italic>p</italic>-value cut-off of 0.01 without any Bonferroni correction. Using a <italic>p</italic>-value cut-off of 0.05 and standard Bonferroni correction, the one-sample <italic>t</italic>-test identified only one significant gene (which is one of the 13 positive controls) and incorrectly assigned the remaining 12 positive controls as 'insignificant'. Similarly, using one-sample SAM as implemented in MeV identified 12 out of 13 positive controls using default parameters.</p>", "<p>The second dataset we used comprises the Affymetrix U133 spike-in data [##UREF##14##26##], which consists of three technical replicates of 14 separate hybridizations of 42 spiked transcripts in a complex human background at varying concentrations. Thirty of the spikes are isolated from a human cell line, four spikes are bacterial controls, and eight spikes are artificially engineered sequences believed to be unique in the human genome. The data were preprocessed using GCRMA [##REF##12582260##27##], resulting in a dataset of 22,300 genes across 42 samples. In addition to the original 42 spiked-in genes, we included an additional 20 genes that consistently showed significant differential expression across the array groups and an additional three genes containing probe sequences exactly matching those for the spiked-in genes [##REF##16447972##28##,##REF##17138586##29##]. As a result, our expanded spiked-in gene list contains 65 entries in total. We used a subset of this spiked-in data consisting of 1,059 genes that include all 65 spiked-in genes across two samples in triplicate. In our comparison, only the 65 spiked-in genes should be identified as differentially expressed.</p>", "<p>BRIDGE identified 45 differentially expressed genes on this data subset. All of these 45 genes identified by BRIDGE are spiked-in genes. On the other hand, the <italic>t</italic>-test with a <italic>p</italic>-value cut-off of 0.01 without any correction for multiple comparison identified a total of 33 significant genes, of which 31 were spiked-in genes. Using a <italic>p</italic>-value cut-off of 0.05 and the standard Bonferroni correction, the <italic>t</italic>-test identified only four significant genes (which are among the spiked-in genes). SAM identified eight spiked-in genes as differentially expressed.</p>", "<p>Our comparison results are summarized in Table ##TAB##1##2##. We have shown that BRIDGE is the only tool that successfully identified all 13 positive controls as 'significant' on the HIV data. In addition, BRIDGE identified the highest number of true positives (spiked-in genes) without any false positives on the Affymetrix spike-in data.</p>", "<title>IterativeBMA: Iterative Bayesian Model Averaging</title>", "<p>We compared the performance of iterativeBMA (abbreviated as iBMA in our MeV+R implementation) to KNN (k-nearest neighbor) [##REF##11779842##30##] and USC (Uncorrelated Shrunken Centroid) [##REF##14659020##31##] implemented in MeV using the well-studied leukemia data [##REF##10521349##32##]. We used the filtered leukemia dataset, which consists of 3,051 genes, 38 samples in the training data and 34 samples in the test set. The data consist of samples from patients with either acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML). On the leukemia data, iterativeBMA produced 2 classification errors using 11 selected genes over 11 models (Figures ##FIG##3##4## and ##FIG##4##5##). On the other hand, KNN does not have a gene selection procedure and produced 2 classification errors using all 3,051 genes. Similarly, USC produced 2 classification errors using 51 selected genes.</p>", "<p>The second dataset we used is the breast cancer prognosis dataset [##REF##11823860##33##], which consists of 4,919 genes with 76 samples in the training set, and 19 samples in the test set [##REF##15713736##17##]. The patient samples are divided into two categories: the good prognosis group (patients who remained disease free for at least five years) and the poor prognosis group (patients who developed distant metastases within five years). The iterativeBMA algorithm produced three classification errors using four genes averaged over three models. On the other hand, KNN does not have a gene selection procedure and produced five classification errors using all genes. Similarly, USC produced four classification errors using 662 genes.</p>", "<p>Our results are summarized in Table ##TAB##2##3##. On the breast cancer prognosis data, iterativeBMA produced higher prediction accuracy using much fewer genes. On the leukaemia data, iterativeBMA produced comparable prediction accuracy using much fewer genes.</p>", "<title>Using other MeV modules in an integrated data analysis</title>", "<p>The previous sub-sections showed that our MeV+R modules achieved superior performance when compared to other existing tools implemented in MeV. Here we demonstrate how the R packages that we incorporated into MeV can be used in combination with other existing tools in MeV. This illustrates the fact that the MeV+R framework has extended the capabilities of MeV, and that using these R packages through the MeV GUI adds value to the integrated analysis of microarray data.</p>", "<p>In this case study, we will follow-up on the results from applying the iterativeBMA algorithm to the leukemia data [##REF##10521349##32##]. The iterativeBMA algorithm is a multivariate gene selection method designed to select a small set of predictive genes for the classification of microarray data. In the case of the leukemia data, the iterativeBMA algorithm selected 11 genes that produced two classification errors on the 34-sample test set. It would be interesting to identify the biological theme in this 11-gene list. Towards this end, we applied EASE [##REF##14519205##13##] as implemented in MeV to determine the over-represented Gene Ontology categories in this gene list relative to all the genes on the microarray. Figure ##FIG##5##6## shows the tabular view from the EASE analysis.</p>", "<p>Since iterativeBMA identifies a small set of predictive genes for classification, other genes that exhibit similar expression patterns to the selected genes are likely of biological interest. For example, we would like to explore the gene with the highest posterior probability 'X95735_at' from the iterativeBMA analysis on the leukemia data [##REF##10521349##32##]. We applied PTM (Template Matching) [##REF##11597334##34##] as implemented in MeV to identify genes that are highly correlated with 'X95735_at'. Using a <italic>p</italic>-value threshold of 0.0001, PTM identified 209 genes that are highly correlated with 'X95735_at'. Our next task was to find the biological theme among these 209 genes, so we applied EASE and TEASE (Tree-EASE). TEASE is a combined analytical tool for hierarchical clustering and EASE. TEASE computes the dendrogram using the hierarchical clustering method and displays the significantly enriched Gene Ontology categories for each subtree in the dendrogram. Please refer to the supplementary materials [##UREF##8##18##] for the details of our case studies.</p>", "<title>Incorporating additional R packages</title>", "<p>We have developed a framework with built-in functions for the integration of Bioconductor packages into MeV. Detailed documentation of these built-in functions is provided on our project web site for software developers. Using this framework, we have integrated three Bioconductor packages (RAMA, BRIDGE and iterativeBMA) into MeV as proof of concept. To integrate additional Bioconductor packages into MeV, a software developer can simply call our built-in functions except for complex and non-standard data views.</p>", "<title>Abbreviations</title>", "<p>API, application programming interface; BMA, Bayesian Model Averaging; BRIDGE, Bayesian Robust Inference for Differential Gene Expression; BSS/WSS, ratio of between-group to within-group sum of squares; EASE, Expression Analysis Systematic Explorer; GUI, graphical user interface; iterativeBMA, iterative Bayesian Model Averaging; KNN, k-nearest neighbor; MAV, Multiple Array Viewer; MeV, MultiExperiment Viewer; PTM, Template Matching; RAMA, Robust Analysis of MicroArray; SAM, Significance Analysis of Microarrays; USC, Uncorrelated Shrunken Centroid.</p>", "<title>Authors' contributions</title>", "<p>VC carried out the software implementation, and drafted part of the initial manuscript. RG and AER designed and wrote the Bioconductor packages RAMA and BRIDGE, and assisted in incorporating these packages into MeV. REB conceived of the study, and designed and coordinated the project. KYY participated in the design and coordination of the study, wrote the iterativeBMA Bioconductor package, carried out the case studies and prepared the manuscript. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We would like to give special thanks to Dr John Quakenbush and his research group for the original development of MeV and for insightful discussions related to this work. We would also like to thank Drs Chris Volinsky and Ian Painter for their help on the development of the iterativeBMA Bioconductor package. We would also like to thank Dr Renee Ireton for editing the manuscript. VTC is supported by NIH-NCI grant K25CA106988 and 5R01HL072370. RG's research is supported by the Natural Sciences and Engineering Research Council of Canada. AER's research was supported by NICHD grant R01 HD054511, NIH grant 8 R01 EB002137-02, NSF grant ATM 0724721 and ONR grant N00014-01-10745. REB is funded by NIH-NIAID grants 5P01 AI052106-02, 1R21AI052028-01 and 1U54AI057141-01, NIH-NIEHA grant 1U19ES011387-02, NIH-NHLBI grants 5R01HL072370-02 and 1P50HL073996-01. KYY is supported by NIH-NCI grant K25CA106988.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Our integration effort is composed of three separate entities: MeV as the GUI, Rserve as the communication layer, and R as the language and environment in which the analysis packages run.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>The results of applying RAMA to the HIV data. The log ratios computed from RAMA are sorted in descending order, and the top 13 genes with log ratios greater than one are the positive controls.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>The significant genes identified by applying BRIDGE to the HIV data.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>The results of applying iterativeBMA to the leukemia data. A heatmap showing the selected genes from iterativeBMA under the training samples labeled as class 0 and the test samples assigned to class 0 by the algorithm.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>The results of applying iterativeBMA to the leukemia data. A heatmap showing the selected genes from iterativeBMA under the training samples labeled as class 1 and the test samples assigned to class 1 by the algorithm.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>The results of applying EASE to the 11 genes selected by iterativeBMA on the leukemia data.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Comparing the results of RAMA to the averaged log ratios on the HIV data and the like and like data</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Data</td><td align=\"left\">Benchmark</td><td align=\"left\">RAMA</td><td align=\"left\">Averaged log ratio</td></tr></thead><tbody><tr><td align=\"left\">HIV data</td><td align=\"left\">13 positive controls</td><td align=\"left\">All 13 positive controls have log ratios &gt;1</td><td align=\"left\">All 13 positive controls have log ratios &gt;1</td></tr><tr><td/><td align=\"left\">24 negative controls</td><td align=\"left\">All 24 negative controls have log ratios &lt;1</td><td align=\"left\">3 negative controls have log ratios &gt;1</td></tr><tr><td align=\"left\">Like and like data</td><td align=\"left\">No genes expected to be differentially expressed</td><td align=\"left\">All log ratios &lt;1</td><td align=\"left\">6 genes with log ratios &gt;1</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Comparing the results of BRIDGE to <italic>t</italic>-test and SAM on the HIV data and the Affymetrix spike-in data</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td/><td align=\"center\" colspan=\"2\"><italic>t</italic>-test</td><td/></tr><tr><td/><td/><td/><td colspan=\"2\"><hr/></td><td/></tr><tr><td align=\"left\">Dataset</td><td align=\"left\">Benchmark</td><td align=\"center\">BRIDGE</td><td align=\"center\"><italic>p</italic>-value cut-off 0.01, no correction</td><td align=\"center\"><italic>p</italic>-value cut-off 0.05, standard Bonferroni correction</td><td align=\"center\">SAM</td></tr></thead><tbody><tr><td align=\"left\">HIV data</td><td align=\"left\">13 positive controls, 24 negative controls</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> DE</td><td/><td align=\"center\">13</td><td align=\"center\">14</td><td align=\"center\">1</td><td align=\"center\">12</td></tr><tr><td align=\"left\"> TP</td><td/><td align=\"center\"><bold>13</bold></td><td align=\"center\"><bold>13</bold></td><td align=\"center\">1</td><td align=\"center\">12</td></tr><tr><td align=\"left\"> FP</td><td/><td align=\"center\"><bold>0</bold></td><td align=\"center\">1</td><td align=\"center\"><bold>0</bold></td><td align=\"center\"><bold>0</bold></td></tr><tr><td align=\"left\">Affymetrix spike-in data</td><td align=\"left\">65 spike-in genes</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> DE</td><td/><td align=\"center\">45</td><td align=\"center\">33</td><td align=\"center\">4</td><td align=\"center\">8</td></tr><tr><td align=\"left\"> TP</td><td/><td align=\"center\"><bold>45</bold></td><td align=\"center\">31</td><td align=\"center\">4</td><td align=\"center\">8</td></tr><tr><td align=\"left\"> FP</td><td/><td align=\"center\"><bold>0</bold></td><td align=\"center\">2</td><td align=\"center\"><bold>0</bold></td><td align=\"center\"><bold>0</bold></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Comparing the results of iterativeBMA to KNN and USC on the leukemia data and the breast cancer prognosis data</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Data</td><td align=\"left\">Size of data</td><td align=\"center\">iterativeBMA</td><td align=\"center\">KNN</td><td align=\"center\">USC</td></tr></thead><tbody><tr><td align=\"left\">Leukemia data [##REF##10521349##32##]</td><td align=\"left\">38 training samples</td><td align=\"center\"><bold>11 genes</bold></td><td align=\"center\">3,051 genes</td><td align=\"center\">51 genes</td></tr><tr><td/><td align=\"left\">34 test samples</td><td align=\"center\">2 errors</td><td align=\"center\">2 errors</td><td align=\"center\">2 errors</td></tr><tr><td align=\"left\">Breast cancer prognosis data [##REF##11823860##33##]</td><td align=\"left\">76 training samples</td><td align=\"center\"><bold>4 genes</bold></td><td align=\"center\">4,919 genes</td><td align=\"center\">662 genes</td></tr><tr><td/><td align=\"left\">19 test samples</td><td align=\"center\"><bold>3 errors</bold></td><td align=\"center\">5 errors</td><td align=\"center\">4 errors</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
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[]
[ "<table-wrap-foot><p>RAMA produced the desired results on both datasets while the averaged log ratio produced three and six false positives, respectively, on these two datasets.</p></table-wrap-foot>", "<table-wrap-foot><p>For each dataset and each method, the number of differentially expressed (DE) genes, true positives (TP) and false positives (FP) are shown. For each dataset, the maximum TP and the minimum FP across all methods are shown in bold. BRIDGE produced the best results on both datasets in identifying the highest number of true positives without any false positives.</p></table-wrap-foot>", "<table-wrap-foot><p>The number of selected genes and the number of classification errors are shown for each method. For each dataset, the smallest number of genes and the smallest number of classification errors across all three methods are shown in bold. On the leukemia data, iterativeBMA produced the same number of classification errors using much fewer genes. On the breast cancer prognosis data, iterativeBMA produced fewer errors using much fewer genes.</p></table-wrap-foot>" ]
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[]
[{"article-title": ["The Bioconductor Project"]}, {"surname": ["Ihaka", "Gentleman"], "given-names": ["R", "RC"], "article-title": ["R: a language for data analysis and graphics."], "source": ["J Computational Graphical Stat"], "year": ["1996"], "volume": ["5"], "fpage": ["299"], "lpage": ["314"], "pub-id": ["10.2307/1390807"]}, {"article-title": ["The R Project for Statistical Computing"]}, {"surname": ["Dalgaard", "Hornik K, Leisch F"], "given-names": ["P"], "article-title": ["The R-Tcl/Tk interface."], "source": ["Proceedings of the Second International Workshop on Distributed Statistical Computing: 15-17 March 2001; Vienna, Austria"]}, {"surname": ["Fox"], "given-names": ["J"], "article-title": ["The R commander: a basic-statistics graphical user interface to R."], "source": ["J Stat Software"], "year": ["2005"], "volume": ["14"]}, {"surname": ["Grosjean", "Hornik K, Leisch F, Zeileis A"], "given-names": ["P"], "article-title": ["SciViews: an object-oriented abstraction layer to design GUIs on top of various calculation kernels."], "source": ["Proceedings of the Third International Workshop on Distributed Statistical Computing: 20-22 March 2003; Vienna, Austria"]}, {"article-title": ["SourceForge.net"]}, {"surname": ["Gottardo", "Raftery", "Yeung", "Bumgarner"], "given-names": ["R", "AE", "KY", "RE"], "article-title": ["Quality control and robust estimation for cDNA microarrays with replicates."], "source": ["J Am Stat Assoc"], "year": ["2006"], "volume": ["101"], "fpage": ["30"], "lpage": ["40"], "pub-id": ["10.1198/016214505000001096"]}, {"article-title": ["MeV+R Supplementary Web Site"]}, {"surname": ["Urbanek", "Hornik K, Leisch F, Zeileis A"], "given-names": ["S"], "article-title": ["Rserve - a fast way to provide R functionality to applications."], "source": ["Proceedings of the Third International Workshop on Distributed Statistical Computing: 20-22 March 2003; Vienna, Austria"]}, {"article-title": ["Rserve"]}, {"article-title": ["MeV Manual"]}, {"surname": ["Raftery"], "given-names": ["AE"], "article-title": ["Bayesian model selection in social research (with discussion)."], "source": ["Sociol Methodol"], "year": ["1995"], "volume": ["25"], "fpage": ["111"], "lpage": ["196"], "pub-id": ["10.2307/271063"]}, {"surname": ["Dudoit", "Fridlyand", "Speed"], "given-names": ["S", "J", "TP"], "article-title": ["Comparison of discrimination methods for the classification of tumors using gene expression data."], "source": ["J Am Stat Assoc"], "year": ["2002"], "volume": ["97"], "fpage": ["77"], "lpage": ["87"], "pub-id": ["10.1198/016214502753479248"]}, {"article-title": ["Affymetrix U133 Spike-in Data"]}]
{ "acronym": [], "definition": [] }
34
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 24; 9(7):R118
oa_package/16/7e/PMC2530872.tar.gz
PMC2530873
18638379
[ "<title>Background</title>", "<p>The protozoan parasite <italic>Leishmania </italic>is distributed worldwide and is responsible for a wide spectrum of diseases, including cutaneous, mucocutaneous and visceral leishmaniasis. No vaccines are presently available against <italic>Leishmania </italic>infections [##REF##11292637##1##] and treatments rely primarily on chemotherapy. The chemotherapeutic arsenal is limited and resistance to the mainstay of pentavalent antimonials has reached epidemic proportions in parts of India [##REF##11049798##2##]. Several studies dealing with drug resistance in <italic>Leishmania </italic>have highlighted the plasticity of the <italic>Leishmania </italic>genome [##REF##1741620##3##,##REF##8561467##4##]. The antifolate methotrexate (MTX) has been one of the first and most widely used drugs for understanding drug-induced plasticity and resistance mechanisms [##REF##6572966##5##, ####REF##6467372##6##, ##REF##3726545##7##, ##REF##3724795##8####3724795##8##]. While <italic>Leishmania </italic>is sensitive to MTX, the drug is not used clinically to treat leishmaniasis. However, <italic>Leishmania </italic>is a folic acid auxotroph and studies of MTX resistance mechanisms have highlighted several novel aspects of folate metabolism in this parasite that could be exploited for drug interventions [##REF##9309772##9##,##REF##11849635##10##]. Indeed, the development of novel antifolate molecules for <italic>Leishmania </italic>and related parasites has been ongoing in several laboratories [##REF##9371081##11##, ####REF##11327604##12##, ##REF##15755663##13####15755663##13##].</p>", "<p><italic>Leishmania </italic>resists MTX by a number of mechanisms. <italic>Leishmania </italic>has the capacity to transport folic acid, but this activity is often impaired in MTX resistant cells [##REF##3724795##8##,##REF##3654626##14##, ####REF##3366764##15##, ##REF##8414986##16##, ##REF##7912069##17####7912069##17##]. The main <italic>Leishmania </italic>folate transporter FT1 has been isolated [##REF##11295174##18##,##REF##15466466##19##] and is part of a large family of folate biopterin transporter (FBT) proteins with 14 members in <italic>Leishmania </italic>(AA Ouameur <italic>et al</italic>., unpublished data). Rearrangements of <italic>FBT </italic>genes are correlated with MTX resistance [##REF##15466466##19##, ####REF##12023977##20##, ##REF##14981076##21####14981076##21##]. A frequent mechanism of drug resistance in <italic>Leishmania </italic>is gene amplification [##REF##1741620##3##]. Small chromosomal regions of 20-70 kb that are part of one of the 36 <italic>Leishmania </italic>chromosomes are amplified as part of extrachromosomal elements [##REF##1741620##3##]. These elements are usually formed by recombination between repeated homologous sequences [##REF##1672636##22##, ####REF##8098523##23##, ##REF##8668175##24####8668175##24##]. Amplification of the gene coding for the target dihydrofolate reductase-thymidylate synthase (DHFR-TS) has been described in MTX resistant parasites [##REF##6572966##5##,##REF##6467372##6##,##REF##2822697##25##, ####REF##2476667##26##, ##REF##8144647##27##, ##REF##10471733##28##, ##REF##14530437##29####14530437##29##]. Work on MTX resistance also led to the characterization of the pteridine reductase PTR1, whose main function is to reduce pterins. However, when overexpressed it can also reduce folic acid and lead to MTX resistance by by-passing DHFR-TS activity [##REF##1339441##30##, ####REF##1396560##31##, ##REF##9153248##32##, ##REF##9186479##33####9186479##33##]. The <italic>PTR1 </italic>gene is frequently amplified as part of extrachromosomal circular or linear amplicons [##REF##6467372##6##,##REF##8414986##16##,##REF##1672636##22##,##REF##3182827##34##, ####REF##3244352##35##, ##REF##3182826##36##, ##REF##7659507##37##, ##REF##9649621##38####9649621##38##]. In addition to these three main mechanisms of resistance, perturbation in folate metabolism [##REF##12963486##39##,##REF##16876889##40##], in one carbon metabolism [##REF##12644573##41##] or in DNA metabolism [##REF##15850702##42##] have also been associated with MTX resistance. Several of these mutations can co-exist in the same cell, demonstrating that resistance can be a complex multi-gene phenomenon. Genome wide expression profiling scans represent a useful tool for understanding complex resistance mechanisms and may lead either to the discovery of novel resistance mechanisms and/or could provide clues about mechanisms of gene rearrangements.</p>", "<p>Indeed, DNA microarrays have been useful for investigating the mode of action of drugs [##REF##10536008##43##] and mechanisms of resistance (reviewed in [##REF##15335284##44##, ####REF##16213417##45##, ##UREF##0##46####0##46##]). DNA microarrays for <italic>Leishmania </italic>have evolved from random genomic DNA clones [##REF##12798511##47##, ####REF##15138069##48##, ##REF##17204342##49##, ##REF##17553180##50####17553180##50##], cDNA clones [##REF##15138070##51##,##REF##17306059##52##], targeted PCR fragments [##REF##14530437##29##], selected 70-mer oligonucleotides [##REF##16705753##53##,##REF##17030997##54##] to full genome microarrays [##REF##16430978##55##,##REF##17188763##56##]. Targeted microarrays have been used previously for the study of drug resistance in <italic>Leishmania </italic>[##REF##14530437##29##,##REF##17306059##52##,##REF##17030997##54##,##REF##15855523##57##]. We present here the generation of full genome DNA microarrays for both <italic>L. major </italic>and <italic>L. infantum </italic>and their use in the study of one <italic>L. major </italic>and one <italic>L. infantum </italic>MTX resistant mutant. These genome wide expression profiling experiments illustrate the complexity of resistance mechanisms present in the same cell. They allowed the definition of the precise mechanisms leading to the formation of extrachromosomal circular and linear amplicons, the definition of gene deletion events and revealed the involvement of aneuploidy in the complex genotype of MTX resistance.</p>" ]
[ "<title>Materials and methods</title>", "<title>Cell culture</title>", "<p>The wild-type strain <italic>L. major </italic>LV39 and the mutants <italic>L. major </italic>MTX60.4 have been described previously [##REF##15190060##65##]. The <italic>L. infantum </italic>strain (MHOM/MA/67/ITMAP-263) was selected <italic>in vitro </italic>in a stepwise fashion starting with its EC<sub>50 </sub>(0.5 μM) with doubling concentrations of MTX when cells were adapted to yield <italic>L. infantum </italic>MTX20.5 growing at 20 μM of MTX. All cells were grown in M199 medium supplemented with 10% heat-inactivated fetal bovine serum and 5 μg/ml hemin at 25°C.</p>", "<title>DNA manipulation</title>", "<p>Chromosomes in agarose blocks were prepared and separated by PFGE as described previously [##REF##9649621##38##]. For Southern blot and PCR, genomic DNA was isolated using the DNAzol technique (Invitrogen, Carlsbad, CA, USA) as recommended by the manufacturer. Southern blots, hybridization, and washing conditions were done following standard protocols [##UREF##2##80##]. For chromosome copy number investigation, Southern spots were quantified using ImageQuant 5.2 (GE Healthcare, Upsala, Sweden) and the reference gene <italic>α-tubulin </italic>was used for normalization.</p>", "<title><italic>L. infantum </italic>and <italic>L. major </italic>DNA oligonucleotides full genome microarray design</title>", "<p>The recent completion of the sequence of the <italic>L. major </italic>[##REF##16020728##81##] and <italic>L. infantum </italic>[##REF##17572675##82##] genomes, allowed the generation of multispecies high-density oligonucleotide microarrays. Our analysis of open reading frame sequence conservation between <italic>L. major </italic>and <italic>L. infantum </italic>revealed that these two species share 91-96% nucleotide identity, suggesting that interspecies microarray probes can be designed. Therefore, 70-mer oligonucleotides were designed for each open reading frame of <italic>L. infantum </italic>and <italic>L. major </italic>using automated bioinformatic procedures. The genomes of both species were first compared using BLAST and homologous genes were grouped together. Probes were designed with consistent thermodynamic properties. Probes were initially designed for <italic>L. infantum </italic>with the added requirement that the region targeted by the probes had perfect homology between both species. For common probes, up to 2 mismatches (out of 70 nucleotides) were tolerated. In the case that more than two mismatches were present in a given gene between <italic>L. infantum </italic>and <italic>L. major</italic>, a new probe was designed specifically for <italic>L. major </italic>(956 probes). The microarray included a total of 8,978 70-mer probes that recognized with no mismatches all <italic>L. infantum </italic>genes (8,184, GeneDB version 3) and also all <italic>L. major </italic>genes (8,370, GeneDB version 5.1) with a small percentage of the probes having at most 2 mismatches. Also, 372 control probes were included in the microarray for assessing synthesis variability, and location of the probe within a given open reading frame and of mismatches on hybridization. The probes were synthesized in 384-well plates by Invitrogen. The microarrays were printed on SuperChip (Erie Scientific, Portsmouth, NH, USA) using a BioRobotics MicroGrid (Genomic solutions Inc, Ann Arbor, MI, USA). Each probe was printed in duplicate. Our microarray platform is described in the Gene Expression Omnibus (GEO) with accession number GPL6855.</p>", "<title>Total RNA preparation and labeling</title>", "<p>Total RNA was isolated from 10<sup>8 </sup><italic>Leishmania </italic>cells during the mid-log phase using RNeasy Plus Mini Kit (QIAGEN, Hilden, Germany). The RNA preparation was treated with TURBO DNase (Ambion, Austin, TX, USA) to avoid any genomic contamination. The purity, integrity and quantity of the RNA were assessed on the Agilent 2100 bioanalyzer with the RNA 6000 Nano LabChip reagent set (Agilent Technologies, Santa Clara, CA, USA). For each probe, 10 μg of RNA were converted to aminoallyl-dUTP incorporated cDNA using random hexamers (Roche, Basel, Switzerland) and the SuperScript III RNase H Reverse Transcriptase (Invitrogen). Probes were thereafter coupled to the fluorescent dye Alexa Fluor555 or Alexa Fluor647 (Invitrogen) following the manufacturer's recommendations. Fluorescent probes were then purified with MinElute Spin Columns (QIAGEN) and quantified spectrophotometrically.</p>", "<title>Genomic DNA preparation and labeling</title>", "<p>Genomic DNA from 10<sup>8 </sup>cells was isolated using the DNAzol technique (Invitrogen) as recommended by the manufacturer. Total DNA was then fragmented by successive passages through 22G1\" and 27G 1/2\" needles (Becton Dickinson Franklin Lakes, NJ, USA). Fragmented DNA was then double digested with <italic>Pvu</italic>II and <italic>Mse</italic>I restriction enzymes. Digested DNA was purified by phenol-chloroform, followed by an ethanol precipitation. For each probe, 4 μg of purified fragmented and digested genomic DNA were converted to fluorescently labeled DNA using Cy5- or Cy3-dCTP (Amersham, Piscataway, NJ, USA), random hexamers (Roche) and the exo<sup>- </sup>Klenow DNA polymerase (NEB, Ipswich, MA, USA). Fluorescent probes were then purified with ArrayIt columns (TeleChem International, Sunnyvale, CA, USA) and quantified spectrophotometrically.</p>", "<title>Microarray hybridization</title>", "<p>Prehybridization and hybridization were performed at 42°C under immersion (Corning chambers, Corning, NY, USA). Slides were prehybridized for 90 minutes in PreHYB Solution (5× Denhardt, 30% formamide, 6× SSPE, 0.5% SDS, 100 μg/ml salmon sperm DNA). Then, slides were first washed 2 times at 42°C for 5 minutes in 2× SSC, 0.1% SDS with gentle agitation. Subsequent washes were at room temperature, 3 minutes each, in 1× SSC, 0.2× SSC and 0.05× SSC. Slides were then dipped in 100% isopropanol and dried by centrifugation. For hybridization, Alexa Fluor555 and 647 cDNA probes were dried and resuspended in the HYB solution (2.5× Denhardt, 30% formamide, 6× SSPE, 0.5% SDS, 100 μg/ml salmon sperm DNA, 750 μg/ml yeast tRNA), then mixed, denatured 5 minutes at 95°C and cooled slowly to 42°C. Mixed probes were applied on the array under a lifterslip. Hybridization was performed for 16 h. Washes after hybridization were the same as those described for the prehybridization.</p>", "<title>Fluorescence detection, data processing and statistical analysis</title>", "<p>The Perkin Elmer ScanArray 4000XL Scanner was used for image acquisition (Perkin Elmer, Waltham, MA, USA). GenePix Pro 6.0 image analysis software (Axon Instruments, Union City, CA, USA) was used to quantify the fluorescence signal intensities of the array features. Four different RNA preparations of each mutant and their respective wild-type strain were analyzed, including dye-swaps. Raw data from GenePix were imported in R 2.2.1 for normalization and statistical analyses were performed using the LIMMA (version 2.7.3) package [##REF##16646809##83##, ####REF##14597310##84##, ##REF##15657102##85####15657102##85##]. Before processing, probes were flagged according to the hybridization signal quality [##REF##17384014##86##]. Weights were assigned to each array in order to give less weight to arrays of lesser quality [##REF##16712727##87##]. Data were corrected using background subtraction based on convolution of normal and exponential distributions [##REF##17720982##88##]. Intra-array normalization was carried out using the 'print-tip loess' statistical method and inter-array normalization was done by using the 'quantiles of A' method for each array [##REF##12804089##89##]. Statistical analysis was done using linear model fitting and standard errors were moderated using a simple empirical Bayes [##REF##16646809##83##]. Multiple testing corrections were done using the FDR method with a threshold <italic>p</italic>-value of 0.05. Only genes statistically significant with an absolute log ratio greater than 0.58 (log<sub>2 </sub>1.5) were considered as differentially expressed. Species comparison was performed only on probes that had less than two mismatches when hybridized to either <italic>Leishmania </italic>species. GeneSpring GX 3.1 was used for the generation of scatter plots and for chromosome by chromosome analysis. The entire data set has been deposited in GEO under the accession number series GSE9949. The comparative genomic hybridization data are deposited under reference number GSE11623.</p>", "<title>qRT-PCR</title>", "<p>Three independent RNA preparations were conducted for each condition. First-strand cDNA was synthesized from 2 μg of total RNA using the Superscript III RNase H Reverse Transcriptase enzyme and random hexamers (Roche) according to the manufacturer's instructions. The resulting cDNA samples were stored at -20°C until use. Control PCR amplification was carried out using primers from different internal controls (<italic>GAPDH </italic>and <italic>actin</italic>) to evaluate the uniformity of cDNA synthesis in different samples. Primers, TaqMan probes, experimental procedures and quantification for qRT-PCR of the folate transporter genes was as described (AA Ouameur <italic>et al</italic>., unpublished data) using the glyceraldehyde-3-phosphate dehydrogenase gene (<italic>GAPDH</italic>) for normalization. For all other genes, equal amounts of cDNA were run in triplicate and amplified in a 15 μl reaction containing 7.5 μl of 2× Universal PCR Master Mix (Applied Biosystems, Foster City, CA, USA), 10 nM of Z-tailed forward primer, 100 nM of reverse primer, 250 nM of Amplifluor Uniprimer probe (Chemicon Int., Temecula, CA, USA), and 1 μl of cDNA target. Reactions were performed at the Gene Quantification core laboratory of the Centre de Génomique de Québec using the Applied Biosystems Prism 7900 Sequence Detector [##UREF##3##90##]. Amplification was normalized to two genes showing a highly stable expression in wild-type and resistant strains: <italic>LinJ18_V3.0630</italic>/<italic>LmjF18.0620 </italic>encoding a putative 60S ribosomal protein L10a, and <italic>LinJ36_V3.0850/LmjF36.2500 </italic>encoding a chromatin assembly factor 1 subunit b-like protein.</p>" ]
[ "<title>Results</title>", "<title>RNA expression profiling in methotrexate resistant <italic>Leishmania </italic>cells</title>", "<p>Completion of the <italic>L. major </italic>genome has allowed the generation of arrays containing 60-mer oligonucleotide probes designed by NimbleGen Systems [##REF##16430978##55##,##REF##17188763##56##] and in this work, we present the generation of a full genome DNA microarray composed of 70-mer oligonucleotide probes suitable for both <italic>L. major </italic>and <italic>L. infantum </italic>analysis (see Materials and methods for a full description of the arrays). These full genome arrays were used for deciphering how <italic>Leishmania </italic>resists the antifolate model drug MTX. Two MTX resistant mutants, <italic>L. major </italic>MTX60.4, which has previously been studied with small targeted arrays [##REF##14530437##29##], and <italic>L. infantum </italic>MTX20.5, were studied using the full-genome microarrays. Mutants of both species are highly resistant to MTX (Figure ##FIG##0##1a##), and since they were selected in a stepwise fashion, it is likely that multiple resistance mechanisms may exist in these mutants and could thus be uncovered by these arrays. The resistant cells had a similar generation time as the wild-type parent cells.</p>", "<p>The DNA microarrays were first validated by hybridizing fluorescently labeled digested DNA of wild-type <italic>L. major </italic>and <italic>L. infantum </italic>cells. The arrays were found to yield uniform and reproducible results (not shown) and were deemed appropriate for RNA expression profiling experiments. Total RNAs were thus purified for both wild-type and mutant strains, used to synthesize fluorescent probes, and hybridized to the microarrays as described in Materials and methods. Scanning and normalization led to expression data that were first represented as scatter plots. As evident from these plots (inserts in Figure ##FIG##1##2a,b##), most genes in both species are equally expressed between the sensitive and resistant strains. Indeed, the bulk of expression (RNA level) ratios between sensitive and resistant strains were close to 1. Nonetheless, there were notable differences. First, the RNA levels of a total of 61 genes were found to be modulated (cut-off of 2, <italic>p </italic>&lt; 0.05) in the <italic>L. infantum </italic>MTX20.5 mutant compared to the wild-type strain (Figure ##FIG##1##2a##; Table S1 in Additional data file 1) and the expression levels of 75 genes were changed significantly (cut-off of 2, <italic>p </italic>&lt; 0.05) in the <italic>L. major </italic>MTX60.4 mutant compared to the wild-type strain (Figure ##FIG##1##2b##; Table S1 in Additional data file 1). Secondly, a majority of genes whose expression was modulated by more than two-fold had increased expression levels in <italic>L. infantum </italic>MTX20.5 but the majority of another set of genes had decreased expression levels in <italic>L. major </italic>MTX60.4 (inserts of Figure ##FIG##1##2##; Table S1 in Additional data file 1). If the expression modulation cut-off was changed from 2 to 1.5 (<italic>p </italic>&lt; 0.05), we found 251 and 372 genes that were differentially expressed in <italic>L. infantum </italic>MTX20.5 and <italic>L. major </italic>MTX60.4, respectively (Figure ##FIG##1##2##). Surprisingly, few differentially expressed genes were found to be modulated similarly in both mutants (Figure ##FIG##2##3##; Table S1 in Additional data file 1). One notable exception is a region of chromosome 6 that corresponds to a six gene locus including the <italic>DHFR-TS </italic>gene. DHFR-TS is the main target for MTX and its gene was frequently found amplified in <italic>L. major </italic>MTX resistant mutants as part of extrachromosomal circles (reviewed in [##REF##1741620##3##,##REF##8561467##4##]).</p>", "<p>The DNA microarray data were supported by selected quantitative real-time reverse transcription PCR (qRT-PCR) assays in both the <italic>L. major </italic>and <italic>L. infantum </italic>mutants (Figure ##FIG##2##3##). In only two cases we found a discrepancy between the two techniques. <italic>LmjF04.0160 </italic>and its orthologue <italic>LinJ04_V3.0160 </italic>were found down-regulated in both mutants using DNA microarrays, but this was confirmed only in the <italic>L. major </italic>mutant by qRT-PCR (Figure ##FIG##2##3##). The other discrepancy between microarray and qRT-PCR data was for <italic>FT1</italic>, but this is explained by a gene deletion event (see below). The only other gene that was modulated similarly in the two mutants was the ABC protein gene <italic>ABCA2 </italic>and this was confirmed by qRT-PCR (Figure ##FIG##2##3##). Other genes were modulated in both mutants but in different ways. While <italic>LmjF31.0720 </italic>was down-regulated in <italic>L. major </italic>MTX60.4, its orthologue <italic>LinJ31_V3.0750 </italic>in <italic>L. infantum </italic>MTX20.5 was overexpressed (Figure ##FIG##2##3##). Otherwise, genes differentially expressed were specific to individual mutants.</p>", "<p>The differential gene expression of the MTX resistant mutants was also represented in a chromosome by chromosome fashion (Figure ##FIG##1##2##). This has permitted us to visualize regions that are differently expressed (red/orange, corresponding to overexpressed genes in the mutants). Two regions were clearly overexpressed in the <italic>L. infantum </italic>MTX20.5 mutant. One region was on chromosome 6 (<italic>DHFR-TS </italic>loci) and the second was in the left portion of chromosome 23 (Figure ##FIG##1##2a##). For the <italic>L. major </italic>MTX60.4 mutant, we also saw an increase in expression of selected genes present on chromosome 6 (<italic>DHFR-TS </italic>loci), but we also observed a number of whole chromosomes (for example, chromosome 22; colored predominantly red in Figure ##FIG##1##2b##).</p>", "<title>Extrachromosomal circular amplification of <italic>DHFR-TS</italic></title>", "<p><italic>DHFR-TS </italic>is present on chromosome 6 and by close examination of the expression data derived from the arrays we were able to precisely define the genes with increased expression in both the <italic>L. major </italic>and <italic>L. infantum </italic>mutants. In <italic>L. infantum</italic>, the genomic region overexpressed is delimited by genes <italic>LinJ06_V3.0860 </italic>and <italic>LinJ06_V3.0910 </italic>(Figure ##FIG##3##4a##). Most interestingly, the same region is overexpressed in <italic>L. major </italic>MTX60.4 (Figure ##FIG##3##4a##). As <italic>Leishmania </italic>is devoid of control for the initiation of transcription (no pol II promoter has yet been isolated in this parasite [##REF##11953307##58##]), it is possible that the amplification of a small genomic region containing the <italic>DHFR-TS </italic>gene is responsible for the increased gene expression as determined by DNA microarrays. This was tested by hybridization of a blotted pulsed-field gel electrophoresis (PFGE) gel with a <italic>DHFR </italic>probe. Wild-type cells gave rise to two hybridizing bands, suggesting that the two homologous chromosomes 6 have different sizes (Figure ##FIG##3##4b##, lanes 1 and 3), a well established phenomenon in <italic>Leishmania </italic>[##REF##1922200##59##]. The two mutants had an extra band hybridizing to the <italic>DHFR </italic>probe, which with its hybridizing smear is characteristic of extrachromosomal circles (Figure ##FIG##3##4b##, lanes 2 and 4). The genesis of circular DNA in <italic>Leishmania </italic>has been studied and is often due to homologous recombination between direct repeats bordering the regions amplified [##REF##1672636##22##, ####REF##8098523##23##, ##REF##8668175##24####8668175##24##]. Close examination of the sequences flanking the regions amplified indeed pointed to the presence of repeated sequences (Figure ##FIG##3##4a##). The repeated sequences were highly similar between <italic>L. major </italic>(575 bp) and <italic>L. infantum </italic>(837 bp) (Figure S1 in Additional data file 2). To provide evidence that the <italic>DHFR-TS </italic>containing circles were generated through homologous recombination between these direct repeated sequences, we used two primers (6a and 6b in Figure ##FIG##3##4a,c##) that should give rise to a PCR amplification product only when an extrachromosomal circle is formed (Figure ##FIG##3##4c##). Indeed, when using this primer pair, PCR fragments of the expected size were observed in <italic>L. infantum </italic>MTX20.5 and <italic>L major </italic>MTX60.4 (Figure ##FIG##3##4d##, lanes 2 and 4) while no amplification was observed in the wild-type cells (Figure ##FIG##3##4d##, lanes 1 and 3). The difference in size of the PCR fragments between <italic>L. major </italic>and <italic>L. infantum </italic>is due to the difference in size of the repeats in the two species (Figure S1 in Additional data file 2). Sequencing of the PCR generated amplicon derived from <italic>L. major </italic>MTX60.4 [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EU346088\">EU346088</ext-link>] confirmed the scenario of homologous recombination between the repeated sequences (Figure S1d in Additional data file 2).</p>", "<title>Linear amplification of <italic>PTR1</italic></title>", "<p>In mutant <italic>L. infantum </italic>MTX20.5 we observed a region of chromosome 23 that was overexpressed (increased RNA levels; Figure ##FIG##1##2a##). This region contains the gene for pteridine reductase 1 (<italic>PTR1</italic>), a well established MTX resistance gene whose product can reduce folic acid, hence by-passing the need for DHFR-TS [##REF##1339441##30##,##REF##1396560##31##]. Similarly to the <italic>DHFR-TS </italic>loci, the microarray expression data have allowed the precise determination of the region that was overexpressed, which started at the telomeric end and extended 120 kb up to gene <italic>LinJ23_V3.0380 </italic>(Figure ##FIG##4##5a##). The putative presence of telomeric sequences would suggest a linear amplification instead of a circular amplification. Hybridization of a chromosome PFGE blot has shown that <italic>PTR1 </italic>hybridized to the approximately 800 kb chromosome in both wild-type and resistant cells but also to a smaller linear amplicon of approximately 230 kb in <italic>L. infantum </italic>MTX20.5 (Figure ##FIG##4##5b##). This amplicon also hybridized to a telomere probe (Figure ##FIG##4##5b##). The size of the amplicon suggests that the amplified region was duplicated. The <italic>LinJ23_V3.0390 </italic>gene is clearly not overexpressed and thus not part of the amplicon (Figure ##FIG##4##5a##). Three genes, <italic>LinJ23_V3.0360</italic>, <italic>LinJ23_V3-0370 </italic>and <italic>Lin23_V3.0380</italic>, were less overexpressed than the other genes that are part of the amplicon (Figure ##FIG##4##5a##). Examination of the sequences where expression changed enabled the detection of inverted homologous repeats of 578 bp (Figure S2 in Additional data file 2) between <italic>LinJ23_V3.0350 </italic>and <italic>Lin23_V3.0360</italic>, and between <italic>LinJ23_V3.0380 </italic>and <italic>Lin23_V3.0390 </italic>(Figure ##FIG##4##5a##). Interestingly, similar repeats of 574 bp with 91% identity were found at the same position in the <italic>L. major </italic>genome [##UREF##1##60##]. The presence of these inverted repeats and the microarray expression data would suggest the formation of a linear amplicon with large inverted duplications that was formed by annealing of the identical 578 bp inverted repeats (Figure ##FIG##4##5c##). To obtain support for this scenario, we used PCR primer pairs (23a and 23b, or 23c and 23d) that would lead to a PCR product only if the rearrangement had occurred at the level of the inverted repeats (as, for example, during a block in DNA replication). Indeed, we obtained a product of the expected size with these pairs of primers in <italic>L. infantum </italic>MTX20.5 but no product was obtained from DNA derived from wild-type cells (Figure ##FIG##4##5d##). The nucleotide sequence of the PCR amplicon obtained with primer pair 23a/23b [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EU346089\">EU346089</ext-link>] is entirely consistent with the model shown in Figure ##FIG##4##5c## (Figure S2 in Additional data file 2).</p>", "<title>Decrease in gene expression due to deletion of folate transporter genes</title>", "<p><italic>Leishmania </italic>spp. have a large gene family of conserved folate transporters with 14 FBT members (AA Ouameur <italic>et al</italic>., unpublished data). Part of this family located on chromosome 10 is shown in Figure ##FIG##5##6a##. Microarray expression data indicated that <italic>FT1</italic>, coding for the main <italic>Leishmania </italic>folate transporter [##REF##11295174##18##,##REF##15466466##19##], is down-regulated in <italic>L. major </italic>MTX60.4 but not in <italic>L. infantum </italic>MTX20.5 (Figure ##FIG##2##3##). The level of conservation of the various FBTs precluded that the 70-mer oligonucleotides spotted on the arrays would discriminate several of these closely related genes. The use of qRT-PCR to confirm the microarray data indicated that <italic>FT1 </italic>may be absent (Figure ##FIG##2##3##). This was suggestive of a gene deletion event and indeed a Southern blot of <italic>L. major </italic>MTX60.4 DNA hybridized with a probe recognizing the majority of <italic>FBT </italic>genes confirmed this extensive gene rearrangement (Figure ##FIG##5##6b##) and bands corresponding to <italic>LmjF10.0380</italic>, <italic>LmjF10.0385 </italic>(<italic>FT1</italic>) and <italic>LmjF10.0390 </italic>were either lacking or rearranged. Using PCR primers (labeled F and R in Figure ##FIG##5##6a,c##), we were able to demonstrate that <italic>FT1 </italic>(<italic>LmjF10.0385</italic>) was deleted following an event of homologous recombination between conserved sequences between <italic>LmjF10.0380 </italic>and <italic>LmjF10.0390 </italic>(Figure ##FIG##5##6c##). Indeed, primers F and R gave rise to a PCR fragment of 2.2 kb in <italic>L. major </italic>MTX60.4 (Figure ##FIG##5##6d##, lane 2) while under the conditions tested no fragments were found with <italic>L. major </italic>wild-type cells. Sequencing of the amplicon [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"EU346090\">EU346090</ext-link>] validated the scenario of homologous recombination between two <italic>FBT </italic>genes leading to the diploid deletion of <italic>FT1 </italic>(Figure ##FIG##5##6c##; Figure S3 in Additional data file 2).</p>", "<title>Selection for MTX resistance and chromosome aneuploidy</title>", "<p>Analysis of gene expression on a chromosome by chromosome basis (Figure ##FIG##1##2##) suggested that the expression of whole chromosomes is modulated in <italic>L. major </italic>MTX60.4. Indeed, the majority of genes present on chromosomes 11 and 12 appeared down-regulated while the expression of genes located on chromosomes 7, 22, 28 and 32 seemed up-regulated (Figure ##FIG##1##2##). Chromosome 6 of <italic>L. infantum </italic>MTX20.5 also appears to be in more than two copies. This chromosome-wide uniform modulation of expression was represented more thoroughly for selected chromosomes by plotting the fold modulation in gene expression along the chromosome (Figure ##FIG##6##7##). The normalized microarray data indicated that genes of chromosomes 22 and 28 were overexpressed 1.7- and 1.5-fold, respectively, in the resistant strain <italic>L. major </italic>MTX60.4 compared to the wild-type strain. The expression of genes on chromosomes 11 and 12 seemed, in general, to be 50% underexpressed in the mutant strain compared to wild-type cells (Figure ##FIG##6##7##).</p>", "<p>A number of hypotheses can explain this whole chromosome-specific gene regulation and we tested whether the copy number of specific chromosomes changed upon MTX selection in <italic>L. major </italic>MTX60.4. Quantitative Southern blot analyses with two distinct probes derived from chromosome 22 revealed that if the wild-type cells contain two homologous copies of chromosome 22 (<italic>Leishmania </italic>is a diploid organism), <italic>L. major </italic>MTX60.4 had four copies (Figure ##FIG##6##7a##, lanes 1 and 2). Similarly, <italic>L. major </italic>MTX60.4 had three copies of chromosome 28 compared to wild-type cells (Figure ##FIG##6##7b##, lanes 1 and 2). The probes used are physically far apart, indicating a change in ploidy of the whole chromosome. However, this change in chromosome copy number was not observed for chromosomes 11 and 12 (Figure ##FIG##6##7c,d##). Aneuploidy of specific chromosomes and drug resistance has been described in cancer cells (reviewed in [##REF##17387035##61##]) and fungi [##REF##16857942##62##,##REF##17693596##63##]. To test this possibility, we generated a revertant line of <italic>L. major </italic>MTX60.4 by successive passages in the absence of MTX; under these conditions, resistance to the drug decreased (Figure ##FIG##0##1b##). Revertant cells were not as sensitive as wild-type cells to MTX but this is expected as a deletion of <italic>FT1 </italic>(Figure ##FIG##5##6##) will lead to resistant parasites [##REF##15466466##19##]. The aneuploidy of chromosomes 22 and 28 regressed to diploidy (similar to wild-type diploidy) after 30 passages, thus circumstantially linking resistance levels (Figure ##FIG##0##1b##) and copy number of these chromosomes (Figure ##FIG##6##7a,b##, lanes 2-6). With the cells now diploid, additional passages (for example, passage 42) did not decrease resistance further.</p>", "<title>Comparative genomic hybridization</title>", "<p>Since several of the changes in RNA levels were correlated with gene amplification or gene deletion, we undertook a comparative genomic hybridization (CGH) study using the full genome array. The DNA of mutant <italic>L. major </italic>MTX60.4 was labeled and changes in copy number in comparison to sensitive wild-type cells were measured using CGH. The CGH data are represented in a chromosome by chromosome fashion in Figure S4 in Additional data file 3. A qualitative correlation was observed between CGH and RNA-based hybridization (Figure ##FIG##7##8##). Indeed, amplification of the <italic>DHFR-TS </italic>locus, derived from chromosome 6, was easily detected by both techniques and quantification of the DNA amplification was compared to RNA levels (Figure ##FIG##3##4##). The deletion of <italic>FT1 </italic>was also detected by CGH and the latter technique was found to be quantitative. Indeed, the 70-mers recognizing <italic>FT1 </italic>recognized three conserved FT genes. In the MTX60.4 mutant two of these genes are deleted, hence explaining the ratio of 0.33 obtained by CGH (Figure ##FIG##5##6##). Polyploidy was also easily detected by CGH (Figure ##FIG##7##8##). Indeed, a similar qualitative pattern of hybridization intensities was obtained for both RNA expression profiling and CGH (Figure ##FIG##7##8##). Interestingly, while RNA expression profiling showed that chromosome 11 was down-regulated, quantitative Southern blots indicated that the copy number of the chromosome remained unchanged (Figure ##FIG##6##7##). This was also confirmed by CGH (Figure ##FIG##7##8##). There are some differences, however, between RNA expression profiling and CGH. For example, the latter technique showed that chromosome 2 is polyploid (Figure S4 in Additional data file 3) but this is likely due to the dynamic process of cell culture and parasite evolution, as DNA and RNA were prepared 1.5 years apart, rather than a difference in the techniques.</p>" ]
[ "<title>Discussion</title>", "<p>The use of DNA microarrays is now useful to understand both the mode of action of drugs and the mechanisms of drug resistance (reviewed in [##REF##15335284##44##, ####REF##16213417##45##, ##UREF##0##46####0##46##]). Since <italic>Leishmania </italic>has no control at the level of transcription initiation [##REF##11953307##58##], it is unlikely that drug response profiling using microarrays will be helpful to understand the mode of action of drugs in <italic>Leishmania</italic>. Results using MTX as a lead drug and qRT-PCR to monitor key genes, such as <italic>DHFR-TS</italic>, <italic>PTR1</italic>, and <italic>FT1</italic>, appeared to confirm this lack of RNA modulation of target genes upon drug exposure (unpublished observations). This is unfortunate, as the mode of action of most anti-<italic>Leishmania </italic>drugs is unknown. Nonetheless, microarrays are likely to be useful for studying resistance in <italic>Leishmania </italic>since it is often mediated by gene amplification [##REF##1741620##3##,##REF##8561467##4##] and we show here that DNA arrays hybridized to cDNAs were most valuable for detecting gene amplification events (Figures ##FIG##1##2##, ##FIG##3##4##, and ##FIG##4##5##). Since resistance is mostly correlated with gene amplification, we also used CGH and found a good qualitative correlation between RNA expression profiling and CGH (Figure ##FIG##7##8##). The technique of CGH was found to be technically simpler, but since there are clear examples of modulation in RNA level (for example, increased RNA stability) without changes in copy number of DNA in drug resistant <italic>Leishmania </italic>[##REF##10564512##64##, ####REF##15190060##65##, ##REF##16135234##66####16135234##66##] (Figure ##FIG##2##3##, and Figure ##FIG##6##7## for chromosomes 11 and 12), hybridization with cDNAs is likely to be more comprehensive. Nonetheless, modulation in RNA levels without changes in copy number of a gene is an infrequent event in drug resistant <italic>Leishmania</italic>. The use of both <italic>L. infantum </italic>and <italic>L. major </italic>MTX resistant mutants validated the design of our multi-species array but has also illustrated that the cellular resistance genotype can be complex and differ considerably between different mutants selected for resistance to the same drug. The modulation in expression of a few genes was common to both mutants, and only <italic>ABCA2 </italic>and <italic>DHFR-TS </italic>could be confirmed by qRT-PCR (Figure ##FIG##2##3##). Down-regulation of the ABC protein gene <italic>ABCA2 </italic>has never been described in MTX resistant <italic>Leishmania </italic>cells and additional investigations would be required to test whether it has any role in MTX resistance.</p>", "<p><italic>DHFR-TS </italic>was the first amplified gene studied in a protozoan parasite [##REF##6572966##5##] but its exact mechanism of amplification has never been reported. In addition to detecting gene amplification events, microarray data, whether derived from RNA expression profiling or CGH, were also useful in mapping the exact regions that were amplified. We show that <italic>DHFR-TS </italic>is amplified in <italic>L. major </italic>MTX60.4 as an extrachromosomal circle through homologous recombination between non-coding repeated sequences (Figure ##FIG##3##4##). This is consistent with other loci that were also found to be amplified by homologous recombination between relatively long repeated sequences [##REF##1672636##22##, ####REF##8098523##23##, ##REF##8668175##24####8668175##24##]. Blast searches have shown that these exact repeated sequences are found only on chromosome 6. Remarkably, the same similar repeated sequences (albeit with different sizes) have also been conserved in <italic>L. infantum </italic>(Figure S1 in Additional data file 2). The same observation was made for the inverted repeats close to <italic>PTR1 </italic>that were conserved between <italic>L. major </italic>and <italic>L. infantum</italic>. <italic>L. major </italic>and <italic>L. infantum </italic>are thought to have diverged 0.5 million years ago [##REF##12798051##67##] and it thus seems that there is considerable selective pressure to keep these repeated sequences intact. Since folates and pterins are important for <italic>Leishmania </italic>growth, it is possible that the presence of these repeats may allow a strategy to rapidly increase DHFR-TS or PTR1 levels in conditions of limited substrates. With its lack of transcription initiation control, <italic>Leishmania </italic>may utilize this alternative strategy of flanking key metabolic genes by repeated sequences to amplify these genes when required. Consistent with this proposal, DNA amplification has been observed in <italic>Leishmania </italic>cells subjected to nutrient shocks [##REF##1542306##68##].</p>", "<p>PTR1 is a well established MTX resistance gene product [##REF##1339441##30##,##REF##1396560##31##] and the amplification of its gene was first reported as part of extrachromosomal circles [##REF##6467372##6##,##REF##3182827##34##, ####REF##3244352##35##, ##REF##3182826##36####3182826##36##]. Linear amplification of <italic>PTR1 </italic>with inverted duplications was described later [##REF##8414986##16##,##REF##8668175##24##,##REF##7659507##37##] and linear amplicons could be precursors of circular amplicons [##REF##9649621##38##]. Linear amplicons derived from other loci than the <italic>PTR1 </italic>region with inverted duplications have also been described in <italic>Leishmania </italic>[##REF##1682806##69##, ####REF##7891749##70##, ##REF##8946380##71##, ##REF##11470881##72##, ##REF##15781496##73####15781496##73##]. The microarray hybridization data have enabled the elaboration of a plausible model for the generation of a linear amplicon that contained large inverted duplications formed at the site of inverted repeats (Figure ##FIG##4##5##). This is consistent with other models of gene amplification in <italic>Leishmania </italic>[##REF##8414986##16##,##REF##7659507##37##] where inverted repeats seem to be a major pathway to generate amplified large DNA palindromes (inverted duplications), as described in <italic>Tetrahymena </italic>[##REF##8524279##74##], yeast [##REF##10712506##75##] and mammalian cancer cells [##REF##12060719##76##,##REF##17242211##77##]. One of the large inverted duplications extends from the inverted repeats, where rearrangement has occurred, to the telomeric sequences (Figure ##FIG##4##5##). These data exclude the necessity of chromosomal breaks/rearrangements at two independent positions, but it remains to be determined whether a double-stranded break, a single-stranded break or blocks in replication are facilitating inverted repeat annealing.</p>", "<p>Gene deletions were thought to be associated with MTX resistance in <italic>Leishmania </italic>[##REF##15466466##19##,##REF##12023977##20##] but had not yet been characterized at the molecular level. The microarray data, either derived from RNA expression profiling or CGH, has led to the observation that a diploid non-conservative deletion occurred by homologous recombination between two members of the large <italic>FBT </italic>gene family (Figure ##FIG##5##6##). The mechanism of gene deletion thus resembles the mechanism of amplification. Usually, amplification in <italic>Leishmania </italic>is conservative, and only a few instances of non-conservative amplification (loss of one allele) have been described in it [##REF##1741620##3##,##REF##1672636##22##,##REF##8098523##23##]. In the <italic>L. major </italic>MTX60.4 mutant, we observed a diploid deletion of the <italic>FT1 </italic>gene (Figure ##FIG##5##6##). It is not known whether the second allele is deleted by homologous recombination or by a gene conversion event such as a loss of heterozygosity, but there is a strong selection pressure to delete <italic>FT1</italic>, the main folate (and MTX) transporter in <italic>Leishmania</italic>. Without FT1, cells can become resistant to MTX but folates or related molecules will still need to be transported. It will be of interest to determine whether the fusion FBT protein produced by the recombination event (Figure ##FIG##5##6##) is active or not.</p>", "<p>The microarray approach has shown that modulation of gene expression could (rarely) be due to differential RNA expression without changes in copy number (Figure ##FIG##2##3##) [##REF##14530437##29##]; it could be more frequently due to gene amplification (Figures ##FIG##3##4## and ##FIG##4##5##) and, as determined now, to gene deletion (Figure ##FIG##5##6##). Two novel strategies were highlighted through the use of microarrays. In the <italic>L. major </italic>MTX60.4 mutant, the entire set of genes of chromosomes 11 and 12 is down-regulated while all the genes present on chromosomes 22 and 28 and possibly a few other chromosomes are overexpressed. The mechanism underlying an upregulation in gene expression results from a change in chromosome ploidy (Figure ##FIG##6##7##). Changes in ploidy have been observed when attempting to inactivate essential genes in <italic>Leishmania </italic>[##REF##8381972##78##], but not in resistant parasites. We recently observed a similar phenomenon with other resistant <italic>Leishmania </italic>cells (P Leprohon <italic>et al</italic>., unpublished data), suggesting that chromosome aneuploidy is part of the <italic>Leishmania </italic>arsenal for responding to drug pressure. There was a good correlation between resistance levels and the copy number of these supernumerary chromosomes (Figures ##FIG##0##1## and ##FIG##6##7##), linking this genetic event to the resistance phenotype. Obviously, additional studies will be required to determine which gene(s) is (are) responsible for resistance. A putative mechanism for increasing the levels of a gene product in <italic>Leishmania </italic>would thus be to generate supernumerary chromosomes. This may occur when direct or inverted repeats are absent in the vicinity of a gene conferring a selective advantage. While this is plausible, especially for an organism lacking control at the level of transcription initiation, this drug induced aneuploidy has been well documented in cells with transcriptional control, such as cancer cells (reviewed in [##REF##17387035##61##]) or fungi [##REF##16857942##62##,##REF##17693596##63##]. The mechanism of down-regulation of whole chromosome expression does not seem to involve a change in chromosome number (Figures ##FIG##6##7## and ##FIG##7##8##) and may involve epigenetic factors that will need to be investigated.</p>" ]
[ "<title>Conclusion</title>", "<p>The microarray approach was useful in highlighting several mechanisms used by resistant cells to modulate the copy number of genes by: gene deletion or extrachromosomal circular or linear amplicons; through supernumerary chromosomes; and by decreasing the expression of whole chromosomes by a mechanism that remains to be identified. In the case of the first two events, the rearrangements have occurred at the site of repeated (direct or inverted) sequences. It is possible that these repeats are not randomly distributed to allow the amplification of specific chromosomal regions. Using DNA microarrays it was shown that inverted duplications are frequent in cancer cells; these are not randomly distributed, and a subset are associated with gene amplification [##REF##15711546##79##]. The availability of DNA microarrays for <italic>Leishmania </italic>has highlighted the role of repeated sequences and of chromosome ploidy in responding to environmental changes. Aneuploidy has been suggested as an important cause of cancer specific drug resistance [##REF##17387035##61##] and further work should reveal the potential importance of this phenomenon in drug resistance in <italic>Leishmania</italic>.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Gene expression and DNA copy number analyses using full genome oligonucleotide microarrays of <italic>Leishmania</italic> reveal molecular mechanisms of methotrexate resistance.</p>", "<title>Background</title>", "<p>Drug resistance can be complex, and several mutations responsible for it can co-exist in a resistant cell. Transcriptional profiling is ideally suited for studying complex resistance genotypes and has the potential to lead to novel discoveries. We generated full genome 70-mer oligonucleotide microarrays for all protein coding genes of the human protozoan parasites <italic>Leishmania major </italic>and <italic>Leishmania infantum</italic>. These arrays were used to monitor gene expression in methotrexate resistant parasites.</p>", "<title>Results</title>", "<p><italic>Leishmania </italic>is a eukaryotic organism with minimal control at the level of transcription initiation and few genes were differentially expressed without concomitant changes in DNA copy number. One exception was found in <italic>Leishmania major</italic>, where the expression of whole chromosomes was down-regulated. The microarrays highlighted several mechanisms by which the copy number of genes involved in resistance was altered; these include gene deletion, formation of extrachromosomal circular or linear amplicons, and the presence of supernumerary chromosomes. In the case of gene deletion or gene amplification, the rearrangements have occurred at the sites of repeated (direct or inverted) sequences. These repeats appear highly conserved in both species to facilitate the amplification of key genes during environmental changes. When direct or inverted repeats are absent in the vicinity of a gene conferring a selective advantage, <italic>Leishmania </italic>will resort to supernumerary chromosomes to increase the levels of a gene product.</p>", "<title>Conclusion</title>", "<p>Aneuploidy has been suggested as an important cause of drug resistance in several organisms and additional studies should reveal the potential importance of this phenomenon in drug resistance in <italic>Leishmania</italic>.</p>" ]
[ "<title>Abbreviations</title>", "<p>CGH, comparative genomic hybridization; DHFR, dihydrofolate reductase; DHFR-TS, DHFR-thymidylate synthase; FBT, folate biopterin transporter; FT, folate transporter; GEO, Gene Expression Omnibus; MTX, methotrexate; PFGE, pulsed-field gel electrophoresis; PTR, pteridine reductase; qRT-PCR, quantitative real-time reverse transcription PCR.</p>", "<title>Authors' contributions</title>", "<p>JM carried out the molecular genetic studies and all the microarray hybridizations performed in this study, participated in the bioinformatic analyses of microarray data and drafted the manuscript. AHO helped in the design of qRT-PCR assays. DL developed and optimized the comparative genomic hybridization protocol. PR designed the 70-mer <italic>Leishmania </italic>oligonucleotide microarrays. FR performed the microarray normalization and statistical analysis. SB developed the LIMS that was used to integrate microarray results storage and analysis. JC, MOl, MOu, BP and MJT are part of a CIHR group grant and have supervised all the experiments presented in this paper. All authors read and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data file ##SUPPL##0##1## contains Table S1, which lists the differential expression measured by the full-genome microarray analysis. Additional data file ##SUPPL##1##2## contains supplementary Figures S1-S3. Additional data file ##SUPPL##2##3## contains supplementary Figure S4, which shows the results of the comparative genomic hybridization analyses of <italic>L. major </italic>MTX60.4 versus the respective wild-type cells.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>We are grateful to Dr Eric Madore from the Centre Génomique du Centre de Recherche en Infectiologie for help during the optimization process of the microarray hybridizations. This work was funded in part by a CIHR group grant to JC, MOl, MOu, BP and MJT and operating grants to MOu. JMU is a Strategic Training Fellow of the Strategic Training Program in Microbial Resistance, a partnership of the CIHR Institute of Infection and Immunity and the Fonds de Recherche en Santé du Québec. AAO and FR are recipients of CIHR studentships. JC holds the Canada Research Chair in Medical Genomics, MJT holds the Canada Research Chair in Human Immuno-Retrovirology. BP and MOl are Burroughs Wellcome Fund New Investigator in Molecular Parasitology and the holders of FRSQ senior scholarships. MOu is a Burroughs Wellcome Fund Scholar in Molecular Parasitology and holds the Canada Research Chair in Antimicrobial Resistance.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Methotrexate susceptibility in <italic>Leishmania </italic>cells. <bold>(a) </bold><italic>Leishmania </italic>cells were grown in M199 medium and their growth was monitored at 72 hours by measuring their OD<sub>600 nm </sub>with varying concentrations of MTX. White circles, <italic>L. major </italic>wild-type cell; black circles, <italic>L. major </italic>MTX60.4; white squares, <italic>L. infantum </italic>wild-type cells; black squares, <italic>L. infantum </italic>MTX20.5. <bold>(b) </bold>The mutant <italic>L. major </italic>MTX60.4 was grown in the absence of drug for 5, 12, 25, 30 and 42 passages. The average of triplicate measurements is shown.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Modulation of gene expression in <italic>Leishmania </italic>cells resistant to methotrexate. DNA microarrays were analyzed as described in Materials and methods and the software GeneSpring version GX3.1 was used to represent fold modulation either on a chromosome by chromosome basis (1 to 36) or as a scatter plot (inserts) for both <bold>(a) </bold><italic>L. infantum </italic>MTX20.5 and <bold>(b) </bold><italic>L. major </italic>MTX60.4. Vertical bars refer to individual genes on each chromosome and their location above or below the strand represents the transcribed strand. Transcription in <italic>Leishmania </italic>leads to polycistronic RNAs. Red (increased expression) and blue (decreased expression) dashed lines in the scatter plots indicate 1.5-fold differences in gene expression, with the y-axis representing the expression ratios between the mutant and wild-type cells and the x-axis the signal intensity in the mutant. The color scale indicates the modulation of hybridization signals in the resistant mutants compared to wild-type cells. The spots corresponding to genes that are part of the <italic>DHFR-TS </italic>amplicons are circled in the scatter plots. The entire data set was deposited in GEO under the accession number series GSE9949.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Validation of DNA microarray expression data by qRT-PCR. The mean log10 ratios of selected genes from microarray expression data (grey bars) are compared to qRT-PCR data (black bars) for <bold>(a) </bold><italic>L. infantum </italic>MTX20.5 and <bold>(b) </bold><italic>L. major </italic>MTX60.4. The microarray data are the average of four biological replicates (with two dye swaps), while the qRT-PCR data are the average of three biological replicates repeated two times each. The asterisk indicates that the related gene transcript was not detected by qRT-PCR. The upper panel shows the expression of orthologous genes where the expression changes in the two species; the middle panel shows the modulation in the expression of FBT genes; the lower panel shows the expression of individual genes specific for each mutant.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Extrachromosomal circular amplification of a genomic region of <italic>Leishmania </italic>chromosome 6 that includes the <italic>DHFR-TS </italic>locus. <bold>(a) </bold>Genomic organization of the <italic>DHFR-TS </italic>locus in both <italic>L. infantum </italic>MTX20.5 and <italic>L. major </italic>MTX60.4. Relative gene expression data (RNA) were determined using DNA microarrays and relative hybridization data were obtained by comparative genomic hybridization (DNA). Asterisks indicate that the microarray data of these genes were not found to be reliable. Direct repeats are shown with thick arrows and the approximate position of primers 6a and 6b are indicated with half arrows. <bold>(b) </bold>Chromosome size blot of <italic>Leishmania </italic>cells hybridized to a <italic>DHFR-TS </italic>probe. Sizes were determined using a yeast molecular weight marker (Biorad. Hercules, CA, USA). <bold>(c) </bold>Model for the formation of the extrachromosomal <italic>DHFR-TS </italic>circular DNA generated through homologous recombination between direct repeats (Figure S1 in Additional data file 2). <bold>(d) </bold>PCR with primers 6a and 6b to support the model shown in (c). Lane 1, <italic>L. infantum </italic>wild-type cells; lane 2, <italic>L. infantum </italic>MTX 20.5; lane 3, <italic>L. major </italic>wild-type cells; lane 4, <italic>L. major </italic>MTX60.4.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Linear amplification of <italic>PTR1 </italic>as a large inverted duplication. <bold>(a) </bold>Genomic organization of the <italic>PTR1 </italic>locus in <italic>L. infantum </italic>and relative gene expression data as determined by DNA microarrays in <italic>L. infantum </italic>MTX20.5. Note that all genes from the telomere up to <italic>LinJ23_V3.0380 </italic>showed increased levels of expression in the MTX20.5 mutant compared to wild-type cells. <bold>(b) </bold>Chromosome size PFGE of <italic>Leishmania </italic>cells. Ethidium bromide (Et-Br) stained gel, or blotted gels hybridized to a <italic>PTR1 </italic>probe or to a probe containing the telomeric repeats are shown. Sizes were determined using a yeast molecular weight marker (Biorad). <bold>(c) </bold>Model for the formation of the extrachromosomal <italic>PTR1 </italic>linear amplicon generated through annealing of homologous inverted repeats (Figure S2 in Additional data file 2). This annealing could be facilitated by a block in replication. <bold>(d) </bold>PCR with primer pairs 23a and 23b or 23c and 23d to support the model shown in (c). Lane 1, <italic>L. infantum </italic>wild-type cells; lane 2, <italic>L. infantum </italic>MTX20.5.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Mechanism of deletion of the main folate transporter gene <italic>FT1 </italic>in <italic>L. major </italic>selected for MTX resistance. <bold>(a) </bold>A portion of the <italic>L. major </italic>chromosome 10 showing some of the FT genes. Approximate location of <italic>Pvu</italic>I sites (crosses) and their size are shown. Primers F and R are indicated by half arrows. The relative hybridization data obtained from RNA expression profiling (RNA) and comparative genomic hybridization (DNA) are shown. Due to conservation between the FT genes, the 70-mer probes for <italic>LmjF10.0380</italic>, <italic>FT1 </italic>and <italic>LmjF10.0390 </italic>are not discriminatory. <bold>(b) </bold>Southern blot of <italic>Leishmania </italic>total DNA digested with <italic>Pvu</italic>I and hybridized to a probe recognizing conserved sequences of most <italic>FBT </italic>genes (indicated by bars underneath the genes in (a,c)). The genes corresponding to some hybridizing bands are indicated. <bold>(c) </bold>Model for the deletion of <italic>FT1 </italic>mediated by the homologous recombination of the conserved sequences between the folate transporter genes <italic>LmjF10.0380 </italic>and <italic>LmjF10.0390 </italic>(Figure S3 in Additional data file 2). <bold>(d) </bold>PCR with primers F and R to support the model shown in (c). Lane 1, <italic>L. major </italic>wild-type cells; lane 2, <italic>L. major </italic>MTX60.4.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>Chromosome aneuploidy in <italic>L. major </italic>selected for MTX resistance. The relative expression ratio of each individual gene of chromosomes <bold>(a) </bold>22, <bold>(b) </bold>28, <bold>(c) </bold>11 and <bold>(d) </bold>12 of <italic>L. major </italic>MTX60.4 was contrasted with the expression levels of the same genes <italic>in L. major </italic>wild-type cells, which were arbitrarily set at 1. Quantitative Southern blots were performed; two distant probes per chromosome were hybridized to <italic>Hpa</italic>II digested DNA from <italic>L. major </italic>wild-type (lane 1), and <italic>L. major </italic>MTX60.4 (lane 2) (only one hybridization is shown for chromosomes 11 and 12). The hybridization signals of an α-<italic>tubulin </italic>(α-<italic>tub</italic>) probe, whose related gene is unchanged in the resistant strain, were used to standardize all the hybridization signals. <italic>Hpa</italic>II digested total DNA from revertant <italic>L. major </italic>MTX60.4 parasites after 5, 12, 25, and 30 passages without MTX (lanes 3, 4, 5, and 6, respectively) were added, showing the progressive loss of aneuploid chromosomes in revertants.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p>Comparison of relative hybridization data between RNA expression profiling and comparative genomic hybridization. RNA or genomic DNA derived probes were prepared from <italic>L. major </italic>MTX60.4 and the sensitive parent strain and hybridized to DNA microarrays. A subset of whole chromosome comparisons showing the correlation between RNA and DNA hybridization data are depicted. Examples shown are: chromosome 1 used as a no change control; chromosome 6 and the overexpression/amplification of the <italic>DHFR-TS </italic>locus (for quantification see Figure 4); and chromosome 22, where DNA and RNA are increased. For chromosome 11, RNA is decreased while DNA appears the same but the latter was also confirmed by Southern blots (Figure 7).</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Differential expression measured by the full-genome microarray analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Figure S1 shows the direct repeats flanking the <italic>DHFR-TS </italic>locus of <italic>L. major </italic>and <italic>L. infantum </italic>chromosome 6, and also provides the circular junction sequence formed by homologous recombination. Figure S2 shows the inverted repeats present on chromosome 23 of <italic>L. infantum</italic>, and provides the sequence of the new junction formed through the inverted duplication. Figure S3 shows the sequence of the <italic>L. major </italic>chimera gene <italic>LmjF10.0380/0390</italic>.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Results of the comparative genomic hybridization analyses of <italic>L. major </italic>MTX60.4 versus the respective wild-type cells.</p></caption></supplementary-material>" ]
[]
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[{"surname": ["Ouellette", "Drummelsmith", "Leprohon", "El Fadili", "Foucher", "Vergnes", "L\u00e9gar\u00e9"], "given-names": ["M", "J", "P", "K", "A", "B", "D"], "source": ["Drug Resistance in Leishmania"], "year": ["2007"], "publisher-name": ["Norwich, UK: Horizon Press"]}, {"article-title": ["Sanger Institute Pathogen Sequencing Unit"]}, {"surname": ["Sambrook", "Fritsch", "Maniatis"], "given-names": ["J", "EF", "T"], "source": ["Molecular Cloning"], "year": ["1989"], "publisher-name": ["New York: Cold Spring Harbour Laboratory Press"]}, {"article-title": ["Quebec Genomics Center"]}]
{ "acronym": [], "definition": [] }
90
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 18; 9(7):R115
oa_package/ec/29/PMC2530873.tar.gz
PMC2530874
18644147
[ "<title>Background</title>", "<p><italic>Toxoplasma gondii </italic>is an obligate intracellular protozoan parasite that infects a wide range of animals, including humans. It is a member of the phylum Apicomplexa, which includes parasites of considerable clinical relevance, such as <italic>Plasmodium</italic>, the causative agent of malaria, as well as important veterinary parasites, such as <italic>Theileria</italic>, <italic>Eimeria</italic>, <italic>Neospora </italic>and <italic>Cryptosporidium</italic>, some of which like <italic>Toxoplasma </italic>are zoonotic. In common with the other Apicomplexa, <italic>T. gondii </italic>has a complex life-cycle with multiple life-stages. The asexual cycle can occur in almost any warm-blooded animal and is characterized by the establishment of a chronic infection in which fast dividing invasive tachyzoites differentiate into bradyzoites that persist within the host tissues. Ingestion of bradyzoites via consumption of raw infected meat is an important transmission route of <italic>Toxoplasma</italic>. By contrast, the sexual cycle, which results in the excretion of infectious oocysts in feces, takes place exclusively in felines.</p>", "<p>The genome of <italic>Toxoplasma </italic>has been sequenced, with draft genomes of three strains of <italic>Toxoplasma </italic>(ME49, GT1, VEG) as well as chromosomes Ia and Ib of the RH strain available via ToxoDB [##REF##18003657##1##]. ToxoDB is a functional genomic database for <italic>T. gondii </italic>that incorporates sequence and annotation data and is integrated with other genomic-scale data, including community annotation, expressed sequence tags (ESTs) and gene expression data. It is a component site of ApiDB, the Apicomplexan Bioinformatics Resource Center, which provides a common research platform to facilitate data access among this important group of organisms [##REF##17098930##2##]. ToxoDB reflects pioneering efforts that have been made toward the annotation of the <italic>Toxoplasma </italic>genome. Nevertheless, although the assembly and annotation of the <italic>Toxoplasma </italic>genome is far in advance of most other eukaryotic pathogens, significant deficiencies still remain; in common with many other genome projects, annotation has thus far not taken into account information provided by global protein expression data and neither have these data been available to the user community in the context of other genome resources.</p>", "<p>There is now an abundance of transcriptional expression data for <italic>Toxoplasma</italic>, including expression profiling of the three archetypal lineages of <italic>T. gondii</italic>. Transcriptional studies have also provided evidence for stage-specific expression via EST libraries, microarray analysis and SAGE (serial analysis of gene expression) [##REF##9529091##3##, ####REF##12455982##4##, ##REF##12618375##5##, ##REF##16324218##6####16324218##6##]. Clusters of developmentally regulated genes, dispersed throughout the genome, have been identified that vary in both temporal and relative abundance, some of which may be key to the induction of differentiation [##REF##12455982##4##,##REF##16324218##6##]. Global mRNA analysis indicates that gene expression is highly dynamic and stage-specific rather than constitutive [##REF##16324218##6##]. However, the study of individual proteins has also implicated the involvement of both post-transcriptional and translational control [##REF##11429165##7##, ####REF##9016946##8##, ##REF##17040862##9####17040862##9##] and the potential regulation of ribosome expression has also been proposed [##REF##15147680##10##]. Evidence may also point to possible epigenetic control of gene expression, following observations of a strong correlation between regions of histone modification and active promoters [##REF##16697685##11##,##REF##17559302##12##].</p>", "<p>Until now the study of global gene expression in <italic>T. gondii </italic>and the use of expression data to inform gene annotation has been almost exclusively confined to transcriptional analyses. Whilst a relatively small number of proteins have been studied in considerable detail, published proteomic expression data are limited to small studies employing two-dimensional electrophoresis (2-DE) separation of tachyzoite proteins [##REF##11796121##13##,##REF##15652819##14##], or to specific analysis of <italic>Toxoplasma </italic>sub-proteomes that have been implicated in the invasion and establishment of the parasite within the host cell [##REF##16002398##15##, ####REF##14982962##16##, ##REF##16002397##17##, ##REF##16518471##18####16518471##18##].</p>", "<p>This paper reports the first multi-platform global proteome analysis of <italic>Toxoplasma </italic>tachyzoites resulting in the identification of nearly one-third of the entire predicted proteome of <italic>T. gondii </italic>and represents a significant advance in our understanding of protein expression in this important pathogen. We describe also the development of a proteomics platform within ToxoDB to act as a public repository for these, and other, proteomic datasets for <italic>T. gondii</italic>. Our data are now available as a public resource and add a vital hitherto missing dimension to the expression data within ToxoDB. Moreover, the addition of detailed protein expression information within an integrated genomic platform highlights the value of protein expression data not only in interpreting transcriptional data (both ESTs and microarray data), but also provides valuable insights into the annotation of the genome of <italic>T. gondii</italic>.</p>" ]
[ "<title>Materials and methods</title>", "<title>Chemicals and materials</title>", "<p>Chemicals were AnalaR or HPLC grade and from VWR (Poole, UK) except: amidosulphobetaine-14 (ASB-14; Calbiochem, Nottingham, UK); deoxycholate (Sigma-Aldrich, Steinheim, Germany); iodoacetamide (Sigma-Aldrich); Invitrosol (Invitrogen, Carlsbad, CA, USA); Mini complete protease inhibitor cocktail (Roche, Penzberg, Germany); bovine pancreas sequencing grade trypsin (Roche); thiourea (Sigma-Aldrich); TCEP (tris (2-carboxyethyl) phosphine hydrochloride (Pierce, Rockford, IL, USA); 2-DE consumables (Amersham Biosciences, Little Chalfont, UK).</p>", "<title>Parasite culture</title>", "<p>Tachyzoites of <italic>T. gondii </italic>strain RH were maintained in confluent layers of Vero cells (ECACC, Salisbury, UK). <italic>T. gondii </italic>tachyzoites were harvested 3 or 4 days post-infection as previously described [##REF##11796121##13##].</p>", "<title>One-dimensional PAGE analysis</title>", "<p>A pellet of 1.1 × 10<sup>8 </sup>tachyzoites (approximately 220 μg) was solubilized in 40 μl of 100 mM Tris/HCl pH 6.8, 10% (v/v) glycerol, 4% (w/v) SDS, 0.01% (w/v) Bromophenol Blue, 200 mM dithiothreitol (DTT), with three cycles of 5 minutes at 90°C and 2 minutes vortexing, then spun at 16,000 g for 3 minutes. The supernatant was run on a 16 cm 12% (v/v) acrylamide gel using the denaturing Tris-glycine method of Laemmli [##REF##5432063##43##], at 16 mA for 30 minutes and 24 mA for 6-7 h at 15°C. The gel was stained with colloidal Coomassie blue, the lane cut into 129 slices of &lt; 1 mm thickness and each digested with trypsin. For the Tris-fractionated sample, a pellet of 9.85 × 10<sup>7 </sup>tachyzoites was solubilized on ice for 1 h in 50 μl of 100 mM Tris/HCl pH 8.5 and vortexed every 10 minutes. Three cycles of freeze-thaw using liquid nitrogen, and 2 minutes of vortexing followed, and the sample spun at 16,000 g at 4°C for 30 minutes to partition Tris-soluble protein (supernatant) from Tris-insoluble protein (pellet). The latter was further solubilized in 50 μl of 2% (v/v) SDS, 100 mM DTT using three cycles of 5 minutes at 90°C and 2 minutes vortexing, with a final spin at 16,000 g for 15 minutes. An aliquot of 20 μl of 100 mM Tris/HCl pH 6.8, 10% (v/v) glycerol, 4% (w/v) SDS, 0.01% (w/v) Bromophenol Blue, 200 mM DTT was added to 30 μl of Tris-insoluble protein (approximately 130 μg), and to 30 μl of Tris-soluble protein (approximately 120 μg) and resolved on a 12% (w/v) acrylamide gel as described above. Twenty-five gel slices were excised from a region of the gel deemed to exhibit maximum density and variation in protein banding.</p>", "<title>Two-dimensional PAGE analysis</title>", "<p>Frozen pellets of <italic>T. gondii </italic>tachyzoites were solubilized in 7 M urea, 2 M thiourea, 4% (w/v) Chaps, 2% (w/v) ASB14, 20 mM Tris base, 60 mM DTT, 1 mM EDTA, 1 × Mini Complete protease cocktail inhibitor, 0.5% (v/v) immobilized pH gradient (IPG) strips buffer (pH 4-7 linear gradient, 1 × 10<sup>8 </sup>tachyzoites, approximately 200 μg; pH3-10 non-linear gradient, 2.58 × 10<sup>8 </sup>tachyzoites, approximately 516 μg). The samples were incubated at room temperature for 4-5 h with a vigorous vortex every half an hour and spun at 16,000 g for 5 minutes. The supernatants were made to a final volume of 450 μl with 8 M urea, 2% (w/v) CHAPS (3- [(3-cholamidopropyl)-dimethylammonio]-1-propane sulphonate), 0.002% (w/v) Bromophenol Blue, 40 mM DTT, supplemented with 0.5% (v/v) pH 3-10 NL or pH 4-7 L IPG buffer and used to rehydrate 24 cm Immobiline IPG strips for a minimum of 10 h at room temperature. The rehydrated strips were placed on an Ettan™ IPGphor II™ with a loading manifold (GE Healthcare, Bucks, UK) and isoelectric focusing (IEF) was run at 20°C, 75 μA per strip as follows: stepped voltage, 500 V for 2 h; gradient voltage, 1,000 V over 8 h; gradient voltage, 10,000 V over 3 h; stepped voltage, 10,000 V for 4 h and 15 minutes (approximately 65, 000 Volt hours). The IPG strips were equilibrated for 15 minutes each in 6 M urea, 50 mM Tris/HCL pH 8.8, 30% (v/v) glycerol, 2% (w/v) SDS, 0.002% (w/v) Bromophenol Blue supplemented with 1% (w/v) DTT, then with 2.5% (w/v) iodoacetamide and mounted on DALT 12.5% (w/v) pre-cast 24 cm acrylamide gels resolved using an Ettan DALT™ 6-MultiTemp III apparatus and buffering kit (Amersham Biosciences). Gels were run at 20°C, 3 W for 0.5 hour and 17 W per gel thereafter.</p>", "<title>Colloidal Coomassie staining</title>", "<p>Gels were fixed in 40% (v/v) ethanol, 10% (v/v) acetic acid overnight at room temperature, rinsed in distilled deionized water, stained for 5 days with colloidal Coomassie stain (20% (v/v) methanol, 0.08% (w/v) CBB G250, 0.8% (v/v) phosphoric acid, 8% (w/v) ammonium sulfate), rinsed in distilled deionized water and stored in 1% (v/v) acetic acid at 4°C.</p>", "<title>In-gel tryptic digestion</title>", "<p>Gel plugs/slices were destained at 37°C using 50 mM ammonium bicarbonate/50% acetonitrile. One-dimensional gel slices were incubated at 37°C with 10 mM DTT/100 mM ammonium bicarbonate for 30 minutes, then 100 mM iodoacetamide/55 mM ammonium bicarbonate for 1 h in the dark. Gel plugs/slices were dehydrated with 100% (v/v) acetonitrile at 37°C and rehydrated at 37°C with 10 μl of 10 ng/μl sequencing grade trypsin in 25 mM ammonium bicarbonate. After 1 h, 25 mM ammonium bicarbonate was added to cover the gel pieces, which were left at 37°C overnight. The reaction was stopped with 2 μl of 2.6 M formic acid and the samples stored at -20°C.</p>", "<title>Tandem mass spectrometry (LC-MS/MS)</title>", "<p>LC-MS/MS was performed on an LTQ ion-trap mass spectrometer (Thermo-Electron, Hemel Hempstead, UK) coupled on-line to a Dionex Ultimate 3000 (Dionex Company, Amsterdam, The Netherlands) HPLC system equipped with a nano pepMap100 C18 RP column (75 μm; 3 μm, 100 Angstroms) equilibrated in 98.9% water/2% acetonitrile/0.1% (v/v) formic acid at 300 nl/minute. Tryptic peptides were desalted on a C18 TRAP, and resolved with a linear gradient of 0-50% (v/v) acetonitrile/0.1% (v/v) formic acid over 30 minutes, followed by 80% (v/v) acetonitrile/0.1% (v/v) formic acid for 5 minutes. Ionized peptides were analyzed using the 'triple play' mode (0-10<sup>6 </sup>m/z, global and Ms<sup>x</sup>), consisting initially of a survey (MS) spectrum from which the three most abundant ions were determined (threshold = 200-500 TIC [total ion chromatogram]). The charge state of each ion was assigned from the C13 isotope envelope 'zoom scan', fragmented (collision energy 35% for 30 ms) and subjected to a MS/MS scan. The LTQ was tuned using a 500 fmol/μl solution of glufibrinopeptide (m/z 785.8, [M+2H]<sup>2+</sup>). The resulting MS/MS spectra were submitted to TurboSequest Bioworks version 3.1 (Thermo Fisher Scientific Inc., Waltham, MA, USA) (threshold cut-off 0-1000; group scan default 100; minimum group count 1; minimum ion count 15; peptide tolerance 1.5), the individual spectra (dta files) merged into an mgf file and submitted to Mascot (Matrix Science, London, UK) and searched against a locally mounted <italic>Toxoplasma </italic>genome database comprising ORFs &gt; 50 amino acids; clustered ESTs; whole genome shotgun (10×); TwinScan, TigrScan and GlimmerHMM protein predictions; and <italic>T. gondii </italic>annotated proteins_ToxoDB release 4.1. Search parameters were: fixed carbamidomethyl modification of cysteine; variable oxidation of methionine; peptide tolerance ± 1.5 Da; MS/MS tolerance ± 0.8 Da; +1, +2, +3 peptide charge state; single missed trypsin cleavage.</p>", "<title>Manual validation of Mascot results</title>", "<p>Additional manual validation of the proteins identified by Mascot was carried out on the 1-DE and 2-DE results. Proteins identifications that were based on a single peptide and proteins that returned a Mascot score &lt; 60 were accepted if: a matching peptide possessed an individual ion score above the significant threshold for identity or extensive homology (typically &gt; 44); or upon manual inspection of individual peptide MS/MS spectra at least 60% of the candidate y-ions were at a minimum signal to noise ratio of 10%. Spectra that failed to pass either rule were regarded as false positive identifications, which can result from an accumulation of several peptides with low ion scores.</p>", "<title>Sample preparation for MudPIT</title>", "<p>A pellet of 10<sup>9 </sup>tachyzoites resuspended to approximately 800 μg/ml in 500 μl 100 mM Tris buffer pH 8.5 were lysed by three cycles of freeze/thaw and the Tris-soluble and insoluble protein fractions separated at 16,000 g for 30 minutes. Digestion of soluble fractions: MS compatible detergent Invitrosol was added to 1% (v/v), the solution heated to 60°C for 5 minutes, vortexed for 2 minutes, denatured with 2 M urea, reduced with 5 mM Tris (2-carboxyethyl) phosphine hydrochloride (TCEP), carboxyamidomethylated with 10 mM iodoacetamide, followed by addition of 1 mM CaCl<sub>2 </sub>and trypsin at a ratio of 1:100 (enzyme:protein) and incubated at 37°C overnight. Digestion of insoluble fractions: 10% (v/v) Invitrosol was added to the pellet, which was heated to 60°C for 5 minutes, vortexed for 2 minutes and sonicated for 1 h. The sample was diluted to 1% (v/v) Invitrosol with 8 M urea/100 mM Tris/HCl pH 8.5, reduced and carboxyamidomethylated as before, and digested with endoproteinase Lys-C for 6 h. The solution was diluted to 4 M urea with 100 mM Tris/HCl pH 8.5 and digested with trypsin as described above.</p>", "<title>Mass spectrometric analysis by MudPIT</title>", "<p>Five soluble replicates and four insoluble samples were each subjected to MudPIT analysis with modifications to the method of Link <italic>et al</italic>. [##REF##10404161##44##], using a quaternary Agilent 1100 series HPLC coupled to a Finnigan LTQ-ion trap mass spectrometer (Thermo, San Jose, CA, USA) with a nano-LC electrospray ionization source [##REF##9750149##45##]. Peptide mixtures were resolved by strong cation exchange LC upstream of reverse phase LC as described [##REF##11231557##46##]. Each sample (approximately 100 μg) was loaded onto separate microcolumns and resolved by fully automated 12 step chromatography. Protein databases: a <italic>Toxoplasma </italic>database was assembled (see above). To identify contaminant host proteins, the parasite database was supplemented with a contaminant database (the complete prokaryote and mammalian databases from NCBI). To estimate the amount of false positives, a reverse database was added [##REF##12643542##47##]. Poor quality spectra were removed from the dataset using an automated spectral quality assessment algorithm [##REF##15262780##48##]. Tandem mass spectra remaining after filtering were searched with the SEQUEST algorithm version 27 [##UREF##0##49##]. All searches were in parallel and were performed on a Beowulf computer cluster consisting of 100 1.2 GHz Athlon CPUs [##REF##12645897##50##]. No enzyme specificity was considered for any search. SEQUEST results were assembled and filtered using the DTASelect (version 2.0) program [##REF##12643522##51##], which uses a quadratic discriminate analysis to dynamically set XCorr and DeltaCN thresholds for the entire dataset to achieve a user-specified false positive rate (&lt; 5% peptides false positive in this analysis). The false positive rates are estimated by the program from the number and quality of spectral matches to the decoy database.</p>", "<title>Bioinformatics prediction</title>", "<p>Prediction programs used were: SignalP to predict proteins that contain signal peptides; TMHMM to predict transmembrane domains; results returned from PATS, PlasMit, and WoLF PSORT together with release4 gene description and GO cellular component prediction provided by ToxoDB were combined to obtain subcellular localization prediction of proteins.</p>", "<title>Mapping of proteome data to the genome scaffold</title>", "<p>Peptides that hit release4 gene annotation could be directly mounted upon the ToxoDB genome scaffold. Where the database search identified preferentially an alternative gene model or an ORF, the sequences were mapped onto the genome using the following algorithm: rule 1, if all the peptides from the alternative models could be mapped to a release4 gene, the release4 annotation is adopted and this is termed a 100% match; rule 2, if more than 50% of the peptides from an alternative model can be mapped to an official release4 gene, this is considered a valid mapping and the matching peptides are aligned with the corresponding release4 gene; rule 3, if a certain set of peptides from an alternative model can be mapped to more than one release4 gene, the gene that can host most peptides will be reported; rule 4, alternative models not conforming to rule 2 will then be mapped to ORFs; rule 5, an alternative model will be mapped to an ORF only if 100% of the peptides can be mapped to that ORF. If 100% of the peptides from the alternative model cannot be mapped to a single release4 gene (rule 1) or to a single ORF (rule 5), the peptides are also mapped to the alternative gene model (for example, TgTwinscan, TgGLEAN, and so on), which can be viewed in GBrowse by selecting the relevant option. This enables ToxoDB users to directly visualize proteomics evidence for alternative gene annotation. All raw data associated with this manuscript may now be downloaded from the Tranche Project [##UREF##1##52##], using the following hash: Ulv/yTYTaaHin5Tv4InpsgoUY1uTJQtdoLRi9HbdtypXqztv+BiVE/wZieBkqu6d3kU20Vyejo0HYCfswgwiGyPHQPAAAAAAAAOhng==</p>" ]
[ "<title>Results</title>", "<title>Two-dimensional electrophoresis proteome map of <italic>T. gondii </italic>tachyzoites</title>", "<p>Urea-soluble lysates from cultured <italic>T. gondii </italic>tachyzoites were resolved using broad (pH 3-10) and narrow (pH 4-7) range 2-DE gels (Figures ##FIG##0##1## and ##FIG##1##2##; Additional data files 1 and 2). The protein identity of individual protein spots was obtained using electrospray mass spectrometry (Additional data files 3 and 4). In total, 1,217 individual protein spots were identified by 2-DE analysis, 783 detected by the pH 3-10 separation and 434 by the pH 4-7 separation. In many instances proteins from separate spots shared the same identity. Examples of clusters of proteins with the same identification are shown boxed in Figures ##FIG##0##1## and ##FIG##1##2##, and these most likely represent isoenzymes, or proteins with post-translational modification. Many gel plugs contained more than one protein and this is represented by overlapping boxes in the figures. Accounting for redundancy between gels and assuming post-translational variants are the products of a single gene, these data represent the expression of 616 non-redundant <italic>Toxoplasma </italic>genes, of which 547 correspond to release4 gene annotation and 69 are described by alternative gene models or open reading frames (ORFs) that do not correspond to a release4 annotation (discussed further in the 'Genome annotation' section below). Forty release4 genes (which exhibited a range of masses, isoelectric points and functional annotations) were uniquely identified using 2-DE analysis; that is, they were not detected by either the gel liquid chromatography (LC)-linked tandem mass spectrometry (MS/MS) or multidimensional protein identification technology (MudPIT) approaches described in the following sections.</p>", "<title><italic>T. gondii </italic>tachyzoite proteome analysis by one-dimensional electrophoresis gel LC MS/MS</title>", "<p>Whole tachyzoite protein, solubilized in SDS, was resolved using a large format one-dimensional electrophoresis (1-DE) gel (Figure ##FIG##2##3##). We excised 129 contiguous gel slices from the entire length of the resolving gel and each gel slice was submitted to LC-MS/MS. This approach combines the resolving power of SDS gel-based protein separation with that of the liquid chromatography separation coupled on-line to the mass spectrometer and resulted in the generation of large, high quality datasets of SDS-soluble proteins. An average of 20 proteins was identified from each 1 mm gel slice and the complete dataset comprising 2,778 individual protein identifications is shown in Additional data file 5. A further 1-DE experiment, using prior Tris solubilization, led to the identification of 82 additional release4 genes and 9 alternative gene models (Additional data files 6 and 7). Some proteins were identified in multiple gel slices again, likely due to isozymes or post-translational modifications. When redundancy between proteins with the same identification was removed, 1,012 individual gene products (939 release4 and 73 alternative gene models) were identified from <italic>T. gondii </italic>tachyzoites by gel LC-MS/MS analysis (Additional data files 8 and 9).</p>", "<title>MudPIT analysis of <italic>T. gondii </italic>tachyzoites</title>", "<p>Whole tachyzoite protein was partitioned into Tris-soluble and Tris-insoluble fractions, and each processed for MudPIT analysis; this resulted in 1,300 and 2,328 protein identifications, respectively, and a total non-redundant dataset comprising 2,409 proteins, which comprises 2,121 release4 and 288 alternative gene models (Additional data files 10 and 11). Of the release4 genes identified, 15.3% were identified uniquely in the Tris-soluble fraction and 48.0% were identified uniquely in the Tris-insoluble fraction.</p>", "<p>When the results using all three proteomic platforms were combined, a total of 2,252 non-redundant release4 protein identifications were obtained from the tachyzoite stage of the parasite. This represents expression from approximately 29% of the total number of currently predicted release4 genes. Figure ##FIG##3##4## illustrates the degree of overlap between the datasets derived using each of the three proteomic platforms. MudPIT generated the largest number of identifications; however, a number of proteins were uniquely identified using the gel-based approaches (59 for 1-DE; 40 for 2-DE). Other studies have also highlighted the benefits of a multi-platform proteomic approach and the advantages and disadvantages of each platform have been discussed extensively elsewhere [##REF##18306179##19##]. Notably, the gel-based proteomic platforms detected, on average, more peptides per protein identification than MudPIT. Overall across all platforms, only approximately 6% of the 2,252 proteins identified were based on single peptide evidence; this represents a relatively low proportion compared to other apicomplexan proteomic studies [##REF##18306179##19##, ####REF##12368866##20##, ##REF##12368870##21####12368870##21##] and is probably accounted for partly by the extensive data from gel-based proteomics in addition to the MudPIT analysis. In addition to the release4 genes, 394 non-redundant alternative gene models and ORFs were also identified from the entire dataset. These data represent sets of peptides that map more comprehensively to alternative models and ORFs than the release4 gene models, and have considerable implications for genome annotation, as discussed below.</p>", "<title>Functional analyses and key pathways of the tachyzoite proteome</title>", "<p>Each individual protein detected by proteomics was submitted to the motif prediction algorithms SignalP [##REF##15223320##22##] and TMHMM [##REF##11152613##23##] and also to subcellular localization prediction programs, for example, PATS (apicoplast) [##REF##11738814##24##], PlasMit (mitochondrion) [##REF##14599665##25##], WoLF PSORT (general) [##REF##17517783##26##] and Gene Ontology (GO) cellular component prediction downloaded from ToxoDB. <italic>Toxoplasma </italic>genome predictions suggest that 11% of proteins contain a signal peptide and 18% contain transmembrane domains (information available at ToxoDB). Virtually identical proportions were detected in this study in the expressed proteome of tachyzoites (10% and 18%, respectively). Analysis of the 394 alternative gene models and ORFs gave closely similar proportions (results not shown). This represents expression of more than one-quarter of the predicted numbers of membrane and secreted proteins within one life-cycle stage of the parasite. Assuming non-biased sampling, these results imply no enrichment for membrane proteins in tachyzoites. Similar proportions of signal peptide and transmembrane containing proteins were observed in the expressed proteome of <italic>Plasmodium falciparum </italic>[##REF##12368866##20##]. The <italic>Toxoplasma </italic>proteins showed a wide distribution of sub-cellular localizations, demonstrating broad sampling, with cytoplasmic, nuclear and mitochondrial locations well represented (Figure ##FIG##4##5a##; Additional data file 12). Many proteins were also potentially involved in secretory pathways and were assigned to the endoplasmic reticulum-Golgi, the plasma membrane and extracellular locations.</p>", "<p>The functional analysis of the expressed proteome presented in Figure ##FIG##4##5b## (see also Additional data file 13) was constructed using the GO classifications listed on ToxoDB, which are largely based on bioinformatics interpretation. Each release4 gene was then assigned to a specific Munich Information Centre for Protein Identification (MIPS) category within the FunCatDB functional catalogue [##REF##15486203##27##]. Some genes are without a GO classification and were assigned a putative MIPS category using additional information provided by Blast similarities, Pfam domain alignments [##REF##16381856##28##], InterPro [##REF##17202162##29##], orthologs, <italic>Toxoplasma </italic>paralogs, and from independent literature searches. Functional categories that are highly represented are metabolism, protein fate, protein synthesis, cellular transport, transcription and proteins with binding functions. A large proportion (36%) of the proteins have 'unknown function', indicating the difficulty of obtaining functional information using sequence similarity methods alone. Functional assignments were also constructed for hits to alternative gene models and ORFs, revealing similar relative proportions of functional categories, except for a larger proportion (70%) of proteins with unknown function, presumably due to the sequences being atypical, or incompletely predicted (Additional data file 14). The implications of the functional categories discovered are examined in the Discussion.</p>", "<p>Tachyzoites are thought to rely upon both glycolysis and the tricarboxylic acid cycle, unlike the bradyzoites, which are thought to be largely dependent upon glycolysis [##REF##11429165##7##]. Virtually every component of the glycolysis/gluconeogenesis pathway predicted for <italic>Toxoplasma </italic>was identified as being expressed in tachyzoites by proteomic analysis, as illustrated in Figure ##FIG##5##6##. Additionally, considerable coverage of the oxidative phosphorylation and tricarboxylic acid cycle pathways was also identified from the expressed proteome dataset (data not shown; see ToxoDB for further details). Several enzymes of the glycolytic pathway have been shown to be modulated during differentiation [##REF##16324218##6##,##REF##11429165##7##], with some showing stage-specific isoforms, such as enolase and lactate dehydrogenase [##REF##9016946##8##]. The level of mRNA expression does not always mirror that of the expressed protein, indicating a degree of translational control or changes in mRNA stability [##REF##9016946##8##]. However, it should be noted that detecting low levels of protein can be problematic. One example is glucose-6-phosphate isomerase (<italic>76.m00001</italic>). Western analysis detected expressed protein in bradyzoites but not tachyzoites despite the presence of abundant mRNA transcripts in both stages [##REF##10455162##30##]. However, glucose-6-phosphate isomerase was successfully detected in tachyzoites in this whole cell proteome analysis (Additional data file 5, gel slices 40-42), again illustrating the sensitivity of our proteome approach.</p>", "<title>Comparison with EST expression data</title>", "<p>Figure ##FIG##6##7a## illustrates the degree of correlation between release4 genes for which EST expression data are available and genes for which the total proteome dataset identified in this study has provided evidence of expression. By including all the tachyzoite and bradyzoite cDNA evidence from RH, ME49, VEG, CAST, COUG and MAS strains (available at ToxoDB), most (91%) of the proteins found in this study were corroborated by EST data. Approximately half of these were confirmed in both bradyzoite and tachyzoite stages by EST analysis, suggesting that many of the proteins may have common, house-keeping functions. Although the EST coverage of the total number of release4 genes listed at ToxoDB is relatively high (68% for tachyzoite ESTs alone), for 266 release4 genes detected in this study using proteomics there was no corresponding tachyzoite EST evidence, apparently reflecting inadequacies in the coverage of the EST data. The distribution of cellular functions amongst these 266 expressed proteins is representative of the entire proteome dataset, indicating that EST evidence is lacking for many different proteins and not specific for a particular type or category of function (data not shown).</p>", "<p>Conversely, comparison of RH strain-specific tachyzoite ESTs with the proteome dataset revealed that 57% of genes for which there was EST transcript evidence were not corroborated by the detection of expressed protein in this study. This is likely to be explained by a number of contributing factors, including the difficulty in detecting low copy number, transient and unstable proteins. It is also possible that a small number of non-coding ESTs are present in the database for which no protein product would be expected.</p>", "<title>Comparison with microarray data</title>", "<p>Microarray analysis of the RH strain of <italic>T. gondii </italic>has been performed previously (data available through ToxoDB; A Bahl and DS Roos unpublished). The analysis provides extensive coverage of the genome (99.5% of release4 genes were assayed), and the results have been cross-referenced with the proteins identified. As it is difficult to determine the correct signal:noise ratio above which mRNA levels can be considered to be indicative of a gene being switched on (all genes represented on the array exhibit some signal, yet not all are expressed), the microarray results were divided into quartiles of mRNA expression level for the purposes of this comparison. Those genes in the bottom 25% were described as zero detectable mRNA above baseline, and alternatively those in the bottom 50% were described as having zero or low detectable mRNA level. The Venn diagrams in Figure ##FIG##6##7b## illustrate the degree of overlap between release4 genes, for which ≥ 25 percentile and ≥ 50 percentile mRNA expression was detected by microarray analysis, and the genes identified by our proteomic study. The results illustrate that some genes with zero or low mRNA can still be identified in a proteome study (204 proteins matching the &lt; 25% group and 632 proteins matching the &lt; 50% group). The detection of these proteins is intriguing and there may be several possible explanations. For example, these proteins may be highly stable and do not require new transcription for the protein to be detected, or perhaps substantial quantities of protein can be produced from very low mRNA. Three examples from this group are: 'bi-functional aminoacyl-tRNA synthetase, putative/prolyl-tRNA synthetase, putative' (<italic>38.m00021</italic>, 254 peptide hits), 'clathrin heavy chain, putative' (<italic>80.m02298</italic>, 148 peptide hits) and 'KH domain-containing protein' (<italic>35.m00901</italic>, 136 peptide hits). The high number of peptide hits demonstrates that these proteins are clearly present in high copy number yet have little or no detectable mRNA; such proteins are interesting candidates for understanding the relationship between mRNA and protein abundance levels in <italic>Toxoplasma</italic>.</p>", "<p>Figure ##FIG##6##7c## displays the comparison of the number of proteins identified matching each quartile of genes, according to mRNA expression level. There is a general trend for more proteins to have been detected for genes with higher mRNA expression levels (from the top quartile, 972 proteins have been detected, and only 204 have been detected from the bottom quartile), indicating, as expected, that there is some correlation between mRNA abundance and protein abundance.</p>", "<title>Genome annotation and generation of a public proteome interface for <italic>Toxoplasma</italic></title>", "<p>The mass spectrometry data in this study were searched against a database containing the current set of predicted proteins from ToxoDB (referred to here as release4), predicted proteins derived from alternative gene models (GLEAN, TigrScan, TwinScan and Glimmer), ESTs and a translation of all six ORFs (see Materials and methods). As such, the proteome data can provide evidence that an alternative gene model is the correct prediction, or that a gene has not been predicted at all in the genome.</p>", "<p>The release4 annotation available in ToxoDB release 4.2 was provided by the Toxoplasma Genome Sequencing Project. The proteome data have been aligned with release4 gene annotations where possible for identified peptide sequences that exactly match a protein predicted in the release4 set. These peptides can be viewed in relation to the predicted protein and the genomic region from which the sequence is predicted to have been produced. The peptide identifications can be viewed in the ToxoDB genome browser GBrowse by selecting the option 'Mass Spec Peptides (Wastling, <italic>et al</italic>.)'. This dataset comprises 2,252 release4 genes. In addition, identified peptides that are more likely to have arisen from a translation of an alternative gene model have been aligned, and can be viewed in GBrowse by selecting the option 'Mass Spec Peptides (Alternative Models)'.</p>", "<p>For the majority of annotated genes, integration of the expressed peptide data has provided direct confirmation of the correct prediction of ORFs and positioning of exon-intron boundaries, including a large number of hitherto 'hypothetical proteins'. The further significance and importance of this corroboratory evidence become more apparent when considering the minority of cases where the peptide expression data are in conflict with the gene prediction algorithms. Approximately 15% of the complete proteome dataset consists of peptide hits to regions of the scaffold where there are discrepancies with the new gene annotation and peptides mapped more convincingly to alternative gene models or ORFs (that is, 394 protein coding sequences). Of the 394 alternative gene models and ORFs detected, most are described as 'hypothetical' with minimal information available and were detected using MudPIT analysis. These hits can be viewed at ToxoDB using the queries and tools option that guides the user to a main menu page from which gene expression confirmation via mass spectrometry can be accessed. The option of refining the search to a single or combination of proteomic approaches, and of searching either annotated genes or ORFs, is available. By adopting the GBrowse viewing option, the user can examine in detail individual ORFs and the integrated peptide sequence data.</p>", "<p>An example is illustrated in Figure ##FIG##7##8## of a region of the scaffold where peptide evidence supports the presence of an expressed ORF but the new prediction algorithm has not assigned a gene in the corresponding region. Eleven peptides map to <italic>TgGlmHMM_3355 </italic>and <italic>TgTigrScan_5280 </italic>but the release4 annotation does not predict an exon in this region. Additional peptides in this region map to exons of the neighboring gene <italic>46m.02877</italic>; however, these peptides could also be assigned to the coding sequence of <italic>TgGlmHMM_3355 </italic>and/or <italic>TgTigrScan_5280</italic>. In this case, the peptide evidence appears to indicate that gene <italic>46m.02877 </italic>could have an incorrect start methionine and be missing an amino-terminal exon.</p>", "<p>In other cases, peptide identifications are able to identify errors in the predicted reading frame or strand orientation as illustrated in Figure ##FIG##8##9##. Here 12 peptides derived from 35 individual spectra originating from both 1-DE and MudPIT approaches provided matching hits to <italic>TgGlmHMM_1717</italic>, <italic>TgTwinScan_4462 </italic>and <italic>TgGLEAN_7850</italic>, whereas the new gene prediction algorithm (assigned <italic>50.m05694</italic>) is predicted to lie on the opposite strand and <italic>TgTigrScan_8273 </italic>uses a different reading frame. The various algorithms also differ in the predictions of the length and number of exons, although peptide evidence supports a single exon. In this example, the peptide expression data have provided supporting evidence for the correct reading frame and the large number of peptide hits to one region only indicates that the gene is likely to comprise a single exon.</p>", "<p>Other discrepancies involving the positioning of the exon-intron boundaries exist and, in some cases, the alternative gene annotation models such as TgGlmHMM, TgTigrScan, TgTwinScan and TgGLEAN correlate more closely with the co-ordinates of the peptide data. In Figure ##FIG##9##10##, 12 peptides from MudPIT analysis map to a region of the scaffold (X: 3917326-3920484) that is annotated with gene <italic>28.m00300</italic>, comprising two exons. Five of the twelve peptides match the second exon of gene <italic>28.m00300</italic>. While it appears that peptides match the scaffold in the region of <italic>28.m00300 </italic>exon 1, these peptides have been predicted from a different frame translation. Of further note is that one peptide maps to the predicted intron region of gene <italic>28.m00300</italic>. Alternative gene models vary considerably in this region of the scaffold in both the number and positioning of the exons and all 12 peptides only appear in <italic>TgGlmHMM_2666</italic>, which does not have an intron at this location, providing evidence that this model is most likely to be correct.</p>", "<p>An important use of peptide identification is to confirm that intron-exon (splice) boundaries have been correctly predicted; these are notoriously difficult to predict accurately in genome sequence using informatics approaches alone. If a peptide sequence spans an intron, matching regions from the splice donor and acceptor of two exons, this provides strong evidence that splicing has been correctly predicted for these exons. In total, our study identified 2,477 intron spanning peptides in the official release4 annotation, providing supporting evidence that these splice sites have been correctly predicted. In addition, peptides aligning across 421 splice boundaries predicted from alternative gene models only have been identified. This number is highly significant, as the identifications provide strong evidence that the alternative gene model is correct for this region, allowing the genome annotation to be improved. One example of a peptide spanning an intron is shown in Figure ##FIG##7##8##, where peptides have been identified that span an intron between exons predicted by TwinScan and Glimmer only.</p>" ]
[ "<title>Discussion</title>", "<p>Draft genomes now exist for the majority of clinically important protozoa, including most Apicomplexa. Providing an accurate interpretation of gene annotation and expression from these genomes is essential to understanding the biology of host-pathogen interactions and in gaining a better understanding of the relationship between gene transcription and protein expression. Of particular importance is an appreciation of the limitations that transcriptional data alone place on our interpretation of how pathogens respond as they develop through different life-stages, or during key processes such as invasion and establishment within their hosts. Such an observation has potentially huge implications for expression profiling and for the reliance on microarray data to describe changes in gene expression. In this paper we describe how global proteomic data for <italic>T. gondii </italic>provides important insights into both genome annotation and gene expression in this model apicomplexan parasite.</p>", "<p>Proteomic data enable us to understand what is actually expressed, as opposed to what might be, or has the potential to be, expressed in an organism. In general, the functional characterization and protein localization profile detected in <italic>T. gondii </italic>in this study fits well with that of the rapidly dividing and invasive tachyzoites, which would be expected to be highly metabolically active, with gene expression, protein synthesis, remodeling and degradation all necessary processes involved in active parasite cell division and required for successful host cell invasion. A similar profile was recently obtained for the expressed proteome of the invasive form of <italic>Cryptosporidium </italic>[##REF##18306179##19##]. Penetration and maintenance within the host cell would require expression of many apical organelle proteins involved in invasion (category: cell rescue, defense and virulence), as has been observed for the invasive stages of <italic>Plasmodium </italic>and <italic>Cryptosporidium </italic>[##REF##18306179##19##,##REF##12368866##20##,##REF##15342554##31##]. In agreement, 44 proteins were assigned to an apical organelle location in Figure ##FIG##4##5a##. Recent work has also shown the recruitment of host endoplasmic reticulum, mitochondria and networks of intimately proximal microtubules facilitating active transport of host nutrients to the parasite [##REF##16630815##32##, ####REF##9378762##33##, ##REF##11448993##34##, ##REF##16778769##35####16778769##35##]. Notably, proteins involved in cellular transport are well represented, with more than 200 expressed in this life cycle stage. A significant proportion of proteins falls into the broad category 'proteins with binding functions', including proteins involved in the cytoskeleton that are also required for motility, an important function during invasion. Many proteins were also detected that would be expected to be expressed at low or temporal levels within the cell, such as those involved in cell cycle control (<italic>641.m01576</italic>, <italic>38.m00005</italic>) or signal transduction (<italic>65.m01199</italic>, <italic>59.m06067</italic>, <italic>55.m04992</italic>, <italic>49.m05708</italic>, <italic>50.m05649</italic>). This suggests that the sensitivity of our proteomic analyses was high.</p>", "<p>Perhaps most notable were the large number of proteins (36%) for which no information is available and these proteins are listed as unclassified. A similarly large proportion (39%) of proteins with unknown function were detected in just one life cycle stage (the sporozoites) of <italic>Cryptosporidium </italic>by proteomic analysis [##REF##18306179##19##] and in the proteome of four life cycle stages of <italic>P. falciparum </italic>(that is, 51%) [##REF##12368866##20##]. More than half the predicted genes of <italic>Toxoplasma </italic>are annotated as 'hypothetical' in the genome. In this analysis, around 800 genes annotated as 'hypothetical protein' were identified, allowing these annotations to be updated to 'confirmed protein'. Functional analysis was also carried out on the 394 alternative gene models and ORFs and revealed a far greater proportion of proteins for which a functional assignment could not be determined (70% compared to 36%). This result reflects the limited annotation available for alternative gene models and ORFs, partially due to the short length of many of these sequences and difficulties obtaining functional information by sequence similarity search if the predicted ORF or alternative gene models do not closely resemble the correct gene sequence.</p>", "<p><italic>Toxoplasma </italic>has a complex life cycle comprising four additional life cycle stages not studied here: the infective sporozoite, two sexual stages and the encysted bradyzoite. Many house-keeping proteins will be common to all stages, although the proportion of shared proteins is not currently known. In this analysis, approximately one-third of the predicted number of release4 genes were detected in the proteome of the tachyzoite, although it is important to remember that these predicted genes will include stage-specific genes not expressed in the tachyzoite stage, so the actual proportion of proteins detected compared to those expected is likely to be considerably higher, although how much higher is impossible to determine at this stage. Whole cell proteome analysis of the related apicomplexan parasite, <italic>Cryptosporidium parvum</italic>, indicated expression of a similar proportion of the genome from the infective sporozoite stage [##REF##18306179##19##], and this parasite also exhibits multiple life cycle stages. Whether the protein set detected is close to the complete proteome of the life cycle stage or limited by the detection levels of the mass spectrometric techniques is not yet clear. Previous microarray analysis of sporozoites, gametocytes and blood stage life cycle stages of <italic>Plasmodium </italic>indicated 35% of genes were shared [##REF##12893887##36##] whereas this figure decreased to 6% at the proteome level [##REF##12368866##20##,##REF##15637271##37##]. It is likely that some of this discrepancy results from technical limitations associated with detecting low abundance proteins, although it is possible that post-transcriptional regulation also plays a role. In <italic>Toxoplasma</italic>, analysis of 568 EST assemblies from three life cycle stages, tachyzoites, bradyzoites and oocysts, indicated 16% of genes are stage-specific and, hence, that a large proportion of the genes is shared [##REF##12618375##5##]. A similar figure of 18% was obtained via SAGE analysis [##REF##16324218##6##].</p>", "<p>The comparison of the detected proteome with microarray results also reveals some interesting discrepancies. Of the least abundant 25% mRNA values, which would usually be described as no measurable mRNA signal above baseline, 204 proteins are detected. In contrast, of the genes with most abundant mRNA (top 25%, approximately 1,900 genes), only half of these are detected by proteome analysis. The most abundant proteins are likely to have been sampled preferentially in this analysis, and as such, we can hypothesize that many of the genes expressing high mRNA levels do not exhibit similarly high abundances of protein product. Without an in-depth absolute quantitative study of the complete <italic>Toxoplasma </italic>proteome, which is highly challenging with current technology, these results should not be over-interpreted. However, it appears that there is a considerable degree of control that regulates the level of protein abundance, independent of the rate of transcription in tachyzoites.</p>", "<p>Our proteome data have been integrated and aligned with the genome sequence at ToxoDB. The interface provided enables visual inspection of peptides matched to the most current (in this case 'release4') gene models, as well as to alternative gene models and ORFs. The facility to visualize and query peptide data, in tandem with EST and microarray data, allows users of ToxoDB to place confidence in particular gene assignments and to explore those genes that are expressed in tachyzoites. As demonstrated above, the proteome data will enable continued improvement in gene models through the confirmation of the correct reading frame and intron-exon boundaries. More fundamentally, the proteome analysis raises several issues in relation to the correct determination of gene models. Many gene prediction algorithms work on the basis of sequence similarity to cDNA or protein sequence databases, EST sequences or other genome sequences (where conserved regions are more likely to correspond to genes). As such, gene finders are relatively successful at identifying 'typical' genes that are similar to gene structures previously observed in other organisms. However, where genes are atypical in structure, or have no EST data, gene finding algorithms may miss such sequences altogether. Large-scale proteome scans are able to contribute significantly in this area, by demonstrating peptide hits to regions of the genome where genes have only been weakly predicted or missed completely. Others have recently also recognized the value of so-called 'proteogenomic annotation' of genomes [##REF##17690205##38##, ####REF##16171517##39##, ##REF##16077011##40##, ##REF##16646984##41##, ##REF##15289470##42####15289470##42##]. As more proteome data are produced, and querying algorithms improve, it is likely that the majority of protein-coding genes expressed in <italic>Toxoplasma </italic>will be confirmed by mass spectrometry based evidence.</p>" ]
[ "<title>Conclusion</title>", "<p>This study represents an unprecedented integration of proteomic and genomic data for <italic>Toxoplasma</italic>, which we suggest might serve as a model well beyond this present field. As well as providing novel information on the functional aspects of the proteome, our data demonstrate how proteomics can inform gene predictions and help discover new genes. Moreover, the data reveal some surprising, but potentially highly significant, discrepancies between protein expression and transcript expression data as assessed by both EST analysis and microarrays. We believe that this has important implications for how we interpret transcriptional expression data in the Apicomplexa, such as that derived from microarray experiments, and points to the fact that determining both absolute protein expression and post-translational events will be a key factor in gaining a more complete understanding of the biology of these pathogenic organisms.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>A proteomics analysis identifies one third of the predicted <italic>Toxoplasma gondii</italic> proteins and integrates proteomics and genomics data to refine genome annotation. </p>", "<title>Background</title>", "<p>Although the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of <italic>Toxoplasma gondii</italic>, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource.</p>", "<title>Results</title>", "<p>The draft genome for <italic>Toxoplasma </italic>predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression.</p>", "<title>Conclusion</title>", "<p>Our data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.</p>" ]
[ "<title>Abbreviations</title>", "<p>1-DE, 1 dimensional electrophoresis; 2-DE, two-dimensional electrophoresis; ASB-14, amidosulphobetaine-14; DTT, dithiothreitol; EST, expressed sequence tags; GO, Gene Ontology; LC, liquid chromatography; LC-MS/MS, liquid chromatography linked tandem mass spectrometry; MIPS, Munich Information Centre for Protein Identification; MS/MS, tandem mass spectrometry; MudPIT, multidimensional protein identification technology; ORF, open reading frame.</p>", "<title>Authors' contributions</title>", "<p>JMW and SJS conceived and designed the experiments. DX and HP performed the experiments. JY, BB, ARJ and DSR provided analysis tools and software. DX, SJS and ARJ analyzed the data. SJS, ARJ, DX and JMW wrote the paper.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Data files ##SUPPL##0##1## and ##SUPPL##1##2## are 2-DE gel images showing the spot numbering system that accompanies Figures ##FIG##0##1## and ##FIG##1##2##. Additional data files ##SUPPL##2##3## and ##SUPPL##3##4## are tables listing the MS data and protein identifications corresponding to Figures ##FIG##0##1## and ##FIG##1##2##. Additional data files ##SUPPL##4##5## and ##SUPPL##7##8## are tables listing the MS data and protein identifications (redundant and non-redundant, respectively) for the 1-DE separation illustrated in Figure ##FIG##2##3##. Additional data file ##SUPPL##5##6## is a 1-DE gel image of Tris-fractionated proteins, and Additional data files ##SUPPL##6##7## and ##SUPPL##8##9## are tables listing the corresponding MS data and protein identifications (redundant and non-redundant, respectively). Additional data files ##SUPPL##9##10## and ##SUPPL##10##11## are tables listing the MS data and redundant protein identifications for soluble and insoluble phase proteins analyzed by MudPIT. Additional data files ##SUPPL##11##12## and ##SUPPL##12##13## are tables listing the protein identifiers corresponding to Figure ##FIG##4##5a, b##. Additional data file ##SUPPL##13##14## is a pie chart illustrating functional categories for alternative gene models and ORFs.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This work was supported by the UK Biotechnology and Biological Science Research Council [BBS/B/03807] (to JMW, FMT &amp; RES), the National Institute of Allergy and Infectious Diseases [NIH-NIAID-DMID-BAA-03-38] and National Institute of Health [NIH P41 RR11823] (to JRY); National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Contract No. HHSN266200400037C to DSR. The authors would like to thank Dr Duncan Robertson of the Proteomics and Functional Genomics Group, Faculty of Veterinary Science, University of Liverpool, for his contribution to MS instrumentation support.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>2-DE proteome map (pH 3-10) of <italic>T. gondii </italic>tachyzoite proteins. Protein spots were visualized using colloidal Coomassie. Spots with the same protein identification are boxed (for detailed numbering, see Additional data file 1). Abbreviations: G1/S phase, G1 to S phase transition protein; Arm RP, armadillo/beta catenin-like repeat containing protein; MLC1, mysosin light chain 1; Sec62, translocation protein Sec62; adenyl cyclase AP, adenyl cyclase associated protein; NPACa, nascent polypeptide associated complex, alpha chain; RBP, RNA binding protein; PKC IC thioredoxin, PKC interacting cousin of thioredoxin; TC tumour protein, translationally controlled tumour protein; BHSP, bradyzoite specific small heat shock protein; Mam33, mitochondrial acidic protein mam33; MSA p30, major surface antigen p30; MDH, malate dehydrogenase; gbp1p protein, gbp1p protein (RNA binding protein); P-serine AT, phosphoserine aminotransferase; inosine-5'-P DH, inosine-5'-monophosphate dehydrogenase; RNA recognition, RNA recognition motif containing protein; nucleolin, nucleolar phosphoprotein (nucleolin), putative; SCR protein, sushi domain-containing protein/SCR repeat-containing protein; nucleosome AP, nucleosome assembly related protein; M2AP, MIC2 associated protein; Rhp23, UV excision repair protein rhp23; PPIase, peptidyl prolyl isomerase; S/T phosphatase 2C, serine/threonine phosphatase 2C; vATPase F, vacuolar ATP synthase subunit F; splicing factor 3b/10, splicing factor 3b subunit 10; 40S RP S12, 40S ribosomal protein S12; eTIF1a, eukaryote translation initiation factor 1 alpha; eTIF3d, eukaryote translation initiation factor 3 delta subunit; PPIPK, phosphatidylinositol-4-phosphate 5-kinase; LDH, lactate dehydrogenase; RACK, receptor for activated C kinase; LGL, lactoylglutathione lyase; Ca2+ BP, membrane associated calcium binding protein; IPP2A, inhibitor 1 or protein phosphatase type 2A; HPPK/DHPS, hydroxymethyldihydropterin pyrophosphokinase-dihydropteroate synthase; RNA BP, RNA binding motif protein; La protein, La domain containing protein; Pfs77r, pfs77 related protein; P-protein, phosphoprotein; PPI/WD, protein with peptidylprolyl isomerase domain and WD repeat; dUTP hydrolase, deoxyuridine 5'-triphosphate nucleotidohydrolase; PRE3, proteasome component PRE3 precursor; 10 kDa HSP mito, mitochondrial heat shock protein; PPIase NIMA, peptidyl-prolyl cis-trans isomerase NIMA-interacting 1; CEP52 fusion protein, ubiquitin/ribosomal protein CEP52 fusion protein.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>2-DE proteome map (pH 4-7) of <italic>T. gondii </italic>tachyzoite proteins. Protein spots were visualized using colloidal Coomassie. Spots with the same protein identification are boxed (for detailed numbering, see Additional data file 2). Abbreviations (also refer to Figure 1): PSAT, phosphoserine amino transferase; IF4E, translation initiation factor 4E; BCDC E1, branched-chain alpha-keto acid dehydrogenase; SOD, superoxide dismutase; OGDC E2, dihydrolipoamide succinyltransferase component of 2-oxoglutaratedehydrogenase complex; EGF1b, elongation factor 1 beta; ubiquitin-E2, ubiquitin-conjugating enzyme E2; F-1,6 bisP aldolase, fructose, 1,6 bis phosphate aldolase; PGK, phosphoglycerate kinase; F1,6 b Pase, fructose 1,6 bis phosphatase; U5 snRNP, U5 snRNP-specific 40 kDa protein (hPrp8-binding); Dihydrolipoyl DH, Dihydrolipoyl dehydrogenase, third enzyme of PDC, OGDC, BCDC.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Tachyzoite proteins resolved for 1-DE gel LC-MS/MS. SDS-soluble proteins from 1.1 × 10<sup>8 </sup>tachyzoites were resolved on a 12% (w/v) acrylamide gel under denaturing conditions as follows: protein standards (lane 1); <italic>T. gondii </italic>soluble protein (lane 3). Proteins were visualized using colloidal Coomassie stain.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>The tachyzoite expressed proteome: comparison of proteome strategies. Venn diagram showing the numbers of unique and shared non-redundant release4 gene identifications obtained from each of the three proteomics platforms.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Subcellular localisation and functional categorization of the expressed tachyzoite proteome. The numbers correspond to the total number of identified proteins in each category. <bold>(a) </bold>Protein subcellular localization information was first assigned according to gene descriptions and GO annotation provided by ToxoDB. When no information was available, protein sequences were submitted to PATS, PlasMit and WoLF PSORT. The combined results were manually assessed to obtain subcellular localization predictions. A detailed list of proteins in each subcellular localization to accompany this figure is provided in Additional data file 12. <bold>(b) </bold>Functional categorization was constructed using the GO classifications listed on ToxoDB for each release4 gene, which were then assigned to specific MIPS categories within the FunCatDB functional catalogue. Genes without a GO classification were assigned a putative MIPS category using additional information provided by Blast, Pfam domain alignments, InterPro and from independent literature searches. Notes: protein fate includes protein folding, modification and destination. A detailed list of proteins in each functional category to accompany this figure is provided in Additional data file 13.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Metabolic pathway coverage: glycolysis/gluconeogenesis. Component enzymes of the glycolysis/gluconeogenesis pathways predicted to be present in <italic>Toxoplasma </italic>from genome analysis are colored. Virtually every component of the glycolysis/gluconeogenesis pathway predicted for <italic>Toxoplasma </italic>was identified as being expressed in tachyzoites by proteomic analysis. Green and blue indicate genes for which expression has been confirmed in tachyzoites in this study by mass spectrometric data; blue also signifies genes for which post-translational modification is likely as indicated by the evidence from two-dimensional gels. Red indicates genes for which expression of predicted components has not been confirmed in this study. Coverage of key metabolic pathway component proteins was determined using the Metabolic Pathway Reconstruction for <italic>T. gondii </italic>available on the KEGG Pathway site accessed via ToxoDB [##UREF##2##53##].</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>The tachyzoite expressed proteome: comparison with EST and microarray expression data. A comparison of the expressed proteome of tachyzoites with EST and microarray data reveals discrepancies between protein and transcriptional data. <bold>(a) </bold>Venn diagram comparing the correlation between the number of non-redundant release4 genes detected by EST expression from <italic>T. gondii </italic>tachyzoite and bradyzoites (available from ToxoDB) and those detected by this proteome study. The number of genes unique to each intersection is indicated. <bold>(b) </bold>Venn diagrams comparing the correlation between release4 genes obtained by this proteome study and those detected by microarray analysis of RH strain tachyzoites, including those genes with expression of ≥ 25 and ≥ 50 percentiles. <bold>(c) </bold>Bar chart showing the number of release4 genes also detected by proteomics for each of the four percentile ranges, 0-24%, 25-49%, 50-74%, 75-100%, determined by microarray analysis.</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8</label><caption><p>Peptide evidence indicating an ORF where release4 annotation does not predict an ORF. The position of ORF X-3-4725402-4726856 in the genome scaffold is indicated by a red line on the grey track at the top of the figure and this region is expanded below, the red triangle demarking the ORF length. Different gene annotation models are presented one above the other bellow the scaffold. Predicted exons are indicated as blue boxes, linked by zigzag lines to indicate the position of exon/intron boundaries. The predicted sequence for <italic>TgGlmHMM_3355 </italic>is shown as an insert; sequence for which there is matching peptide evidence is shown in red. The peptide that spans an intron-exon boundary is shown in purple. Peptides aligning with this region are shown in yellow and the detailed MS information for one is shown, including the predicted sequence. Peptides that align with the release4 or alternative gene annotations are indicated on different lines. ESTs are shown as dark blue or brown boxes.</p></caption></fig>", "<fig position=\"float\" id=\"F9\"><label>Figure 9</label><caption><p>Peptide evidence indicating alternative frame shift. The position of ORF XII-4-5562689-5562144 in the genome scaffold is indicated by a red line on the grey track at the top of the figure and this region is expanded below, the red triangle demarking the ORF length. Predicted exons are indicated as red shaded boxes, linked by zigzag lines to indicate the position of exon/intron boundaries. Peptides aligning with this region are shown in yellow. The gene of interest with the release4 annotation (<italic>50.m05694</italic>) is highlighted in blue. Predicted sequences for this gene and the ORF and <italic>TgGlmHMM_1717 </italic>are shown as inserts. Sequence for which there is matching peptide evidence is shown in red. <italic>TgGlmHMM_1717 </italic>comprises several exons and the complete sequence is not given; the start methionine is shown in green. Mass spectrometric evidence for one peptide sequence derived by the 1-DE approach is shown.</p></caption></fig>", "<fig position=\"float\" id=\"F10\"><label>Figure 10</label><caption><p>Peptide evidence indicating alternative exon positioning and sequence annotation. The position of ORF X-1-3917326-3920484 in the genome scaffold is indicated by a red line on the grey track at the top of the figure and this region is expanded below, the red triangle demarking the ORF length. Predicted exons are indicated as blue boxes, linked by zigzag lines to indicate the position of exon/intron boundaries. Gene <italic>28.m00300 </italic>is shown with two exons. ESTs are shown as dark blue or brown boxes. Peptides aligning with this region are shown in yellow. The predicted sequence for ORF X-1-3917326-3920484 is shown as an insert and sequence that matches exon 2 of gene <italic>28.m00300 </italic>is shown in blue. Sequence for which there is matching peptide evidence is shown in red. Purple lettering indicates the positioning of the 'intron-located' peptide, mass spectrometric evidence for which is shown in the right hand insert.</p></caption></fig>" ]
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[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Soluble proteins from 2.53 × 10<sup>8 </sup>tachyzoites (516 μg protein) resolved by IEF over a narrow linear pH 3-10 range followed by molecular mass on a 12.5% (w/v) acrylamide gel under denaturing conditions. Protein spots are visualized using colloidal Coomassie. Individual spots are numbered and the corresponding mass spectrometric data are detailed in Additional data file 3.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Soluble proteins from 1 × 10<sup>8 </sup>tachyzoites (200 μg protein) resolved by IEF over a narrow linear pH 4-7 range followed by molecular mass on a 12.5% (w/v) acrylamide gel under denaturing conditions. Protein spots are visualized using colloidal Coomassie. Individual spots are numbered and the corresponding mass spectrometric data are detailed in Additional data file 4.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>The spot number, matching gene annotation and description, Mascot score, sequence coverage and number of matching peptides are given. Further information concerning peptide sequences is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent (and in Additional data files 4, 5, 7, 8 and 9).</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>The spot number, matching gene annotation and description, Mascot score, sequence coverage and number of matching peptides are given. Further information concerning peptide sequences is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Listed in the columns (from left to right) are: the gel slice number, ranking of each protein hit returned from the Mascot search for that gel slice, corresponding gene annotations and descriptions, Mascot scores, number of matching peptides to each protein and sequence coverage. Further information concerning peptide sequences is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>SDS-soluble proteins from 9.85 × 10<sup>7 </sup>tachyzoites previously fractionated into Tris-soluble (120 μg) and Tris-insoluble (130 μg) fractions were resolved on a 12% (w/v) acrylamide gel under denaturing conditions as follows: protein standards (lane 1), Tris-insoluble protein (lane 2) and Tris-soluble protein (lane 4). Proteins were visualized using colloidal Coomassie. The masses of the protein standards and the position of every gel slice are shown.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Listed in the columns (from left to right) are: the gel slice number, ranking of each protein hit returned from the Mascot search for that gel slice, corresponding gene annotations and descriptions, Mascot scores, number of matching peptides to each protein and sequence coverage. Further information concerning peptide sequences is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p>Listed in the columns (from left to right) are: the gene annotations and descriptions of each protein, the highest individual Mascot score, sequence coverage and number of matching peptides returned for that protein from all the gel slices in which it appeared, and the gel slice number that this refers to. Individual peptide amino acid sequence, MS scores and a measure of the total sequence coverage obtained is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>Listed in the columns (from left to right) are: the gene annotations and descriptions of each protein, the highest individual Mascot score, sequence coverage and number of matching peptides returned for that protein from all the gel slices in which it appeared, and the gel slice number that this refers to. Individual peptide amino acid sequence, MS scores and a measure of the total sequence coverage obtained is available at ToxoDB. For consistency, where the release4 annotation is not identified by the peptide evidence, TwinScan gene annotation is given in preference to other alternative gene annotations assuming the returning Mascot score is equivalent.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional data file 10</title><p>The unprocessed results from MudPIT analysis lists: gene annotations and descriptions for each protein; alternative gene annotation for that region of the scaffold; total Xcorr scores for each protein hit; individual Xcorr scores theoretical mass and pI values and sequences for each individual matching peptide.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S11\"><caption><title>Additional data file 11</title><p>The unprocessed results from MudPIT analysis lists: gene annotations and descriptions for each protein; alternative gene annotation for that region of the scaffold; total Xcorr scores for each protein hit; individual Xcorr scores theoretical mass and pI values and sequences for each individual matching peptide.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S12\"><caption><title>Additional data file 12</title><p>List of protein identifiers according to subcellular localization category. Number of non-redundant proteins is shown in brackets.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S13\"><caption><title>Additional data file 13</title><p>List of protein identifiers according to functional category. Number of non-redundant proteins is shown in brackets.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S14\"><caption><title>Additional data file 14</title><p>The amino acid sequences of alternative genes and ORFs were submitted to BlastP and results returning e-values &lt; e<sup>-30 </sup>were considered. Homology to apicomplexan proteins was prioritized when deciding the protein description to be used to assist the assignment of functional category. Sequences returning no significant BlastP result or with a description 'hypothetical protein' were searched against Amigo Blast [##UREF##3##54##] to determine the potential GO classification. The same e-value cut-off was applied. The above information was then used in conjunction with InterPro and independent literature searches to assign a MIPS category within the FunCatDB functional catalogue. Note: protein fate includes protein folding, modification and destination.</p></caption></supplementary-material>" ]
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[{"surname": ["Eng", "Mccormack", "Yates"], "given-names": ["JK", "AL", "JR"], "article-title": ["An approach to correlate tandem mass-spectral data of peptides with amino-acid-sequences in a protein database."], "source": ["J Am Soc Mass Spectrom"], "year": ["1994"], "volume": ["5"], "fpage": ["976"], "lpage": ["989"], "pub-id": ["10.1016/1044-0305(94)80016-2"]}, {"article-title": ["Tranche Project"]}, {"article-title": ["KEGG PATHWAY for "], "italic": ["Toxoplasma gondii"]}, {"article-title": ["AmiGO!"]}]
{ "acronym": [], "definition": [] }
54
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 21; 9(7):R116
oa_package/62/1f/PMC2530874.tar.gz
PMC2530875
18647395
[ "<title>Background</title>", "<p>Comparative genomics, in the study of different bacterial genera, species, and strains, leads to the definition of two DNA pools in bacterial genomes: a set of genes shared by all genomes in a taxa, namely the 'core' genome; and a set of genes containing mobile and accessory genetic elements, termed the 'flexible' gene pool. Both intergenomic and intragenomic rearrangements occur in this 'flexible' gene pool [##REF##11587932##1##]. Changes in the 'flexible' gene pool are considered to be the motor of bacterial diversification and evolution [##REF##12042773##2##, ####REF##15100694##3##, ##REF##17289390##4####17289390##4##].</p>", "<p>However, comparative genomic analyses of genomic variants within a clonal population are rarely undertaken because of the difficulties involved in using molecular approaches in a mixed population. Initially, researchers focused on local modifications of the DNA sequence occurring during phase variation. Phase variation is an adaptive process by which certain bacteria within a bacterial subpopulation, called phase variants, undergo frequent and reversible phenotypic changes. Phase variation is dependent on DNA sequence plasticity, generating a reversible switch between 'on' and 'off' phases of expression for one or more protein-encoding genes. Variation in the expression of certain genes in some phase variants allows the bacterial population to adapt to environmental change [##REF##11587935##5##, ####REF##7968525##6##, ##REF##15258095##7####15258095##7##]. Other studies have focused on DNA sequence variations that involve large regions of the genome in a clonal population. These extensively distributed and large genomic rearrangements mostly occur through homologous recombination between repeated sequences such as <italic>rrn </italic>loci, duplicated genes, or insertion sequences, which may then lead to the inversion, amplification, or deletion of chromosomal fragments. These events can occur either under strong selective pressure - such as <italic>in vitro </italic>antibiotic selection [##REF##8506337##8##], stressful high temperature [##REF##11149947##9##], long-term storage [##REF##11377870##10##, ####REF##12644482##11##, ##REF##14996798##12####14996798##12##], and chronic clinical carriage [##REF##12700269##13##] - or without specific selective pressure [##REF##10922070##14##, ####REF##11741857##15##, ##REF##12902376##16##, ##REF##10984043##17##, ##REF##14668356##18##, ##REF##11586360##19##, ##REF##9484880##20####9484880##20##].</p>", "<p>The phenotypic consequences of such large rearrangements are variable. In <italic>Streptomyces </italic>spp., genetic instability affects various phenotypical properties, including morphological differentiation, production of secondary metabolites, antibiotic resistance, secretion of extracellular enzymes, and gene expression for primary metabolism, regardless of selective pressure [##REF##9484880##20##]. In other bacterial species and when stressful selective pressure is applied, large-scale genomic variation often correlates with modification of certain phenotypes: reversion from nutritional auxotrophy to prototrophy [##REF##11377870##10##], variation in colony morphology [##REF##12644482##11##], modification of bacterial growth features [##REF##14996798##12##], and adaptation to high temperature [##REF##11149947##9##]. Few data are available on phenotypic variation in the absence of strong selective pressure. A few studies suggest that large genomic architecture modifications can occur with or without slight detectable phenotypic modifications [##REF##11741857##15##,##REF##12902376##16##]. We studied genomic rearrangements in the entomopathogenic bacterium <italic>Photorhabdus luminescens</italic>, for which variants are frequently observed in standard growth conditions, in order to investigate further the link between genomic variation within a bacterial population and the phenotypic consequences.</p>", "<p><italic>P. luminescens </italic>is a member of the Enterobacteriaceae; it is a symbiont of entomopathogenic nematodes and is pathogenic for a wide variety of insects [##UREF##0##21##, ####REF##9343343##22##, ##REF##12586390##23##, ##REF##16480919##24####16480919##24##]. Bacterial variants frequently arise within the <italic>Photorhabdus </italic>genus. Two types of variant exist. The phenotypic variants (PVs) are the most studied. The primary PV is characterized by the presence of numerous phenotypic traits (production of extracellular enzymes, pigments, antibiotics, crystalline inclusion bodies, and ability to generate bioluminescence) that are absent from the secondary PV. Secondary PVs are mostly obtained during prolonged <italic>in vitro </italic>culturing [##UREF##1##25##,##UREF##2##26##]. Only primary PVs support nematode growth and development both in the insect cadaver and <italic>in vitro</italic>. However, both variants are equally virulent to insect hosts [##UREF##3##27##]. This phenomenon differs from classical phase variation because it occurs at low and unpredictable frequency, it is rarely reversible, and numerous phenotypic traits are altered simultaneously [##UREF##3##27##]. Recent studies suggest that generation of PVs in <italic>P. luminescens </italic>may be controlled by several regulatory cascades, each of them involving the products of many different genes [##REF##12603747##28##, ####REF##12003952##29##, ##REF##15073299##30##, ##REF##16548063##31####16548063##31##].</p>", "<p>The other common variants in <italic>Photorhabdus </italic>are colonial variants (CVs). Different colonial morphotypes can be generated from one colony subculture. This variation is unstable; indeed, each morphotype can generate all other morphotypes [##REF##9726862##32##, ####REF##1622273##33##, ##REF##16347906##34##, ##UREF##4##35##, ##REF##16941119##36####16941119##36##]. The most frequent CVs are small-colony variants (SCVs). These SCVs constitute a slow-growing bacterial subpopulation with atypical colony morphology and unusual biochemical characteristics that, in the case of clinical isolates, cause latent or recurrent infections [##REF##16541137##37##]. In <italic>Photorhabdus</italic>, these SCVs can be generated from primary or secondary PV [##REF##16347906##34##]. SCVs have small cells, do not produce crystalline inclusions [##REF##9726862##32##, ####REF##1622273##33##, ##REF##16347906##34####16347906##34##], and have undergone changes in their proteome [##REF##1622273##33##,##REF##16347906##34##]. Some SCVs have modified virulence properties and do not support nematode development and reproduction [##REF##9726862##32##].</p>", "<p>Previous studies, incorporating local genetic [##REF##12603747##28##,##REF##8449874##38##,##REF##2211511##39##] or nonexhaustive genomic comparisons [##REF##1622273##33##,##REF##16347906##34##,##UREF##5##40##,##REF##12100588##41##], have not identified genomic differences within sets of PVs or CVs. We used the recently elucidated complete nucleotide sequence of the <italic>P. luminescens </italic>subspecies <italic>laumondii </italic>strain TT01 [##REF##14528314##42##] to study systematically the link between phenotypic and genomic variations in clonal <italic>Photorhabdus </italic>variants. We undertook whole-genome comparisons between the wild-type TT01 strain and six different PVs or CVs. We showed that large genomic rearrangements occurred <italic>in vivo </italic>and <italic>in vitro</italic>. We described two categories of intragenomic rearrangements: deletion events occurring in the 'flexible gene pool', and an unusual duplication of a 275-kilobase (kb) region, encompassing 4.8% of the TT01 wild-type genome. These rearrangements were not correlated with the generation of PVs, and we did not detect a functional relationship between the genes affected by rearrangements and phenotypic variation. Thus, the consequences of these genomic changes are cryptic.</p>" ]
[ "<title>Materials and methods</title>", "<title>Strains, plasmids, primers, and culture media</title>", "<p>All bacterial strains and plasmids used in this study are listed in Additional data file 6. Primers are listed in Additional data file 7. <italic>P. luminescens </italic>was grown at 28°C in LB broth or on nutrient agar 1.5% (BD Difco™, Franklin Lakes, New Jersey, USA) for 48 hours. <italic>Escherichia coli </italic>was grown at 37°C in LB broth or on LB supplemented with 1.5% agar (BD Difco™, Franklin Lakes, New Jersey, USA). Strains were stored at -80°C in LB broth containing 16% glycerol (vol/vol). Secondary variants were obtained by prolonged culture of primary variants at 28°C for 10 days in Schneider's insect medium (Cambrex Bio Science, Walkersville, Maryland, USA) with shaking (TT01<sub>/II </sub>[##REF##15073299##30##]), for 10 days in LB broth with shaking (TT01α<sub>/II</sub>), or for 3 months in LB broth without shaking (TT01α'<sub>/II</sub>). Secondary variant phenotypes were evaluated from culture on NBTA (nutrient agar 1,5%, 25 mg/l bromothymol blue and 40 mg/l triphenyl-2,3,5-tetrazolium chloride) plates and on TreGNO plates (see below) at 28°C. Secondary variants were identified by performing phenotypic tests as previously described [##UREF##10##76##] and controlled by PCR-restriction fragment length polymorphism of the 16S rRNA gene [##REF##9023937##77##].</p>", "<title>Analysis of phenotypic variants on a new selective medium: TreGNO</title>", "<p><italic>Xenorhabdus </italic>and <italic>Photorhabdus </italic>secondary variants are typically selected on NBTA plates to distinguish red secondary variants colonies from blue primary colonies [##UREF##10##76##]. Because of the high level of pigmentation of <italic>Photorhabdus </italic>colonies, the use of color assays does not allow clear distinction between primary and secondary variants for <italic>Photorhabdus </italic>genus. We found that TT01 secondary variants were able to undergo trehalose fermentation, whereas primary variants can not. On nutrient agar plates supplemented with trehalose (10 g/l) and bromothymol blue (25 mg/l), secondary colonies acidified the bromothymol blue and became yellow at 28°C after 48 hours. Primary colonies remained green. Furthermore, secondary colonies were flat and large with irregular borders. This new medium was named TreGNO medium and was routinely used for the discrimination of <italic>Photorhabdus luminescens </italic>strain TT01 phenotypic variants.</p>", "<title>PFGE and DNA electrophoresis</title>", "<p>Intact genomic DNA was extracted in agarose plugs as follows. Bacterial cells grown on nutrient agar plates were suspended in phosphate-buffered saline (GIBCO<sup>® </sup>Invitrogen, Carlsbad, California, USA) to a turbidity of 1.25 at 650 nm, included in 1% (vol/vol) low melting agarose (SeaPlaque<sup>® </sup>GTG, FMC BioProducts, Rockland, Massachusetts, USA) solution and then subjected to lysis as described previously [##REF##9466259##78##].</p>", "<p><italic>Not</italic>I and <italic>Apa</italic>I hydrolysis were performed by incubation of the agarose plugs overnight with 40 units of the endonuclease in buffer recommended by the supplier (New England Biolabs, Hertfordshire, UK), at 37°C for <italic>Not</italic>I and 25°C for <italic>Apa</italic>I. PFGE was carried out in a contour-clamped homogeneous field electrophoresis apparatus CHEF-DRII (Bio-Rad, Hercule, California, USA) in a 0.8% agarose gel in 0.5× Tris-borate-EDTA (TBE) at 10°C. PFGE conditions were as follows: for <italic>Not</italic>I fragments, a 35 to 5 second pulse ramp for 47 hours followed by a constant pulse time of 50 seconds for 6 hours at 4.5 V/cm; and for <italic>Apa</italic>I fragments, 35 to 5 seconds for 35 hours, followed by 5 seconds to 1 second for 4 hours at 4.5 V/cm.</p>", "<p>I-<italic>Ceu</italic>I hydrolysis was performed as described previously [##REF##15995979##79##]. For the separation of I-<italic>Ceu</italic>I fragments, different electrophoresis conditions were selected according to fragment size: a pulse ramp from 5 to 50 seconds for 24 hours at 6 V/cm for fragments with size below 700 kb; and a pulse ramp from 150 to 400 seconds for 45 hours at 4.5 V/cm for I-<italic>Ceu</italic>I fragments for fragments between 700 kb and 1 megabase. For I-<italic>Ceu</italic>I fragments larger than 1 megabase, PFGE was performed on Rotaphor apparatus (Biometra, Goettingen, Germany) using 0.7% agarose gels in 0.5× TBE buffer. The electrophoresis conditions used were as follows: 50 to 47 V (linear ramp), 6,000 to 1,000 seconds decreasing pulses (logarithmic ramp), with a increasing angle from 96 to 105°, buffer temperature 11°C, for 240 hours. I-<italic>Ceu</italic>I PFGE patterns were compared by calculating the Dice coefficient for each pair [##REF##1779765##80##]. Patterns were clustered by UPGMA using the Phylip program package [##UREF##11##81##].</p>", "<p><italic>Hin</italic>dIII-hydrolyzed DNA was subjected to electrophoresis for 3 hours at 2.6 V/cm in a 0.8% agarose gel in 0.5× TBE using SubCell apparatus (Bio-Rad) [##REF##12700269##13##].</p>", "<title>Southern blotting, probes, and hybridization experiments</title>", "<p>Electrophoresis gels were transferred onto a Nytran N SuperCharge nylon membrane (Schleicher and Schuell, Dassel, Germany) by vacuum blotting in 20 × SSC (Euromedex, Souffelweyersheim, France).</p>", "<p>A digoxigenin-labeled probe targeting 16S rRNA gene was obtained by PCR from genomic DNA of <italic>P. luminescens </italic>strain TT01<sub>/I</sub>, using primers 27f and 1492r with a dNTP mixture containing 0.1 mmol/l digoxigenin-dUTP [##REF##12700269##13##].</p>", "<p>Probes B and H were obtained using respectively small fragment insert from plg2711 and large fragment inserts from plbac4g08, plbac6h12, plbac3a10, plbac3c04, and plbac2f12. Fragment inserts were purified, sonicated into fragments of between 1 and 10 kb if insert size was higher than 10 kb, and labeled with digoxygenin by random priming (Dig DNA labeling Kit; Roche, Meylan, France). Hybridization of the probes was detected using a CSPD chemiluminescent system (Roche).</p>", "<title>Standard DNA manipulations</title>", "<p>Genomic DNA was extracted as previously described [##REF##16385072##56##] and stored at 4°C. We PCR-amplified the <italic>lop</italic>T1 deletion region with Taq polymerase (Invitrogen, Carlsbad, California, USA), in accordance with the manufacturer's recommendations, using the P<italic>lopT1</italic>.fw an P<italic>lopT1</italic>.rev primers. The region H was amplified by PCR with the Herculase Enhanced DNA polymerase (Stratagene, Amsterdam Zuidoost, Pays Bas), in accordance with the manufacturer's recommendations, using the R-3236, F-3249, R-3238bis, and F-3254 primers. For sequencing region H deletions, we purified the 4.8 kb and 5.2 kb fragments using the Montage PCR kit (Millipore, Guyancourt, France) and sequenced using PCR primers and chromosome walking (Millegen, Toulouse, France). Sequencing of the 5.2 kb fragment central region of the fragment failed probably because of the presence of repetitions. A 3.2 kb central region was therefore amplified by PCR with <italic>Pst</italic>IdMutF and <italic>Xba</italic>IdMutR primers. The amplicon was hydrolyzed by <italic>Pst</italic>I and <italic>Xba</italic>I, ligated into <italic>Pst</italic>I- and <italic>Xba</italic>I-hydrolyzed pUC19, and inserted into <italic>E. coli </italic>XL1blue by transformation. The resulting plasmid was purified by Nucleobond AX-100 kit (Macherey-Nagel, Hoerd, France), and the insert was sequenced with <italic>Pst</italic>IdMutF and <italic>Xba</italic>IdMutR primers and then by chromosome walking.</p>", "<title>DNA microarray hybridization and analysis</title>", "<p>DNA microarray hybridization and analysis were performed as previously described [##REF##16385072##56##].</p>", "<title>Quantitative PCR analysis</title>", "<p>Quantitative PCR was performed in triplicate using the LightCycler FastStart DNA Master<sup>PLUS </sup>SYBR Green I kit from Roche Diagnostics with 1 ng genomic DNA and 1 μmol/l specific primers targeting <italic>fliC </italic>(L-1954 and R-1954), <italic>mrfJ </italic>(L-0778 and R-0778), <italic>dnaQ </italic>(L-0943 and R-0943), and <italic>pilN </italic>(L-1051 and R-1051). The enzyme was activated for 10 minutes at 95°C. Reactions were performed in triplicate at 95°C for 5 seconds, 60°C for 5 seconds and 72°C for 10 seconds (45 cycles), and monitored in the Light Cycler (Roche). Melting curves were analyzed for each reaction; all reactions exhibited a single peak. The amount of PCR product was calculated with standard curves obtained from PCR with serially diluted TT01<sub>/I </sub>genomic DNA. All data are presented as ratios, with <italic>gyrB </italic>(primers L-0004 and R-0004) as a control (95% confidence limits).</p>", "<title>Sequence analysis</title>", "<p>Sequence annotation of the TT01<sub>/I </sub>genome was obtained from the MaGe database [##UREF##12##82##]. We evaluated amino-acid and nucleotide similarity using BLASTP and BLASTN software [##REF##9254694##83##]. We used Repseek software, previously Nosferatu [##REF##17038345##46##], to detect approximate repeats in large DNA sequences.</p>", "<title>Pathogenicity assays</title>", "<p><italic>In vivo </italic>infection assays were performed as previously described [##REF##15679839##45##]. We performed three independent experiments for each variant. Statistical analysis were performed as previously described [##REF##12081958##84##].</p>", "<title>Antibiosis plate assays</title>", "<p>Antibiosis assays were performed as previously described [##UREF##10##76##] with the following bacterial species: <italic>Micrococcus luteus</italic>, <italic>Staphylococcus epidermidis </italic>CIP 6821, <italic>Staphylococcus aureus </italic>CIP 7625, <italic>Escherichia coli </italic>CIP 7624, <italic>Proteus vulgaris </italic>CIP 5860, <italic>Pseudomonas aeruginosa </italic>CIP 76.110, <italic>Corynebacterium xerosis</italic>, <italic>Ochrobactrum intermedium </italic>LMG 3301<sup>T</sup>, <italic>Ochrobactrum anthropi </italic>ATCC 49188<sup>T</sup>, <italic>Ochrobactrum </italic>sp. FR49, <italic>Erwinia amylovora </italic>CFBP1430, <italic>Pseudomonas </italic>sp. BW11M, <italic>Salmonella enterica </italic>14028s, and <italic>Yersinia enterocolitica </italic>serotype 08.</p>" ]
[ "<title>Results</title>", "<title>TT01α<sub>/I</sub>: a genomic variant isolated from the laboratory-maintained nematode <italic>Heterorhabditis bacteriophora</italic></title>", "<p>The nematode <italic>Heterorhabditis bacteriophora </italic>TH01, harboring the TT01 wild-type strain, was collected in Trinidad in 1993 [##REF##9797272##43##]. The nematode was maintained in the laboratory and multiplied by infestation in the Lepidopteran <italic>Galleria mellonella </italic>[##UREF##6##44##]. In 1998, a further bacterial isolate was taken from this nematode. During the course of a genetic study of the type III secretion system, we discovered that the bacterium isolated in 1998 is a genomic variant. It differs from the TT01 wild-type strain by a 250 base pair deletion at the 5' end of the gene <italic>lopT1 </italic>(Additional data file 1). This gene encodes a type III secretion system effector that appears to be involved in the depression of the insect innate immune system [##REF##15679839##45##]. Both TT01 wild-type and the <italic>lopT1 </italic>genomic variant produced many of the phenotypes associated with primary PVs, including bioluminescence, lipase activity, antibiotic production, and presence of cytoplasmic crystal (Table ##TAB##0##1##). Therefore, both were primary PVs. To distinguish between them, the TT01 wild-type strain was named TT01<sub>/I </sub>and the <italic>lopT1 </italic>genomic variant, TT01α<sub>/I </sub>(Figure ##FIG##0##1##).</p>", "<title>Isolation and characterization of PVs and CVs from TT01<sub>/I </sub>and TT01α<sub>/I</sub></title>", "<p>We cultured TT01<sub>/I </sub>and TT01α<sub>/I </sub>in liquid broth and selected primary and secondary PVs on NBTA (nutrient agar supplemented with bromothymol blue and triphenyl 2,3,5 tetrazolium chloride) plates. TT01<sub>/II </sub>secondary PV was derived from TT01<sub>/I </sub>(TT01 lineage; Figure ##FIG##0##1##). TT01α<sub>/II </sub>and TT01α'<sub>/II </sub>secondary PVs were obtained from TT01α<sub>/I </sub>(TT01α lineage; Figure ##FIG##0##1##). TT01<sub>/II</sub>, TT01α<sub>/II</sub>, and TT01α'<sub>/II </sub>had classic secondary PV traits (Table ##TAB##0##1##).</p>", "<p>We developed a new agar medium, the TreGNO (nutrient agar with trehalose and and bromothymol blue) medium, for color discrimination of TT01 PVs (see Materials and methods [below] for details). PVs produce green, convex, and mucoid colonies whereas secondary PVs produce yellow, flat, and nonmucoid colonies on this medium. TT01<sub>/II </sub>and TT01α<sub>/II </sub>colonies were homogeneous and had the colonial traits of secondary PVs. However, TT01α'<sub>/II </sub>was composed of three CVs (TT01α' lineage; Figure ##FIG##0##1##). The first was a primary colonial form (green, convex, and mucoid colonies), named REV because it resembled a revertant colony, exhibiting primary PV traits (although bioluminescence, pigmentation, and crystal production were not completely restored; Table ##TAB##0##1##). The second was a secondary colonial form (yellow, flat, and nonmucoid colonies), named VAR because of its secondary PV traits (Table ##TAB##0##1##). The third form had small, green, convex, and mucoid colonies, and was named INT because of its intermediate traits or traits from both the primary and secondary PVs (Table ##TAB##0##1##). These CVs are unstable because each individual TT01α'<sub>/II </sub>colony grown in liquid broth gives rise to a mixture of the three colonial forms on TreGNO medium. We generated a stable secondary PV from the VAR colonial variant by plating a liquid subculture from an individual VAR colony on nutrient agar and picking another VAR colony for a new cycle of liquid/plate culture. We continued this enrichment process until the liquid subculture generated 95% of VAR colonies on TreGNO plates. The stable population was named VAR* (Figure ##FIG##0##1##).</p>", "<p>We PCR-amplified the <italic>lopT1 </italic>5' region from TT01<sub>/II</sub>, TT01α<sub>/II</sub>, TT01α'<sub>/II</sub>, VAR*, and REV (Additional data file 1). The <italic>lopT1 </italic>deletion was only present in the TT01α and TT01α' lineages.</p>", "<title>Virulence of TT01 variants</title>", "<p>We injected TT01<sub>/I</sub>, TT01<sub>/II</sub>, TT01α<sub>/I</sub>, TT01α<sub>/II</sub>, and VAR* into <italic>Spodoptera littoralis </italic>larvae to evaluate the pathogenicity of these variants in insect larvae. TT01<sub>/II</sub>, TT01α<sub>/I</sub>, and TT01α<sub>/II </sub>had the same level of pathogenicity as TT01<sub>/I</sub>; 50% mortality (LT<sub>50</sub>) was reached between 28 and 32 hours after injection for the TT01 wild-type strain and these three variants. By contrast, VAR* had a delayed LT<sub>50 </sub>of 53 hours, although 100% mortality was reached at 3 days after infection (Figure ##FIG##1##2##).</p>", "<title>Extensive rearrangements in genomic architecture correlated with the variant lineages</title>", "<p>We examined the whole genome architecture of each variant using I-<italic>Ceu</italic>I genomic macrorestriction and pulsed field gel electrophoresis (PFGE) in order to detect large rearrangement such as deletions and amplifications by recombination between <italic>rrn </italic>or deletions, amplifications, and translocations inside I-<italic>Ceu</italic>I fragments. I-<italic>Ceu</italic>I is an intron-encoded enzyme that specifically cleaves a 26-base-pair site in the bacterial 23S rRNA gene. The PFGE pattern obtained for the TT01<sub>/I </sub>strain matched the pattern of I-<italic>Ceu</italic>I fragments predicted from the complete TT01<sub>/I </sub>genome sequence (Figure ##FIG##2##3a, b##; also see Additional data file 2 for the details of the gels). Using the TT01<sub>/I </sub>pattern used as a reference, we observed large genomic rearrangements in TT01α<sub>/I</sub>, TT01α<sub>/II</sub>, TT01α'<sub>II</sub>, VAR*, and REV. PFGE patterns revealed identical profiles for primary and secondary PVs within both TT01 and TT01α lineages (Figure ##FIG##2##3b## and Additional data file 2). Therefore, PV status (primary versus secondary) in these variant lineages is independent from global genomic architecture.</p>", "<p>Cluster analysis of the seven observed I-<italic>Ceu</italic>I patterns reveals that variant lineages are in fact genomic lineages (Figure ##FIG##2##3c##). The TT01 and TT01α lineages exhibit genomic homogeneity. The TT01α' lineage shared common genomic features with the TT01α lineage, but exhibited a more polymorphic genomic pattern than TT01 and TT01α lineages.</p>", "<p>The PFGE patterns of TT01α and the TT01α' lineages only reveal six apparent I-<italic>Ceu</italic>I fragments, instead of seven fragments in the TT01<sub>/I </sub>reference chromosome; however, the intensity of the 295-kb band suggests that it may represent two different fragments. We used Southern blot analysis to confirm that the seven <italic>rrn </italic>copies are present in all the variants (Additional data file 3). Therefore, variation in I-<italic>Ceu</italic>I PFGE patterns among the TT01 variants appeared to be unrelated to deletion or amplifications mediated by recombination between <italic>rrn </italic>operons.</p>", "<p>Additionally, the 465 kb faint band in the TT01α'<sub>/II </sub>pattern (white star in Additional data file 2) corresponded to a fragment in the REV pattern, suggesting the existence of a 'REV-like' chromosome subpopulation in TT01α'<sub>/II</sub>.</p>", "<title>Deletions and amplifications in the TT01α<sub>/I </sub>and VAR* variants, representative of the TT01α and TT01α' lineages</title>", "<p>Large genomic rearrangements were present in the TT01α and TT01α' lineages. We further evaluated the nature of these rearrangements by comparing gene content between representative variants of each lineage, TT01<sub>/I</sub>, TT01α<sub>/I </sub>and VAR*, using genomic DNA hybridization on a <italic>P. luminescens </italic>TT01<sub>/I </sub>microarray.</p>", "<p>Totals of 159 and 162 genes were absent from TT01α<sub>/I </sub>and VAR*, respectively (see Additional data file 4). We located these genes on a circular map of the TT01<sub>/I </sub>chromosome (Figure ##FIG##3##4##); they mostly clustered into eight regions absent from both the TT01α<sub>/I </sub>and VAR* genomes (regions A, C, D, E, F, G, I, and J) and one region specifically absent from the VAR* genome (region H). The deleted regions were located throughout the chromosome, with no particular symmetry around the replication origin or termination site. Several regions displayed a GC bias inversion (C, D, E, G, I, and J). Three overlapped with phagic regions (C, G, and I), suggesting that prophage excision occurred during the TT01<sub>/I </sub>to TT01α<sub>/I </sub>transition (Table ##TAB##1##2##). As well as phagic genes, the deleted regions encompass putative mobile and recombination-mediating elements such as insertion sequences and recombination hotspot (Rhs) elements (region A), and plasmid-related protein-encoding genes (region J)(Table ##TAB##1##2##). The regions C, D, E, and F potentially encode peptide synthetases involved in antimicrobial compound synthesis (Table ##TAB##1##2##). However, we did not observe any significant difference in antimicrobial activity between TT01<sub>/I </sub>and TT01α<sub>/I </sub>tested for 14 indicator strains (data not shown).</p>", "<p>A more thorough analysis of hybridization ratios revealed that 122 genes had a ratio higher than 1.4 in the VAR* genome (Additional data file 5). In contrast, comparison of the TT01<sub>/I </sub>and TT01α<sub>/I </sub>genomes revealed only four genes with a ratio higher than 1.4. These findings suggest that numerous genes are amplified in the VAR* genome. Among these potentially amplified genes, 112 are clustered in a unique and large 275-kb region, named B. This region encompasses 4.8% of the TT01<sub>/I </sub>genome (from plu0769 = <italic>mrfA </italic>to plu0980 = <italic>hpaA</italic>; Figure ##FIG##3##4##). Region B is located within the first quarter of the TT01<sub>/I </sub>chromosome and is not delimited by obvious repeat elements. According to TT01<sub>/I </sub>genome annotations, the region B may be involved in numerous and different functions (Table ##TAB##1##2##): basal cellular functions involving the DNA polymerase III ε chain (plu0943 = <italic>dnaQ</italic>), enolase (plu0913 = <italic>eno</italic>), and proteins involved in tryptophan metabolism (plu0799 = <italic>tnaA</italic>; plu0800 = <italic>mtr</italic>); and environment and/or host interactions, involving the major fimbrial biosynthesis locus (plu0769-0778 = the <italic>mrfABCDEFGHJ </italic>operon), insecticidal toxin proteins (plu0805 = <italic>tccA3</italic>; plu0806 = <italic>tccB3</italic>; plu0960 = <italic>tcc2</italic>; plu0961 = <italic>tcdB1</italic>; plu0962 = <italic>tcdA1</italic>; plu0964 = <italic>tccC5</italic>; plu0965 = <italic>tcdA4</italic>; plu0970 = <italic>tcdB2</italic>; plu0971 = <italic>tcdA2</italic>), and proteins similar to pyocins (plu0884; plu0886-0888; plu0892; plu0894).</p>", "<p>To determine whether DNA microarray experiments explain the architectural modifications observed by macrorestriction experiments, we compared the two sets of data. The observed I-<italic>Ceu</italic>I macrorestriction fragments from the TT01α lineage (36 kb, 295 kb, 295 kb, 330 kb, 465 kb, 610 kb, ~3600 kb) were similar to the theoretical I-<italic>Ceu</italic>I fragments calculated after size subtraction of the eight deleted regions from the TT01<sub>/I </sub>I-<italic>Ceu</italic>I fragments (36 kb, 244 kb, 266 kb, 330 kb, 462 kb, 627 kb, ~3478 kb). Therefore, large-scale deletion events appear to underlie the TT01 to TT01α lineage transition. DNA microarray experiments in the TT01α' lineage identified a 275 kb amplification of the TT01<sub>/I </sub>genome. Duplication or triplication of region B may account for the increase in genome size (~100 kb to 650 kb) observed by macrorestriction for the TT01α to TT01α' transition. Therefore, duplication appears to be mainly responsible for the TT01α to TT01α' lineage transition.</p>", "<title>Homologous recombination between long repeats led to serial deletions of the region H in the TT01α and TT01α' lineages</title>", "<p>We first examined the genomic deletions observed in the TT01α<sub>/I </sub>and VAR* variants. We focused on region H, which, by contrast to other deleted regions, did not exhibit typical recombination-mediating elements. Probes targeting different parts of the region H were hybridized on genomic DNA of the wild-type strain and the six variants. Hybridization patterns were identical within each variant lineage and confirmed the presence of a 25 kb deletion within the region H (from <italic>plu3237 </italic>to <italic>plu3253</italic>) for the TT01α' lineage (data not shown). Southern analysis also indicated the presence of a small deletion of about 10 kb (from <italic>plu3238 </italic>to <italic>plu3248</italic>) in the TT01α lineage. To map the deletion borders accurately, primers flanking the 25-kb deletion (R-3236 and F-3254) and the 10-kb deletion (R-3238bis and F-3249) were designed (Figure ##FIG##4##5##) and used for PCR amplification in the TT01α' and TT01α lineages. Amplified fragments of 4.8 kb and 5.2 kb were observed (data not shown). These fragments were sequenced for TT01α<sub>/I </sub>and VAR*, and the deletion was physically mapped (a genetic map of the region H is presented in Figure ##FIG##4##5##). The deletions in TT01α<sub>/I </sub>and VAR* were 12,820 bases (from coordinates 3,833,904 to 3,846,723) and 25,140 bases long (from coordinates 3,830,001 to 3,855,140), respectively.</p>", "<p>We used Nosferatu, software that can detect approximate repeats in large DNA sequences [##REF##17038345##46##]. The region H is rich in pairs of repetition units (RPT) larger than 1 kb (Figure ##FIG##4##5##). Each deletion began at the right-hand extremity of the first repetition and finished at the right-hand extremity of the corresponding second repetition (RPT179385 repetitions for the 10-kb deletion and RPT179383 repetitions for the 25-kb deletion). Therefore, successive deletions mediated by homologous recombination between RPT are likely to have occurred in the region H during the TT01<sub>/I </sub>to TT01α<sub>/I </sub>to VAR* transition, leading to genomic reduction.</p>", "<title>A single block duplication of region B is specific to the TT01α' lineage</title>", "<p>In a second set of analyses, we focused on the gene amplification observed in region B, occurring in the TT01α<sub>/I </sub>to VAR transition. Quantitative PCR was performed for two genes in region B, <italic>mrfA </italic>(plu0769) and <italic>dnaQ </italic>(plu0943). Comparison of VAR and TT01α<sub>/I </sub>data confirmed that these two genes were duplicated in the VAR* genome (Figure ##FIG##5##6a##).</p>", "<p>In order to determine whether region B is duplicated specifically in the VAR* variant or in all variants of the TT01α' lineage, a probe covering the entire region B (the probe B) was prepared and hybridized to genomic DNA of the wild-type strain and the six variants. According to the TT01<sub>/I </sub>genome sequence, <italic>Not</italic>I hydrolysis generates 25 fragments with a unique 1,056-kb fragment containing region B. Hybridization of the probe B to <italic>Not</italic>I-hydrolyzed genomic DNA generated a unique fragment of 1,056 kb in the TT01 lineage and of 1,020 kb in the TT01α lineages (Figure ##FIG##5##6b, c##). By contrast, in the TT01α' lineage, the B probe hybridized to the 1,020-kb fragment and an additional fragment. This second fragment has a similar size in TT01α'<sub>/II </sub>and VAR* variants (610 kb) but is smaller (365 kb) in the REV variant. These findings showed that duplication of region B occurred in all TT01α' lineage variants.</p>", "<p>Region B encompasses 275 kb in the TT01<sub>/I </sub>genome sequence; thus, we determined whether the resulting amplified genes were dispersed in the genome or co-localized in an unique block. The unique additional fragment detected by the probe B in the TT01α'<sub>/II </sub>and VAR* variants indicated that the product of the region B amplification is constituted either of one block or a few blocks co-localized in a genomic region whose size is smaller than 610 kb in TT01α'<sub>/II </sub>and VAR* and smaller than 365 kb in REV. The probe B was also hybridized to <italic>Apa</italic>I-hydrolyzed genomic DNA of the wild-type strain and the six variants. The seven patterns were identical and the probe B hybridized with the two main 74 and 156 kb fragments covering the major part of region B according to the TT01<sub>/I </sub>genome reference sequence (data not shown). Because the duplication did not modify the <italic>Apa</italic>I restriction pattern, we concluded that region B was amplified as a single block.</p>" ]
[ "<title>Discussion</title>", "<title>Variant lineages are genomic lineages characterized by extensive genomic rearrangements</title>", "<p>Our study provides the first extensive investigation into genomic rearrangements in <italic>Photorhabdus </italic>variants. First, we evaluated phenotypic traits of the three variant lineages (Figure ##FIG##0##1##). The TT01 lineage is derived from the TT01<sub>/I </sub>strain, which was isolated from the <italic>H. bacteriophora </italic>TH01 nematode collected in Trinidad in 1993 [##REF##9797272##43##] and whose genome is sequenced [##REF##14528314##42##]. The TT01α lineage is derived from the TT01α<sub>/I </sub>genomic variant, which was collected from <italic>H. bacteriophora </italic>TH01 maintained and multiplied in the laboratory. The TT01α' lineage was derived from the TT01α<sub>/I </sub>variant after prolonged culture in synthetic medium. Each lineage is composed of PVs, whereby the primary form is characterized by the presence of typical phenotypic traits that are absent from the secondary form. The TT01α' lineage has an additional level of complexity, because the PVs exhibit features of CVs such as unstable morphotypes.</p>", "<p>We then examined the genomic architecture of each variant in macrorestriction experiments and used comparative DNA microarray hybridization experiments to analyze the genomic content of representative variants for each lineage. Our findings revealed that large genomic rearrangements characterize each variant lineage. Consequently, these findings provide insight into probable scenarios underlying each lineage transition. The whole-genome organization of the TT01 lineage is described by the TT01 reference genome [##REF##14528314##42##]. Large-scale deletion events in the TT01 flexible gene pool seem to be involved in the TT01 to TT01α lineage transition. Deletion events in the TT01 flexible gene pool and a single block duplication encompassing 4.8% of the TT01 reference genome appear to underlie the TT01 to TT01α' lineage transition. The genomic clusters do not depend on the PV status (see below). Thus, each variant lineage is a genetic lineage.</p>", "<title>Deletion at new recombination hotspot</title>", "<p>To explain the molecular mechanisms involved in the rearrangements in our variants, we investigated potential repetitive elements and recombination-mediating elements flanking the rearranged regions. Large genomic architectural changes are often driven by homologous recombination between repeated sequences. The nature of the change then depends on the relative orientation, size, and spacing of the repeated sequences [##UREF##7##47##, ####REF##11380986##48##, ##REF##15451508##49##, ##UREF##8##50####8##50##]. Recombination events often occur at the <italic>rrn </italic>operon in Gram-negative bacteria, such as <italic>Salmonella</italic>, <italic>Rhizobium</italic>, <italic>Escherichia coli</italic>, and <italic>Ochrobactrum </italic>[##REF##12644482##11##,##REF##12700269##13##,##REF##14668356##18##,##REF##10673005##51##,##REF##6789329##52##]. However, despite the variation detected in PFGE analysis of the <italic>rrn </italic>skeleton for the three variant lineages, we demonstrated that the rearrangements are not the result of <italic>rrn </italic>recombination.</p>", "<p>Apart from homologous recombination, rearrangements can be induced by site-specific recombination, associated with recombination-mediating elements such as mobile elements, or by illegitimate recombination, linked to shortly spaced repeats [##REF##15451508##49##,##UREF##8##50##]. Most of the deleted regions in the TT01α lineage are rich in potential rearrangement-mediating elements, with both repeated sequences - including insertion sequences and Rhs elements - and mobile elements, including phagic and plasmid-related genes.</p>", "<p>Genomic annotation of the region H, which underwent successive deletions in the TT01α<sub>/I </sub>and VAR* variants, did not describe the presence of typical repetitive or recombination-mediating elements. The region H belongs to a large genomic island containing the genes <italic>vgr </italic>and <italic>hcp</italic>, initially described as genes associated with <italic>Rhs </italic>elements. <italic>Rhs </italic>elements are repeated sequences in the <italic>E. coli </italic>genome that mediate major chromosomal rearrangements [##REF##10673005##51##,##REF##9696756##53##,##REF##6086936##54##]. Although the TT01<sub>/I </sub>genome contains <italic>Rhs</italic>-like elements [##REF##14528314##42##], no <italic>Rhs </italic>element is located in the genomic island encompassing the region H. Nevertheless, we identified pairs of approximate long repeated sequences (&gt;1 kb) in direct orientation (RPT) that corresponded to the observed deletion junction points. Therefore, successive deletions in the region H are likely to have been mediated by homologous recombination between RPT during the transition from TT01<sub>/I </sub>to TT01α<sub>/I </sub>to VAR*, leading to genomic reduction.</p>", "<p>There was a strong selective pressure during the TT01α to TT01α' lineage transition (3 months in LB broth without shaking). This environmental constraint could thus be responsible for the rearrangement leading to the region H deletion. However, the region H deletion was already initiated during the former transition (TT01<sub>/I </sub>to TT01α<sub>/I </sub>in the laboratory-maintained nematode). Therefore, the observed reduction genomic size is more likely to be the result of particular genomic features (the RPT) rather than environmental constraints.</p>", "<p>The region H is unique in the TT01<sub>/I </sub>genome. Nevertheless, some RPT elements have similarities with sequences elsewhere in the TT01<sub>/I </sub>genome, in the <italic>Photorhabdus </italic>strain W14 genome [##REF##10919786##55##] or in other Enterobacteriaceae genomes such as <italic>Yersinia pseudotuberculosis </italic>IP32953 (BX936398.1), <italic>Yersinia pestis </italic>Angola (CP000901.1), <italic>Yersinia pestis </italic>Pestoides F (CP000668.1), <italic>Yersinia pestis </italic>CO92 (AL590842.1), <italic>Yersinia pestis </italic>biovar Microtus str. 91001, (AE017042.1, <italic>Yersinia pestis </italic>Antiqua (CP000308.1), <italic>Yersinia pestis </italic>Nepal 516 (CP000305.1), <italic>Yersinia pestis </italic>KIM (AE009952.1), <italic>Yersinia pseudotuberculosis </italic>IP 31758 (CP000720.1), and <italic>E. coli </italic>CFT073 (AE014075.1). Therefore, we propose that the region H represents a new type of bacterial recombination hot spot, which is <italic>vgr</italic>- and <italic>hcp</italic>-rich, but lacks <italic>Rhs </italic>elements.</p>", "<title>A new duplication class</title>", "<p>We described a single block duplication (region B) targeting a 275-kb region of the TT01<sub>/I </sub>genome in the TT01α' lineage. This significant duplication encompasses 4.8% of the TT01<sub>/I </sub>genome. Region B is not located near the replication origin or termination and does not correspond to genomic islands or enterobacterial variable regions previously identified [##REF##14528314##42##,##REF##16385072##56##]. GC content or GC skew deviations are not evident.</p>", "<p>Gene amplifications can occur through three kinds of known mechanism: homologous recombination between direct repeats, illegitimate recombination, or escape replication. No repeated elements flanking region B were detected, despite the use of the Nosferatu software [##REF##17038345##46##], excluding the possibility of homologous recombination underlying this duplication. Region B duplications may result from illegitimate recombination between short repeats [##UREF##7##47##,##REF##9442891##57##,##REF##17661705##58##]. However, amplification copy number resulting from illegitimate recombination events is often high, even for large amplicons, such as in <italic>Acinetobacter </italic>sp. ADP1 or <italic>Streptomyces kanamyceticus </italic>[##REF##15099734##59##,##REF##16766657##60##]. Escape replication involves amplification of large regions of the host genome (several hundred kilobases), next to phage integration sites after induction of the phage lytic cycle [##REF##368588##61##, ####REF##5492021##62##, ##REF##17764558##63##, ##REF##15687213##64####15687213##64##], or around degraded prophages without the induction of specific phage lysis [##REF##16936024##65##]. Although phage remnants represent 4% of the <italic>Photorhabdus </italic>genome [##REF##14528314##42##], lytic phages have not been identified in <italic>Photorhabdus </italic>strains, even after extensive investigation of lytic induction conditions [##UREF##9##66##]. We detected the presence of an 11-kb phagic segment (plu0818-plu0826) in region B, potentially representing a degraded prophage. However, whereas the copy number usually resulting from the escape replication mechanism ranges between three and ten, with its intensity decreasing symmetrically from the center, region B in the TT01α' lineage genomes represents a single block homogeneous duplication. We only identified one other previously reported example of a large duplication without repeated flanking sequences - a 250 kb duplication in <italic>Mycobacterium smegmatis </italic>mc<sup>2 </sup>155 genome [##REF##11739760##67##]. Therefore, the duplication of region B is likely to belong to a new class of duplications.</p>", "<title>Observed phenotypes and global genomic architecture are not systematically correlated</title>", "<p>Large genomic changes such as deletions and duplications are supposed to have important fitness effects. In our study, we firstly demonstrated that the PV status (primary or secondary) is independent from global genomic architecture. This was consistent with previous studies analyzing specific genetic regions [##REF##12603747##28##,##REF##8449874##38##,##REF##2211511##39##] and with partial genome studies [##REF##1622273##33##,##UREF##5##40##,##REF##12100588##41##], but this is the first time it has been demonstrated using a whole-genome approach.</p>", "<p>We showed that the overall genomic pattern corresponds to the variant lineage. Both the phenotype and pathogenic traits of the primary PV (or the secondary PVs) are indistinguishable between the TT01 and TT01α lineages. Therefore, changes in the genomic architecture of these strains did not lead to observable changes in the phenotype. Furthermore, certain regions that were deleted in the TT01α lineage potentially encode biosynthesis pathways for antimicrobial compounds. However, we did not observe any difference in antimicrobial activity between TT01<sub>/I </sub>and TT01α<sub>/I</sub>. This finding suggests that some TT01<sub>/I </sub>genes are redundant. Indeed, genes encoding proteins potentially involved in the biosynthesis of antimicrobial compounds are over-represented in TT01<sub>/I </sub>genome [##REF##14528314##42##]. Moreover, the encoded proteins in the deleted regions may be adaptive factors required for specific conditions that are not encountered in the laboratory or in our antibiosis assays.</p>", "<p>The TT01α' lineage differs from the two other lineages due to its polymorphic genomic pattern. Furthermore, this lineage is composed of three unstable CVs and the virulence of the stabilized VAR* variant is attenuated in insects. This is consistent with previously reports of CVs isolated from the <italic>Photorhabdus </italic>genus [##REF##1622273##33##]. Therefore, changes in genomic architecture might be correlated to phenotypic changes in variants of this lineage. The main rearrangement observed in the TT01α' lineage is the region B duplication. According to TT01<sub>/I </sub>genome annotation, region B may be involved in both basal cellular functions and environment and/or host interactions. Gene duplication events can underlie modification of phenotypes [##REF##17661705##58##]. However, we did not detect any modification of gene transcription in region B using transcriptomic microarray comparison between the VAR* and TT01α<sub>/I </sub>variants (Gaudriault S, unpublished data). Thus, this duplication does not appear to modify gene expression in the VAR* variant. Therefore, the attenuation of virulence of the VAR* variant is not likely to be due to amplified expression in region B. Rather, it is more likely that the 'cost' to the bacteria of the increased genome size is decreased virulence in insects.</p>", "<p>We conclude that the observed phenotypes and overall genomic architecture are not systematically correlated in TT01, TT01α, and TT01α' lineages. It is likely that this result is general in the field of bacterial genomic architecture. Similar observations were previously made between strains of the <italic>Pseudomonas aeruginosa </italic>species [##REF##18287045##68##], but also inside a clonal bacterial population of a wide range of bacterial groups such as <italic>Yersinia pestis </italic>[##REF##11586360##19##], <italic>Pseudomonas aeruginosa </italic>[##REF##10984043##17##], and <italic>Sinorhizobium meliloti </italic>[##REF##12902376##16##].</p>", "<title>Stability and plasticity of bacterial genome architecture</title>", "<p>Do large genomic rearrangements occur randomly or are they shaped by drastic selective evolutionary forces? Several years of comparative genomics between whole bacterial genomes showed that the prokaryotic genome is a heterogeneous entity, with regions of stability and flexibility [##REF##17289390##4##,##REF##15451508##49##,##UREF##8##50##]. Genomic stability is subject to selective pressures such as functional replication [##REF##15184548##69##], gene essentiality [##REF##12847524##70##], or translation [##REF##16683018##71##]. The three main routes of evolution of genome repertoire are lateral gene transfer, when several bacterial communities share a same ecological niche, deletions, and duplications [##REF##17289390##4##,##REF##15451508##49##,##UREF##8##50##]. The dynamism of genome repertoire inside a clonal population only arises by the last two phenomena, as illustrated by our study on <italic>Photorhabdus </italic>variants.</p>", "<p>In <italic>E. coli</italic>, the chromosome is organized in structured macrodomains, limiting genome plasticity. Whereas some genomic rearrangements between these macrodomains have only moderate effects on cell physiology, others have detrimental effects [##REF##18085828##72##]. The rearrangements that we observed in our variants may have been selected to preserve chromosomal configurations that are not detrimental for bacterial fitness in the laboratory or in the nematode. We believe that structured macrodomains that restrict chromosome plasticity are likely to exist in other bacterial genus. Identification of structured macrodomains in <italic>P. luminescens </italic>genome would provide better knowledge on evolutionary forces modeling bacterial genome.</p>", "<title>Clonal variation, environmental adaptation, and bacterial evolution</title>", "<p>The major genomic variations described in TT01 variants have cryptic consequences in our laboratory conditions. The absence of associated phenotypes makes them difficult to identify, explaining why such genomic variations are rarely observed. However, further studies of such genomic variations may be crucial for a better understanding of bacterial adaptation and evolution.</p>", "<p>Indeed, we observed that the extensive genomic rearrangements in <italic>Photorhabdus </italic>variants were often associated with several genomic subpopulations in the same culture. Similar observations were previously made for a <italic>P. luminescens </italic>TT01<sub>/I </sub>locus encoding a phage tail-like structure [##REF##15063490##73##] and the <italic>mrf </italic>locus of the <italic>P. temperata </italic>strain K122 [##REF##14729685##74##]. In <italic>Sinorhizobium meliloti</italic>, <italic>Yersinia pestis</italic>, and <italic>Pseudomonas aeruginosa</italic>, extensive variations of genome architecture, without obvious changes in phenotype, were also observed during bacterial growth in broth medium [##REF##12902376##16##,##REF##10984043##17##,##REF##11586360##19##]. Different pre-existing chromosomal forms in a clonal bacterial population are likely to give this population an adaptive capacity. It is therefore possible that bacterial populations maintain various subpopulations with different genomic structures as a way to cope with different environments during its life cycle.</p>", "<p>Additionally, deletion events in TT01α and TT01α' lineages are located within the TT01<sub>/I </sub>'flexible' gene pool. Whereas intragenomic recombination in the 'flexible' gene pool have been widely studied using comparative genomics for different bacterial genera, species, and strains [##REF##11587932##1##, ####REF##12042773##2##, ##REF##15100694##3##, ##REF##17289390##4####17289390##4##], similar reports for clonal variants are rare. Gene repertoires of the 'flexible' gene pool may evolve through variations in bacterial subpopulations and then become fixed after bacterial speciation. Such pre-existing or currently existing genomic variations have an important role in evolutionary patterns of natural eukaryotic populations [##REF##18006185##75##]. They may also have a determinant role in bacterial evolution.</p>" ]
[ "<title>Conclusion</title>", "<p>The study of molecular mechanisms underlying genomic plasticity in clonal populations is challenging because classical molecular tools only detect the major genomic state of the population. Such studies are easier in bacterial species with a high rate of bacterial variants. With our model, <italic>P. luminescens</italic>, we identified two new genomic rearrangements, allowing a new research axis for gaining a comprehensive knowledge of bacterial chromosome plasticity. The cryptic consequences of large genomic rearrangements in our model also allow prospective comprehensive analysis of bacterial genome evolution. Therefore, we propose that the <italic>P. luminescens </italic>TT01 strain represents a new bacterial model for study of genomic plasticity.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The phenotypic consequences of large genomic architecture modifications within a clonal bacterial population are rarely evaluated because of the difficulties associated with using molecular approaches in a mixed population. Bacterial variants frequently arise among <italic>Photorhabdus luminescens</italic>, a nematode-symbiotic and insect-pathogenic bacterium. We therefore studied genome plasticity within <italic>Photorhabdus </italic>variants.</p>", "<title>Results</title>", "<p>We used a combination of macrorestriction and DNA microarray experiments to perform a comparative genomic study of different <italic>P. luminescens </italic>TT01 variants. Prolonged culturing of TT01 strain and a genomic variant, collected from the laboratory-maintained symbiotic nematode, generated bacterial lineages composed of primary and secondary phenotypic variants and colonial variants. The primary phenotypic variants exhibit several characteristics that are absent from the secondary forms. We identify substantial plasticity of the genome architecture of some variants, mediated mainly by deletions in the 'flexible' gene pool of the TT01 reference genome and also by genomic amplification. We show that the primary or secondary phenotypic variant status is independent from global genomic architecture and that the bacterial lineages are genomic lineages. We focused on two unusual genomic changes: a deletion at a new recombination hotspot composed of long approximate repeats; and a 275 kilobase single block duplication belonging to a new class of genomic duplications.</p>", "<title>Conclusion</title>", "<p>Our findings demonstrate that major genomic variations occur in <italic>Photorhabdus </italic>clonal populations. The phenotypic consequences of these genomic changes are cryptic. This study provides insight into the field of bacterial genome architecture and further elucidates the role played by clonal genomic variation in bacterial genome evolution.</p>" ]
[ "<title>Abbreviations</title>", "<p>CV, colonial variant; kb, kilobase; NBTA, nutrient agar supplemented with bromothymol blue and triphenyl-2,3,5-tetrazolium chloride; PCR, polymerase chain reaction; PFGE, pulsed field gel electrophoresis; PV, phenotypic variant; Rhs, recombination hotspot; RPT, repetition units; SCV, small-colony variant; TBE, Tris-borate-EDTA; TreGNO, nutrient agar with trehalose and bromothymol blue.</p>", "<title>Authors' contributions</title>", "<p>SG, SP, and AG characterized bacterial variants. SG, AL, and CL provided molecular materials. SG and AL performed microarray analysis. SG, CT, and EJ-B provided PFGE analysis. SG analyzed sequence data. SG wrote the paper with contributions from AG and EJ-B.</p>", "<title>Additional data files</title>", "<p>The following additional data files are available with this paper. Additional data file ##SUPPL##0##1## is a figure showing the deletion in the <italic>lopT1 </italic>gene in TT01<sub>/I </sub>strain and the six variants. Additional data file ##SUPPL##1##2## is a figure showing PFGE of I-<italic>Ceu</italic>I-hydrolyzed genomic DNA of TT01<sub>/I </sub>strain and the six variants. Additional data file ##SUPPL##2##3## is a figure showing the copy number of 16S rDNA in TT01<sub>/I </sub>and the six variants. Additional data file ##SUPPL##3##4## is a table listing the TT01<sub>/I </sub>missing genes in TT01α<sub>/I </sub>and VAR* variants according whole-genome comparison using DNA microarray. Additional data file ##SUPPL##4##5## is a table listing the TT01<sub>/I </sub>amplified genes in TT01α<sub>/I </sub>and VAR* variants, according to whole-genome comparison using DNA microarray. Additional data file ##SUPPL##5##6## is a table listing strains and plasmids used in this study. Additional data file ##SUPPL##6##7## is a table listing primers used in this study.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This study received financial support from the Institut National de la Recherche Agronomique (grant SPE 2004-1133-2). We thank Sylviane Derzelle for the TT01<sub>/II </sub>gift, Eric Duchaud and Lionel Frangeul for help with the circular map of DNA microarray data, Agnès Masnou and Emmanuelle d'Alençon for help in PFGE experiments, and Karine Brugirard-Ricaud for <italic>lopT1 </italic>deletion identification. We thank Marie-Christine Guérin and Joël Martin for expert technical assistance with quantitative PCR.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Schematic representation of TT01 variants selection on TreGNO medium. TT01<sub>/I</sub>, TT01α<sub>/I</sub>, and REV colonies are green, convex, and mucoid colonies; TT01<sub>/II</sub>, TT01α<sub>/II</sub>, VAR, and VAR* colonies are yellow, flat, and nonmucoid; and the INT colonies are small, green, convex, and mucoid.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Mortality in <italic>Spodoptera littoralis</italic>. Shown is the mortality in <italic>S. littoralis </italic>infected with the TT01<sub>/I </sub><italic>Photorhabdus luminescens </italic>wild-type strain, the genomic variant TT01α<sub>/I</sub>, the secondary variants TT01<sub>/II </sub>and TT01α<sub>/II</sub>, and the stabilized VAR* colonial variant. Bacteria obtained at the end of the exponential phase were injected into fourth-instar larvae. Mortality values are based on data obtained after injection into 20 larvae. All experiments were repeated at least twice.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Variation in genomic architecture of the TT01 variants. <bold>(a) </bold>Schematic representation of the I-<italic>Ceu</italic>I restriction map of the TT01<sub>/I</sub><italic> Photorhabdus luminescens </italic>reference genome. <bold>(b) </bold>Schematic reconstruction of I-<italic>Ceu</italic>I pulsed field gel electrophoresis (PFGE) patterns for TT01<sub>/I </sub>and the six variants representing gels presented in Additional data file 2. Fragment sizes were calculated using the TT01<sub>/I </sub>genome as a reference. Lane 1: TT01<sub>/I</sub>. Lane 2: TT01<sub>/II</sub>. Lane 3: TT01α<sub>/I</sub>. Lane 4: TT01α<sub>/II</sub>. Lane 5: TT01α'<sub>/II</sub>. Lane 6: VAR*. Lane 7: REV. <bold>(c) </bold>Clustering of the PFGE patterns. Patterns were compared using the Dice coefficient for each pair. Patterns were clustered by UPGMA.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Schematic representation of DNA microarray data as a circular map of the TT01<sub>/I </sub>genome. Circle 1 (from outside to inside): scale marked in megabases. Circle 2: location of transposases (red) and phage-related genes (green) location. Circles 3, 4, and 5: DNA microarray data comparing TT01<sub>/I </sub>and TT01α<sub>/I </sub>genomes (circle 3), TT01<sub>/I </sub>and VAR* genomes (circle 5), and synthesis from both experiments (circle 4). Deleted genes are represented by bars inside the circle. Amplified genes are represented by bars outside the circle. Deleted and amplified regions are circled in blue. Circle 6: GC bias (G-C/G+C). Circle 7: GC content with &lt;32% G+C in light yellow, between 32% and 53.6% G+C in yellow, and with &gt;53.6% G+C in dark yellow.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Successive deletions between homologous repeats in the region H. Genetic map of TT01<sub>/I </sub>region H is shown (blue boxes are open reading frames [ORFs]). Location of repetition units (RPT) larger than 1 kilobase (kb) is indicated (hatched colored boxes). RPT were systematically searched on the whole TT01<sub>/I </sub>genome sequence by using Nosferatu, software that can detect approximate repeat sequences [##REF##17038345##46##]. The RPTs are numbered according their position on the chromosome. DNA microarray data for the TT01α<sub>/I </sub>and VAR* genomes are indicated. '+': the gene is present. '-': the gene is absent. '?': the gene is not represented on the microarray. Schematic representation of TT01α<sub>/I </sub>and the VAR* variant deletions is shown. Deletion borders were obtained from sequencing between the R-3236 and F-3254 primers in the VAR* variant, and between the R-3238bis and F-3249 primers in the TT01α<sub>/I </sub>variant. Green and hatched gray boxes represent regions in TT01α<sub>/I </sub>and VAR* genomes variants that were found to be present or absent, respectively. The deleted regions encompassed sequence between coordinates 3.833.904 and 3,846,724 in TT01α<sub>/I </sub>genome and coordinates 3,830,001 and 3,855,141 in VAR* genome.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Duplication of region B. <bold>(a) </bold>Quantitative PCR was carried out for <italic>mrfA </italic>(plu0769) and <italic>dnaQ </italic>(plu0943) using genomic DNA from TT01α<sub>/I </sub>and VAR* variants and specific internal primers for each gene. <italic>pilN </italic>(plu1051) and <italic>fliC </italic>(plu1954) were used for negative controls. PCR was performed in triplicate and data are presented as ratios, with <italic>gyrB </italic>as the control gene (95% confidence limits). <bold>(b, c) </bold>Pulsed field gel electrophoresis (PFGE) of <italic>Not</italic>I-hydrolyzed genomic DNA from TT01α<sub>/I </sub>and the six variants following by Southern blot and hybridization with a probe covering the region B (probe B). The PFGE conditions allow separation of <italic>Not</italic>I fragments between 50 and 400 kb (panel b) or between 350 and 1,350 kb (panel c). Gray arrows indicate fragments that hybridize with the probe B. Lane 1: TT01<sub>/I</sub>. Lane 2: TT01<sub>/II</sub>. Lane 3: TT01α<sub>/I</sub>. Lane 4: TT01α<sub>/II</sub>. Lane 5: TT01α'<sub>/II</sub>. Lane 6: VAR*. Lane 7: REV.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Phenotypes of <italic>P. luminescens </italic>TT01<sub>/I</sub>, TT01α<sub>/I</sub>, and their respective variants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Phenotype</td><td align=\"left\">TT01<sub>/I</sub></td><td align=\"left\">TT01α<sub>/I</sub></td><td align=\"left\">TT01<sub>/II</sub></td><td align=\"left\">TT01α<sub>/II</sub></td><td align=\"left\">TT01α'<sub>/II</sub></td><td align=\"left\">VAR</td><td align=\"left\">VAR*</td><td align=\"left\">REV</td><td align=\"left\">INT</td></tr></thead><tbody><tr><td align=\"left\">Bioluminescence</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+/w</td><td align=\"left\">w</td></tr><tr><td align=\"left\">Colony morphology</td><td align=\"left\">Convex, mucoid,</td><td align=\"left\">Convex, mucoid,</td><td align=\"left\">Flat, nonmucoid</td><td align=\"left\">Flat, nonmucoid</td><td align=\"left\">Flat, nonmucoid</td><td align=\"left\">Flat, nonmucoid</td><td align=\"left\">Flat, nonmucoid</td><td align=\"left\">Convex, mucoid</td><td align=\"left\">Small, convex, mucoid</td></tr><tr><td align=\"left\">Lipase activity on Tween 20-60</td><td align=\"left\">++</td><td align=\"left\">++</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">++</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">Lipase activity on Tween 80-85</td><td align=\"left\">++</td><td align=\"left\">++</td><td align=\"left\">+/w</td><td align=\"left\">+/w</td><td align=\"left\">+v</td><td align=\"left\">+</td><td align=\"left\">v</td><td align=\"left\">++</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">Pigmentation</td><td align=\"left\">+(Orange)</td><td align=\"left\">+(Orange)</td><td align=\"left\">+(Yellow)</td><td align=\"left\">++(Yellow)</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+(Orange)</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">Antibiotic production</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">+/w</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">Crystal proteins</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">w</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Coloration on TreGNO medium</td><td align=\"left\">Green</td><td align=\"left\">Green</td><td align=\"left\">Yellow</td><td align=\"left\">Yellow</td><td align=\"left\">Yellow</td><td align=\"left\">Yellow</td><td align=\"left\">Yellow</td><td align=\"left\">Green</td><td align=\"left\">Green</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Deleted and amplified regions in the TY01α<sub>/I </sub>and VAR* genomes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"left\">Probable nature of event</td><td align=\"left\">Gene region</td><td align=\"left\">Size (in kb)</td><td align=\"left\">Products of interest (similarity or function)</td><td align=\"left\">Matching GI<sup>a </sup>or EVR<sup>b</sup></td></tr></thead><tbody><tr><td align=\"left\">A</td><td align=\"left\">Deletion</td><td align=\"left\">plu0338-plu0355</td><td align=\"left\">18</td><td align=\"left\">DNA cytosine, ethyl-transferase, mismatch repair endonuclease, unknown proteins, Rhs proteins, IS630 family</td><td align=\"left\">Part of GI plu0310-plu0373</td></tr><tr><td align=\"left\">B</td><td align=\"left\">Amplification</td><td align=\"left\">plu0769-plu0980</td><td align=\"left\">275</td><td align=\"left\">Proteins involved in basal metabolism (DNA polymerase III ε chain, enolase, tryptophan metabolism) and in interaction with environment and/or host (fimbrial biosynthesis, Tc insecticidal toxins, pyocins)</td><td align=\"left\">Encompassed GI plu0884-plu0901, GI plu0914-plu0938, and overlapped a part of GI plu0958-plu1166</td></tr><tr><td align=\"left\">C</td><td align=\"left\">Deletion</td><td align=\"left\">plu1086-plu1123</td><td align=\"left\">44</td><td align=\"left\">Unknown proteins, phage regulators, peptide synthetase, transposase, bacteriophage proteins</td><td align=\"left\">Part of GI plu0958-plu1166</td></tr><tr><td align=\"left\">D</td><td align=\"left\">Deletion</td><td align=\"left\">plu1861-plu1876</td><td align=\"left\">12</td><td align=\"left\">Antibiotic biosynthesis</td><td align=\"left\">Part of GI plu1859-plu1894</td></tr><tr><td align=\"left\">E</td><td align=\"left\">Deletion</td><td align=\"left\">plu2191-plu2200</td><td align=\"left\">11</td><td align=\"left\">Antibiotic synthesis and transport</td><td align=\"left\">Part of EVR plu2179-plu2224</td></tr><tr><td align=\"left\">F</td><td align=\"left\">Deletion</td><td align=\"left\">plu2468-plu2476</td><td align=\"left\">8</td><td align=\"left\">unknown protein, ABC transporter, toxoflavin biosynthesis, transposase</td><td align=\"left\">EVR plu2468-plu2476</td></tr><tr><td align=\"left\">G</td><td align=\"left\">Deletion</td><td align=\"left\">plu2874-plu2960</td><td align=\"left\">54</td><td align=\"left\">Bacteriophage proteins</td><td align=\"left\">Part of GI plu2873-plu3038</td></tr><tr><td align=\"left\">H</td><td align=\"left\">Deletion</td><td align=\"left\">plu3238-plu3252</td><td align=\"left\">22</td><td align=\"left\">Unknown proteins, VgrG proteins</td><td align=\"left\">Part of GI plu3207-plu3275</td></tr><tr><td align=\"left\">I</td><td align=\"left\">Deletion</td><td align=\"left\">plu3380-plu3504</td><td align=\"left\">89</td><td align=\"left\">Bacteriophage proteins</td><td align=\"left\">Part of GI plu3379-plu3538</td></tr><tr><td align=\"left\">J</td><td align=\"left\">Deletion</td><td align=\"left\">plu4324-plu4328</td><td align=\"left\">12</td><td align=\"left\">Unknown and plasmid-related proteins</td><td align=\"left\">EVR plu4319-plu4332</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Presented is a figure showing the deletion in the <italic>lopT1 </italic>gene in TT01<sub>/I </sub>strain and the six variants.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Presented is a figure showing PFGE of I-<italic>Ceu</italic>I-hydrolyzed genomic DNA of TT01<sub>/I </sub>strain and the six variants.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Presented is a figure showing the copy number of 16S rDNA in TT01<sub>/I </sub>and the six variants.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Presented is a table listing the TT01<sub>/I </sub>missing genes in TT01α<sub>/I </sub>and VAR* variants according whole-genome comparison using DNA microarray.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Presented is a table listing the TT01<sub>/I </sub>amplified genes in TT01α<sub>/I </sub>and VAR* variants, according to whole-genome comparison using DNA microarray.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Presented is a table listing strains and plasmids used in this study.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Presented is a table listing primers used in this study.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>+, positive; -, negative; v, variable; w, weak.</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a </sup>Genomic islands described in [##REF##14528314##42##]. <sup>b </sup>Enterobacterial variable regions described in [##REF##16385072##56##].</p></table-wrap-foot>" ]
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[{"surname": ["Boemare", "Gaugler R"], "given-names": ["NE"], "article-title": ["Biology, taxonomy and systematics of "], "italic": ["Photorhabdus ", "Xenorhabdus"], "source": ["Entomopathogenic Nematology"], "year": ["2002"], "publisher-name": ["Wallingford, UK: CABI Publishing"], "fpage": ["35"], "lpage": ["56"]}, {"surname": ["Akhurst"], "given-names": ["RJ"], "article-title": ["Morphological and functional dimorphism in "], "italic": ["Xenorhabdus ", "Neoaplectana ", "Heterorhabditis"], "source": ["J Gen Microbiol"], "year": ["1980"], "volume": ["121"], "fpage": ["303"], "lpage": ["309"]}, {"surname": ["Boemare", "Akhurst"], "given-names": ["NE", "RJ"], "article-title": ["Biochemical and physiological characterization of colony form variants in "], "italic": ["Xenorhabdus ", "Enterobacteriaceae"], "source": ["J Gen Microbiol"], "year": ["1988"], "volume": ["134"], "fpage": ["751"], "lpage": ["761"]}, {"surname": ["Forst", "Clarke", "Gaugler R"], "given-names": ["S", "D"], "article-title": ["Bacteria-nematode symbiosis."], "source": ["Entomopathogenic Nematology"], "year": ["2002"], "publisher-name": ["Oxon, UK: CAB International"], "fpage": ["57"], "lpage": ["77"]}, {"surname": ["Wouts"], "given-names": ["WM"], "article-title": ["The primary form of "], "italic": ["Xenorhabdus ", "Enterobacteriaceae"], "source": ["Nematologica"], "year": ["1990"], "volume": ["36"], "fpage": ["313"], "lpage": ["318"]}, {"surname": ["Smigielski", "Akhurst"], "given-names": ["AJ", "RJ"], "article-title": ["Megaplasmids in "], "italic": ["Xenorhabdus ", "Photorhabdus ", "Steinernema ", "Heterorhabditiae"], "source": ["J Invertebrate Pathol"], "year": ["1994"], "volume": ["64"], "fpage": ["214"], "lpage": ["220"], "pub-id": ["10.1016/S0022-2011(94)90225-9"]}, {"surname": ["Woodring", "Kaya"], "given-names": ["JL", "HK"], "italic": ["Steinernematid ", "Heterorhabditid "], "source": ["Southern Cooperative Series Bulletin 331"], "year": ["1988"], "publisher-name": ["Arkansas Agricultural Experiment Station, Fayetteville, AR: Nematode Subcommittee of the Southern Regional Project S-135:Entomopathogens for Use in Pest-Management Systems"]}, {"surname": ["Roth", "Benson", "Galitski", "Haack", "Lawrence", "Miesel", "Neidhardt F"], "given-names": ["J", "N", "T", "K", "J", "L"], "article-title": ["Rearrangement of the bacterial chromosome: formation and applications."], "source": ["Escherichia coli and Salmonella: Cellular and Molecular Biology"], "year": ["1996"], "volume": ["2"], "edition": ["2"], "publisher-name": ["Washington, DC: ASM Press"], "fpage": ["2256"], "lpage": ["2276"]}, {"surname": ["Mira", "Pushker", "Baquero FCN, Cassell GH, Guti\u00e9rez JA"], "given-names": ["A", "R"], "article-title": ["Genome architecture and evolution of bacterial pathogens."], "source": ["Evolutionary Biology of Bacterial and Fungal Pathogens"], "year": ["2008"], "publisher-name": ["Washington, DC: ASM Press"], "fpage": ["115"], "lpage": ["127"]}, {"surname": ["Baghdiguian", "Boyer-Giglio", "Thaler", "Bonnot", "Boemare"], "given-names": ["S", "M-H", "JO", "G", "N"], "article-title": ["Bacteriocinogenesis in cells of "], "italic": ["Xenorhabdus nematophilus ", "Photorhabdus luminescens"], "source": ["Biol Cell"], "year": ["1993"], "volume": ["79"], "fpage": ["177"], "lpage": ["185"], "pub-id": ["10.1016/0248-4900(93)90254-C"]}, {"surname": ["Boemare", "Thaler", "Lanois"], "given-names": ["N", "J-O", "A"], "article-title": ["Simple bacteriological tests for phenotypic characterization of "], "italic": ["Xenorhabdus ", "Photorhabdus "], "source": ["Symbiosis"], "year": ["1997"], "volume": ["22"], "fpage": ["167"], "lpage": ["175"]}, {"surname": ["Felsenstein"], "given-names": ["J"], "source": ["PHYLIP (Phylogeny Inference Package, Version 36"], "year": ["2004"], "publisher-name": ["Seattle, WA: Department of Genome Sciences, University of Washington, Seattle, WA"]}, {"article-title": ["MaGe"]}]
{ "acronym": [], "definition": [] }
84
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 22; 9(7):R117
oa_package/a0/20/PMC2530875.tar.gz
PMC2530876
18664279
[ "<title>Background</title>", "<p>T cells comprise a heterogeneous population of cells that have different phenotypes and functions. The primary function of T cells is to mount an immune response against invading pathogens, but some T cells can mount an immune response against self-proteins and thus cause a variety of autoimmune diseases if they are not properly controlled by a T cell population known as regulatory T cells (Treg cells). There are several well defined Treg cell subsets and the best studied is the CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells, which possess potent activity in suppressing the proliferation of both CD4<sup>+ </sup>and CD8<sup>+ </sup>effector T cells <italic>in vitro </italic>and <italic>in vivo</italic>. Certain CD8<sup>+ </sup>T cells have also been recognized to have suppressive function but the CD8<sup>+ </sup>Treg is poorly defined. T cells with natural killer (NK) cell activity have been identified in both mice and humans [##REF##1696293##1##, ####REF##1977798##2##, ##REF##3086432##3##, ##REF##3088199##4####3088199##4##] and these cells are referred to as NKT cells. Murine NKT cells express phenotypic markers that are typically found on T cells, such as CD3 and the αβ T-cell receptor (TCR), and markers for NK cells, such as NK1.1 and DX5 [##REF##7650474##5##]. Two major NKT cell populations have been recognized in mice [##REF##10352254##6##,##REF##15039760##7##]. The first population is the well-characterized invariant NKT (<italic>i</italic>NKT) cells that express invariant Vα14-Jα18 TCR in mice [##REF##9143699##8##, ####REF##8046344##9##, ##REF##7908323##10####7908323##10##]. These <italic>i</italic>NKT cells are restricted by the major histocompatibility complex (MHC) class I-like molecule Cd1d and recognize glycolipid antigen α-galactosylceramide, a synthetic variant of a murine sponge-derived glycolipid [##REF##9143699##8##,##REF##12154375##11##]. These <italic>i</italic>NKT cells produce large amounts of interleukin (IL)-4 and interferon (IFN)-γ upon activation and have been shown to play a critical role in regulating the immune response [##REF##9143699##8##,##REF##12154375##11##]. The second population of NKT cells expresses a variable TCR repertoire and is not restricted by Cd1d. These NKT cells express mainly CD8 or are negative for both CD8 and CD4 [##REF##10352254##6##]. The whole αβTCR<sup>+</sup>NK1.1<sup>+ </sup>NKT population represents 1-2% of splenocytes in B6 mice, and, of these cells, approximately 20% are CD8<sup>+ </sup>[##REF##10352254##6##]. It has been shown that neonatal tolerance is associated with increased CD8<sup>+ </sup>NKT-like cells, suggesting that CD8<sup>+ </sup>NKT-like cells may have immunoregulatory properties [##REF##11923704##12##].</p>", "<p>Due to the very low frequency of the CD8<sup>+ </sup>NKT-like cells, their function and the molecular mechanism underlying their function are poorly understood. Therefore, a number of investigators have attempted to develop <italic>in vitro </italic>and <italic>in vivo </italic>expansion protocols to investigate these rare cells. The Cd1d-independent CD8<sup>+ </sup>NKT-like cells are increased in certain genetically manipulated mice. For example, three different MHC class I-restricted TCR-transgenic mouse strains (OT-I, P14 and H-Y) contain higher but still low frequencies of transgenic CD8<sup>+ </sup>T cells that co-express NK cell marker NK1.1 [##REF##16506291##13##]. These transgenic CD8<sup>+ </sup>NKT-like cells are endowed with effector properties, such as cytokine production and antigen-specific cytotoxicity. Tumor-bearing C57BL/6 mice were shown to have a population of NKT cells that co-express CD8 and NK1.1 [##REF##11536181##14##]. These cells can be maintained in long-term culture with IL-4 but produce large amounts of IFN-γ following activation. These CD8<sup>+ </sup>NKT-like cells show a potent NK-like cytotoxic activity against multiple tumor targets and their cytotoxic activity is Cd1d-independent [##REF##11536181##14##]. CD8<sup>+ </sup>cells with NK phenotype can also be expanded <italic>in vitro </italic>using a culture condition that includes IFN-γ, anti-CD3 and IL-2 [##REF##11342413##15##]. Such expanded CD8<sup>+ </sup>NKT-like cells can efficiently kill tumor cells <italic>in vitro </italic>and <italic>in vivo </italic>but have limited capacity to cause graft-versus-host disease [##REF##11342413##15##]. However, the amplification efficiency for these cells is variable and slight changes in culture conditions may result in cells with very different phenotypes and functions. Cell culture with anti-CD3/anti-CD28-coated beads and high dose IL-2 was previously shown to expand CD4<sup>+ </sup>Treg cells that can suppress the proliferation of responder T cells and prevent the development of autoimmune diseases in certain models [##REF##15870014##16##,##REF##15184499##17##]. Using a similar protocol, we can efficiently produce, from the total splenic CD8<sup>+ </sup>T cell population, large numbers of CD8<sup>+ </sup>T cells that co-express various NK markers. These cells are therefore referred to as CD8<sup>+ </sup>NKT-like cells. We demonstrate that these cells possess potent immunosuppressive activity and report the molecular profiles of these cells assayed using microarray analysis coupled with multiple confirmation techniques, including RT-PCR, enzyme-linked immunosorbent assay (ELISA) and flow cytometry. Guided by the genomic information, we further demonstrate that IL-10 and IFN-γ are two key pathways implicated in the function of these immunosuppressive CD8<sup>+ </sup>NKT-like cells.</p>" ]
[ "<title>Materials and methods</title>", "<title>Mice</title>", "<p>C57BL/6 (B6), B6.IL-10<sup>-/- </sup>and B6.IFN-γ<sup>-/- </sup>mice were purchased from Jackson Lab (Bar Harbor, ME, USA) and housed/bred under specific pathogen-free conditions at the Medical College of Georgia Animal Barrier Facility. This study was approved by the Medical College of Georgia IACUC committee.</p>", "<title>Cell sorting and flow cytometry</title>", "<p>CD8<sup>+ </sup>T cells were enriched from spleens by negative selection using an AutoMACS from Miltenyi Biotec (Auburn, CA, USA). The resulting CD8<sup>+ </sup>cell purity was around 90-95%. High purity CD4<sup>+ </sup>T cells were obtained by sorting splenic T cells using a Mo-Flow cytometer (Dako, Carpinteria, CA, USA). Flow cytometric analyses were performed on a FACScalibur™ flow cytometer with CELLQuest™ software (Becton Dickinson, Franklin Lakes, NJ, USA).</p>", "<title><italic>In vitro </italic>culture of CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells</title>", "<p>AutoMACS-purified naïve CD8<sup>+ </sup>or Mo-Flow sorted CD4<sup>+ </sup>T cells at 10<sup>6 </sup>cells/ml were cultured with anti-CD3 and anti-CD28 coupled to 4.5 mm paramagnetic Dynal beads (Invitrogen, Carlsbad, CA, USA) supplemented with 2,000 IU/ml rIL-2 (PeproTech, Rocky Hill, NJ, USA) in complete medium, which consisted of 10% heat-inactivated fetal bovine serum (Sigma-Aldrich, St. Louis, MO, USA), nonessential amino acids, 0.5 mM sodium pyruvate, 5 mM Hepes, 1 mM glutaMax I (all from Invitrogen, Carlsbad, CA, USA), and 55 μM mercaptoethanol (Sigma-Aldrich) in DMEM base. The cultures were monitored daily and maintained at 1-1.5 × 10<sup>6 </sup>cells/ml by diluting with IL-2-supplemented complete medium for 8-13 days. At the end of the culture, the anti-CD3 and anti-CD28 beads were removed using a Dynal MPC-L magnet (Dynal Biotech), and the cells were routinely assayed for CD8, CD4, CD62L, and CD25 expression by flow cytometry and for <italic>in vitro </italic>suppression assays.</p>", "<title><italic>In vitro </italic>suppression assays</title>", "<p>Two different types of suppression assays were performed in this study. Most of the studies were performed using the CFSE system. Briefly, naïve CD4<sup>+</sup>CD25<sup>- </sup>responder T cells were labeled with 2.5 μM CFSE. These labeled responder cells (1 × 10<sup>5</sup>) were cocultured at 37°C with different numbers of suppressor cells (CD8<sup>+ </sup>NKT-like cells, cultured CD4<sup>+ </sup>T cells or CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells) in the presence of 1.5 × 10<sup>5 </sup>irradiated (2,000 rads) splenic APC (T cell depleted spleen cells) and 1.5 μg/ml anti-CD3 in a U-bottomed 96-well plate. After 72 h of culture, responder T cell proliferation was assessed by determining the dilution of CFSE using flow cytometry. The second type of suppression assay has identical culture conditions but the responder T cells were not labeled. The cultures were pulsed with 1 μCi/well [<sup>3</sup>H]thymidine for the last 16 h of 72 h culture and the level of proliferation was assessed by [<sup>3</sup>H]thymidine incorporation using scintillation counting after cell harvesting.</p>", "<title>Cytokine analysis by ELISA</title>", "<p>Cells were cultured in 96-well plates with 1 × 10<sup>5 </sup>CD4<sup>+</sup>CD25<sup>- </sup>T cells/well, 1.5 × 10<sup>5</sup>/well irradiated splenic APC and 1.5 μg/ml anti-CD3 in the presence or absence of 1 × 10<sup>5</sup>/well CD8<sup>+ </sup>NKT-like cells or fresh CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells. The level of IL-10, IFN-γ, IL-4 and IL-2 in the culture supernatant was determined by ELISA kits purchased from R&amp;D Systems (Minneapolis, MN, USA).</p>", "<title>Microarray experiments</title>", "<p>Total RNA was extracted from cultured or fresh T cells using the Qiashredder column and RNeasy Mini kit (Qiagen Inc., Valencia, CA, USA). All RNA extracted was analyzed for quantity and quality using the Agilent 2100 Bioanalyzer system (Agilent Technologies, Palo Alto, CA, USA). Gene expression profiling was performed using the mouse genome 430 2.0 chips (GeneChip™, Affymetrix, Santa Clara, CA, USA). An aliquot of 1 μg of total RNA was converted into double-stranded cDNA (ds-cDNA) by using SuperScript Choice System (Gibco BRL Life Technologies, Carlsbad, CA, USA) with an oligo-dT primer containing a T7 RNA polymerase promoter (Genset, San Diego, CA, USA). After second-strand synthesis, the reaction mixture was extracted with phenol-chloroform-isoamyl alcohol, and ds-cDNA was recovered by ethanol precipitation. <italic>In vitro </italic>transcription was performed on the above ds-cDNA using the Enzo RNA transcript Labeling kit. Biotin-labeled cRNA was purified by using an RNeasy affinity column (Qiagen), and fragmented randomly to sizes ranging from 35-200 bases by incubating at 94°C for 35 minutes. The hybridization solutions contained 100 mM MES [2-(N-morpholino)ethanesulfonic acid], 1 M Na<sup>+</sup>, 20 mM EDTA, and 0.01% Tween 20. The final concentration of fragmented cRNA was 0.05 μg/μl in hybridization solution. Target for hybridization was prepared by combining 40 μl of fragmented transcript with sonicated herring sperm DNA (0.1 mg/ml), bovine serum albumin and 5 nM control oligonucleotide in a buffer containing 1.0 M NaCl, 10 mM Tris.HCl (pH7.6), and 0.005% Triton X-100. Target was hybridized for 16 h at 45°C to a set of oligonuceotide arrays (Affymetrix). Arrays were then washed at 50°C with stringent solution, then again at 30°C with non-stringent washes. Arrays were then stained with streptavidin-phycoerythrin (Invitrogen). DNA chips were read at a resolution of 3 μm with a Hewlett-Packard GeneArray Scanner and were analyzed with the GENECHIP software (Affymetrix GCOS 1.1). Both the CEL and DAT files for each hybridization have been uploaded to our server running GeneTraffic v3.2 (Iobion Informatics LLC, La Jolla, CA, USA).</p>", "<title>Data analysis</title>", "<p>Microarray data were first normalized using RMA [##REF##12538238##68##] and normalized data were subsequently analyzed using the LIMMA [##REF##16646809##69##] package in R. All groups were compared pairwise, and the resulting <italic>p</italic>-values were adjusted using the pFDR of Storey [##UREF##0##70##] and the qvalue package in R. We considered all probesets with a q-value ≤0.01 as being significant. The heatmap was constructed using the heatmap.2 function in R. Differentially expressed genes showing more than five-fold change between CD8<sup>+ </sup>NKT-like and naïve CD8<sup>+ </sup>T cells were annotated with respect to their involvement in biological processes and pathways using the PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification system [##REF##12952881##71##]. Of the 314 genes, biological processes for 257 were found by PANTHER. One-tailed <italic>p</italic>-values for enrichment of particular biological processes were obtained using the standard Fisher's exact test, to determine if the observed number of counts exceeded the expected counts. Gene lists from each significantly enriched (<italic>p </italic>&lt; 0.05) biological process were further analyzed by pathway analysis. A network depicting the molecular interactions between the given set of genes was constructed using Pathway Studio Version 5.0 software and ResNet mammalian database (Ariadne Genomics, Rockville, MD, USA). ResNet is a very large database of molecular interactions and biological relationships extracted from the biomedical literature. The found interactions between the given set of genes were manually curated by reading sentences from which the relationship or interaction was derived. Only manually curated interactions were used for the final pathway visualization.</p>", "<title>RT-PCR analysis</title>", "<p>An aliquot of total RNA (2 μg per sample) were arrayed in 96-well plates and then converted to cDNA using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA) and a programmable thermal cycler (Applied Biosystems). The cDNA products were diluted and an aliquot of cDNA equivalent to 10 ng total RNA was used for quantitative RT-PCR performed using ready-to-use TaqMan gene expression assays from Applied Biosystems. 18srRNA and GAPDH were used as endogenous controls for normalizing RNA concentration. RT-PCR was performed in 384-well plates with the ABI 7900HT Fast Real-Time PCR System (Applied Biosystems). Standard thermal cycling conditions (10 minutes at 95°C, 40 cycles for 15 s at 95°C, 1 minute at 60°C) was used for all genes. All samples were analyzed on the same plate and each sample was analyzed in duplicate. Cycle threshold (C<sub>T</sub>) values for each test gene and 18SrRNA and GAPDH were obtained for each sample using the SDS2.3 and analyzed with RQ Manager 1.2 software (Applied Biosystems). Differences in C<sub>T </sub>values between a test gene and 18srRNA (ΔC<sub>T</sub>) for each sample were calculated and used for statistical analyses.</p>" ]
[ "<title>Results</title>", "<title><italic>In vitro </italic>culture of CD8<sup>+ </sup>T cells</title>", "<p><italic>In vitro </italic>cultures with anti-CD3/anti-CD28-coated beads in the presence of high dose IL-2 can efficiently expand CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells that suppress the proliferation of effector T cells. However, the small number of natural CD4<sup>+ </sup>Treg cells available for expansion limits the use of this approach. Therefore, we attempted to obtain Treg cells from the more abundant total CD4<sup>+ </sup>and CD8<sup>+ </sup>T cell populations from the mouse spleen. Freshly purified splenic CD8<sup>+ </sup>or Mo-Flow sorted CD4<sup>+ </sup>T cells from 7-8-week old mice were cultured with an expansion protocol consisting of anti-CD3/anti-CD28-coated beads and high dose IL-2. By the end of the 10-13 days of expansion, the number of cells had generally increased by over 1,000-fold. The cultured cells were phenotyped for a number of surface markers (Figure ##FIG##0##1##). The vast majority of the cultured cells from CD8<sup>+ </sup>T cells were positive for CD8 (&gt;95%) and the activation marker CD25 (98%) at the end of the culture. Consistent with the activation of these cells, the percentages of CD62L<sup>+ </sup>cells gradually decreased and became very low near the end of the culture (around 10%). Similarly, the culture conditions can efficiently expand CD4<sup>+ </sup>T cells. At the end of the culture, the cultured cells remained CD4<sup>+ </sup>(97%) and became positive for the activation marker CD25 (99%).</p>", "<title>Cultured CD8<sup>+ </sup>T cells possess potent immunosuppressive properties</title>", "<p>The cultured CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells were tested for their ability to inhibit the proliferation of CD4<sup>+</sup>CD25<sup>- </sup>naïve T cells (Tn cells) using two different <italic>in vitro </italic>suppression assays. In the first assay, the naïve T cells were labeled with carboxyfluorescein succinimidyl ester (CFSE) and T cell proliferation was assessed by the dilution of CFSE signal using fluorescence-activated cell sorting (FACS) analysis. As shown in Figure ##FIG##1##2a##, the cultured CD8<sup>+ </sup>T cells efficiently suppressed proliferation of naïve CD4<sup>+</sup>CD25<sup>- </sup>T cells. The suppressive activity of the cultured CD8<sup>+ </sup>T cells is dose-dependent and strong suppression can be seen at the 1:16 expanded CD8<sup>+ </sup>T to Tn cell ratio (Tr/Tn; Figure ##FIG##1##2b##). In the second suppression assay, T cell proliferation was measured by incorporation of [<sup>3</sup>H]thymidine. As shown in Figure ##FIG##1##2c##, the dose-dependent suppression activity of the CD8<sup>+ </sup>T cells was confirmed. Furthermore, the cultured CD8<sup>+ </sup>T cells did not proliferate in response to anti-CD3 and antigen presenting cell (APC) stimulation. This anergic phenotype is consistent with the observation on CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells [##REF##9670041##18##,##REF##10228007##19##]. Finally, the cultured CD8<sup>+ </sup>cells appeared to suppress better than freshly isolated CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells (Figure ##FIG##1##2c##; <italic>p </italic>&lt; 10<sup>-6</sup>). The cultured CD4<sup>+ </sup>T cells also had some suppressive function at the high Tr/Tn ratio of 1:1, while the suppressive activity for the cells gradually became undetectable, suggesting that the suppressive activity of the cultured CD8<sup>+ </sup>T cells was much higher than the CD4<sup>+ </sup>T cells cultured under the same conditions (Figure ##FIG##0##1c##). Therefore, most subsequent studies focused on the phenotype of the cultured CD8<sup>+ </sup>T cells.</p>", "<title>Gene expression profiles of cultured CD8<sup>+ </sup>T cells</title>", "<p>To gain further insight into the phenotypes and functions of the cultured CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells, we carried out microarray analyses using Affymetrix GeneChips that cover the whole mouse transcriptome (&gt;45,000 transcripts). Five independent cultures of CD8<sup>+ </sup>T cells and three independent cultures of CD4<sup>+ </sup>T cells as well as two groups of control cells were included in the microarray analysis. The first group of control cells included two freshly isolated naïve CD8<sup>+ </sup>T cells and the second control group consisted of two CD8<sup>+ </sup>T cells activated by a low dose of soluble anti-CD3 and anti-CD28 (activation protocol). Naïve CD8<sup>+ </sup>T cells as well as activated CD8<sup>+ </sup>T cells do not possess suppression function. This data set was analyzed as described in Materials and methods and the results are summarized in Table ##TAB##0##1##. As expected, the expression of thousands of genes was changed by the expansion protocol and the activation protocol compared to naïve CD8<sup>+ </sup>T cells (Figure ##FIG##2##3##). Surprisingly, over 100 genes were changed by &gt;10-fold and a few dozen genes were changed by 40- to 800-fold in the cultured CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells compared to naïve CD8<sup>+ </sup>T cells.</p>", "<p>To elucidate the molecular basis of the function of the cultured CD8<sup>+ </sup>T cells, we functionally annotated the 314 genes with &gt;5-fold differences (including 113 genes with &gt;10-fold differences) between the cultured and naïve CD8<sup>+ </sup>T cells (Table ##TAB##1##2##). The largest group of differentially expressed genes (17% for &gt;5-fold difference and 31% for &gt;10-fold difference) is, as expected, involved in immunity and defense. The genes with &gt;10-fold differences are enriched by 6-fold compared to the frequency of this functional group in the genome (<italic>p </italic>= 7.7 × 10<sup>-15</sup>). Other significantly enriched gene groups with considerable interest include those involved in apoptosis, cell cycle, cell proliferation and differentiation, and cell adhesion (Table ##TAB##1##2##). Twenty-three cell cycle genes were upregulated by &gt;5-fold, including 11 genes that were upregulated by &gt;10-fold in the cultured CD8<sup>+ </sup>T cells (Table ##TAB##1##2##). Twenty-one genes in the cell proliferation and differentiation category were upregulated and twenty-five upregulated genes belong to the apoptosis group. A number of these genes were selected for confirmation using a combination of real-time RT-PCR, flow cytometry and ELISA. All selected genes have been confirmed and will be discussed in more detail later.</p>", "<title>Up- and downregulation of transcription factors</title>", "<p>The expression of a large number of transcription factors (TFs) was changed in the CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells cultured using the expansion protocol (Table ##TAB##2##3##). Most of the differentially expressed TF genes were upregulated, while a small number were downregulated in the cultured cells. The expression patterns of the TF genes share some similarity but also have significant differences in the cultured CD8<sup>+</sup>, cultured CD4<sup>+</sup>, activated CD8<sup>+ </sup>T cells and naïve CD8<sup>+ </sup>T cells. Many of the TF genes still have unknown biological functions and their roles in T cells have not been investigated. However, several TF factors are known to be critical for the immune system and may play a role in gaining suppressive function for the cultured CD8<sup>+ </sup>T cells. The V-<italic>myc myelocytomatosis viral related oncogene, neuroblastoma derived </italic>(<italic>Mycn</italic>) is essential to cell proliferation and differentiation [##REF##12381668##20##]. This was the most upregulated TF gene (21-fold) in the cultured CD8<sup>+ </sup>T cells but not in cultured CD4<sup>+ </sup>(2-fold) or activated CD8<sup>+ </sup>(1-fold) T cells (Table ##TAB##2##3##). RT-PCR analyses confirmed the expression differences observed with the microarray analysis (Figure ##FIG##3##4##). This may be a key gene for the cultured CD8<sup>+ </sup>T cell phenotype. The Eomesodermin homolog (Eomes) is a T-box transcription factor that is highly homologous to T-bet. Eomes and T-bet may have cooperative or redundant functions in regulating the genes encoding IFN-γ and cytolitic molecules in CD8<sup>+ </sup>T cells [##REF##14605368##21##], and determine the fate of effector and memory CD8<sup>+ </sup>T cells [##REF##16273099##22##]. Furthermore, they are responsible for inducing enhanced expression of <italic>Il2rb </italic>(CD122) [##REF##16273099##22##], a marker for some CD8<sup>+ </sup>Treg cells [##REF##16301610##23##]. <italic>Eomes </italic>was upregulated four-fold in the cultured CD8<sup>+ </sup>T cells while it was downregulated five-fold in the cultured CD4<sup>+ </sup>T cells and was unchanged by our activation protocol (Table ##TAB##2##3##). The upregulation of <italic>Eomes </italic>may be responsible for the increased expression of IFN-γ, perforin, granzymes, CD122 and other genes in cultured CD8<sup>+ </sup>T cells. It could be a critical TF for the suppressive function of the cultured CD8<sup>+ </sup>T cells. Runt related transcription factor 2 (Runx2) may be another critical transcription factor. <italic>Runx2 </italic>was highly upregulated in the cultured CD8<sup>+ </sup>T cells (8.6-fold) and moderately upregulated in the cultured CD4<sup>+ </sup>(3.5-fold) and activated CD8<sup>+ </sup>(1.8-fold) T cells. Runx2 plays an important role in early T cell development [##REF##9182763##24##]. Over-expression of <italic>Runx2 </italic>increases the proportion of single positive CD8<sup>+ </sup>T cells [##REF##12218099##25##]. Other potentially important TFs include Litaf, Jun (AP1), Zbtb32 (Rog), Zfp608 and Rnf13, which had higher expression levels in the cultured CD8<sup>+ </sup>T cells than in the other three types of cells. The expression of Foxp3, which is an important TF for CD4<sup>+ </sup>Treg cells, was not detectable by RT-PCR (data not shown) in the CD8<sup>+ </sup>T cells cultured under this condition.</p>", "<title>The cultured CD8<sup>+ </sup>T cells are CD8<sup>+ </sup>NKT-like cells</title>", "<p>Several genes encoding surface markers on NK cells were highly upregulated in the cultured CD8<sup>+ </sup>T cells (19-fold for CD244, 13-fold for Ly49e, 4.4-fold for NK1.1, 8.0-fold for NKG2A and 6-fold for NKG2D; Figure ##FIG##4##5a##) but not in the cultured CD4<sup>+ </sup>or activated CD8<sup>+ </sup>T cells. To confirm these findings, FACS analysis was carried out for a number of surface markers. As already mentioned, these cultured cells remained positive for CD8 (~99%) and negative for CD4 (Figure ##FIG##0##1##). They were activated T cells as indicated by the high expression levels of CD25 and CD69 as well as the low expression level of CD62L (Figure ##FIG##0##1##). Consistent with the low frequency of NKT cells among naïve CD8<sup>+ </sup>T cells, &lt;1% of the CD8<sup>+ </sup>T cells were positive for these markers after three days of culture (Figure ##FIG##4##5b##), while the majority of the cells became positive for NK1.1 and CD244 after about 10 days of culture. The percentages of cells positive for the NK markers may vary from culture to culture. By day 10-13, 75-95% of the cells were normally positive for NK1.1 and CD244. NKG2A was upregulated by 8-fold in the cultured CD8<sup>+ </sup>T cells according to the microarray data (Figure ##FIG##4##5a##) and 25-30% of the cultured CD8<sup>+ </sup>T cells stained positive for NKG2A. Although CD94 and DX5 were not upregulated in the cultured CD8<sup>+</sup> NKT-like cells according to the microarray data (Figure ##FIG##4##5a##), FACS analyses indicated that 15-30% of the cultured CD8<sup>+ </sup>T cells were positive for these NK markers. It is unclear if these discrepancies are due to an imperfect correlation between gene and protein expression. Since the vast majority of the cultured CD8<sup>+ </sup>T cells expressed NK markers, the cultured CD8<sup>+ </sup>T cells had similar phenotypes to NKT cells, which are defined as cells expressing both T cell and NK cell markers. Furthermore, these cells were negative for the α-galactosylceramide-loaded Cd1d tetramer (data not shown), suggesting that they were not Cd1d-restricted <italic>i</italic>NKT cells. It is unclear at this time what the source of these cultured CD8<sup>+ </sup>NKT-like cells was. As the CD8<sup>+ </sup>NKT-like cell precursors in the total CD8<sup>+ </sup>T cell pool were very rare, we believe that the cultured CD8<sup>+ </sup>NKT-like cells were probably expanded from the conventional CD8<sup>+ </sup>T cells, which acquired NK markers during the expansion. According to a recent classification of NKT cells [##REF##15039760##7##], these cultured cells belong to the CD8<sup>+ </sup>NKT-like category of NKT cells.</p>", "<title>Upregulation of secreted molecules with potential suppression functions</title>", "<p>Using the 318 genes that are upregulated by &gt;5-fold in the cultured CD8<sup>+ </sup>NKT-like cells, we established molecular networks to understand the functional relationships of the genes upregulated in the CD8<sup>+ </sup>NKT-like cells. The largest network consists of genes involved in immunity and defense (Figure ##FIG##5##6##). This network highlights the importance of two central nodes: IL-10 and IFN-γ. Upon stimulation by anti-CD3/CD28 and IL-2, IL-10 and IFN-γ are highly upregulated (by 47- and 51-fold, respectively; Table ##TAB##3##4##). These proteins and pathways are known to influence the expression of many genes involved in immune responses, including those encoding the activation marker IL-2 receptor (Il2ra, or CD25), granzymes, the tumor necrosis factor (TNF) family genes, cytokines, chemokines and their receptors. Many of these genes are significantly upregulated in the CD8<sup>+ </sup>NKT-like cells (Tables ##TAB##3##4## and ##TAB##4##5##). To confirm the microarray data, we used ELISA to measure the levels of secreted cytokines in the culture medium of CD8<sup>+ </sup>NKT-like cells and natural CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells stimulated by anti-CD3 and APC (Figure ##FIG##6##7a##). Consistent with the microarray data, the cultured CD8<sup>+ </sup>NKT-like cells secreted more IL-10 and IFN-γ but a similar level of IL-4 when compared to fresh CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells (Figure ##FIG##6##7a##). The secretion of IFN-γ was particularly high in the CD8<sup>+</sup> NKT-like cells. The expression of IFN-γ and lack of expression of IL-4 are also consistent with the observation on other NKT-like cells [##REF##15039760##7##]. IL-10 and IFN-γ are immunosuppressive cytokines known to be involved in the suppressive function of CD4<sup>+ </sup>Treg cells and may contribute to the suppressive function of the expanded CD8<sup>+ </sup>NKT-like cells. Transforming growth factor (TGF)-β is another important immunosuppressive cytokine that might be important for the suppressive function of the CD8<sup>+ </sup>NKT-like cells. Our microarray and RT-PCR data (Figure ##FIG##3##4##) indicate that the TGF-β mRNA level was about two-fold higher in CD8<sup>+ </sup>NKT-like cells compared to naïve CD8<sup>+ </sup>cells. As TGF-β cannot be accurately measured from serum-containing culture medium, we performed blocking experiments using an anti-TGF-β antibody to assess the role of TGF-β. Our results (Figure S1 in Additional data file 1) indicate that TGF-β blockade cannot block the suppression function of the CD8<sup>+ </sup>NKT-like cells.</p>", "<p>A number of other secreted molecules were also highly upregulated in the CD8<sup>+ </sup>NKT-like cells based on the microarray data (Table ##TAB##3##4##). Many of these secreted molecules are known to have immunosuppressive function or potentially contribute to the suppression function. Perforin (<italic>Prf1</italic>) and granzymes are among the most noticeable. Perforin and granzyme expression is regulated by IFN-γ (Figure ##FIG##5##6##). Both natural and adaptive CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells in human display perforin-dependent cytotoxicity against autologous target cells, suggesting that the perforin/granzyme pathway is one of the mechanisms that Treg cells can use to control immune responses [##REF##15485635##26##]. <italic>Prf1 </italic>was upregulated by 29-fold in the CD8<sup>+</sup> NKT-like cells (with potent suppression activity; Table ##TAB##3##4##), but unchanged in cultured CD4<sup>+ </sup>cells (with weak suppression activity) and activated CD8<sup>+ </sup>T cells (without suppression activity). Several granzymes were highly upregulated (834-, 535-, 446-, 329-, 105-, 63-, 61- and 23-fold for granzymes D, E, C, G, B, F, K and A, respectively). These molecules were generally upregulated to a much lesser degree in the cultured CD4<sup>+ </sup>T cells and were unchanged in activated CD8<sup>+ </sup>T cells (except <italic>Gzmk</italic>). The large expression differences for <italic>Prf1 </italic>and selected granzymes were confirmed using RT-PCR (Figure ##FIG##3##4##).</p>", "<p>Several secreted molecules can potentially be implicated in the immunosuppressive function. The most noticeable include Esm1, Spp1, Fgl2, Tnfrsf11b, Lgals3, Lgals1, and IL-24 (Table ##TAB##3##4##). <italic>Esm1 </italic>(endothelial cell-specific molecule 1) was upregulated in the CD8<sup>+ </sup>NKT-like cells by 75-fold and was only slightly increased in the cultured CD4<sup>+ </sup>T cells (2.8-fold) and was unchanged in the activated CD8<sup>+ </sup>T cells. The expression pattern was confirmed by RT-PCR (Figure ##FIG##3##4##). Esm1 is a proteoglycan mainly secreted by endothelial cells under the control of inflammatory cytokine. It binds to LFA-1 integrin on the surface of lymphocytes and monocytes [##REF##11544294##27##] and therefore inhibits the binding of intercellular adhesion molecules (ICAMs) to LFA-1 and influences leukocyte adhesion and activation. Spp1 (secreted phosphoprotein 1) is better known as osteopondin. In addition to its well known function in bone formation, it functions as a cytokine and chemokine to regulate cell-cell and cell-tissue interaction. Much less well known is its function in suppressing T cells and activating B cells [##REF##8971479##28##]. Osteopondin is believed to be the most abundant protein secreted by activated T cells, which is consistent with our microarray data (7.4-fold higher expression in activated CD8<sup>+ </sup>T cells versus naïve T cells). Osteopondin was upregulated by 252-fold in the CD8<sup>+ </sup>NKT-like cells and 204-fold in the cultured CD4<sup>+ </sup>T cells based on the microarray data (Table ##TAB##3##4##). Based on the RT-PCR data, Osteopondin (Spp1) was greatly increased (by 25,000-fold) in the CD8<sup>+</sup> NKT-like cells compared to the naïve CD8<sup>+</sup> T cells (Figure ##FIG##3##4##). It is possible that Osteopondin contributed to the suppression activity of both the CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells cultured using our protocol. Tnfrsf11b, also known as Osteoprotegerin (Opg), is a member of the TNF receptor superfamily. Opg is a decoy receptor of RANKL and inhibits the binding of RANKL (Receptor activator for nuclear factor κB ligand) to its receptor RANK. Opg is secreted as a disulfide-linked homodimer [##REF##12787563##29##]. Opg can inhibit the inflammatory effect of RANKL secreted by activated T cells [##REF##12101070##30##,##REF##14769514##31##] and RANKL blockade can significantly prolong heart allograft survival [##REF##14734743##32##]. Opg was upregulated by 29-fold in the CD8<sup>+</sup> NKT-like cells and unchanged in the cultured CD4<sup>+ </sup>and activated CD8<sup>+ </sup>T cells. The expression changes were also confirmed by RT-PCR (Figure ##FIG##3##4##). Fgl2 (Fibrinogen-like protein 2) is a member of the fibrinogen-related protein superfamily. In addition to its well established role in triggering thrombosis, it is known to be secreted by T cells under the control of IFN-γ [##REF##14976252##33##]. Fgl2 has been shown to exhibit immunomodulatory properties capable of inhibiting dendritic cells (DC) maturation and T cell proliferation stimulated by alloantigens or anti-CD3/anti-CD28 antibodies in a dose-dependent manner [##REF##12682232##34##]. <italic>Fgl2 </italic>was upregulated by 33-fold in the CD8<sup>+ </sup>NKT-like cells but was unchanged in the cultured CD4<sup>+ </sup>and activated CD8<sup>+ </sup>T cells. Thus, Fgl2 could be a critical factor for the suppression mechanism of the CD8<sup>+ </sup>NKT-like cells. Lgals3 and Lgals1, also known as Galectin (Gal)-3 and Gal1, are members of the beta-galactoside-binding gene family. They are multifunctional proteins implicated in a variety of biological functions, including tumor cell adhesion, proliferation, differentiation, angiogenesis, cancer progression and metastasis. It was recently shown that Gal3 secreted by tumor cells induces T cell apoptosis [##REF##15843888##35##]. The expression of Gal3 has been positively correlated with the level of apoptosis of tumor-associated lymphocytes [##REF##16651632##36##]. Treatment with the <italic>Gal3 </italic>gene is also beneficial against asthma in mice [##REF##16424226##37##]. Finally, IL-24 is a member of the IL-10 family of cytokines [##REF##16264231##38##]. Over-expression of IL-24 induces apoptosis in cancer cells [##REF##16912197##39##]. Therefore, IL-24 appears to be an immunosuppressive cytokine.</p>", "<title>Cultured CD8<sup>+ </sup>NKT-like cells upregulate many suppressive surface markers</title>", "<p>A large number of surface molecules were highly upregulated in the CD8<sup>+ </sup>NKT-like cells, while a few surface molecules were down regulated (Table ##TAB##4##5##). Many of the upregulated molecules have been implicated in immunosuppressive function. Most notably, many of the genes are related to IFN-γ and some belong to the TNF family receptors and ligands. The expression patterns for these genes are clearly different among the cells cultured under different conditions or different cell types cultured under the same condition. The overall pattern seems to correlate well with their cellular functions. The genes already implicated in suppressive function or having suppressive potential were highly upregulated in the CD8<sup>+ </sup>NKT-like cells, which have potent suppression activity, while these genes were only moderately upregulated or unchanged in the cultured CD4<sup>+ </sup>T cells and activated CD8<sup>+ </sup>T cells, which have only weak or no suppression activity.</p>", "<p>Ifitm1 (Interferon induced transmembrane protein 1) is the most upregulated surface molecule in the CD8<sup>+ </sup>NKT-like cells (90-fold increase compared to naïve CD8<sup>+ </sup>T cells; Table ##TAB##4##5##). This gene is not upregulated by the conventional activation protocol and upregulated to a much lesser degree in the cultured CD4<sup>+ </sup>T cells. Ifitm1 has been shown to be a key molecule in the anti-proliferative function of IFN-γ [##REF##16847454##40##]. Two other interferon-induced transmembrane genes (<italic>Ifitm2 </italic>and <italic>Ifitm3</italic>) were also highly upregulated in the CD8<sup>+ </sup>NKT-like cells (45- and 24-fold, respectively). It is highly likely that these proteins are involved in the suppressive function of the CD8<sup>+ </sup>NKT-like cells.</p>", "<p>Lilrb4 (Leukocyte immunoglobulin-like receptor, subfamily B, member 4) is a member of the leukocyte immunoglobulin-like receptor (LIR) family. The encoded protein belongs to the subfamily B class of LIR receptors with a transmembrane domain, extracellular immunoglobulin domains, and cytoplasmic immunoreceptor tyrosine-based inhibitory motifs. The receptor expressed on immune cells binds to MHC class I molecules on antigen-presenting cells and transduces a negative signal that inhibits stimulation of an immune response. The receptor can also function in antigen capture and presentation. It may be involved in controlling inflammatory responses and cytotoxicity to help focus the immune response and limit autoreactivity. This gene was highly upregulated in both the CD8<sup>+ </sup>NKT-like cells and cultured CD4<sup>+ </sup>T cells.</p>", "<p>Havcr2 (Hepatitis A virus cellular receptor 2), more commonly known as Tim3, was upregulated by 36-fold in the CD8<sup>+ </sup>NKT-like cells and 5-fold in the cultured CD4<sup>+ </sup>T cells compared to naïve CD8<sup>+ </sup>T cells. Tim3<sup>-/- </sup>mice have exacerbated diabetes due partly to a defect in CD4<sup>+</sup>CD25<sup>+ </sup>Treg cell function [##REF##14556005##41##]. Therefore, Tim3 may be important for CD8<sup>+ </sup>NKT-like cell suppression function. Tnfrsf9, also known as 4-1BB and CD137, was highly upregulated in the CD8<sup>+ </sup>NKT-like cells (30-fold) and only slightly upregulated in the cultured CD4<sup>+ </sup>T cells (5-fold). 4-1BB is a costimulatory molecule that may be very important for Treg cell function. 4-1BB-primed CD8<sup>+ </sup>T cells possess suppressive function [##REF##15944263##42##] and an agonist monoclonal antibody specific for 4-1BB can mitigate autoimmunity [##REF##11801689##43##, ####REF##12426559##44##, ##REF##12750400##45##, ##REF##15312139##46##, ##REF##15448685##47####15448685##47##]. 4-1BB<sup>-/- </sup>mice exhibit enhanced T cell proliferation [##REF##12023342##48##]. GITR (Tnfrsf18) is an important molecule for CD4<sup>+ </sup>Treg cell function. GITR was upregulated 5-fold in the CD8<sup>+ </sup>NKT-like cells and 10-fold in the cultured CD4<sup>+ </sup>T cells. Clearly, GITR could contribute to the suppressive function of both CD4<sup>+ </sup>and CD8<sup>+ </sup>Treg cells. Other upregulated costimulatory molecules, such as Pdcd1 (5.6-fold), PDL2 (4.6-fold) and Icos (4.3-fold), Ctla4 (5-fold) and CD28 (3.9-fold), may be good candidate molecules involved in the suppressive function. CD24a is one of the few costimulatory molecules that was downregulated in the CD8<sup>+ </sup>NKT-like cells (0.03-fold) but unchanged in the cultured CD4<sup>+ </sup>T cells (1.2-fold).</p>", "<p>Genes for several TNF family proteins, such as Fas-L, RANKL, TRAIL and OX40, were all upregulated in the CD8<sup>+ </sup>NKT-like cells and the cultured CD4<sup>+ </sup>T cells. All these molecules could be implicated in the suppression function. Fas-L expression was 25-fold higher in the CD8<sup>+</sup> NKT-like cells compared to naïve CD8<sup>+ </sup>T cells while CD4<sup>+ </sup>T cells cultured in the same condition did not upregulate Fas-L. Fas/Fas-L is one of the two pathways of lymphocyte-mediated cell killing [##REF##8717513##49##].</p>", "<p>Several lymphocyte receptors were upregulated - Gpr105 (65-fold), Ptger3 (13.1-fold), Ptger2 (4.3-fold), Fcer1g (13.7-fold) - and a few other receptors were downregulated by the expansion protocol (Table ##TAB##3##4##). Prostaglandin E receptor 3 (subtype EP3; Ptger3) may have pro-inflammatory or anti-inflammatory properties depending on the physiological condition [##REF##15369681##50##]. EP2, EP3 and EP4 receptors have been shown to be important for the immunosuppressive function of PGE2 [##REF##15914318##51##]. Cytokine and chemokine receptors were up- or downregulated by the expansion protocol (Table ##TAB##4##5##). Notably, Il2ra (CD25) was highly upregulated in the CD8<sup>+ </sup>NKT-like and cultured CD4<sup>+ </sup>T cells as well as activated CD8<sup>+ </sup>T cells. These expected results were confirmed by FACS analysis (Figure ##FIG##0##1##). Ccr5 and Ccr2 were highly upregulated in the CD8<sup>+ </sup>NKT-like cells but unchanged or slightly upregulated in the cultured CD4<sup>+ </sup>T cells and activated CD8<sup>+ </sup>T cells. Ccr5 has been shown to be important for the suppression function of CD4<sup>+</sup>CD25<sup>+ </sup>T cells [##REF##16002422##52##]. CCR5 and several other CCR members (CCR2) were highly upregulated in the CD8<sup>+ </sup>NKT-like cells (Table ##TAB##2##3##) and they should be excellent candidate molecules for further studies.</p>", "<p>A large number of adhesion molecules were highly upregulated in the CD8<sup>+ </sup>NKT-like cells and only slightly upregulated/unchanged or even decreased in the cultured CD4<sup>+ </sup>T cells (Table ##TAB##4##5##). Several of these genes (<italic>Tjp1</italic>, <italic>Emilin2</italic>, <italic>Nov</italic>) were confirmed by RT-PCR (Figure ##FIG##3##4##) and Sell (CD62L) was confirmed by FACS analysis (Figure ##FIG##0##1##).</p>", "<title>IL-10 and IFN-γ are two key pathways for the conversion and function of the CD8<sup>+ </sup>NKT-like cells</title>", "<p>Immunosuppressive cytokines can enhance the suppressive activity of Treg cells. For example, naturally occurring CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells use a combination of IL-10 and TGF-β to suppress immune responses [##REF##10899916##53##, ####REF##10510089##54##, ##REF##11801641##55##, ##REF##12466842##56##, ##REF##12848981##57####12848981##57##]. As IL-10 and IFN-γ are two key nodes of the molecular network modified in the CD8<sup>+</sup> NKT-like cells (Figure ##FIG##5##6##), we further tested their role in the function and generation of these cells using two different approaches. First, an anti-IL-10 antibody was used in the suppression assay to determine whether IL-10 neutralization could block or reduce the suppression function of the CD8<sup>+ </sup>NKT-like cells. Blockade using anti-IL-10 could slightly reduce the suppression but could not completely block the suppressive function of the CD8<sup>+ </sup>NKT-like cells (data not shown). Since the levels of IL-10 and IFN-γ secreted by the CD8<sup>+ </sup>NKT-like cells was very high, antibody blocking may not have been efficient. Furthermore, the antibody blockade experiment evaluated only the role of these cytokines in the suppression function but did not allow us to assess the role of these molecules in the generation of the CD8<sup>+ </sup>NKT-like cells. Therefore, we tested the potential roles of IL-10 and IFN-γ in CD8<sup>+ </sup>NKT-like function/generation using IL-10<sup>-/- </sup>and IFN-γ<sup>-/- </sup>mice. CD8<sup>+</sup> NKT-like cells can be cultured using naïve CD8<sup>+ </sup>T cells from both knockout mice; however, at the later stages of culture, the viability of cells from IFN-γ<sup>-/- </sup>mice is not as good as those from wild-type mice and IL-10<sup>-/- </sup>mice. Addition of IFN-γ in the culture medium can improve the viability of the cultured cells (Figure S2 in Additional data file 1). Although the CD8<sup>+ </sup>NKT-like cells cultured from both knockout mice had good suppressive activity at a high ratio (Tr/Tn = 1:1) of suppressor (CD8<sup>+ </sup>NKT-like) to responder (naïve CD4<sup>+ </sup>T cells), the reduction in suppressive activity for the CD8<sup>+ </sup>NKT-like cells cultured with naïve CD8<sup>+ </sup>T cells from both knockout mice became very clear at lower suppressor to responder T cell ratios (Figure ##FIG##6##7b##), which provides a more accurate estimate of the suppressive activity. At the 1:16 Tr/Tn ratio, the suppressive activity of CD8<sup>+ </sup>NKT-like cells cultured from IL-10<sup>-/- </sup>mice was reduced to 30-35% of that for CD8<sup>+ </sup>NKT-like cells cultured from wild-type B6 mice (Figure ##FIG##6##7b##), suggesting that the vast majority of the suppressive activity of the CD8<sup>+ </sup>NKT-like cells can be attributed to the IL-10 pathway. Similarly, almost 50% of the CD8<sup>+ </sup>NKT-like suppression could be attributed to the IFN-γ pathway (Figure ##FIG##6##7b##). These results together suggest that IL-10 and IFN-γ play an important role in the <italic>in vitro </italic>generation and function of CD8<sup>+ </sup>NKT-like cells.</p>" ]
[ "<title>Discussion</title>", "<p>Naïve CD8<sup>+ </sup>T cells cultured with anti-CD3/anti-CD28-coated beads in the presence of a high concentration of IL-2 can generate CD8<sup>+ </sup>NKT-like cells. This highly efficient culture system can produce large numbers of CD8<sup>+ </sup>NKT-like cells. The cultured CD8<sup>+ </sup>NKT-like cells can potently suppress the proliferation of responder T cells. In this study, we extensively characterized the molecular mechanisms underlying the suppression function of the cultured CD8<sup>+ </sup>NKT-like cells using a variety of techniques. We first compared the gene expression profiles of four different cells with different phenotypes with regard to suppression function. The CD8<sup>+ </sup>NKT-like cells cultured with the expansion protocol were highly potent suppressor cells (Figure ##FIG##1##2##). The suppressive activity of these NKT-like cells was actually much more potent than that of natural CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells, which are known to be highly suppressive. To identify candidate genes that might be related to the suppressive activity, the CD8<sup>+ </sup>NKT-like cells with potent suppressive activity were compared to three different control cells: CD4<sup>+ </sup>T cells (with very weak suppression) cultured under the same conditions as a control for culture condition; naïve CD8 T cells as the baseline expression level; and CD8<sup>+ </sup>T cells activated using conventional activation protocols. The last two cell populations do not possess suppressive activity. This data set allows us to examine a number of different questions; however, we focus on the suppressive mechanism of the cultured CD8<sup>+ </sup>NKT-like cells in this paper.</p>", "<p>The number of genes modified by the expansion protocol in both CD8<sup>+ </sup>and CD4<sup>+ </sup>T cells was quite extensive. Approximately 3,000 genes were changed by the culture. While some genes were downregulated, most genes were upregulated by the expansion protocol. In contrast, the number of genes significantly changed by the activation protocol was much less. Another surprise with this dataset is the extent of the gene expression changes in a large number of genes. Several dozens of genes were changed by 40- to 800-fold. Whereas the exact extent of gene expression changes may not be accurately measured for all genes, confirmatory studies using a variety of techniques did provide evidence that many of the genes showed large differences.</p>", "<p>If one focuses on the upregulated genes with a false discovery rate (FDR) (q) of &lt;0.01 and &gt;5-fold difference between CD8<sup>+ </sup>NKT-like cells and naïve CD8<sup>+ </sup>T cells, a large number of genes encode proteins that have already been implicated in immune suppression or have functions consistent with immune suppression, the critical phenotype for these cultured CD8<sup>+ </sup>NKT-like cells. The proteins with immunosuppressive properties include both surface molecules and soluble/secreted molecules. Many of the surface molecules belong to the TNF family and interferon-regulated proteins (Table ##TAB##4##5##). Many of these molecules were highly upregulated in the CD8<sup>+ </sup>NKT-like cells but not upregulated in the cultured CD4<sup>+ </sup>T cells or activated CD8<sup>+ </sup>T cells. Which of these molecules are involved in suppressing T cell proliferation by the CD8<sup>+ </sup>NKT-like cells remains to be investigated in future studies. It is likely that these molecules may work cooperatively to confer suppressive function. Multiple molecules may have to be blocked to demonstrate the suppression function of these molecules.</p>", "<p>Furthermore, the CD8<sup>+ </sup>NKT-like cells upregulated a large number of genes encoding secreted proteins that are known, or have the potential, to be implicated in the suppression function. These molecules include Fas-L, perforin, granzymes, Spp1, Lgals3, Lgal1 and others. Again, any of these molecules may confer some suppression function and the potent suppressive function of the CD8<sup>+ </sup>NKT-like cells may be related to more than one of these molecules. Our cell culture system provides an excellent model to further investigate the function of these molecules in immune suppression.</p>", "<p>Pathway analysis of the expression data identified IL-10 and IFN-γ as two critical nodes linking many of the upregulated genes that may be implicated in immune suppression (Figure ##FIG##5##6##). Microarray and ELISA data both suggest that the CD8<sup>+ </sup>NKT-like cells express high levels of IL-10 and IFN-γ. Previous studies suggested that these immunosuppressive cytokines could be directly involved in the immune suppressive function. The suppressive activities of some regulatory T cells like Tr1 cells have been attributed to their IL-10 production [##REF##15128781##58##,##REF##11877483##59##], while CD4<sup>+</sup>CD25<sup>+ </sup>T cells produce less IL-10 and can suppress via IL-10-dependent or -independent mechanisms [##REF##10510089##54##,##REF##12466842##56##]. Some studies demonstrated production of both IL-10 and IFN-γ by CD4<sup>+ </sup>Treg cells [##REF##11714761##60##]. IFN-γ has also been reported to be a suppressive cytokine secreted by T cells [##REF##15944263##42##,##REF##1386311##61##]. In certain models, IFN-γ is identified as part of a suppressive pathway [##REF##14578884##62##]. Thus, despite its pro-inflammatory functions, IFN-γ may contribute to the regulation of T-cell responses [##REF##11086037##63##,##REF##15173889##64##] as shown in IFN-γ<sup>-/- </sup>mice [##REF##8456300##65##] and in graft versus host responses [##REF##8450227##66##,##REF##3129505##67##]. Our studies using IL-10<sup>-/- </sup>and IFN-γ<sup>-/- </sup>mice clearly indicate that IL-10 and IFN-γ both play a role in the generation and/or function of CD8<sup>+ </sup>NKT-like cells. However, IL-10 or IFN-γ alone cannot completely explain the suppressive function of the cultured CD8<sup>+ </sup>NKT-like cells. It will be interesting to find out whether IL-10<sup>-/-</sup>-IFN-γ<sup>-/- </sup>double knockout can completely abolish the conversion and/or function of CD8<sup>+ </sup>NKT-like cells. It is possible that other compensatory pathways may exist for the production and/or function of CD8<sup>+ </sup>NKT-like cells. In addition, it will be interesting to identify the signaling pathways upstream of IL-10 and IFN-γ. In both regards, further investigation of the large number of differentially expressed transcription factors may provide important clues.</p>" ]
[ "<title>Conclusion</title>", "<p>This study demonstrates that CD8<sup>+ </sup>NKT-like cells generated from <italic>in vitro </italic>culture possess potent immune suppressive activity. Gene and protein expression studies using a variety of techniques, including microarray analysis, RT-PCR, ELISA and FACS analyses as well as functional characterization using knockout mice, demonstrate the involvement of two key molecular pathways, IL-10 and IFN-γ, in the function of these potent suppressor T cells. Our culture system and the molecular information provide a valuable platform for the further dissection of the molecular and functional pathways implicated in the conversion and function of suppressor T cells.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Global gene expression profiling of <italic>in vitro</italic> cultured CD8<sup>+ </sup>T cells that express natural killer cell markers revealed differential expression of about 3,000 genes between these cells and naïve CD8<sup>+ </sup>T cells.</p>", "<title>Background</title>", "<p>CD8<sup>+ </sup>NKT-like cells are naturally occurring but rare T cells that express both T cell and natural killer cell markers. These cells may play key roles in establishing tolerance to self-antigens; however, their mechanism of action and molecular profiles are poorly characterized due to their low frequencies. We developed an efficient <italic>in vitro </italic>protocol to produce CD8<sup>+ </sup>T cells that express natural killer cell markers (CD8<sup>+ </sup>NKT-like cells) and extensively characterized their functional and molecular phenotypes using a variety of techniques.</p>", "<title>Results</title>", "<p>Large numbers of CD8<sup>+ </sup>NKT-like cells were obtained through culture of naïve CD8<sup>+ </sup>T cells using anti-CD3/anti-CD28-coated beads and high dose IL-2. These cells possess potent activity in suppressing the proliferation of naïve responder T cells. Gene expression profiling suggests that the cultured CD8<sup>+ </sup>NKT-like cells and the naïve CD8<sup>+ </sup>T cells differ by more than 2-fold for about 3,000 genes, among which 314 are upregulated by more than 5-fold and 113 are upregulated by more than 10-fold in the CD8<sup>+ </sup>NKT-like cells. A large proportion of the highly upregulated genes are soluble factors or surface markers that have previously been implicated in immune suppression or are likely to possess immunosuppressive properties. Many of these genes are regulated by two key cytokines, IL-10 and IFN-γ. The immunosuppressive activities of cells cultured from IL-10<sup>-/- </sup>and IFN-γ<sup>-/- </sup>mice are reduced by about 70% and about 50%, respectively, compared to wild-type mice.</p>", "<title>Conclusion</title>", "<p>Immunosuppressive CD8<sup>+ </sup>NKT-like cells can be efficiently produced and their immunosuppressive activity is related to many surface and soluble molecules regulated by IL-10 and IFN-γ.</p>" ]
[ "<title>Abbreviations</title>", "<p>APC, antigen presenting cell; CFSE, carboxyfluorescein succinimidyl ester; ds-cDNA, double-stranded cDNA; ELISA, enzyme-linked immunosorbent assay; FACS, fluorescence-activated cell sorting; FDR, false discovery rate; Fgl, Fibrinogen-like protein; Gal, Galectin; Ifitm, Interferon induced transmembrane protein; IFN, interferon; IL, interleukin; MHC, major histocompatibility complex; NK, natural killer; NKT, natural killer T cell; Opg, Osteoprotegerin; RANK, Receptor activator for nuclear factor κB; RANKL, RANK ligand; TCR, T cell receptor; TF, transcription factor; TGF, transforming growth factor; Tn, naïve T; TNF, tumor necrosis factor; Treg, regulatory T.</p>", "<title>Authors' contributions</title>", "<p>LZ performed most experiments and drafted the manuscript. HW carried out most RT-PCR analyses. XZ participated in the cellular studies. YJ performed RT-PCR analyses. QM participated in experimental design and data interpretation. RAM participated in data analysis and interpretation. NG and RP performed statistical analysis. J-XS participated in experimental design, data analysis and interpretation, writing and editing of the manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available. Additional data file ##SUPPL##0##1## contains two figures showing that anti-TGF-β cannot block the suppression function of CD8<sup>+ </sup>NKT-like cells and that IFN-γ restores the viability of <italic>in vitro </italic>cultured CD8<sup>+ </sup>NKT-like cells from IFN-γ<sup>-/- </sup>mouse.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>The study was partially supported by a grant from the National Institutes of Health (2P01 AI-42288) to JXS. Ashok Sharma performed pathway analysis.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Surface marker expression of cultured CD8<sup>+ </sup>T cells. The expression profiles of CD8, CD4, CD25, CD62L, CD69, CD122, GITR and CTLA-4 were analyzed by flow cytometory in the tenth day of culture for CD8<sup>+ </sup>T cells.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Cultured CD8<sup>+ </sup>T cells suppress naïve T cell proliferation. <bold>(a) </bold>Dose-dependent suppression of CD4<sup>+</sup>CD25<sup>- </sup>responder T cells by cultured CD8<sup>+ </sup>T cells. CFSE-labeled CD4<sup>+</sup>CD25<sup>- </sup>naïve T cells (Tn) isolated from B6 spleens were stimulated with anti-CD3 (1.5 μg/ml) in the presence of irradiated splenic APCs with graded numbers of cultured CD8<sup>+ </sup>T cells (Tr). After 72 h in the culture, CFSE dilution in the responder CD4<sup>+ </sup>T cells was analyzed by flow cytometry. T cells in the M2 zone are undivided cells and T cells in the M3 zone with lower CFSE are divided cells. Data are representative of five independent experiments. <bold>(b) </bold>Naïve CD4<sup>+</sup>CD25<sup>- </sup>splenic T cells were cultured in the same condition as shown in (a). The cultures were pulsed with 1 μCi/well [<sup>3</sup>H]thymidine at 72 h and the level of proliferation was assessed by [<sup>3</sup>H]thymidine incorporation in the last 16 h of culture. <bold>(c) </bold>Cultured CD8<sup>+ </sup>T cells (NKT-like), freshly isolated CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells and cultured CD4<sup>+ </sup>cells (cCD4) were compared for their ability to suppress the proliferation of CD4<sup>+</sup>CD25<sup>- </sup>responder T cells. Data are presented as percentage of suppression based on the CFSE dilution with standard deviation. ANOVA test suggests that the suppressive ability is significantly different between these cells (<italic>p </italic>&lt; 10<sup>-6</sup>).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Heat map for genes differentially expressed among the four groups of T cells. Only those genes with a FDR (q) ≤0.01 and fold change ≥5 are included in this map. Data for each gene are standardized separately before being plotted, as is standard in drawing heat maps, so that all genes have a similar scale and the relative differences for all genes can be visualized on a single plot.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>RT-PCR analysis of selected genes in four cell groups. Quantitative RT-PCR was performed in duplicate using cDNA (equivalent of 10 ng total RNA) and already-developed TaqMan gene expression assays (Applied Biosystems) on the ABI 7900 HT Fast Real-Time PCR System. Data were normalized based on 18srRNA and GAPDH expression. The mean expression level for naïve CD8<sup>+</sup> T cells was artificially scaled to one for each tested gene. Data are presented as mean ± standard deviation.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Expression of NK cell markers. <bold>(a) </bold>Summary of microarray data for NK cell markers. Ratios of expression values and FDR (q) values are presented. <bold>(b) </bold>NK cell marker expression on the surface of cultured CD8<sup>+ </sup>NKT-like cells.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Molecular network for the highly upregulated immunity and defense genes. The network was created by extracting the direct interactions between these genes from the literature. Three types of relationship are shown in the pathway, binding, expression and regulation. Binding refers to physical interactions between molecules. Expression indicates that the regulator changes the protein level of the target by means of regulating its gene expression or protein stability. Regulation indicates that the regulator changes the activity of the target; the mechanism of the regulation is either unknown or has not been specified in the sentence describing the relationship. This network highlights the importance of two key nodes, IFN-γ and IL-10, which regulate many genes in this network. These genes are also critical for the immunosuppressive function of the CD8<sup>+</sup> NKT-like cells.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>The role of IL-10 and IFN-γ in the generation and function of the CD8<sup>+ </sup>NKT-like cells. <bold>(a) </bold>Cytokine levels in the cell culture media. Cultured CD8<sup>+ </sup>NKT-like cells and freshly isolated CD4<sup>+</sup>CD25<sup>+ </sup>Treg cells were stimulated with anti-CD3 (1.5 μg/ml) and splenic APCs. At 72 h of culturing, the culture supernatant was saved and used for measuring IL-10, IL-4 and IFN-γ using ELISA. Results are representative of two independent experiments. <bold>(b) </bold>Suppression activity of CD8<sup>+ </sup>NKT-like cells cultured from IFN-γ<sup>-/- </sup>and IL-10<sup>-/- </sup>mice. CD8<sup>+ </sup>NKT-like cells (Tr) cultured from knockout mice and wild-type B6 (WT) mice were co-cultured with naïve CD4<sup>+</sup>CD25<sup>- </sup>responder T cells (Tn) at different Tr/Tn ratios in the presence of splenic APCs and anti-CD3. The cultures were pulsed with 1 μCi/well of [<sup>3</sup>H]thymidine at 72 h and proliferation (cpm) was measured by [<sup>3</sup>H]thymidine incorporation in the last 16 h. Results are expressed as the mean of triplicate cultures. ANOVA <italic>p</italic>-values are &lt;0.0004 for IFN-γ<sup>-/- </sup>and 0.001 for IL-10<sup>-/- </sup>when compared to wild-type mice. Error bars are standard deviation.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Summary of differentially expressed genes*</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Fold change</td><td align=\"center\">NKT/nCD8</td><td align=\"center\">cCD4/nCD8</td><td align=\"center\">aCD8/nCD8</td><td align=\"center\">NKT/cCD4</td><td align=\"center\">NKT/aCD8</td><td align=\"center\">cCD4/aCD8</td></tr></thead><tbody><tr><td align=\"left\">&gt;10 fold up</td><td align=\"center\">113</td><td align=\"center\">126</td><td align=\"center\">40</td><td align=\"center\">26</td><td align=\"center\">64</td><td align=\"center\">36</td></tr><tr><td align=\"left\">5-10 fold up</td><td align=\"center\">201</td><td align=\"center\">300</td><td align=\"center\">27</td><td align=\"center\">46</td><td align=\"center\">67</td><td align=\"center\">47</td></tr><tr><td align=\"left\">2-5 fold up</td><td align=\"center\">1,742</td><td align=\"center\">1,647</td><td align=\"center\">3</td><td align=\"center\">161</td><td align=\"center\">190</td><td align=\"center\">47</td></tr><tr><td align=\"left\">2-5 fold down</td><td align=\"center\">681</td><td align=\"center\">693</td><td align=\"center\">6</td><td align=\"center\">243</td><td align=\"center\">76</td><td align=\"center\">4</td></tr><tr><td align=\"left\">5-10 fold down</td><td align=\"center\">100</td><td align=\"center\">100</td><td align=\"center\">35</td><td align=\"center\">59</td><td align=\"center\">24</td><td align=\"center\">15</td></tr><tr><td align=\"left\">&gt;10 fold down</td><td align=\"center\">56</td><td align=\"center\">59</td><td align=\"center\">36</td><td align=\"center\">32</td><td align=\"center\">15</td><td align=\"center\">9</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Major biological processes modified in the cultured CD8<sup>+ </sup>NKT-like cells</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"4\">≥5 fold (314)</td><td align=\"center\" colspan=\"4\">≥10 fold (113)</td></tr><tr><td/><td colspan=\"4\"><hr/></td><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Biological process</td><td align=\"center\">Number of genes</td><td align=\"center\">% genes</td><td align=\"center\"><italic>p</italic>-value</td><td align=\"center\">OR</td><td align=\"center\">Number of genes</td><td align=\"center\">% genes</td><td align=\"center\"><italic>p</italic>-value</td><td align=\"center\">OR</td></tr></thead><tbody><tr><td align=\"left\">Immunity and defense</td><td align=\"center\">56</td><td align=\"center\">17.80%</td><td align=\"center\">2.0E-11</td><td align=\"center\">3.1</td><td align=\"center\">35</td><td align=\"center\">31.00%</td><td align=\"center\">7.7E-15</td><td align=\"center\">6.3</td></tr><tr><td align=\"left\">Apoptosis</td><td align=\"center\">25</td><td align=\"center\">8.00%</td><td align=\"center\">8.8E-10</td><td align=\"center\">4.8</td><td align=\"center\">19</td><td align=\"center\">16.80%</td><td align=\"center\">2.2E-13</td><td align=\"center\">11.3</td></tr><tr><td align=\"left\">Lipid, fatty acid metabolism</td><td align=\"center\">25</td><td align=\"center\">8.00%</td><td align=\"center\">7.5E-06</td><td align=\"center\">2.9</td><td align=\"center\">6</td><td align=\"center\">5.30%</td><td align=\"center\">0.1161</td><td align=\"center\">1.9</td></tr><tr><td align=\"left\">Signal transduction</td><td align=\"center\">74</td><td align=\"center\">23.60%</td><td align=\"center\">3.4E-05</td><td align=\"center\">1.8</td><td align=\"center\">28</td><td align=\"center\">24.80%</td><td align=\"center\">0.0041</td><td align=\"center\">1.9</td></tr><tr><td align=\"left\">Cell structure and motility</td><td align=\"center\">25</td><td align=\"center\">8.00%</td><td align=\"center\">0.0004</td><td align=\"center\">2.2</td><td align=\"center\">11</td><td align=\"center\">9.70%</td><td align=\"center\">0.0036</td><td align=\"center\">2.8</td></tr><tr><td align=\"left\">Cell cycle</td><td align=\"center\">23</td><td align=\"center\">7.30%</td><td align=\"center\">0.0004</td><td align=\"center\">2.3</td><td align=\"center\">11</td><td align=\"center\">9.70%</td><td align=\"center\">0.0015</td><td align=\"center\">3.1</td></tr><tr><td align=\"left\">Oncogenesis</td><td align=\"center\">14</td><td align=\"center\">4.50%</td><td align=\"center\">0.0005</td><td align=\"center\">3.0</td><td align=\"center\">8</td><td align=\"center\">7.10%</td><td align=\"center\">0.0004</td><td align=\"center\">4.9</td></tr><tr><td align=\"left\">Protein metabolism and modification</td><td align=\"center\">60</td><td align=\"center\">19.10%</td><td align=\"center\">0.0009</td><td align=\"center\">1.6</td><td align=\"center\">23</td><td align=\"center\">20.40%</td><td align=\"center\">0.0152</td><td align=\"center\">1.8</td></tr><tr><td align=\"left\">Cell proliferation and differentiation</td><td align=\"center\">21</td><td align=\"center\">6.70%</td><td align=\"center\">0.0023</td><td align=\"center\">2.1</td><td align=\"center\">13</td><td align=\"center\">11.50%</td><td align=\"center\">0.0001</td><td align=\"center\">3.8</td></tr><tr><td align=\"left\">Carbohydrate metabolism</td><td align=\"center\">14</td><td align=\"center\">4.50%</td><td align=\"center\">0.0053</td><td align=\"center\">2.3</td><td align=\"center\">2</td><td align=\"center\">1.80%</td><td align=\"center\">0.6720</td><td align=\"center\">0.9</td></tr><tr><td align=\"left\">Sulfur metabolism</td><td align=\"center\">5</td><td align=\"center\">1.60%</td><td align=\"center\">0.0059</td><td align=\"center\">4.6</td><td align=\"center\">0</td><td/><td/><td/></tr><tr><td align=\"left\">Other metabolism</td><td align=\"center\">13</td><td align=\"center\">4.10%</td><td align=\"center\">0.0159</td><td align=\"center\">2.0</td><td align=\"center\">6</td><td align=\"center\">5.30%</td><td align=\"center\">0.0319</td><td align=\"center\">2.6</td></tr><tr><td align=\"left\">Cell adhesion</td><td align=\"center\">12</td><td align=\"center\">3.80%</td><td align=\"center\">0.0528</td><td align=\"center\">1.7</td><td align=\"center\">6</td><td align=\"center\">5.30%</td><td align=\"center\">0.0424</td><td align=\"center\">2.5</td></tr><tr><td align=\"left\">Others</td><td align=\"center\">176</td><td align=\"center\">56.10%</td><td/><td/><td align=\"center\">53</td><td align=\"center\">24.80%</td><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Transcription factors differentially expressed in CD8<sup>+ </sup>NKT-like cells</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Symbol</td><td align=\"left\">Function</td><td align=\"center\">NKT/nCD8</td><td align=\"center\">CD4/nCD8</td><td align=\"center\">aCD8/nCD8</td><td align=\"center\">NKT/nCD8 (q)</td><td align=\"center\">CD4/nCD8 (q)</td><td align=\"center\">aCD8/nCD8 (q)</td></tr></thead><tbody><tr><td align=\"left\">Nfil3</td><td align=\"left\">NF</td><td align=\"center\">19.7</td><td align=\"center\">22.3</td><td align=\"center\">4.3</td><td align=\"center\">4.0E-04</td><td align=\"center\">3.6E-04</td><td align=\"center\">3.4E-02</td></tr><tr><td align=\"left\">Mycn (Nmyc1)</td><td align=\"left\">TF</td><td align=\"center\">20.8</td><td align=\"center\">2.2</td><td align=\"center\">1.1</td><td align=\"center\">8.0E-05</td><td align=\"center\">2.7E-02</td><td align=\"center\">6.8E-01</td></tr><tr><td align=\"left\">Irf8 (Icsbp1)</td><td align=\"left\">TF</td><td align=\"center\">14.4</td><td align=\"center\">1.9</td><td align=\"center\">19.2</td><td align=\"center\">1.7E-04</td><td align=\"center\">3.2E-02</td><td align=\"center\">3.7E-03</td></tr><tr><td align=\"left\">Irf4</td><td align=\"left\">TF</td><td align=\"center\">4.7</td><td align=\"center\">24.7</td><td align=\"center\">18.2</td><td align=\"center\">1.6E-03</td><td align=\"center\">4.7E-05</td><td align=\"center\">6.2E-03</td></tr><tr><td align=\"left\">Litaf</td><td align=\"left\">TF</td><td align=\"center\">10.3</td><td align=\"center\">5.0</td><td align=\"center\">4.8</td><td align=\"center\">4.5E-05</td><td align=\"center\">1.4E-04</td><td align=\"center\">1.6E-02</td></tr><tr><td align=\"left\">Runx2</td><td align=\"left\">TF</td><td align=\"center\">8.6</td><td align=\"center\">3.5</td><td align=\"center\">1.8</td><td align=\"center\">2.2E-04</td><td align=\"center\">5.2E-02</td><td align=\"center\">3.4E-01</td></tr><tr><td align=\"left\">Pbx3</td><td align=\"left\">TF</td><td align=\"center\">5.8</td><td align=\"center\">6.2</td><td align=\"center\">1.6</td><td align=\"center\">4.1E-04</td><td align=\"center\">4.8E-04</td><td align=\"center\">3.6E-01</td></tr><tr><td align=\"left\">Jun (AP1)</td><td align=\"left\">TF</td><td align=\"center\">5.8</td><td align=\"center\">2.9</td><td align=\"center\">2.6</td><td align=\"center\">8.9E-04</td><td align=\"center\">8.8E-02</td><td align=\"center\">1.2E-01</td></tr><tr><td align=\"left\">Cgrrf1</td><td align=\"left\">TF</td><td align=\"center\">4.9</td><td align=\"center\">4.9</td><td align=\"center\">2.8</td><td align=\"center\">2.2E-04</td><td align=\"center\">2.7E-03</td><td align=\"center\">7.7E-02</td></tr><tr><td align=\"left\">Eomes</td><td align=\"left\">TF</td><td align=\"center\">4.0</td><td align=\"center\">0.2</td><td align=\"center\">0.6</td><td align=\"center\">9.7E-04</td><td align=\"center\">3.8E-04</td><td align=\"center\">4.5E-01</td></tr><tr><td align=\"left\">Atf4</td><td align=\"left\">TF</td><td align=\"center\">3.8</td><td align=\"center\">2.0</td><td align=\"center\">3.1</td><td align=\"center\">6.4E-03</td><td align=\"center\">3.1E-03</td><td align=\"center\">2.0E-02</td></tr><tr><td align=\"left\">Zbtb32 (Rog)</td><td align=\"left\">TF (ZF)</td><td align=\"center\">9.3</td><td align=\"center\">1.8</td><td align=\"center\">3.9</td><td align=\"center\">1.2E-03</td><td align=\"center\">3.9E-02</td><td align=\"center\">1.6E-02</td></tr><tr><td align=\"left\">Zdhhc2</td><td align=\"left\">TF (ZF)</td><td align=\"center\">4.9</td><td align=\"center\">2.6</td><td align=\"center\">1.5</td><td align=\"center\">9.3E-04</td><td align=\"center\">1.5E-03</td><td align=\"center\">4.6E-01</td></tr><tr><td align=\"left\">Zfp313</td><td align=\"left\">TF (ZF)</td><td align=\"center\">4.3</td><td align=\"center\">2.2</td><td align=\"center\">1.2</td><td align=\"center\">1.9E-02</td><td align=\"center\">6.6E-03</td><td align=\"center\">6.8E-01</td></tr><tr><td align=\"left\">Zfp608</td><td align=\"left\">TF (ZF)</td><td align=\"center\">3.9</td><td align=\"center\">1.2</td><td align=\"center\">1.7</td><td align=\"center\">4.2E-04</td><td align=\"center\">1.1E-01</td><td align=\"center\">1.3E-01</td></tr><tr><td align=\"left\">Rnf128</td><td align=\"left\">TF (RF)</td><td align=\"center\">6.2</td><td align=\"center\">10.4</td><td align=\"center\">1.0</td><td align=\"center\">5.8E-04</td><td align=\"center\">7.8E-04</td><td align=\"center\">8.0E-01</td></tr><tr><td align=\"left\">Rnf13</td><td align=\"left\">TF (RF)</td><td align=\"center\">4.0</td><td align=\"center\">1.7</td><td align=\"center\">1.2</td><td align=\"center\">8.5E-04</td><td align=\"center\">7.3E-02</td><td align=\"center\">7.0E-01</td></tr><tr><td align=\"left\">Socs2</td><td align=\"left\">Suppressor</td><td align=\"center\">48.6</td><td align=\"center\">104.0</td><td align=\"center\">27.8</td><td align=\"center\">3.5E-05</td><td align=\"center\">3.3E-05</td><td align=\"center\">3.9E-03</td></tr><tr><td align=\"left\">Cish (Socs)</td><td align=\"left\">Suppressor</td><td align=\"center\">8.8</td><td align=\"center\">9.5</td><td align=\"center\">4.9</td><td align=\"center\">1.2E-04</td><td align=\"center\">5.2E-05</td><td align=\"center\">1.3E-02</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Tcf7</td><td align=\"left\">TF</td><td align=\"center\">0.012</td><td align=\"center\">0.061</td><td align=\"center\">0.274</td><td align=\"center\">5.7E-07</td><td align=\"center\">1.4E-03</td><td align=\"center\">7.7E-02</td></tr><tr><td align=\"left\">Klf3</td><td align=\"left\">TF (KR)</td><td align=\"center\">0.012</td><td align=\"center\">0.014</td><td align=\"center\">0.034</td><td align=\"center\">9.4E-08</td><td align=\"center\">5.9E-06</td><td align=\"center\">8.1E-03</td></tr><tr><td align=\"left\">Klf2</td><td align=\"left\">TF (KR)</td><td align=\"center\">0.04</td><td align=\"center\">0.03</td><td align=\"center\">0.01</td><td align=\"center\">4.3E-04</td><td align=\"center\">5.5E-04</td><td align=\"center\">2.7E-03</td></tr><tr><td align=\"left\">Klf1</td><td align=\"left\">TF (KR)</td><td align=\"center\">0.05</td><td align=\"center\">0.05</td><td align=\"center\">0.06</td><td align=\"center\">3.5E-05</td><td align=\"center\">1.7E-04</td><td align=\"center\">1.0E-02</td></tr><tr><td align=\"left\">Rkhd3</td><td align=\"left\">TF (RF)</td><td align=\"center\">0.15</td><td align=\"center\">0.17</td><td align=\"center\">0.16</td><td align=\"center\">1.1E-04</td><td align=\"center\">1.7E-04</td><td align=\"center\">9.1E-03</td></tr><tr><td align=\"left\">Bcl11a</td><td align=\"left\">TF (ZF)</td><td align=\"center\">0.15</td><td align=\"center\">0.16</td><td align=\"center\">0.15</td><td align=\"center\">2.4E-04</td><td align=\"center\">1.1E-03</td><td align=\"center\">2.3E-02</td></tr><tr><td align=\"left\">Zbtb20</td><td align=\"left\">TF (ZF)</td><td align=\"center\">0.20</td><td align=\"center\">0.15</td><td align=\"center\">0.16</td><td align=\"center\">1.2E-04</td><td align=\"center\">3.2E-04</td><td align=\"center\">6.3E-03</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Expression of genes encoding secreted molecules with potential suppressive function</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Symbol</td><td align=\"left\">Function</td><td align=\"center\">NKT/nCD8</td><td align=\"center\">CD4/nCD8</td><td align=\"center\">aCD8/nCD8</td><td align=\"center\">NKT/nCD8 (q)</td><td align=\"center\">CD4/nCD8 (q)</td><td align=\"center\">aCD8/nCD8 (q)</td></tr></thead><tbody><tr><td align=\"left\">Spp1</td><td align=\"left\">Suppression</td><td align=\"center\">251.4</td><td align=\"center\">203.8</td><td align=\"center\">7.4</td><td align=\"center\">9.9E-06</td><td align=\"center\">2.6E-06</td><td align=\"center\">1.2E-02</td></tr><tr><td align=\"left\">Lgals3 (Gal3)</td><td align=\"left\">Suppression</td><td align=\"center\">87.0</td><td align=\"center\">38.5</td><td align=\"center\">3.1</td><td align=\"center\">2.6E-07</td><td align=\"center\">1.7E-04</td><td align=\"center\">4.9E-02</td></tr><tr><td align=\"left\">Esm1</td><td align=\"left\">Suppression</td><td align=\"center\">74.5</td><td align=\"center\">2.8</td><td align=\"center\">1.2</td><td align=\"center\">4.2E-04</td><td align=\"center\">2.2E-02</td><td align=\"center\">5.8E-01</td></tr><tr><td align=\"left\">Fgl2</td><td align=\"left\">Suppression</td><td align=\"center\">32.7</td><td align=\"center\">0.9</td><td align=\"center\">0.9</td><td align=\"center\">9.2E-05</td><td align=\"center\">3.0E-01</td><td align=\"center\">6.3E-01</td></tr><tr><td align=\"left\">Tnfrsf11b (Opg)</td><td align=\"left\">Suppression</td><td align=\"center\">29.0</td><td align=\"center\">1.1</td><td align=\"center\">1.1</td><td align=\"center\">2.4E-04</td><td align=\"center\">3.0E-01</td><td align=\"center\">7.0E-01</td></tr><tr><td align=\"left\">Lgals1 (Gal1)</td><td align=\"left\">Suppression</td><td align=\"center\">27.1</td><td align=\"center\">22.3</td><td align=\"center\">6.0</td><td align=\"center\">1.4E-04</td><td align=\"center\">7.8E-04</td><td align=\"center\">6.1E-02</td></tr><tr><td align=\"left\">Gzmd</td><td align=\"left\">Killing</td><td align=\"center\">834.8</td><td align=\"center\">25.8</td><td align=\"center\">1.1</td><td align=\"center\">2.1E-07</td><td align=\"center\">1.0E-02</td><td align=\"center\">6.6E-01</td></tr><tr><td align=\"left\">Gzme</td><td align=\"left\">Killing</td><td align=\"center\">524.9</td><td align=\"center\">27.3</td><td align=\"center\">0.9</td><td align=\"center\">2.3E-06</td><td align=\"center\">6.8E-03</td><td align=\"center\">6.4E-01</td></tr><tr><td align=\"left\">Gzmc</td><td align=\"left\">Killing</td><td align=\"center\">446.4</td><td align=\"center\">25.7</td><td align=\"center\">2.1</td><td align=\"center\">2.7E-07</td><td align=\"center\">3.3E-02</td><td align=\"center\">1.8E-01</td></tr><tr><td align=\"left\">Gzmg</td><td align=\"left\">Killing</td><td align=\"center\">328.8</td><td align=\"center\">5.5</td><td align=\"center\">1.0</td><td align=\"center\">1.2E-05</td><td align=\"center\">1.8E-02</td><td align=\"center\">8.0E-01</td></tr><tr><td align=\"left\">Gzmb</td><td align=\"left\">Killing</td><td align=\"center\">104.6</td><td align=\"center\">60.2</td><td align=\"center\">85.6</td><td align=\"center\">2.2E-06</td><td align=\"center\">2.4E-04</td><td align=\"center\">2.8E-03</td></tr><tr><td align=\"left\">Gzmf</td><td align=\"left\">Killing</td><td align=\"center\">63.5</td><td align=\"center\">1.8</td><td align=\"center\">1.3</td><td align=\"center\">6.9E-04</td><td align=\"center\">9.4E-03</td><td align=\"center\">4.1E-01</td></tr><tr><td align=\"left\">Gzma</td><td align=\"left\">Killing</td><td align=\"center\">61.5</td><td align=\"center\">17.1</td><td align=\"center\">0.4</td><td align=\"center\">1.2E-05</td><td align=\"center\">6.8E-03</td><td align=\"center\">1.2E-01</td></tr><tr><td align=\"left\">Prf1</td><td align=\"left\">Killing</td><td align=\"center\">29.0</td><td align=\"center\">1.0</td><td align=\"center\">1.7</td><td align=\"center\">4.4E-04</td><td align=\"center\">4.1E-01</td><td align=\"center\">2.8E-01</td></tr><tr><td align=\"left\">Gzmk</td><td align=\"left\">Killing</td><td align=\"center\">23.3</td><td align=\"center\">0.5</td><td align=\"center\">0.6</td><td align=\"center\">1.5E-04</td><td align=\"center\">1.7E-02</td><td align=\"center\">2.7E-01</td></tr><tr><td align=\"left\">Ifng</td><td align=\"left\">Cytokine</td><td align=\"center\">51.2</td><td align=\"center\">50.3</td><td align=\"center\">258.7</td><td align=\"center\">3.3E-03</td><td align=\"center\">1.5E-02</td><td align=\"center\">8.2E-04</td></tr><tr><td align=\"left\">Il10</td><td align=\"left\">Cytokine</td><td align=\"center\">47.2</td><td align=\"center\">122.8</td><td align=\"center\">2.1</td><td align=\"center\">1.8E-03</td><td align=\"center\">1.6E-05</td><td align=\"center\">4.0E-02</td></tr><tr><td align=\"left\">Il24</td><td align=\"left\">Cytokine</td><td align=\"center\">5.5</td><td align=\"center\">33.0</td><td align=\"center\">1.0</td><td align=\"center\">5.1E-02</td><td align=\"center\">3.5E-04</td><td align=\"center\">8.1E-01</td></tr><tr><td align=\"left\">Lta</td><td align=\"left\">Cytokine</td><td align=\"center\">3.9</td><td align=\"center\">5.1</td><td align=\"center\">27.8</td><td align=\"center\">3.1E-03</td><td align=\"center\">2.2E-04</td><td align=\"center\">2.7E-03</td></tr><tr><td align=\"left\">Ccl3</td><td align=\"left\">Chemokine</td><td align=\"center\">86.2</td><td align=\"center\">162.4</td><td align=\"center\">177.9</td><td align=\"center\">3.4E-04</td><td align=\"center\">1.2E-03</td><td align=\"center\">1.3E-03</td></tr><tr><td align=\"left\">Ccl4</td><td align=\"left\">Chemokine</td><td align=\"center\">25.5</td><td align=\"center\">20.8</td><td align=\"center\">18.7</td><td align=\"center\">6.6E-04</td><td align=\"center\">3.5E-03</td><td align=\"center\">4.1E-03</td></tr><tr><td align=\"left\">Ccl9</td><td align=\"left\">Chemokine</td><td align=\"center\">11.3</td><td align=\"center\">53.7</td><td align=\"center\">4.4</td><td align=\"center\">1.5E-02</td><td align=\"center\">5.2E-04</td><td align=\"center\">4.5E-02</td></tr><tr><td align=\"left\">Cklfsf7</td><td align=\"left\">Chemokine</td><td align=\"center\">4.7</td><td align=\"center\">1.2</td><td align=\"center\">0.9</td><td align=\"center\">3.4E-03</td><td align=\"center\">1.8E-01</td><td align=\"center\">7.1E-01</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Gene expression levels for genes encoding critical surface markers</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Symbol</td><td align=\"left\">Function</td><td align=\"center\">NKT/nCD8</td><td align=\"center\">CD4/nCD8</td><td align=\"center\">aCD8/nCD8</td><td align=\"center\">NKT/nCD8 (q)</td><td align=\"center\">CD4/nCD8 (q)</td><td align=\"center\">aCD8/nCD8 (q)</td></tr></thead><tbody><tr><td align=\"left\">Ifitm1</td><td align=\"left\">Suppression</td><td align=\"center\">89.9</td><td align=\"center\">7.1</td><td align=\"center\">1.5</td><td align=\"center\">2.8E-07</td><td align=\"center\">2.2E-03</td><td align=\"center\">2.4E-01</td></tr><tr><td align=\"left\">Lilrb4 (ILT3)</td><td align=\"left\">Suppression</td><td align=\"center\">50.3</td><td align=\"center\">43.8</td><td align=\"center\">7.6</td><td align=\"center\">3.6E-04</td><td align=\"center\">4.0E-05</td><td align=\"center\">5.6E-03</td></tr><tr><td align=\"left\">Ifitm2</td><td align=\"left\">Suppression</td><td align=\"center\">45.6</td><td align=\"center\">11.4</td><td align=\"center\">1.6</td><td align=\"center\">6.0E-07</td><td align=\"center\">2.1E-03</td><td align=\"center\">2.8E-01</td></tr><tr><td align=\"left\">Havcr2 (Tim3)</td><td align=\"left\">Suppression</td><td align=\"center\">36.5</td><td align=\"center\">10.4</td><td align=\"center\">0.7</td><td align=\"center\">1.6E-04</td><td align=\"center\">2.0E-03</td><td align=\"center\">2.4E-01</td></tr><tr><td align=\"left\">Tnfrsf9 (4-1BB)</td><td align=\"left\">Suppression</td><td align=\"center\">28.2</td><td align=\"center\">12.5</td><td align=\"center\">12.8</td><td align=\"center\">2.4E-04</td><td align=\"center\">3.2E-03</td><td align=\"center\">1.1E-02</td></tr><tr><td align=\"left\">Tnfsf6 (FASL)</td><td align=\"left\">Suppression</td><td align=\"center\">24.4</td><td align=\"center\">4.3</td><td align=\"center\">3.2</td><td align=\"center\">1.2E-05</td><td align=\"center\">6.1E-02</td><td align=\"center\">4.5E-02</td></tr><tr><td align=\"left\">Ifitm3</td><td align=\"left\">Suppression</td><td align=\"center\">24.3</td><td align=\"center\">15.6</td><td align=\"center\">0.8</td><td align=\"center\">2.5E-05</td><td align=\"center\">1.4E-03</td><td align=\"center\">6.6E-01</td></tr><tr><td align=\"left\">Ctla4</td><td align=\"left\">Suppression</td><td align=\"center\">5.3</td><td align=\"center\">18.7</td><td align=\"center\">21.5</td><td align=\"center\">2.8E-03</td><td align=\"center\">4.8E-03</td><td align=\"center\">2.7E-03</td></tr><tr><td align=\"left\">Tnfrsf18 (GITR)</td><td align=\"left\">Suppression</td><td align=\"center\">4.8</td><td align=\"center\">11.1</td><td align=\"center\">4.4</td><td align=\"center\">2.7E-03</td><td align=\"center\">4.3E-05</td><td align=\"center\">1.7E-02</td></tr><tr><td align=\"left\">Pdcd1lg2</td><td align=\"left\">Suppression</td><td align=\"center\">4.6</td><td align=\"center\">7.3</td><td align=\"center\">3.3</td><td align=\"center\">5.4E-04</td><td align=\"center\">2.5E-04</td><td align=\"center\">3.0E-02</td></tr><tr><td align=\"left\">Icos</td><td align=\"left\">Suppression</td><td align=\"center\">4.6</td><td align=\"center\">4.0</td><td align=\"center\">3.6</td><td align=\"center\">2.8E-03</td><td align=\"center\">3.5E-03</td><td align=\"center\">3.2E-02</td></tr><tr><td align=\"left\">Tnfsf11 (RANKL)</td><td align=\"left\">Suppression</td><td align=\"center\">3.9</td><td align=\"center\">8.1</td><td align=\"center\">27.0</td><td align=\"center\">7.6E-03</td><td align=\"center\">2.4E-03</td><td align=\"center\">3.4E-03</td></tr><tr><td align=\"left\">Tnfsf10 (TRAIL)</td><td align=\"left\">Suppression</td><td align=\"center\">3.1</td><td align=\"center\">3.2</td><td align=\"center\">1.8</td><td align=\"center\">2.7E-03</td><td align=\"center\">9.5E-03</td><td align=\"center\">6.6E-02</td></tr><tr><td align=\"left\">Tnfrsf4 (OX40)</td><td align=\"left\">Suppression</td><td align=\"center\">3.0</td><td align=\"center\">23.8</td><td align=\"center\">11.3</td><td align=\"center\">1.6E-02</td><td align=\"center\">7.6E-06</td><td align=\"center\">5.7E-03</td></tr><tr><td align=\"left\">P2ry14 (Gpr105)</td><td align=\"left\">Receptor</td><td align=\"center\">65.4</td><td align=\"center\">6.9</td><td align=\"center\">1.1</td><td align=\"center\">2.1E-05</td><td align=\"center\">2.3E-03</td><td align=\"center\">6.8E-01</td></tr><tr><td align=\"left\">Fcer1g</td><td align=\"left\">Receptor</td><td align=\"center\">12.7</td><td align=\"center\">0.7</td><td align=\"center\">0.7</td><td align=\"center\">7.4E-04</td><td align=\"center\">1.4E-02</td><td align=\"center\">2.2E-01</td></tr><tr><td align=\"left\">Ptger3</td><td align=\"left\">Receptor</td><td align=\"center\">12.0</td><td align=\"center\">1.2</td><td align=\"center\">1.1</td><td align=\"center\">3.5E-04</td><td align=\"center\">2.3E-01</td><td align=\"center\">6.9E-01</td></tr><tr><td align=\"left\">Il12rb1</td><td align=\"left\">Receptor</td><td align=\"center\">10.6</td><td align=\"center\">11.3</td><td align=\"center\">4.0</td><td align=\"center\">8.9E-04</td><td align=\"center\">2.7E-05</td><td align=\"center\">1.8E-02</td></tr><tr><td align=\"left\">Ltb4r1</td><td align=\"left\">Receptor</td><td align=\"center\">8.8</td><td align=\"center\">1.2</td><td align=\"center\">0.9</td><td align=\"center\">2.9E-03</td><td align=\"center\">1.8E-01</td><td align=\"center\">7.0E-01</td></tr><tr><td align=\"left\">Gabarapl1</td><td align=\"left\">Receptor</td><td align=\"center\">8.7</td><td align=\"center\">5.3</td><td align=\"center\">3.4</td><td align=\"center\">2.4E-04</td><td align=\"center\">1.8E-03</td><td align=\"center\">1.4E-01</td></tr><tr><td align=\"left\">Ly6a</td><td align=\"left\">Receptor</td><td align=\"center\">7.3</td><td align=\"center\">11.3</td><td align=\"center\">3.0</td><td align=\"center\">3.5E-04</td><td align=\"center\">1.7E-04</td><td align=\"center\">5.3E-02</td></tr><tr><td align=\"left\">Tcrg</td><td align=\"left\">Receptor</td><td align=\"center\">6.3</td><td align=\"center\">0.2</td><td align=\"center\">0.3</td><td align=\"center\">1.9E-03</td><td align=\"center\">1.4E-03</td><td align=\"center\">6.9E-02</td></tr><tr><td align=\"left\">Il12rb2</td><td align=\"left\">Receptor</td><td align=\"center\">5.8</td><td align=\"center\">18.2</td><td align=\"center\">6.2</td><td align=\"center\">1.1E-04</td><td align=\"center\">1.6E-04</td><td align=\"center\">1.0E-02</td></tr><tr><td align=\"left\">Pilrb</td><td align=\"left\">Receptor</td><td align=\"center\">5.8</td><td align=\"center\">0.9</td><td align=\"center\">0.8</td><td align=\"center\">1.1E-03</td><td align=\"center\">1.5E-01</td><td align=\"center\">5.2E-01</td></tr><tr><td align=\"left\">Tmem2</td><td align=\"left\">Receptor</td><td align=\"center\">5.8</td><td align=\"center\">4.6</td><td align=\"center\">2.0</td><td align=\"center\">5.4E-03</td><td align=\"center\">8.5E-04</td><td align=\"center\">1.1E-01</td></tr><tr><td align=\"left\">Gpr171</td><td align=\"left\">Receptor</td><td align=\"center\">4.6</td><td align=\"center\">7.4</td><td align=\"center\">2.9</td><td align=\"center\">8.8E-03</td><td align=\"center\">1.3E-03</td><td align=\"center\">1.2E-01</td></tr><tr><td align=\"left\">Gpr34</td><td align=\"left\">Receptor</td><td align=\"center\">4.5</td><td align=\"center\">0.6</td><td align=\"center\">0.7</td><td align=\"center\">2.7E-02</td><td align=\"center\">1.1E-02</td><td align=\"center\">3.6E-01</td></tr><tr><td align=\"left\">Gpr160</td><td align=\"left\">Receptor</td><td align=\"center\">3.7</td><td align=\"center\">1.1</td><td align=\"center\">1.4</td><td align=\"center\">4.8E-03</td><td align=\"center\">3.0E-01</td><td align=\"center\">3.5E-01</td></tr><tr><td align=\"left\">Oprm1</td><td align=\"left\">Receptor</td><td align=\"center\">0.20</td><td align=\"center\">0.08</td><td align=\"center\">0.49</td><td align=\"center\">1.6E-03</td><td align=\"center\">3.6E-04</td><td align=\"center\">1.1E-01</td></tr><tr><td align=\"left\">Tlr1</td><td align=\"left\">Receptor</td><td align=\"center\">0.12</td><td align=\"center\">0.11</td><td align=\"center\">0.19</td><td align=\"center\">1.3E-04</td><td align=\"center\">3.5E-04</td><td align=\"center\">2.2E-02</td></tr><tr><td align=\"left\">Trat1</td><td align=\"left\">Receptor</td><td align=\"center\">0.12</td><td align=\"center\">0.15</td><td align=\"center\">0.25</td><td align=\"center\">1.3E-04</td><td align=\"center\">2.2E-03</td><td align=\"center\">4.3E-02</td></tr><tr><td align=\"left\">Edg1</td><td align=\"left\">Receptor</td><td align=\"center\">0.10</td><td align=\"center\">0.17</td><td align=\"center\">0.13</td><td align=\"center\">2.9E-04</td><td align=\"center\">1.0E-02</td><td align=\"center\">3.0E-02</td></tr><tr><td align=\"left\">Il2ra (CD25)</td><td align=\"left\">CR</td><td align=\"center\">48.4</td><td align=\"center\">66.9</td><td align=\"center\">28.1</td><td align=\"center\">1.7E-05</td><td align=\"center\">1.2E-05</td><td align=\"center\">2.7E-03</td></tr><tr><td align=\"left\">Il2rb (CD122)</td><td align=\"left\">CR</td><td align=\"center\">7.1</td><td align=\"center\">4.3</td><td align=\"center\">1.9</td><td align=\"center\">1.4E-03</td><td align=\"center\">1.4E-03</td><td align=\"center\">2.7E-01</td></tr><tr><td align=\"left\">Il7r</td><td align=\"left\">CR</td><td align=\"center\">0.12</td><td align=\"center\">0.17</td><td align=\"center\">0.05</td><td align=\"center\">3.4E-04</td><td align=\"center\">1.0E-04</td><td align=\"center\">4.9E-03</td></tr><tr><td align=\"left\">Il6ra</td><td align=\"left\">CR</td><td align=\"center\">0.11</td><td align=\"center\">0.26</td><td align=\"center\">0.16</td><td align=\"center\">6.3E-06</td><td align=\"center\">1.0E-02</td><td align=\"center\">9.2E-03</td></tr><tr><td align=\"left\">Il6st</td><td align=\"left\">CR</td><td align=\"center\">0.08</td><td align=\"center\">0.11</td><td align=\"center\">0.16</td><td align=\"center\">2.7E-07</td><td align=\"center\">3.7E-05</td><td align=\"center\">5.3E-03</td></tr><tr><td align=\"left\">Sema6d</td><td align=\"left\">Costimulation</td><td align=\"center\">6.1</td><td align=\"center\">0.9</td><td align=\"center\">1.4</td><td align=\"center\">2.1E-02</td><td align=\"center\">3.2E-01</td><td align=\"center\">2.5E-01</td></tr><tr><td align=\"left\">Pdcd1</td><td align=\"left\">Costimulation</td><td align=\"center\">4.5</td><td align=\"center\">4.0</td><td align=\"center\">4.6</td><td align=\"center\">8.3E-03</td><td align=\"center\">5.2E-03</td><td align=\"center\">1.7E-02</td></tr><tr><td align=\"left\">Ptger2</td><td align=\"left\">Costimulation</td><td align=\"center\">4.5</td><td align=\"center\">18.3</td><td align=\"center\">5.3</td><td align=\"center\">1.1E-04</td><td align=\"center\">6.8E-05</td><td align=\"center\">1.5E-02</td></tr><tr><td align=\"left\">Cd28</td><td align=\"left\">Costimulation</td><td align=\"center\">4.0</td><td align=\"center\">6.3</td><td align=\"center\">2.2</td><td align=\"center\">2.0E-03</td><td align=\"center\">2.6E-04</td><td align=\"center\">1.1E-01</td></tr><tr><td align=\"left\">Cd80</td><td align=\"left\">Costimulation</td><td align=\"center\">3.6</td><td align=\"center\">2.1</td><td align=\"center\">1.3</td><td align=\"center\">7.8E-03</td><td align=\"center\">5.3E-03</td><td align=\"center\">4.7E-01</td></tr><tr><td align=\"left\">Cd24a</td><td align=\"left\">Costimulation</td><td align=\"center\">0.03</td><td align=\"center\">0.82</td><td align=\"center\">0.78</td><td align=\"center\">4.2E-04</td><td align=\"center\">2.8E-01</td><td align=\"center\">6.4E-01</td></tr><tr><td align=\"left\">Ccr5</td><td align=\"left\">CCR</td><td align=\"center\">18.3</td><td align=\"center\">3.9</td><td align=\"center\">1.6</td><td align=\"center\">1.1E-05</td><td align=\"center\">4.3E-04</td><td align=\"center\">1.4E-01</td></tr><tr><td align=\"left\">Ccr2</td><td align=\"left\">CCR</td><td align=\"center\">15.7</td><td align=\"center\">0.7</td><td align=\"center\">0.5</td><td align=\"center\">2.4E-04</td><td align=\"center\">6.1E-02</td><td align=\"center\">4.0E-02</td></tr><tr><td align=\"left\">Cxcr6</td><td align=\"left\">CCR</td><td align=\"center\">5.0</td><td align=\"center\">1.9</td><td align=\"center\">0.1</td><td align=\"center\">5.5E-03</td><td align=\"center\">1.9E-01</td><td align=\"center\">3.6E-02</td></tr><tr><td align=\"left\">Ccr7</td><td align=\"left\">CCR</td><td align=\"center\">0.10</td><td align=\"center\">0.39</td><td align=\"center\">1.43</td><td align=\"center\">1.1E-03</td><td align=\"center\">6.2E-02</td><td align=\"center\">7.0E-01</td></tr><tr><td align=\"left\">Cxcr4</td><td align=\"left\">CCR</td><td align=\"center\">0.06</td><td align=\"center\">0.46</td><td align=\"center\">0.10</td><td align=\"center\">6.3E-06</td><td align=\"center\">2.1E-02</td><td align=\"center\">9.2E-03</td></tr><tr><td align=\"left\">Ccr9</td><td align=\"left\">CCR</td><td align=\"center\">0.03</td><td align=\"center\">0.03</td><td align=\"center\">0.04</td><td align=\"center\">3.1E-07</td><td align=\"center\">7.6E-06</td><td align=\"center\">2.7E-03</td></tr><tr><td align=\"left\">Adam8</td><td align=\"left\">Adhesion</td><td align=\"center\">16.9</td><td align=\"center\">6.1</td><td align=\"center\">0.8</td><td align=\"center\">3.9E-04</td><td align=\"center\">4.4E-03</td><td align=\"center\">4.5E-01</td></tr><tr><td align=\"left\">Tjp1</td><td align=\"left\">Adhesion</td><td align=\"center\">14.6</td><td align=\"center\">0.4</td><td align=\"center\">0.4</td><td align=\"center\">2.9E-05</td><td align=\"center\">6.7E-03</td><td align=\"center\">5.0E-02</td></tr><tr><td align=\"left\">Emp1</td><td align=\"left\">Adhesion</td><td align=\"center\">14.4</td><td align=\"center\">13.7</td><td align=\"center\">1.3</td><td align=\"center\">1.2E-03</td><td align=\"center\">2.2E-03</td><td align=\"center\">6.3E-01</td></tr><tr><td align=\"left\">Emilin2</td><td align=\"left\">Adhesion</td><td align=\"center\">13.6</td><td align=\"center\">0.8</td><td align=\"center\">1.1</td><td align=\"center\">2.4E-04</td><td align=\"center\">2.0E-01</td><td align=\"center\">7.6E-01</td></tr><tr><td align=\"left\">Itgav</td><td align=\"left\">Adhesion</td><td align=\"center\">10.6</td><td align=\"center\">3.6</td><td align=\"center\">2.3</td><td align=\"center\">3.6E-05</td><td align=\"center\">1.0E-03</td><td align=\"center\">8.1E-02</td></tr><tr><td align=\"left\">Nov</td><td align=\"left\">Adhesion</td><td align=\"center\">10.6</td><td align=\"center\">1.0</td><td align=\"center\">1.1</td><td align=\"center\">1.4E-02</td><td align=\"center\">4.3E-01</td><td align=\"center\">7.5E-01</td></tr><tr><td align=\"left\">Alcam</td><td align=\"left\">Adhesion</td><td align=\"center\">8.2</td><td align=\"center\">18.3</td><td align=\"center\">8.5</td><td align=\"center\">3.0E-04</td><td align=\"center\">2.9E-04</td><td align=\"center\">1.9E-02</td></tr><tr><td align=\"left\">Cdh1</td><td align=\"left\">Adhesion</td><td align=\"center\">6.9</td><td align=\"center\">0.8</td><td align=\"center\">0.9</td><td align=\"center\">3.3E-03</td><td align=\"center\">8.6E-02</td><td align=\"center\">6.2E-01</td></tr><tr><td align=\"left\">Adam9</td><td align=\"left\">Adhesion</td><td align=\"center\">6.9</td><td align=\"center\">7.1</td><td align=\"center\">2.3</td><td align=\"center\">4.0E-04</td><td align=\"center\">1.2E-03</td><td align=\"center\">2.6E-01</td></tr><tr><td align=\"left\">Tjp2</td><td align=\"left\">Adhesion</td><td align=\"center\">5.7</td><td align=\"center\">4.7</td><td align=\"center\">2.0</td><td align=\"center\">1.8E-04</td><td align=\"center\">9.1E-05</td><td align=\"center\">1.0E-01</td></tr><tr><td align=\"left\">Dsc2</td><td align=\"left\">Adhesion</td><td align=\"center\">5.0</td><td align=\"center\">0.9</td><td align=\"center\">0.9</td><td align=\"center\">5.8E-02</td><td align=\"center\">2.0E-01</td><td align=\"center\">7.1E-01</td></tr><tr><td align=\"left\">Itga6</td><td align=\"left\">Adhesion</td><td align=\"center\">0.14</td><td align=\"center\">0.12</td><td align=\"center\">0.48</td><td align=\"center\">4.2E-04</td><td align=\"center\">1.6E-04</td><td align=\"center\">1.5E-01</td></tr><tr><td align=\"left\">Itgae</td><td align=\"left\">Adhesion</td><td align=\"center\">0.08</td><td align=\"center\">0.10</td><td align=\"center\">0.09</td><td align=\"center\">3.8E-06</td><td align=\"center\">7.1E-05</td><td align=\"center\">4.2E-03</td></tr><tr><td align=\"left\">Sell (CD62L)</td><td align=\"left\">Adhesion</td><td align=\"center\">0.04</td><td align=\"center\">0.23</td><td align=\"center\">0.19</td><td align=\"center\">2.4E-04</td><td align=\"center\">3.6E-04</td><td align=\"center\">1.4E-02</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Figure S1: anti-TGF-β cannot block the suppression function of CD8<sup>+ </sup>NKT-like cells. Figure S2: IFN-γ restores the viability of <italic>in vitro </italic>cultured CD8<sup>+ </sup>NKT-like cells from IFN-γ<sup>-/- </sup>mouse.</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>*Only those genes with q-values &lt;0.01 are included in this table. NKT, CD8<sup>+ </sup>NKT-like cells; nCD8, naïve CD8<sup>+ </sup>T cells; cCD4, cultured CD4<sup>+ </sup>T cells; aCD8, activated CD8<sup>+ </sup>T cells.</p></table-wrap-foot>", "<table-wrap-foot><p>OR, odds ratio.</p></table-wrap-foot>", "<table-wrap-foot><p>NKT, CD8<sup>+ </sup>NKT-like cells; nCD8, naïve CD8<sup>+ </sup>T cells; cCD4, cultured CD4<sup>+ </sup>T cells; aCD8, activated CD8<sup>+ </sup>T cells. KR, Kruppel-like factor; NF, nuclear factor; RF, ring finger; TF, transcription factor; ZF, zinc finger. The table is split into two parts based on the expression ratio of NKT/nCD8.</p></table-wrap-foot>", "<table-wrap-foot><p>NKT, CD8<sup>+ </sup>NKT-like cells; nCD8, naïve CD8<sup>+ </sup>T cells; cCD4, cultured CD4<sup>+ </sup>T cells; aCD8, activated CD8<sup>+ </sup>T cells.</p></table-wrap-foot>", "<table-wrap-foot><p>NKT, CD8<sup>+ </sup>NKT-like cells; nCD8, naïve CD8<sup>+ </sup>T cells; cCD4, cultured CD4<sup>+ </sup>T cells; aCD8, activated CD8<sup>+ </sup>T cells. CCR, chemokine receptor; CR, cytokine receptor.</p></table-wrap-foot>" ]
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[ "<media xlink:href=\"gb-2008-9-7-r119-S1.ppt\" mimetype=\"application\" mime-subtype=\"vnd.ms-powerpoint\"><caption><p>Click here for file</p></caption></media>" ]
[{"surname": ["Storey"], "given-names": ["JD"], "article-title": ["A direct approach to false discovery rates."], "source": ["J Roy Stat Soc B Stat Meth"], "year": ["2002"], "volume": ["64"], "fpage": ["479"], "lpage": ["498"], "pub-id": ["10.1111/1467-9868.00346"]}]
{ "acronym": [], "definition": [] }
71
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 29; 9(7):R119
oa_package/63/4e/PMC2530876.tar.gz
PMC2530877
18671868
[ "<title>Background</title>", "<p>The ability to sense the environment and respond appropriately is a crucial factor for organism survival. One of the primary sensing mechanisms used by metazoans involves G-protein coupled receptor (GPCR) signaling cascades. These cascades are composed of, at the most simplistic level, a plasma membrane localized stimulus-sensing GPCR that transduces the extracellular signal to an intracellular heterotrimeric G-protein complex, thereby activating downstream signaling cascades. Because GPCR sequence conservation even within a single GPCR family of an organism can be lower than 25% [##REF##10611402##1##], GPCRs are identified not by sequence homology but rather by their ability to couple with an intracellular heterotrimeric G-protein α subunit and by their two-dimensional topology, which classically consists of an extracellular amino terminus, seven membrane spanning domains connected by three intracellular and three extracellular loops, and an intracellularly located carboxy-terminal tail.</p>", "<p>Signaling from the exterior of the cell is initiated when the GPCR becomes activated by ligand binding, stimulating an exchange of guanosine diphosphate for guanosine triphosphate on the Gα subunit, and a subsequent dissociation of the heterotrimer into Gα and a βγ subunit dimer. Gα and the βγ dimer then proceed to initiate downstream signaling cascades [##REF##10508237##2##,##REF##12183178##3##].</p>", "<p>GPCRs comprise the largest class of transmembrane signaling molecules present in metazoan organisms and have been shown to recognize ligands and effectors such as photons, ions, nucleotides, amino acids, peptides, glycoproteins, hormones and lipids [##REF##10696571##4##,##REF##11519072##5##]. Although GPCRs appear to be strictly limited to the eukaryota, they are ubiquitous and have been cloned from a wide range of evolutionarily distant organisms, including yeast [##REF##2839507##6##], coral [##REF##17196770##7##], nematodes [##REF##17082916##8##], arthropods [##REF##18316733##9##], human [##REF##6589631##10##], and even from the preserved DNA of the woolly mammoth [##REF##16825562##11##]. GPCRs play central roles in processes as diverse as yeast mating and insect taste perception [##REF##17919910##12##], and in mammals, GPCR signaling plays critical roles in development and metabolism. Aberrant mammalian GPCR activity has been directly linked to such maladies as blindness, asthma, heart disease, obesity, and cancer [##REF##14746508##13##,##REF##18370233##14##].</p>", "<p>Whole genome sequencing efforts have shown that heterotrimeric G-protein signaling can be highly complex. The human proteome is known to contain 23 Gα, 5 Gβ, and 12 Gγ subunits [##REF##15747061##15##], leading to over 1,300 theoretical heterotrimeric complexes. Factoring in the over 850 predicted human GPCRs [##REF##15687224##16##], many of which are known to homo- and heterodimerize [##REF##14636888##17##], the number of potential signaling pathways becomes enormous. In sharp contrast, the number of known heterotrimeric signaling complex components in plants is dramatically less. The fully sequenced model plant <italic>Arabidopsis thaliana </italic>has only one canonical Gα subunit (GPA1), one Gβ subunit (AGB1), and two identified Gγ subunits (AGG1 and AGG2) [##REF##16210528##18##,##REF##15170476##19##]. <italic>Arabidopsis </italic>also has a single regulator of G-protein signaling (RGS) protein (RGS1), which has been shown to directly accelerate the intrinsic guanosine triphosphatase activity of Gα [##REF##14500984##20##]. Interestingly, RGS1 contains a heptahelical domain as well as an RGS box domain, and might also function as a receptor or co-receptor [##REF##17951432##21##]. For the past decade there has been only one putative GPCR (GCR1) identified and experimentally investigated in <italic>Arabidopsis </italic>[##REF##17322342##22##, ####REF##12972659##23##, ##REF##11930019##24##, ##REF##9512416##25####9512416##25##]. Recently, a new GPCR, GCR2, has been reported in <italic>Arabidopsis </italic>[##REF##17347412##26##], although this protein sequence does not appear to have the canonical seven transmembrane (TM) topology of known GPCRs and some discrepancies exist regarding its purported plant hormone signaling function [##REF##17894782##27##]. Thus, the question that arises, and which is the focus of the present study, is whether the <italic>Arabidopsis </italic>genome is as depauperate of GPCRs as it is of heterotrimeric G-protein subunits, or whether additional <italic>Arabidopsis </italic>GPCRs exist that have not yet been identified. In other words, given that <italic>Arabidopsis </italic>has only one canonical Gα subunit and one canonical Gβ subunit [##REF##17201690##28##,##REF##11522903##29##], and only two identified Gγ subunits [##REF##11121078##30##,##REF##11513956##31##], is it reasonable that GCR1, and potentially RGS1, are the only candidate GPCRs in <italic>Arabidopsis</italic>, or are there other as yet undiscovered candidate GPCRs? The large number of plant responses that are affected upon genetic knockout of <italic>GPA1</italic>, <italic>AGB1</italic>, <italic>AGG1</italic>, or <italic>AGG2 </italic>[##REF##17468261##32##,##REF##15491922##33##] suggests that the latter hypothesis may prove true.</p>", "<p>The great physiological importance of GPCRs, combined with the ever-increasing availability of nucleic acid sequence data, has prompted the development and use of bioinformatic tools to predict and identify new GPCRs. Using both functionally characterized GPCRs and their predicted sequence homologs as a starting point, new predicted GPCRs have been identified and shown to be plentiful in a broad range of organisms from slime molds to humans [##REF##15687224##16##]. Analyses based on sequence conservation are useful for identifying GPCRs that are highly similar to known GPCRs, but the low sequence conservation within the GPCR superfamily, and even within each GPCR family, limits this approach. To circumvent this problem, more comprehensive bioinformatic methods have been developed to identify and characterize potential GPCRs.</p>", "<p>More than ten bioinformatic programs designed to identify transmembrane domains are publicly available, and programs such as TMHMM2 [##REF##11152613##34##] and HMMTOP2 [##REF##9769220##35##,##REF##11590105##36##], and Phobius [##REF##17483518##37##,##REF##15111065##38##] can be used to identify sequences with the classic 7TM domain topology of GPCRs. In a comparative study, Cuthbertson <italic>et al</italic>. [##REF##15932905##39##] found TMHMM2 and HMMTOP2 to consistently perform better than other programs, and Phobius was reported to perform comparably [##REF##15111065##38##]. To attain greater accuracy in the number of predicted TMs, signal peptide prediction programs such as Phobius and Signal-P [##REF##15223320##40##] can be used in conjunction with dedicated TM prediction programs, since TM domain predictors alone have a tendency to mistakenly predict signal peptides as amino-terminal transmembrane domains [##REF##11448883##41##, ####REF##12490439##42##, ##REF##16225690##43####16225690##43##].</p>", "<p>At the level of directly predicting a sequence as a GPCR there are only a few prediction methods available, and the diversity in their approach is an indicator of the difficulty of this task. The quasi-periodic feature classifier (QFC) developed by Kim <italic>et al</italic>. [##REF##11108699##44##] maps statistical values derived from protein sequence attributes into an n-dimensional feature space and classifies the query sequence as either a GPCR or a non-GPCR through the use of a discriminate function. The QFC relies on four parameters for classification: amino acid usage index; log of the average periodicity of the hydrophobicity function; log of average periodicity of the polarity scale; and variance of the first derivative of the polarity scale. Notably, the QFC has been used successfully to identify <italic>Drosophila </italic>odorant and gustatory receptors [##REF##10710312##45##,##REF##10069338##46##] and <italic>Anopheles </italic>odorant receptors [##REF##12364795##47##].</p>", "<p>The GPCRHMM [##REF##16452613##48##] prediction method is based on variances in amino acid composition and topological segment lengths between GPCR families. While not explicitly predicting a 7TM topology, GPCRHMM describes the typical 7TM topology by creating different hidden state compartments to model each of the three extracellular segments, the three intracellular segments, and the seven transmembrane segments that connect them. The amino and carboxyl termini are additionally broken into two compartments (close to the membrane and globular) and the distal amino-terminal compartment also includes a signal peptide model. GPCRHMM also includes a secondary filter that takes sequences passing the global prediction model and re-analyzes the central core 7TM region of the query using only the corresponding local compartment models in order to reduce the number of false positives arising from amino acid composition bias derived from long amino and carboxyl termini.</p>", "<p>Recently, Moriyama <italic>et al</italic>. [##REF##17064408##49##] combined the alignment free methods of discriminant function analyses, support vector machines, and partial least squares regression (LDA, QDA, KNN, SVM-AA, SVM-di, and PLS-ACC) to identify a preliminary list of 652 <italic>Arabidopsis </italic>candidate 7TM receptors. This initial list was reduced by filtering with HMMTOP2 [##REF##11590105##36##] to tentatively identify 394 putative 7TM receptor proteins (7TMpRs) with 5-10 predicted TM domains. A subsequent requirement of exactly seven predicted TM domains and an extracellular amino terminus identified 54 non-redundant proteins as 7TMpRs. This prediction method has not been challenged in biological experiments in order to determine if the predicted GPCRs actually couple to a Gα subunit.</p>", "<p>In our work we use a combination of direct GPCR prediction methods, multiple TM domain prediction analyses, and signal peptide prediction to identify and rank candidate GPCRs in the <italic>Arabidopsis </italic>proteome. Once potential candidate GPCRs have been identified in a proteome, it is possible to classify them using software such as the four level classifier GPCRsIdentifier [##REF##17032692##50##], which classifies GPCRs as belonging to GPCR superfamily, family, sub-family, and sub-family types based on amino acid composition and dipeptide frequencies. Beyond classification, candidate GPCRs can be characterized using coupling specificity prediction software such as Pred-Couple 2 [##REF##16174684##51##], which predicts the type of Gα subunit with which the candidate GPCR should physically interact. We further characterize our candidate GPCRs by using GPCRsIdentifier to classify our candidate plant GPCRs and Pred-Couple 2 to predict their coupling specificity. We also show evidence for evolutionary conservation of our identified candidate GPCRs using the fully sequenced genomes of rice (<italic>Oryza sativa</italic>) and poplar <italic>(Populus trichocarpa</italic>), and search the Pfam database [##REF##18039703##52##] to investigate domain similarities. Most importantly, we also provide positive results from <italic>in vivo </italic>protein-protein coupling assays between some of our highest ranking <italic>Arabidopsis </italic>candidate GPCRs and the sole Gα subunit in <italic>Arabidopsis</italic>, thus confirming the efficacy of our bioinformatic scheme for identifying novel, divergent GPCRs.</p>" ]
[ "<title>Materials and methods</title>", "<title>Sequence and annotation acquisition</title>", "<p>All <italic>A. thaliana </italic>sequences were obtained from The <italic>Arabidopsis </italic>Information Resource (TAIR) ftp Gene download site [##UREF##2##85##]. All our bioinformatic analyses performed on <italic>Arabidopsis </italic>sequences were performed using protein sequence from the updated TAIR ATH1 version 7.0 annotation of the genome, except for those performed using PRED-GPCR, which was performed on version 6.0. Although the TAIR ATH1 annotation of the <italic>Arabidopsis </italic>genome advanced from version 7 to version 8 during manuscript review, none of the sequences of our <italic>Arabidopsis </italic>candidate GPCRs changed and our predictions are still valid. All <italic>O. sativa </italic>sequences were obtained from The Institute for Genomic Resource (TIGR) and downloaded from the pseudomolecules ftp site [##UREF##3##86##]. All <italic>Oryza </italic>bioinformatic analyses were performed on the TIGR release 5 of the Osa1 Rice Pseudomolecules and Genome Annotation database. All <italic>P. trichocarpa </italic>sequences were obtained from the DOE Joint Genome Initiative (JGI) and downloaded from the ftp data download site [##UREF##4##87##]. All <italic>Populus </italic>bioinformatic analyses were performed on the JGI version 1.1 release of the proteome. All three proteomes were the most currently available versions at the time of analysis.</p>", "<title>Locus abbreviations for the <italic>Oryza </italic>and <italic>Populus </italic>proteomes</title>", "<p>For brevity, the official locus identifiers used in the <italic>Oryza </italic>and <italic>Populus </italic>proteomes have been abbreviated. For the <italic>Oryza </italic>data set, the locus identifier has been shortened by removing the characters 'LOC_' prior to each loci (for example, Os01g01010.1 corresponds to LOC_Os01g01010.1). For the <italic>Populus </italic>data set, the locus identifier has been shortened to a three letter abbreviation to indicate the <italic>Populus </italic>proteome followed by the unique numerical identifier for each sequence (for example, Pop171407 corresponds to jgi|Poptr1_1|171407).</p>", "<title>Computational analyses</title>", "<p>To identify candidate GPCRs, bioinformatic analyses were performed with software designed to directly predict putative GPCRs, to predict protein topology, to predict the presence of signal peptides, and to classify putative GPCRs into family, subfamily, and type.</p>", "<p>Computational analyses to directly predict candidate GPCRs were initiated by analyzing the complete proteome of <italic>Arabidopsis </italic>with the QFC algorithm [##REF##11108699##44##], GPCRHMM [##REF##16452613##48##], and PRED-GPCR [##REF##15215415##83##]. The QFC algorithm from Kim <italic>et al</italic>. [##REF##11108699##44##,##UREF##5##88##] was run using the default feature set and discriminant cutoff values; the results were further filtered by a discriminant function for ion channels based on amino acid usage frequency difference between GPCRs and channel proteins (J Kim, unpublished data). Analyses using GPCRHMM were performed with the local scoring option turned on. The <italic>Arabidopsis </italic>proteome version 6.0 was independently analyzed twice with PRED-GPCR. The first analysis was performed with the default parameters and the second analysis was performed with a less stringent user defined filtering option: combined family motif off, Global E-Value motif cutoff set to 1.1, and CAST low complexity filtering off. The <italic>Oryza </italic>and <italic>Populus </italic>proteomes were analyzed by GPCRHMM and QFC with the same software settings as those used for <italic>Arabidopsis</italic>.</p>", "<p>Topology prediction was performed on the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes by analyzing the complete proteomes with TMHMM version 2.0 [##REF##11152613##34##], HMMTOP version 2.0 [##REF##11590105##36##], and Phobius [##REF##17483518##37##]. TMHMM2 was run using the 'one line per protein' option. HMMTOP2 was run in the advanced mode with the parameters: FASTA format, Single Sequence type, Reliable prediction type, text output, and the results in one line. Phobius was run in the Normal prediction mode with the short output format mode selected.</p>", "<p>Signal peptide predictions were performed on the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes using Phobius. Only a single run of Phobius is necessary to obtain signal peptide predictions and TM domain prediction as they are co-predicted. For those protein sequences identified as having a signal peptide by Phobius, the sequences were cleaved '<italic>in silico</italic>' and the predicted mature protein sequences were analyzed using TMHMM2 and HMMTOP2. Phobius was not utilized for TM domain prediction of the predicted mature protein sequences because the co-prediction analytical method of Phobius could lead to additional <italic>in silico </italic>cleavage of the mature proteins and consequent inaccurate TM domain prediction.</p>", "<p>GPCR classification was performed using the GPCRsIdentifier executable program [##REF##17032692##50##] and was applied to analyze the set of heptahelical proteins identified in our topological analyses of the <italic>Arabidopsis</italic>, <italic>Oryza </italic>and <italic>Populus </italic>proteomes. All of our candidate GPCRs from all three proteomes were assessed for coupling specificity using Pred-Couple 2 [##REF##16174684##51##] and examined for the presence of domains catalogued in the Pfam database [##REF##18039703##52##].</p>", "<p>GPCRHMM, HMMTOP2, Phobius, Pred-Couple 2, and the Pfam queries were run using their respective public web servers, while the QFC algorithm was run locally on a LINUX cluster. The initial whole proteome analyses using TMHMM2 were kindly provided by Dr Jannick Bendtsen while subsequent analyses by TMHMM2 were performed over the internet. PRED-GPCR analyses of the <italic>Arabidopsis </italic>proteome were kindly provided by Dr Pantelis Bagos (University of Athens, Greece). The stand alone executable GPCRsIdentifier program was obtained from the author [##REF##17032692##50##] and run locally. Results from the BTP method as published by Inoue <italic>et al</italic>. [##REF##15022640##80##] were downloaded from the publisher's supplemental information website. The published BTP analysis was performed on the 2001 version of the <italic>Arabidopsis </italic>proteome and only protein sequences from their published results retaining an exact match to a protein sequence in the TAIR ATH1 version 7.0 were considered further.</p>", "<p>It is notable that almost all of our whole proteome analyses were performed, or could have been performed, using the publicly available web servers in a reasonable amount of time with the exception of PRED-GPCR, which appears to time out while analyzing large batch submissions.</p>", "<p>All the raw output files from the computational analyses were formatted, coded where appropriate, and used to create a relational database where the single unifying field between all tables for each respective proteome was the Locus identifier with splice variant information where available. BLASTClust (NCBI) was used to create the set of non-redundant proteins for each proteome with the percent identity and sequence length options set to 100% and the alignment length threshold enforced for all sequences. Redundant proteins were handled by using the lowest numerical identifier within a redundant protein set as a representative identifier. The data sets of corresponding splice variant or other protein redundancies within each proteome are available as Additional data files 12, 13, and 14.</p>", "<title>Identification of candidate GPCR homologs</title>", "<p><italic>Arabidopsis</italic>, <italic>Oryza </italic>and <italic>Populus </italic>protein sequences potentially orthologous to our <italic>Arabidopsis </italic>high ranking candidate GPCRs were identified using the BLOSUM62 scoring matrix and the BLAST algorithm implemented as a module in the BioEdit software package [##UREF##6##89##], with a cutoff value of e<sup>-20</sup>. Additional analyses performed to identify homologous sequences were performed using the public BLAST service at NCBI. Multiple sequence alignments were prepared in DAMBE [##REF##11535656##90##] using ClustalW and the Blosum series protein matrix. Phylogenetic trees were reconstructed in MEGA4 [##REF##17488738##91##] using the neighbor joining method with pairwise deletion of alignment gaps, Poisson correction for amino acid substitutions, and 1,000 bootstrap replicates.</p>", "<title>Protein-protein interaction assays</title>", "<p>Coupling of the <italic>Arabidopsis </italic>heterotrimeric G-protein α subunit, GPA1, and proteins selected from the highest ranking pool of candidate GPCRs was experimentally investigated using the membrane-based split-ubiquitin system assay [##REF##15299147##68##,##REF##15003597##82##]. Split-ubiquitin system linker adapted gene specific primer pairs were designed to include a 5' translation initiation codon but not a 3' termination codon and were used to amplify the full length open reading frame cDNAs of the candidate GPCRs and GPA1. The cDNAs were cloned into the TOPO-BLUNT II vector (Invitrogen, Carlsbad, California, U.S.A), sequenced, and the inserts were recovered by restriction digestion and gel purification. The Nub<sub>wt</sub>, Nub<sub>G </sub>and Cub fusion constructs were created by homologous recombination following co-transformation of 50-100 ng of insert and 50-100 ng of linearized split-ubiquitin system vector into the haploid AP5 and AP4 yeast strains. AP4 transformants containing a Cub fusion construct were mated to AP5 transformants having one of the four Nub fusion constructs and then selected on SD minimal media.</p>", "<p>Protein-protein interaction was assayed by patching diploid cultures to SD minimal media plates lacking His and Ade but containing either 0 μM, 200 μM, or 1 mM methionine and scored by visualization of yeast growth after 3-5 days. All experiments were independently replicated at least twice starting from the co-transformation stage.</p>" ]
[ "<title>Results</title>", "<title>Identification of candidate GPCRs <italic>in Arabidopsis</italic></title>", "<p>Due to the low sequence similarity of GPCRs, alternative methods beyond BLAST are required to identify novel GPCRs. Because the QFC algorithm was reported to have an approximately 98% success rate in classifying GPCRs from non-GPCRs [##REF##11108699##44##], and GPCRs are classically described by their 7TM topology, our criterion to identify a protein sequence as a candidate GPCR comprises the requirements of direct prediction as a GPCR by the QFC algorithm and the presence of exactly seven TM domains as predicted by at least two of the three TM prediction programs used (TMHMM2, HMMMTOP2, and Phobius) after correction for signal peptide misprediction (Figure ##FIG##0##1##).</p>", "<p>We identified 2,469 <italic>Arabidopsis </italic>proteins that satisfied the QFC requirement (Figure ##FIG##0##1##). To predict proteins containing seven TM domains, we performed whole proteome analyses with the dedicated TM prediction programs TMHMM2 and HMMTOP2, and the signal peptide/TM domain co-prediction program Phobius. The mature proteins of sequences with signal peptides detected by Phobius were subsequently re-analyzed by TMHMM2 and HMMTOP2 (Figure ##FIG##1##2a##). A total of 401 non-redundant protein sequences were predicted to have seven TM domains by at least one of the three programs and 83 were predicted to have seven TM domains by all three. We identified 178 proteins that satisfied our '2/3 predictions' rule for the presence of exactly seven TM domains (Figure ##FIG##1##2a##). The intersecting set of these 178 proteins with the 2,469 proteins identified by QFC analysis contains 127 candidate GPCRs, which we call the 'intermediate pool'; of these, 71 are predicted to have exactly seven TM domains by all three TM domain predictors (Figure ##FIG##0##1##; Additional data file 1).</p>", "<p>From this intermediate pool of 127 proteins, we designated a sequence as a high ranking candidate GPCR if it also satisfied the criterion of prediction as a GPCR by GPCRHMM using the relaxed global threshold of -10. Because GPCRHMM appears to have high specificity for selecting GPCRs, and it has been reported that reducing the GPCRHMM global cutoff threshold to as low as -53 still allows GPCRHMM to function with a false positive rate of only approximately 1% when analyzing data sets composed of proteins containing 6-8TMs [##REF##16452613##48##], we chose to use the relaxed global threshold of -10 in order to select more divergent GPCR candidates while still minimizing the number of false positives. Whole <italic>Arabidopsis </italic>proteome analysis by GPCRHMM using this threshold identified a non-redundant set of 99 sequences (Figure ##FIG##0##1##). Of these 99 sequences, 16 also satisfied the prediction criteria from our QFC and 7TM analyses; thus, we designated these 16 as 'high ranking' candidate GPCRs (Figure ##FIG##0##1##; Table ##TAB##0##1##), while the remaining 111 proteins from the intermediate pool were designated as second tier GPCR candidates. Further filtering of the 16 sequences through the use of a stricter global threshold in combination with a local GPCRHMM filter, namely a GPCRHMM global filter threshold level of 0 and a positive GPCRHMM local score, identified 11 of the 16 candidates as belonging to an upper bin within the set of high ranking candidate GPCRs (Table ##TAB##0##1##).</p>", "<p>Twelve of the sixteen high ranking candidate GPCR sequences were predicted to have seven TM domains by all three methods (Table ##TAB##0##1##), with ten of the consensus 7TM proteins found within the eleven member upper bin. Two of the upper bin consensus 7TM predictions (Cand6, At5g02630.1; Cand7, At5g18520.1) are only apparent after removal of the signal peptide (Table ##TAB##0##1##).</p>", "<title>Empirical testing of <italic>Arabidopsis </italic>candidate GPCR Gα-coupling ability</title>", "<p>Although the identification of candidate GPCRs by bioinformatic means is informative, the validity of the predictions can only be determined empirically. One obvious criterion that GPCR proteins should logically satisfy is that they should physically interact with a G-protein α subunit. As wet-bench evaluation of such protein-protein interactions is not a trivial task, we chose half of our <italic>Arabidopsis </italic>high ranking candidate GPCRs for <italic>in vivo </italic>analysis, and did so using additional information beyond our initial criteria of direct GPCR prediction and TM domain analysis.</p>", "<p>Candidates Cand2 and Cand8 were chosen based on their limited similarity to GPR175, a mammalian GPCR. Heptahelical protein 2 (HHP2) was selected for analysis since the HHP family shows similarity to the atypical GPCRs of the human adiponectin receptor and membrane progestin receptor family [##REF##16263907##53##]. The Tobamovirus replication protein TOM1 sequence was selected for analysis since both TOM1 and TOM3 were shown to be essential for tobamovirus pathogenicity in <italic>Arabidopsis </italic>[##REF##11836427##54##] and mammalian GPCRs are essential for HIV pathogenesis [##REF##15573137##55##]. Two of the splice variant products encoded by the At3g59090 locus (Cand3 and Cand5) were chosen based on the fact that they differ primarily in their amino-terminal regions and both are annotated as being similar to TOM1. Our BLAST analyses show that Cand3 and Cand5 have only limited similarity to TOM1 or TOM3, with BLAST e-values ranging between e<sup>-12 </sup>and e<sup>-07 </sup>(data not shown).</p>", "<p>A high proportion of GPCRs, especially class A GPCRs, are known to be intronless [##REF##15302402##56##], and this information was used to select Cand1 and Cand7 instead of other candidates that, like Cand1 and Cand7, are also annotated only as expressed proteins. Additional support for selecting Cand7 came from domain prediction analyses using the conserved domain database at NCBI, which indicated that Cand7 has a Lung 7TM receptor domain with a query e-value of 3.1e<sup>-35</sup>.</p>", "<p>After choosing these candidates, we applied the split-ubiquitin system to test their ability to interact with GPA1, the sole canonical G-protein α subunit of <italic>Arabidopsis</italic>. The split-ubiquitin system variant of the yeast two hybrid assay is based on the ability of the amino-terminal (Nub<sub>wt</sub>) and carboxy-terminal (Cub) domains of ubiquitin to spontaneously reassemble and become a functionally recognized target for ubiquitin specific proteases, which cleave an artificial transcription factor, PLV, that is fused downstream of Cub (Figure ##FIG##2##3a##). PLV translocation to the nucleus and subsequent induction of reporter gene expression leads to functional complementation of auxotrophic yeast and positive interactions are easily visualized through yeast growth. Protein-protein interaction test assays are possible through the use of NubG, a mutant version of Nub<sub>wt </sub>that has reduced affinity for Cub; thus, a functional ubiquitin is reassembled only if the two test proteins (in our case, a candidate GPCR and GPA1) interact. Increased assay stringency is achieved by modulating test protein expression levels through the application of methionine, which downregulates the methionine repressible Met25 promoter that drives Cub fusion protein expression. In our split-ubiquitin system assays we separately fused the Nub<sub>wt </sub>and NubG domains to both the amino terminus and carboxyl terminus of the candidate GPCR and tested the ability of these fusion proteins to interact with the GPA1-Cub-PLV fusion protein (Figure ##FIG##2##3b,c##, sectors 1-4). Fusion with the Nub<sub>wt </sub>is a positive control that should always yield protein-protein interaction. Because the fusion of additional protein sequence can cause physiochemical changes in protein structure and loss of function, we also performed the reciprocal assay in which the Nub domains were fused to GPA1, and the candidate GPCR was fused to Cub-PLV (Figure ##FIG##2##3c##, sectors 5-8). The two reciprocal assays were performed on the same methionine supplemented media plate (Figure ##FIG##2##3c##). Since a lack of yeast growth indicates a lack of protein-protein interaction, all interaction assay cultures were simultaneously verified as capable of growing on minimal media alone (data not shown). All of the positive interactions, as determined by yeast growth due to complementation of the <italic>his3 </italic>mutation, were also accompanied by the expected color change of the diploid yeast due to complementation of the <italic>ade2 </italic>mutation (data not shown).</p>", "<p>Figure ##FIG##2##3d## illustrates one positive result, while Figure ##FIG##2##3e## illustrates the sole negative result from our tests of candidate GPCRs. As shown in sector 2 of Figure ##FIG##2##3d##, the candidate GPCR Cand5 interacts with GPA1 as shown by the presence of yeast growth; however, this interaction does not occur when the Cand5 protein has a carboxy-terminal NubG or Cub fusion protein (Figure ##FIG##2##3d##, sectors 4, 6 and 8), consistent with the known importance of GPCR carboxyl termini in binding G-proteins as well as other GPCR interacting proteins [##REF##15494032##57##]. As shown in Figure ##FIG##2##3e##, sectors 2, 4, 6 and 8 all lack yeast growth, demonstrating that this candidate GPCR, TOM1, does not interact with GPA1 regardless of the orientation of the fusion proteins.</p>", "<p>Our complete results, summarized in Table ##TAB##1##2##, demonstrate that seven of the eight candidate GPCRs we tested indeed interact with GPA1. All of the positive control interactions using Nub<sub>wt</sub>-candidate fusion proteins showed heavy yeast growth, as expected. Fusion proteins made using Cand1, 2, 3, 5, 7, 8 and HHP2 in the NubG-candidate orientation interacted with the GPA1-Cub-PLV fusion protein as indicated by yeast growth (Table ##TAB##1##2##). The NubG TOM1 fusion protein did not interact with GPA1-Cub-PLV in our highly stringent conditions with 1 mM methionine (Figure ##FIG##2##3e##), nor did it show any interaction when the methionine concentration was reduced five-fold to 200 μM (data not shown). None of the test assays involving candidate-NubG constructs showed any interaction with GPA1, while all of the control assays showed heavy yeast growth except the assay involving HHP2, which did not show any growth (Table ##TAB##1##2##). From these data we can conclude that a free carboxyl terminus is required for Cand1, 2, 3, 5, 7, and 8 to interact with GPA1.</p>", "<p>All of the reciprocal interaction assays using the GPA1 Nub fusion proteins and the candidate-Cub-PLV fusion protein were negative, while all of the control assays involving either the Nub<sub>wt</sub>-GPA1 or the GPA1-Nub<sub>wt </sub>fusion proteins were positive except Cand8 (Table ##TAB##2##3##). Taken together, the results from the reciprocal assays provide further evidence that a free carboxyl terminus is required for candidate GPCR interaction with GPA1. Because the interaction of GPA1-Nub<sub>wt </sub>and Cand8-Cub-PLV did not show any yeast growth, the negative results for interaction between GPA1-NubG and Cand8-Cub-PLV are inconclusive (Table ##TAB##2##3##).</p>", "<title>Classification of our <italic>Arabidopsis </italic>high ranking candidate GPCRs</title>", "<p>Although GPCRs are highly divergent and generally have low sequence similarity, extensive study has led to the ability to categorize metazoan GPCRs into receptor families and subfamilies, and even subfamily categories [##REF##12520006##58##]. Importantly, GPCR classification systems are based on the pharmacological properties of GPCR function [##REF##12520006##58##]; therefore, classification of candidate GPCRs may give clues regarding their functional relatedness. The comprehensive GPCR classification software GPCRsIdentifier [##REF##17032692##50##] was utilized to classify our candidate GPCRs in order to compare classifications of plant candidate GPCRs with those from metazoan systems.</p>", "<p>As the GPCRsIdentifier method is independent of primary sequence and also does not attempt to verify a query sequence as having the typical 7TM topology of GPCRs prior to classification, we applied GPCRsIdentifier to proteins that we had previously predicted to contain 7TM domains (Figure ##FIG##1##2a##). GPCRsIdentifier was able to classify the great majority of these proteins: 94.74% of the 7TM proteins identified by TMHMM2, 90.56% of the 7TM proteins identified by HMMTOP2, and 91.52% of the 7TM sequences identified by Phobius were classifiable by GPCRsIdentifier.</p>", "<p>We next specifically applied GPCRsIdentifier to classify our high ranking candidate GPCRs in the <italic>Arabidopsis </italic>proteome. All 16 of these candidates were classified as being class A GPCRs, and 12 of these were identified as belonging to the Olfactory subfamily (Table ##TAB##3##4##). GCR1 was the only sequence to be classified as belonging to the Olfactory I subfamily type category, and nine of the Olfactory classified sequences were diversely classified into the Olfactory II subfamily type category numbers 1, 2, 4, 5, 8, 10, and 13. Two of the sequences classified into the Olfactory II subfamily were classified into the FOR-like category. The remaining four <italic>Arabidopsis </italic>high ranking candidate GPCRs were only classified to the subfamily level: three sequences were identified as belonging to the Peptide subfamily of class A, while one sequence was classified as belonging to the Viral subfamily of class A.</p>", "<title>Application of our GPCR detection method to the <italic>Oryza </italic>proteome</title>", "<p>To identify candidate GPCRs in <italic>Oryza </italic>the same bioinformatics pipeline was applied as was used for <italic>Arabidopsis</italic>. Application of the QFC algorithm to the <italic>Oryza </italic>non-redundant proteome identified 3,344 proteins as GPCRs. Topology predictions using TMHMM2, HMMTOP2, and Phobius identified 187 proteins that were predicted to have 7 TMs by at least two of these programs, after considering the presence of signal peptides (Figure ##FIG##1##2b##). As summarized in Figure ##FIG##3##4##, we identified an intermediate pool of 151 non-redundant <italic>Oryza </italic>candidate GPCRs that satisfied the criterion of direct prediction as a GPCR by the QFC algorithm and a majority 7TM topology prediction. Sixty-seven proteins in this intermediate pool were predicted to have exactly seven TM domains by consensus prediction (Additional data file 2). Application of GPCRHMM with a relaxed global threshold to the intermediate pool resulted in identification of 138 second tier candidate GPCRs (Additional data file 2) and 13 high ranking candidate GPCRs (Table ##TAB##4##5##). Seven of these sequences were further segregated into an upper bin of high ranking candidates using the additional filtering steps of requiring a GPCRHMM global score greater than 0 and a positive GPCRHMM local score (Table ##TAB##4##5##).</p>", "<p>Four of the thirteen high ranking candidates were predicted to have seven TM domains by all three TM predictors, although 7TM consensus predictions became evident for three of the sequences (Os01g61970.1, Os03g36790.1, and Os02g40550.1) only after considering the confounding effect of signal peptides on amino-terminal TM domain prediction (Table ##TAB##4##5##).</p>", "<title>Classification of the <italic>Oryza </italic>high ranking candidate GPCRs</title>", "<p>GPCRsIdentifier classified all but one of the high ranking candidate GPCRs in the <italic>Oryza </italic>proteome into the class A family of GPCRs (Table ##TAB##3##4##). Interestingly, the two <italic>Oryza </italic>putative paralogs most closely related to Cand1 were classified differently; Os04g36630.1 was classified as belonging to the class A family while Os01g66190.1 was classified as belonging to the class C family. This may indicate that these <italic>Oryza </italic>candidate GPCRs have functionally diverged and have differential ligand specificities since GPCR classification systems are based on pharmacological function.</p>", "<p>Classification of the <italic>Oryza </italic>class A candidate GPCRs identified a greater diversity of subfamily representation than that seen in the <italic>Arabidopsis </italic>analysis (Table ##TAB##3##4##). Only 5 of the 13 candidates were classified into the Olfactory subfamily and of these, two were identified as Olfactory I family sequences. The other three were classified into the Olfactory II subfamily type category numbers 4, 5, and 10. Another 5 of the 13 candidates were classified into the Rhodopsin subfamily, and the remaining 2 sequences were divided between the Peptide and Lysosphingolipid subfamilies.</p>", "<title>Application of our GPCR detection method to the <italic>Populus </italic>proteome</title>", "<p>Direct detection of potential candidate GPCRs by the QFC algorithm identified 2,678 sequences within the non-redundant <italic>Populus </italic>proteome (Figure ##FIG##4##5##) and our protein topology analysis identified a total of 249 protein sequences predicted to be heptahelical by two out of the three prediction programs (Figure ##FIG##1##2c##). The intermediate pool of <italic>Populus </italic>candidate GPCRs, defined as those proteins that satisfied both the QFC and majority 7TM prediction requirements, contains 202 proteins of which 96 are 7TM by prediction consensus (Additional data file 3).</p>", "<p>Using the same GPCRHMM criteria as previously employed, we identified 20 high ranking candidate GPCRs in the non-redundant <italic>Populus </italic>proteome (Table ##TAB##5##6##), 12 of which compose an upper bin of candidates as they were co-predicted by the QFC and GPCRHMM using our most stringent criteria. Of these 20 high ranking candidate GPCRs, 16 are predicted to have 7 TM domains by agreement of TMHMM2, HMMTOP2, and Phobius. Nine of the consensus 7TM prediction sequences are found within the twelve sequence upper bin (Table ##TAB##5##6##).</p>", "<title>Classification of the <italic>Populus </italic>high ranking candidate GPCRs</title>", "<p>Out of the 20 <italic>Populus </italic>high ranking candidate GPCRs, 18 protein sequences were classified as class A GPCRs by GPCRsIdentifier (Table ##TAB##3##4##). Of these, eleven were classified into the Olfactory family with three identified as belonging to the Olfactory I subfamily and eight identified as belonging in one of the Olfactory II subfamily type category numbers 2, 4, 5, 6, 9, and 10. The remaining seven class A sequences were identified as belonging to the Peptide (four), Nucleotide (two), and Thyrotropin (one) subfamilies. One sequence, Pop 279432, which was not classified as a class A GPCR, was classified into class C, while the remaining non-class A sequence, Pop796139, was classified by GPCRsIdentifier as a globular protein.</p>", "<title>Conservation of high ranking candidate GPCRs across monocot and dicot plants and metazoa</title>", "<p>Since individual GPCRs and GPCR families are known to be evolutionarily conserved across species [##REF##16091152##59##], we sought to identify sequences in the <italic>Oryza </italic>and <italic>Populus </italic>proteomes that are homologous to our <italic>Arabidopsis </italic>candidate GPCRs that we empirically demonstrated to interact with GPA1 (Tables ##TAB##1##2## and ##TAB##2##3##). Specifically, we hypothesized that our <italic>Arabidopsis </italic>candidate GPCRs shown to interact with GPA1 should have likely orthologs in the <italic>Oryza </italic>and <italic>Populus </italic>proteomes and that these likely orthologs should also have been predicted as candidate GPCRs using our most stringent identification scheme.</p>", "<p>To evaluate the hypothesis that our <italic>Arabidopsis </italic>high ranking candidate GPCRs shown to physically interact with GPA1 exhibit sequence conservation in higher plants, we performed phylogenetic analyses using potential orthologs identified by BLAST analyses of the <italic>Arabidopsis</italic>, <italic>Oryza </italic>and <italic>Populus </italic>proteomes (Figure ##FIG##5##6##). This molecular evolutionary analysis supported both parts of our hypothesis. First, as described in more detail below, all seven of our interacting GPCRs as well as GCR1, previously shown to interact with GPA1 [##REF##15155892##60##], indeed have close homologs (E-values &lt; e<sup>-60</sup>) in <italic>Oryza </italic>and <italic>Populus</italic>, while RGS1 [##REF##14500984##20##] has a close homolog only in <italic>Populus </italic>(Tables ##TAB##3##4## and ##TAB##4##5##, Figure ##FIG##5##6##). Second, nearly all of the orthologous sequences uncovered by phylogenetic analyses were independently predicted as GPCRs using our GPCR prediction pipeline (Figure ##FIG##5##6##), despite differences in primary sequence, physiochemical characteristics, and topological boundaries.</p>", "<p>Using GCR1 as the input sequence, we identified single homologous proteins in both the <italic>Oryza </italic>and <italic>Populus </italic>proteomes (Os06g09930.1, Pop820940), and both these proteins were among those independently predicted by our bioinformatic pipeline as high-ranking candidates in these proteomes. Queries using the RGS1 sequence did not identify a homolog in the proteome of the monocot, <italic>Oryza</italic>, but did identify a sole homologous protein in the proteome of the dicot, <italic>Populus </italic>(Pop720911). This sole <italic>Populus </italic>RGS1 homolog was identified as second tier candidate GPCR by our bioinformatic analysis. Further queries of publicly available databases show that GCR1 is highly conserved across the plant kingdom, including dicots and monocots, while RGS1 sequences are highly conserved within the dicotyledonous species (data not shown).</p>", "<p>BLAST analyses showed that Cand1 has no homologs within the <italic>Arabidopsis </italic>proteome, but it does have two highly similar proteins in the <italic>Oryza </italic>proteome and three homologs in the <italic>Populus </italic>proteome (Figure ##FIG##5##6d##), all of which we had previously identified as high ranking candidate GPCRs. Although highly similar sequences (BLAST &lt; e<sup>-95</sup>) were identified in other plant species, the identification of non-plant possible homologs of Cand1 was limited to a single <italic>Dictyostelium </italic>protein [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XP_637589\">XP_637589</ext-link>] with an expected value of 5e<sup>-07 </sup>(data not shown).</p>", "<p>Candidate GPCRs Cand2 and Cand8, which share 83% identity and compose a two gene family in <italic>Arabidopsis</italic>, identified a similarly closely related protein pair in <italic>Populus </italic>but only identified a single homolog in the <italic>Oryza </italic>proteome; we had previously identified all three of these proteins as belonging to the high ranking candidate GPCR gene sets of these proteomes. The Cand2/8 family is not only widely conserved across monocot and dicot plant lineages, but is also conserved across higher metazoa as BLAST searches identify homologs in mouse (GPR175) and honeybee (Figure ##FIG##6##7##).</p>", "<p>Queries with all three splice variants of At3g59090 (Cand3, 4, and 5) detected a single sequence in the <italic>Oryza </italic>proteome (Os01g54784.1) and two sequences (Pop205267, Pop551235) in the <italic>Populus </italic>proteome (Figure ##FIG##5##6e##). BLAST analyses using these At3g59090 splice variants did not detect any non-plant sequences, suggesting that this family, like the TOM1/3 family with which it is weakly associated, is plant-specific.</p>", "<p>The HHP family has five members in <italic>Arabidopsis </italic>and has previously been reported to be similar to human adiponectin and progestin receptors [##REF##16263907##53##]. BLAST searches of the <italic>Oryza </italic>and <italic>Populus </italic>proteomes using HHP2 identify five homologs in the <italic>Oryza </italic>proteome and six homologs in the <italic>Populus </italic>proteome (Figure ##FIG##5##6g##), and all but 2 of these 11 sequences were found by our independent GPCR candidate search.</p>", "<p>BLAST searches using Cand6 and Cand7, which compose half of a four gene family in <italic>Arabidopsis</italic>, identified six homologous protein sequences in the <italic>Oryza </italic>proteome and five in <italic>Populus</italic>. A broader BLAST analysis, using all of these sequences as queries, showed that the Cand6/7 'super-family' contains 29 non-redundant members within the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes. The majority of these sequences (20/29) are independently identified by our GPCR prediction pipeline as candidate GPCRs, with 13 of the 20 sequences identified as high ranking candidate GPCRs. Molecular evolutionary analyses using all 29 members show that the superfamily strongly and equally bifurcates into two clades (Additional data file 4), with one clade containing Cand6, Cand7, and all of their close homologs identified in the initial BLAST analyses (Figure ##FIG##5##6f##). Subsequent BLAST searches using Cand7 as the query show that this large plant family of sequences is similar to the human GPR107 family of GPCRs and conserved across plants, insects, fish, and mammals (Additional data file 5 and data not shown).</p>", "<p>To further characterize the phylogenetic relationships identified by our molecular evolutionary analyses, we queried the Pfam database [##REF##18039703##52##] using all of our candidate GPCRs (Additional data file 6). Domain search analyses using the Pfam database confirm the previous descriptions of GCR1 having a Dicty_CAR domain, and we find that this attribute is also found in the <italic>Oryza </italic>and <italic>Populus </italic>homologs of GCR1 (Figure ##FIG##5##6##; Additional data file 6). Our analyses show that the plant Cand6/7 superfamily members all contain the Lung_7TMR domain (Figure ##FIG##5##6##; Additional data file 6), which is common to the mammalian GPR107/108 family. Plant sequences in the TOM1/3 family have a domain of unknown function, DUF1084, while Cand9 and Cand10 both have a DUF300 domain. The haemolysin-III domain of the HHP2 family of <italic>Arabidopsis </italic>sequences was previously noted [##REF##16263907##53##] and we show that this domain is conserved across the greater HHP family in <italic>Oryza </italic>and <italic>Populus </italic>(Figure ##FIG##5##6##; Additional data file 6). Interestingly, several of our candidate GPCRs and candidate GPCR families (Cand1-5 and Cand8) do not have any of the domains included in the PfamA database. This provides additional support that these are novel, uncharacterized proteins, but does not provide negative support for their identification as a candidate GPCR: analysis of all of the human sequences available in the GPCRDB using the Pfam database shows that 21.4% do not have any associated PfamA domains, and 6.2% of the sequences have domains that are other than those annotated as GPCR specific (data not shown). Similar to the number observed for the human GPCRDB sequences, 20.4% of our <italic>Arabidopsis </italic>candidate GPCRs did not have matches in the PfamA database. Although PfamB family domains are not annotated and are of lower quality, all of our candidate GPCRs without PfamA domains were assessed for the possibility of functionally conserved domains in order to computationally characterize these proteins to the fullest extent. After PfamB analyses, we find that nearly all of these candidate GPCRs have some type of uncharacterized domain (Additional data file 7). Interestingly, in some cases the associated domain is based exclusively on data from members of the candidate GPCR family or superfamily. For instance, members of the Cand2/8 family have Pfam-B_26759 and Pfam-B_14631 domains, but these domains are based on the ProDom alignment of Cand2/8, a sorghum homologue, and the human GPR175 sequence (see also Figure ##FIG##6##7##). This domain analysis suggests that these two unannotated PfamB domains may be uncharacterized GPCR domains, but this remains to be proven.</p>", "<p>Taken together, our results show that the high ranking <italic>Arabidopsis </italic>candidate GPCRs that we have empirically shown to interact with GPA1 are widely conserved in plant species, and that homologous sequences in other plant proteomes are indeed independently predicted as high ranking GPCRs by our approach, further supporting the validity of this method.</p>" ]
[ "<title>Discussion</title>", "<title>Bioinformatic identification of <italic>Arabidopsis </italic>candidate GPCRs</title>", "<p>The experimental elucidation of candidate plant GPCRs has so far been limited to the discovery of <italic>Arabidopsis </italic>GCR1 [##REF##9512416##25##] and its homolog in pea [##REF##17587233##61##], <italic>Arabidopsis </italic>RGS1 [##REF##17201690##28##], and, recently, <italic>Arabidopsis </italic>GCR2 [##REF##17347412##26##]. Within the <italic>Arabidopsis </italic>genome no other genes have any appreciable similarity to GCR1 or RGS1 by BLAST analysis. GCR2 and its two homologs within the <italic>Arabidopsis </italic>genome are homologous to the lanthionine synthetase C family [##REF##17894782##27##], and furthermore, all of the key LanC-like family GXXG motifs as well as the catalytic residues are conserved between GCR2 (At1g52920.1) and lantibiotic cyclase, for which a crystal structure is known [PDB:<ext-link ext-link-type=\"pdb\" xlink:href=\"2g02\">2g02</ext-link>, PDB:<ext-link ext-link-type=\"pdb\" xlink:href=\"2g0d\">2g0d</ext-link>] [##REF##18086512##62##]. Although GCR2 was reported by Liu and co-workers [##REF##17347412##26##] as a GPCR, none of the 16 TM prediction programs used to create the ARAMEMNON membrane protein database [##REF##12529511##63##] predict this protein to have seven TM domains, including DAS and TM-PRED, which were included in the Liu <italic>et al</italic>. report [##REF##17347412##26##]. Our whole proteome analysis using our multiple topology prediction approach did not predict a single TM domain within this protein. Illingworth <italic>et al</italic>. [##REF##18086512##62##] mathematically describe how GCR2 can be misconstrued to have transmembrane domains and show that GCR2, similar to other lanthionine synthetases, does have short hydrophobic stretches but these short regions encompass the conserved GXXG motifs and map to a single face of the 2g02 crystal structure. Interestingly LANCL1, another lanthionine synthetase, was initially identified as a GPCR [##REF##10944443##64##] prior to biochemical experimentation, which confirmed its subcellular localization as a peripheral membrane protein. Additional discrepancies have also arisen regarding the description of GCR2 as a GPCR that functions as a receptor for the plant hormone abscisic acid. Gao <italic>et al</italic>. [##REF##17894782##27##] report that GCR2 is not genetically or physiologically coupled to GPA1 and is not required for abscisic acid perception during seed germination and seedling development</p>", "<p>GCR1 has no homologs within the <italic>Arabidopsis </italic>proteome. BLAST searches of other plant proteomes, including <italic>Oryza </italic>and <italic>Populus</italic>, do readily identify sequences highly similar to GCR1, but subsequent BLAST searches using these identified putative orthologs of GCR1 suggest that these genes also have no homologs within their respective proteomes. The lack of obvious homologs of GCR1 in each proteome precludes the ability to discover new potential GPCRs through the use of simple homology-based searches. Attempts to identify plant candidate GPCRs through the use of publicly available GPCR specific databases were also not productive; the GPCRDB database [##REF##12520006##58##] and the SEVENS database [##UREF##0##65##] contain only GCR1 and sequences from the mildew locus o (MLO) family, although SEVENS also includes GCR2 and its two homologs. Searches of the GPCR/G-protein/effector database gpDB [##REF##15619328##66##] did not identify any plant sequences in the GPCR category.</p>", "<p>To circumvent these problems, we have developed a combinatorial approach to identify novel GPCRs based on the direct prediction of GPCRs by the QFC algorithm and GPCRHMM; signal peptide detection by Phobius; TM domain prediction by TMHMM2, HMMTOP2, and Phobius; and subsequent GPCR classification by GPCRsIdentifier. Our bioinformatic analyses of the <italic>Arabidopsis </italic>proteome identified a primary tier of 16 high ranking candidate GPCRs using the criteria that sequences were required to be co-predicted as a GPCR by the QFC algorithm and GPCRHMM and have a predicted 7TM topology by at least two of the transmembrane prediction programs.</p>", "<p>Notably, both GCR1 and RGS1, two proteins experimentally confirmed to functionally couple to the sole Gα subunit in <italic>Arabidopsis </italic>[##REF##14500984##20##,##REF##15155892##60##], are found within our primary tier of candidate GPCRs (Table ##TAB##0##1##). RGS1, which has both a 7TM domain and a long carboxy-terminal RGS domain, was directly predicted as a GPCR by GPCRHMM only when analyses were performed using the 7TM domain of the protein. This is because inclusion of the carboxy-terminal RGS domain introduced sequence bias from the intrinsic amino acid composition and dipeptide frequency of this domain, resulting in a lower Global score of -15.27.</p>", "<p>Also found in this primary tier of GPCR candidates is HHP2, one of five members of the <italic>Arabidopsis </italic>HHP family with sequence similarities to human adiponectin receptors and membrane progestin receptors [##REF##16263907##53##], and two members of the TOM1/3 family implicated in tobamovirus multiplication [##REF##11836427##54##]. Of the 16 proteins in our primary tier, seven have not been previously studied and are only annotated as expressed proteins. The inclusion of these biologically uncharacterized proteins in our candidate GPCR list provides both a clue as to their function and a framework to guide the design of future experimental work.</p>", "<p>Since GPCRHMM appears to be highly specific, or at least highly conservative, in identifying novel plant GPCRs, we reasoned that our strict criteria for identifying the highest ranking sequences most likely excluded the identification of potentially useful candidates. Removal of the high ranking candidates identified by GPCRHMM from the intermediate pool led to the identification of 111 second tier candidate GPCRs, including HHP1, HHP3, and three members of the MLO family: MLO7, MLO10, and MLO13. The plant-specific MLO family is named after a barley MLO protein that was experimentally shown to have a 7TM GPCR-like topology and play a key role in mediating fungal infection [##REF##11919636##67##]. Aside from the 7TM topology, there is no evidence to suggest that any MLO family members couple to Gα. Additional MLO family members (MLOs 2, 3, 4, 6, 8, 11 and 14) are identified by our QFC analysis but are subsequently removed by our ion channel filter step (Additional data file 8).</p>", "<p>Attempts to reconstruct an overall evolutionary relationship using our 16 high ranking candidate GPCRs proved fruitless, and the inclusion of the remaining set of 111 candidate GPCRs did not provide any greater resolution beyond the obvious small gene family clusters identified by BLAST analyses of the <italic>Arabidopsis </italic>proteome alone (Figure ##FIG##5##6##). These phylogenetic results were expected based on the lack of sequence homology between our candidate GPCR clusters, which mirrors the well-established lack of a comprehensive phylogenetic relationship linking all metazoan GPCRs of an organism.</p>", "<title>Prediction of candidate GPCR coupling specificity</title>", "<p>In the human system, the heterotrimeric G-protein contains one of 23 different Gα subunits and some GPCRs are described as promiscuous because they can couple to more than one type of Gα subunit. Although <italic>Arabidopsis </italic>contains only one canonical Gα subunit, GPA1, which is most similar to a G<sub>z </sub>variant of the G<sub>i/o </sub>subunit family [##REF##12183178##3##], we used Pred-Couple 2 to predict the coupling specificity of our candidate GPCRs. Since GPA1 belongs to the G<sub>i/o </sub>subunit family, it follows that <italic>Arabidopsis </italic>candidate GPCRs associated with GPA1 should be predicted to couple with members of the G<sub>i/o </sub>family. Our analyses show that 92.2% of our <italic>Arabidopsis </italic>candidate GPCRs for which Pred-Couple 2 provides a coupling prediction are indeed predicted to couple to the G<sub>i/o </sub>family (Additional data file 9). Note that the absence of a coupling prediction does not indicate that a sequence is not a GPCR, because Pred-Couple 2 initially filters sequences using parameters based on established GPCRs and is not, therefore, designed to detect novel or divergent GPCRs [##REF##16174684##51##].</p>", "<title><italic>In vivo </italic>testing of protein coupling</title>", "<p>With the information provided by our bioinformatic analyses, we turned towards providing empirical evidence that some of our top candidates truly have the potential to function as a GPCR. The split-ubiquitin system, a membrane-based variant of the yeast two-hybrid assay, has been used to demonstrate coupling of the candidate GPCRs, GCR1 and RGS1, to the sole <italic>Arabidopsis </italic>Gα subunit, GPA1 [##REF##14500984##20##,##REF##15155892##60##]. GPA1 has been shown to act specifically in this binding assay as it does not bind the inward potassium channel KAT1 or a truncated version of GCR1 [##REF##15155892##60##]. Our <italic>in vivo </italic>protein-protein binding experiments demonstrated that the great majority (7/8) of the candidate GPCRs that we tested do interact with GPA1. We show that candidates Cand1, 2, 3, 5, 7, 8, and HHP2 all couple to GPA1, provided that the carboxyl terminus of the candidate is not blocked by a fused protein tag. This requirement for a free carboxyl terminus was observed previously for GCR1 [##REF##15155892##60##]. TOM1 did not interact with GPA1 in any of our assays regardless of protein fusion orientation. Given the apparent specificity of GPA1 in the split-ubiquitin system, our positive protein-protein interactions now await confirmation of interaction <italic>in planta</italic>. The ability to not only bind Gα but to stimulate the exchange of guanosine diphosphate for guanosine triphosphate is a key characteristic of classically functioning GPCRs that could also be assessed in future studies.</p>", "<p>Within the <italic>Arabidopsis </italic>proteome, the candidates we tested for <italic>in vivo </italic>coupling to GPA1 ranged from a single gene to members of small families. Cand1 has no homologs within the proteome, candidates Cand2 and Cand8 comprise a small two gene family, Cand3 and Cand5 are two splice variant products of the same locus, and Cand7 is a member of a small four gene family in <italic>Arabidopsis</italic>. Interestingly, Cand6 (At5g02630.1), which is the second closest homolog to Cand7, is also identified in our top tier of candidate GPCRs, suggesting that the other two members of this family may also couple to GPA1. HHP2 is part of the five gene HHP family [##REF##16263907##53##], and we predict all but HHP5 to be GPCRs, suggesting these sequences also may compose a GPCR family.</p>", "<p>Given the positive correlation between our high stringency computational analysis identifying candidate GPCRs and our subsequent <italic>in vivo </italic>assay showing physical interaction with GPA1 under high stringency (1 mM methionine) conditions [##REF##15299147##68##], it is likely that these proteins, and their close homologs, actually function as GPCRs.</p>", "<title>Bioinformatic method application to the <italic>Oryza </italic>and <italic>Populus </italic>proteomes</title>", "<p>As our method for identifying novel plant candidate GPCRs successfully identified a set of <italic>Arabidopsis </italic>high ranking proteins, most of which were demonstrated to physically couple with GPA1, we next applied our method to the proteomes deduced from the fully sequenced <italic>Oryza </italic>and <italic>Populus </italic>genomes. Our analyses identified 13 and 20 high ranking candidate GPCRs, and an additional 138 and 182 second tier candidate GPCRs in the <italic>Oryza </italic>and <italic>Populus </italic>proteomes, respectively. Similarly as described for our <italic>Arabidopsis </italic>candidate GPCRs, those <italic>Oryza </italic>and <italic>Populus </italic>candidates for which coupling predictions were obtained using Pred-Couple 2 were primarily predicted to couple to the G<sub>i/o </sub>type of Gα subunit (Additional data file 9). And for <italic>Oryza</italic>, in which the Gα subunit has been characterized, our results are consistent as the <italic>Oryza </italic>Gα subunit shows sequence similarities to subunits of the human G<sub>i </sub>family (data not shown).</p>", "<title>Evolutionary dynamics of candidate GPCRs in plants</title>", "<p>One hallmark of metazoan GPCRs is the conservation of individual GPCRs across divergent organisms. BLAST analyses using the <italic>Arabidopsis </italic>candidate GPCRs that we experimentally demonstrated to couple with GPA1 in the protein binding assays identified a number of homologous sequences in the <italic>Oryza </italic>and <italic>Populus </italic>proteomes, the great majority of which (47/53) were also independently predicted as GPCRs by our bioinformatic method (Figure ##FIG##5##6##). With the exception of HHP2, all of the <italic>Arabidopsis </italic>candidate GPCRs that we demonstrated to couple with GPA1 have homologs within both the <italic>Oryza </italic>and <italic>Populus </italic>top tier candidate lists.</p>", "<p>The small <italic>Arabidopsis </italic>gene family of Cand2 and Cand8, which shows approximately 22% identity, and approximately 43% similarity to the mammalian GPR175 GPCR family, has a corresponding two gene family in <italic>Populus</italic>, but only a single homolog is identifiable in the <italic>Oryza </italic>proteome, suggesting a potential evolutionary loss. Additional homologs can be identified within other plant (grape and sorghum), insect (honeybee), and mammal (human, mouse, rat) predicted proteomes. Multiple sequence alignments show that the intracellular carboxyl terminus, which has been described as the 'magic tail' due to its ability to couple with multiple GPCR-interacting proteins including Gα [##REF##15494032##57##], has near complete identity (Figure ##FIG##6##7##) within the group of plant sequences. This high sequence conservation across diverse plant genera points towards conserved binding partners and potential signaling mechanisms.</p>", "<p>In contrast to the small number of sequences that compose the other high ranking candidate GPCR families, the <italic>Arabidopsis </italic>candidate GPCR Cand7 belongs to a small gene family, which, in turn, belongs to a large 29 member protein 'superfamily' found within the <italic>Arabidopsis</italic>, <italic>Oryza </italic>and <italic>Populus </italic>proteomes. Phylogenetic analyses show that the Cand6/7 superfamily deeply bifurcates to form two distinct clades. One clade contains the three closest <italic>Arabidopsis </italic>homologs of Cand7 (including Cand6), and the other clade contains three only distantly related <italic>Arabidopsis </italic>sequence (Additional data file 4). The two clades are linked by a single <italic>Arabidopsis </italic>sequence found at the midpoint of the reconstructed tree. The divergence of the <italic>Arabidopsis </italic>sequences exemplifies the difficulty of finding potential GPCRs by homology alone; without the results from our independent GPCR prediction pipeline these distant <italic>Arabidopsis </italic>homologs, which are phylogenetically surrounded by candidate GPCRs from the <italic>Oryza </italic>and <italic>Populus </italic>proteomes, would not have been discovered as belonging to the Cand6/7 superfamily. All of the proteins within the Cand6/7 superfamily contain a Lung 7TM receptor domain (Additional data file 6) and are related to the GPR107/108 orphan GPCR superfamily that contains the same domain. Interestingly, one residue, Cand7 Trp<sup>193</sup>, located near the interior membrane junction of TM1, is invariant in all 29 non-redundant sequences of the 3 plant proteomes, and this conservation extends across kingdoms to almost all members of the greater GPR107/108 family identified, including insects, fish, and mammals (Additional data file 5), suggesting its functional importance.</p>", "<p>Overall, while most of our first tier candidates have homologs across the three proteomes as well as other taxa, the distribution pattern of putative orthologs and putative paralogs is heterogenous. As can be seen in Figure ##FIG##5##6a-g##, the gene trees of homologs show diverse patterns with none of the seven trees in Figure ##FIG##5##6## showing a consistent set of putative orthologs/paralogs across the three proteomes. Thus, it seems likely that while each species retained many of the ancestral GPCRs, each seems to have specialized through both gene duplications and gene losses.</p>", "<p>Although GPCR prediction algorithms all use sequence-derived information as a starting point, sequence homology is not the key component in our method of candidate GPCR identification. For example, our analysis identifies Cand7 and its second closest homolog, Cand6, as candidate GPCRs, but not At3g09570.1 and At5g42090.1, Cand7's first and third closest homologs. Both of these 'un-chosen' sequences were excluded from our intermediate pool by the QFC, but they were directly predicted to be GPCRs by our GPCRHMM analyses and did pass our '2/3' topology prediction requirement. The four other <italic>Arabidopsis </italic>sequences identified in the Cand6/7 superfamily are surrounded in the phylogenetic tree by candidate GPCRs from both <italic>Oryza </italic>and <italic>Populus </italic>and would have been candidate GPCRs had we not applied the QFC ion channel filter (Additional data file 4; Table ##TAB##3##4##). It was deemed more valuable to include rather than discard the ion channel filter (see Materials and methods), because this filter removes a large number (70) of protein sequences annotated as having channel or transport activity, 43 of which have already been identified and named, while only excluding 19 potential second tier proteins (Additional data file 8).</p>", "<p>Although our bioinformatic approach identified a number of candidate GPCRs within each of the three proteomes analyzed, the sequences identified do not compose a homogenous group. Using the primary sequence independent four-level GPCR classification system in GPCRsIdentifier [##REF##17032692##50##], we show the majority of our primary tier candidate GPCRs appear to be most similar to the class A family of GPCRs, the most abundant type of metazoan GPCRs [##REF##15687224##16##], but belong to a wide range of subfamilies and subfamily types. Furthermore, the subfamily classification distribution varied between proteomes. The majority of the <italic>Arabidopsis </italic>primary tier sequences were classified into the Olfactory subfamily while the <italic>Oryza </italic>and <italic>Populus </italic>primary tier of candidate GPCRs showed greater diversity in amino acid composition and dipeptide frequency.</p>", "<p>The direct meaning of these classifications is unclear relative to their descriptive names since, for example, plants do not possess an olfactory system. The GPCR classifications provided by GPCRsIdentifier may simply provide a ready-made system to catalog the breadth and diversity of plant GPCRs, and eventually, new plant-centric descriptive names should be devised for these families and subfamilies. Alternatively, these results may suggest an evolutionary relationship and indicate that mammalian GPCRs and plant GPCRs are derived from a common class of ancient GPCRs. Along these lines, it is known that some of the mammalian GPCRs bind plant secondary metabolites; for example, the ligands of opiate and cannabinoid receptors were first identified as plant-derived compounds, and only later it was discovered that mammals themselves manufacture analogous compounds: the endorphins [##REF##12184727##69##] and the endocannabinoids [##REF##12097154##70##]. In fact, the previously thought plant-specific compound morphine is now know to be biosynthesized <italic>de novo </italic>by humans [##REF##15383669##71##], and morphine as well as its biosynthetic precursors have been shown to activate Gα subunits through GPCR signaling [##REF##17616524##72##]. These data further suggest an evolutionary link between mammalian GPCRs and plant GPCRs.</p>", "<p>Just as relevant evolutionary and physiological links exist between our candidate plant GPCRs and human GPCR function, plausible links also exist between plant and insect receptors. Herbivory induces plant production of volatile compounds and one such compound, methyl salicylate (MeSA), is a mobile signal that induces plant defenses in spatially distant organs of the plant under attack as well as in neighboring plants [##REF##17916738##73##,##REF##11459069##74##]. MeSA also activates unique neuron specific receptors of the cabbage moth <italic>Mamestra brassicae </italic>[##REF##17846100##75##] and females of that species avoid ovipositing on plants and artificial plants equipped with MeSA emitting dispensers [##REF##17846100##75##], apparently in an attempt to avoid plants already occupied by herbivores. In contrast, volatiles from herbivore-damaged plants attract wasps that parasitize insect herbivores [##UREF##1##76##], and MeSA has been shown to attract, as well as elicit electrophysiologically significant responses in, lady beetles [##REF##16222805##77##], which are predators of aphids and plant mites. Homology modeling and ligand docking simulations [##REF##14691239##78##,##REF##18175323##79##] using our plant candidate GPCRs, predicted insect receptors, and tentative ligands such as MeSA may be helpful in identifying prospective receptors that respond to the same ligand, for example, MeSA, in both plants and insects.</p>", "<title>Comparison to previous plant GPCR prediction attempts</title>", "<p>The study of heterotrimeric G-protein signaling in metazoan systems is mature and, as a result, most of the bioinformatic analyses of the GPCR family of signaling proteins are based on metazoan proteins and are designed to predict metazoan GPCRs. In comparison to both wet-bench and computational researchers studying mammalian systems, researchers in plant laboratories have relatively little information with which to identify novel candidate GPCRs. To our knowledge there have been only three published attempts at predicting GPCRs in plants, in papers by Fredriksson and Schioth [##REF##15687224##16##], Inoue <italic>et al</italic>. [##REF##15022640##80##], and Moriyama <italic>et al</italic>. [##REF##17064408##49##].</p>", "<p>Fredriksson and Schioth applied a hidden Markov model (HMM) approach. Although published in the year 2005, the GPCR prediction attempt by Fredriksson and Schioth [##REF##15687224##16##] was performed on a pre-genome sequencing NCBI Genscan data set containing only 6,600 <italic>Arabidopsis </italic>predicted proteins [##REF##15687224##16##] compared to the current 29,988. Their analyses identified only GCR1 and five of the MLO family proteins as GPCRs in the <italic>Arabidopsis </italic>data set. The identification of GCR1 is not surprising as the GCR1 protein sequence was already shown to have sufficient similarity to sequences from several classes of GPCRs to be identified as a GPCR by BLAST and PSI-BLAST analyses [##REF##10548735##81##]. The identification of five <italic>Arabidopsis </italic>MLO sequences as GPCRs is also not surprising as Fredriksson and Schioth [##REF##15687224##16##] utilized HMMs trained on the highly similar MLO family [##REF##15003597##82##]. Fredriksson and Schioth [##REF##15687224##16##] did extend their prediction attempts to another plant system consisting of 2,400 predicted proteins from the incomplete <italic>Oryza </italic>proteome, and this analysis identified only a single protein, an MLO, as a GPCR. It should be noted that the plant-specific MLOs have been described as GPCRs based solely on their 7TM topology and there are no genetic, physiological, or biochemical experimental data to support their identification as GPCRs. In fact, the one experimental test of Gα coupling, with barley MLO1, yielded negative results [##REF##11919636##67##].</p>", "<p>During the course of our study, we examined another HMM-based approach, PRED-GPCR [##REF##15215415##83##], but this approach was ultimately excluded from our final analyses. PRED-GPCR utilizes a homology-oriented probabilistic approach based on identifying query sequence similarities to descriptive GPCR family-specific 'signature' motifs. Profile HMM GPCR family signatures were derived from low entropy regions of multiple sequence alignments based on GPCRs identified in the Swiss-Prot and TrEMBL databases and sorted into families. Notably, this approach does not explicitly use or assume any topological information.</p>", "<p>Our PRED-GPCR analysis of the version 6 <italic>Arabidopsis </italic>proteome using the default settings identified only seven sequences (Additional data file 10). Because PRED-GPCR is based on multiple sequence alignment profile HMMs, relaxing the default settings may allow for identification of candidate GPCRs that are evolutionarily divergent from the previously identified GPCRs within the PRED-GPCR training set. Using relaxed user defined settings increased our candidate list to 19 non-redundant sequences (Additional data file 10), and the results for the PRED-GPCR default and user defined setting analyses have only one sequence in common (At4g19050.1). None of these PRED-GPCR predicted sequences were identified by our GPCR prediction pipeline. This was due to their identification as a non-GPCR by the QFC algorithm, either with or without the ion channel filter, and their predicted non-7TM topology, with the exception of At2g36630.1, which was predicted as a GPCR by the QFC but has 9 or 11 predicted TM domains, and At1g52780.1, which was not predicted by the QFC and was predicted only by Phobius to have 7 TM domains. The remainder of the PRED-GPCR predicted sequences have 0-3 or 23-24 TM domains. None of the 19 PRED-GPCR predicted sequences were identified as a GPCR by GPCRHMM.</p>", "<p>The apparent inability of PRED-GPCR to identify <italic>Arabidopsis </italic>candidate GPCRs may reflect the fact that PRED-GPCR was developed and trained using a data set composed of only class B, C, D, and F GPCR sequences with a high relative proportion of sequences coming from class F, the frizzled/smoothened group. By contrast, our classification analyses using GPCRsIdentifier [##REF##17032692##50##] identifies nearly all of our high ranking candidates in all three plant proteomes as class A GPCRs (Table ##TAB##3##4##), and our whole proteome analyses suggests class A type candidate GPCRs comprise the majority in plants (data not shown). This comparison provides a rationale for why these proteins were not identified by the PRED-GPCR methodology, and indicates that HMM-based approaches will prove more useful in plants when retrained using plant-specific HMMs derived from candidate plant GPCRs verified to couple with Gα, such as those identified in the present report.</p>", "<p>In 2004, Inoue <italic>et al</italic>. [##REF##15022640##80##] described the binary topology pattern (BTP) approach and applied it to the analyses of several proteomes. The BTP method [##REF##15022640##80##] is entirely different from the QFC and HMM-based approaches in that it does not directly use any primary sequence information. The BTP method is based on the observation that although the sequences of extra-transmembrane regions (the loops and tails) of GPCRs are highly variable, there is a tendency for the lengths of these regions to be similar within GPCR families. By an iterative process, Inoue <italic>et al</italic>. [##REF##15022640##80##] coded the extra-transmembraneous regions of known GPCRs as having a short (0) or long (1) length and found that a binary code representation of protein topology (for example, 10011001) could be used for GPCR identification and classification.</p>", "<p>The BTP data set published by Inoue <italic>et al</italic>. [##REF##15022640##80##] was derived from the August 13, 2001 release of the <italic>Arabidopsis </italic>proteome (25,542 sequences) and most predictions are invalidated by the subsequent high quality refinements of the <italic>Arabidopsis </italic>proteome. Examination of the <italic>Arabidopsis </italic>candidate GPCR data set predicted by Inoue <italic>et al</italic>. [##REF##15022640##80##] using the BTP method showed that only 57 of the 100 predicted candidates had sequences that remain identical to a protein sequence in the current version (v7.0) of the <italic>Arabidopsis </italic>proteome. An additional 8 sequences have 100% identity over the aligned region, but have protein lengths that differ from the current sequence. We discount these because the BTP method is based on coding residue segment lengths. One BTP predicted GPCR sequence was identified by Inoue <italic>et al</italic>. as At1g42560, but actually shows the highest identity (86%) to At2g33670.1. A comparison of the still-valid 49 BTP-predicted sequences with our candidate GPCR data set shows that there are 11 sequences in common. Most notably, GCR1 and Cand7, both found within our high ranking candidate GPCR set, are identified by the BTP method. The BTP identification of GCR1, which has previously been shown to couple to GPA1 [##REF##15155892##60##], and Cand7, which we show in the present study to couple to GPA1, indicates they have true GPCR topological characteristics beyond their heptahelical nature and provides further computational support for their identification as likely GPCRs. It would be interesting to see how the method of Inoue <italic>et al</italic>. [##REF##15022640##80##] would perform on the current proteome; however, the BTP code was not made available.</p>", "<p>The study performed by Moriyama <italic>et al</italic>. [##REF##17064408##49##] is the most recent attempt at predicting GPCRs in <italic>Arabidopsis</italic>. Moriyama <italic>et al</italic>. [##REF##17064408##49##] utilized multiple alignment free computational methods, along with TM prediction by HMMTOP2, to identify 394 sequences with predicted 5-10 TM domains. Although Moriyama <italic>et al</italic>. [##REF##17064408##49##] further constricted this set to 54 sequences by a 7TM prediction by HMMTOP2, reliance on a single TM predictor can lead to both false positives and false negatives. Combinatorial approaches have been shown to greatly increase discrimination of 7TM sequences [##REF##16274668##84##] because topology prediction programs' strengths and weaknesses vary, even in the top rated topology prediction programs [##REF##15932905##39##]. Importantly, other GPCR prediction studies, including the analysis by Moriyama <italic>et al</italic>. [##REF##17064408##49##], often failed to utilize signal peptide prediction to account for the confounding effect of signal peptides on TM domain prediction [##REF##17483518##37##]. We found 6,739 non-redundant membrane proteins in the <italic>Arabidopsis </italic>proteome using Phobius, of which 1,209 also had predicted signal peptides. Had we also not accounted for signal peptide misprediction, we would have mistakenly discarded 2/11 proteins from our upper bin of high ranking GPCRs, including Cand7, which does physically couple with GPA1.</p>", "<p>Although we report the predicted amino terminus location of our candidate GPCRs, and nearly all of our high ranking candidate GPCRs do indeed have a predicted extracellular amino terminus, we differ from Moriyama <italic>et al</italic>. [##REF##17064408##49##] in that we did not specifically integrate that information into our GPCR prediction pipeline. However, had we integrated this criterion, our high ranking candidate lists would not have changed significantly (data not shown).</p>", "<p>Our use of the alignment-free HMM GPCRHMM is another methodological difference from Moriyama <italic>et al</italic>. [##REF##17064408##49##]. Another machine learning approach, an alignment based support vector machine method, SAM, was utilized by Moriyama <italic>et al</italic>. [##REF##17064408##49##], but the results were not used to select their broad list of candidates as that method was found to have insufficient predictive power: SAM identified only GCR1 and 14 sequences from the 15 member <italic>Arabidopsis </italic>MLO family as candidate GPCRs. In contrast, we utilized the apparent high specificity of the GPCRHMM software in two serial filtering steps to identify candidate GPCRs with increasing stringency. These steps were exceedingly important as our focus went beyond computational analyses towards selecting candidate GPCRs for our functional analyses. Our GPCR prediction pipeline, which ended with our high stringency GPCRHMM filter, enabled the identification of 11 target sequences out of 29,988 non-redundant sequences in the <italic>Arabidopsis </italic>proteome.</p>", "<p>Of the 394 sequences listed in Moriyama <italic>et al</italic>.'s [##REF##17064408##49##] larger data set of possible 7TM putative receptors, we found 18 sequences that are actually redundant with other sequences and four sequences that are no longer found within the current version (v7) of the proteome. Comparing our high ranking candidate data set to Moriyama <italic>et al</italic>.'s high priority list shows that we predict only 14.8% (8/54) of their list to be GPCRs, and their list is missing half of our high ranking candidate GPCRs (Additional data file 11). Comparing our complete set of 127 candidate GPCRs with Moriyama <italic>et al</italic>.'s remaining present and non-redundant 372 sequences shows a similar story as there are only 63 sequences in common; we predict only 16.9% of Moriyama <italic>et al</italic>.'s list to actually be GPCRs (Additional data file 11). Perhaps this is due to a difference in research focus as Moriyama <italic>et al</italic>. attempt to cast the broadest net possible while identifying candidate 7TMpRs while we attempt to find the most highly likely candidate GPCRs.</p>", "<p>Although there is overlap between our high ranking candidate GPCRs and Moriyama <italic>et al</italic>.'s high priority list, there are some interesting differences between the two, especially in light of our <italic>in vivo </italic>coupling results. The list of highest priority candidates identified by Moriyama <italic>et al</italic>. [##REF##17064408##49##] includes Cand8, but not its closest homolog, Cand2; in fact, Cand2 is not identified by Moriyama <italic>et al</italic>. [##REF##17064408##49##] as a candidate GPCR even using their broadest definition. Likewise, the method of Moriyama <italic>et al</italic>. [##REF##17064408##49##] lists Cand3 as a high priority candidate GPCR but fails to identify the highly similar splice variant Cand5. We have shown here that all four proteins do in fact physically couple with GPA1. We also show by direct biological experimentation that Cand7 and HHP2 also interact with GPA1; these proteins are found only in Moriyama <italic>et al</italic>.'s broader list of nearly 400 candidates. This suggests the power and focus of our high stringency combinatorial analyses.</p>", "<p>Biologically, GPCRs are interesting because of their omnipresence in metazoa and their physiological importance, while computationally, the GPCR family is interesting due the extreme range of sequence divergence, which provides an interesting case for testing the limits of bioinformatic prediction. GPCR signaling via the heterotrimeric G-protein in <italic>Arabidopsis </italic>is especially interesting because the G-protein complex contains only single canonical Gα and Gβ subunits, which leads to the obvious question as to whether the complement of <italic>Arabidopsis </italic>candidate GPCRs is similarly limited. Our data now provide an answer to this question as we show, using the same protein-protein coupling assay used for GCR1 and RGS1, that at least seven additional candidate GPCRs are present in <italic>Arabidopsis</italic>. Although we provide evidence showing the physical coupling of these heptahelical proteins to GPA1, we follow the convention of the GPCR community and still call these proteins candidate GPCRs to reflect the fact that a signaling ligand has not yet been identified and they, therefore, cannot unequivocally be called GPCRs. To date, this is also the situation for GCR1, and RGS1, as well as for the GCR1 homolog in pea [##REF##17587233##61##].</p>", "<p>While our study appears highly specific, it is complemented by the efforts of Moriyama <italic>et al</italic>. [##REF##17064408##49##] and Inoue <italic>et al</italic>. [##REF##15022640##80##], who used different prediction methods. The combinatorial approach has strength in that it considers diverse information sources before arriving at a conclusion, and thus further combination of these three independent studies should provide an even greater level of confidence that the intersecting sets of predicted GPCRs are truly G-protein coupled receptors.</p>" ]
[ "<title>Conclusion</title>", "<p>We have used a combinatorial approach to identify novel GPCRs based on the direct prediction of GPCRs by the QFC algorithm and GPCRHMM; signal peptide detection by Phobius; transmembrane domain prediction by TMHMM2, HMMTOP2, and Phobius; and subsequent GPCR classification by GPCRsIdentifier and coupling specificity prediction by Pred-Couple 2. After identification of candidate GPCRs, we bridged the gap between computational biology and wet-bench biology by experimental demonstration that the majority of our upper bin high ranking GPCRs, as well as the one lower bin high ranking GPCR that we tested, can physically interact with the Gα subunit of the <italic>Arabidopsis </italic>heterotrimer. Notably, this extension to wet bench analysis was not performed in the previous plant GPCR prediction attempts, and is rarely, if at all, performed in bioinformatic studies predicting novel GPCRs in metazoans. With experimental evidence in hand to validate our method, we classified our high ranking candidate GPCRs to examine their possible functional diversity using a non-linear sequence dependent method and examined our candidates for annotated functional protein domains. Additionally, our within-proteome and cross-proteome molecular evolutionary analyses show that our high ranking candidate GPCRs are evolutionarily conserved and that our method can be used not only to identify individual candidate GPCRs but also to identify evolutionarily conserved candidate GPCR families. Some high ranking candidate GPCRs and GPCR families are uniquely conserved within plants, while others show evolutionary conservation that extends to metazoans. These evolutionary relationships reinforce the probable functional importance of the candidate GPCRs that we have identified, and the present study is the first step towards determining their physiological roles in G-protein signaling.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Computational prediction and <italic>in vivo</italic> protein coupling experiments identify candidate plant G-protein coupled receptors in <italic>Arabidopsis</italic>, rice and poplar.</p>", "<title>Background</title>", "<p>The classic paradigm of heterotrimeric G-protein signaling describes a heptahelical, membrane-spanning G-protein coupled receptor that physically interacts with an intracellular Gα subunit of the G-protein heterotrimer to transduce signals. G-protein coupled receptors comprise the largest protein superfamily in metazoa and are physiologically important as they sense highly diverse stimuli and play key roles in human disease. The heterotrimeric G-protein signaling mechanism is conserved across metazoa, and also readily identifiable in plants, but the low sequence conservation of G-protein coupled receptors hampers the identification of novel ones. Using diverse computational methods, we performed whole-proteome analyses of the three dominant model plant species, the herbaceous dicot <italic>Arabidopsis thaliana </italic>(mouse-eared cress), the monocot <italic>Oryza sativa </italic>(rice), and the woody dicot <italic>Populus trichocarpa </italic>(poplar), to identify plant protein sequences most likely to be GPCRs.</p>", "<title>Results</title>", "<p>Our stringent bioinformatic pipeline allowed the high confidence identification of candidate G-protein coupled receptors within the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes. We extended these computational results through actual wet-bench experiments where we tested over half of our highest ranking <italic>Arabidopsis </italic>candidate G-protein coupled receptors for the ability to physically couple with GPA1, the sole Gα in <italic>Arabidopsis</italic>. We found that seven out of eight tested candidate G-protein coupled receptors do in fact interact with GPA1. We show through G-protein coupled receptor classification and molecular evolutionary analyses that both individual G-protein coupled receptor candidates and candidate G-protein coupled receptor families are conserved across plant species and that, in some cases, this conservation extends to metazoans.</p>", "<title>Conclusion</title>", "<p>Our computational and wet-bench results provide the first step toward understanding the diversity, conservation, and functional roles of plant candidate G-protein coupled receptors.</p>" ]
[ "<title>Abbreviations</title>", "<p>7TMpR, 7TM putative receptor; BTP, binary topology pattern; Cub, carboxy-terminal half of ubiquitin; GCR, G-protein coupled receptor (from plants); GPCR, G-protein coupled receptor; HHP, heptahelical protein; HMM, hidden Markov model; MeSA, methyl salicylate; MLO, mildew resistance locus o; NubG, low affinity mutant of Nub<sub>wt</sub>; Nub<sub>wt</sub>, wild type amino-terminal half of ubiquitin; QFC, quasi-periodic feature classifier; RGS, regulator of G-protein signaling; TM, transmembrane; TOM, tobamovirus replication protein.</p>", "<title>Authors' contributions</title>", "<p>SMA conceived of and supervised the study. TEG contributed study design and performed the computational and wet-bench analyses. SMA and TEG co-wrote the manuscript. JK contributed to both the analysis and manuscript preparation. All authors edited and approved the final manuscript.</p>", "<title>Additional data files</title>", "<p>The following additional data are available with the online version of this paper. Additional data files ##SUPPL##0##1##, ##SUPPL##1##2##, ##SUPPL##2##3## are tables containing bioinformatic characterizations of our second tier candidate G-protein coupled receptors from the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes, respectively. Additional data file ##SUPPL##3##4## contains a reconstructed phylogenetic tree of the Cand6/7 GPCR 'superfamily'. Additional data file ##SUPPL##4##5## contains a multiple sequence alignment of Cand7 (At5g18520) and its closest homologs. Additional data files ##SUPPL##5##6## and ##SUPPL##6##7## contain tables listing results from our Pfam A and Pfam B database analyses, respectively. Additional data file ##SUPPL##7##8## contains a table listing the <italic>Arabidopsis </italic>sequences removed from our analysis by our QFC ion channel filter. Additional data file ##SUPPL##8##9## is a table containing the PredCouple 2 predicted coupling specificities of our candidate GPCRs from all three proteomes. Additional data file ##SUPPL##9##10## is a table presenting our bioinformatic characterization of the <italic>Arabidopsis </italic>proteome (version 6) sequences predicted to be candidate GPCRs by PRED-GPCR. Additional data file ##SUPPL##10##11## is a Venn diagram detailing the extent of overlap between candidate GPCRs predicted by our analysis and that of Moriyama <italic>et al</italic>. [##REF##17064408##49##]. Additional data files ##SUPPL##11##12##, ##SUPPL##12##13##, and ##SUPPL##13##14## are tables identifying protein redundancies within the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes, respectively. Additional data files ##SUPPL##11##12##, ##SUPPL##12##13##, and ##SUPPL##13##14## also contain the complete list of <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>protein sequence identifiers, including splice variant identifiers, used in this study.</p>", "<title>Supplementary Material</title>" ]
[ "<title>Acknowledgements</title>", "<p>This research was supported by the National Science Foundation grant MCB-0618402. JK was supported in part by the Penn Genome Frontiers Institute and a grant from the Pennsylvania Department of Health. The authors would like to thank Dr Markus Wistrand and Dr Lukas Käll (GPCRHMM, Phobius), Dr Jannick Bendtsen (TMHMM), and Dr Pantelis Bagos (PRED-GPCR) for their invaluable assistance and discussion. The authors appreciate the input from Dr Sona Pandey and Ms Liza Wilson concerning an early version of the <italic>Arabidopsis </italic>QFC dataset, and the timely and excellent technical assistance of Ms Stephanie Gookin throughout this work.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Flowchart detailing our <italic>A. thaliana </italic>candidate GPCR identification and <italic>in vivo </italic>analysis scheme. Numbers in parentheses include redundant protein sequences. A complete list of splice variants and redundant proteins for the <italic>Arabidopsis </italic>proteome is supplied in Additional data file 12.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Proportional Venn diagrams detailing the number of predicted and co-predicted 7TM protein sequences in the non-redundant <bold>(a) </bold><italic>Arabidopsis</italic>, <bold>(b) </bold><italic>Oryza</italic>, and <bold>(c) </bold><italic>Populus </italic>proteomes. Signal peptides were removed <italic>in silico </italic>prior to topology analyses.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Experimental organization and two representative results for GPA1-candidate GPCR interaction assessed by the split-ubiquitin system. <bold>(a) </bold>Schematic showing a simplified outline of the split-ubiquitin system assay: protein A is fused to the amino-terminal half of ubiquitin as an amino- or carboxy-terminal fusion (only the amino-terminal fusion orientation is shown here); protein B is fused to the carboxy-terminal half of ubiquitin, which in turn has a fused artificial transcription factor (PLV). Interaction of protein A with protein B brings the two halves of ubiquitin into close proximity and a functional ubiquitin molecule is restored. Ubiquitin specific proteases cleave off PLV, which translocates to the nucleus and activates transcription of target genes allowing for yeast growth. <bold>(b) </bold>Cartoon detailing the control (Nub<sub>wt</sub>) and test (NubG) fusion protein construct orientations for sectors 1-4 in (c). <bold>(c) </bold>Schematic depicting the organization of the interaction assay plates in (d,e). The X represents the candidate GPCR open reading frame (ORF). Sectors 5-8 show the reciprocal assay. <bold>(d,e) </bold>Representative results for the ability of candidate GPCRs to interact with GPA1, the Gα subunit, on 1 mM methionine repression media. Diploid yeast containing GPA1 fusion constructs and either candidate Cand5 (d) or TOM1 (e) fusion constructs both grow on minimal media (not shown), but Cand5 specifically interacts with GPA1 and allows growth on the repression media (d, boxed sector) while TOM1 does not (e, boxed sector).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Flowchart detailing our <italic>O. sativa </italic>candidate GPCR identification scheme. Numbers in parentheses include redundant protein sequences. A complete list of splice variants and redundant proteins for the <italic>Oryza </italic>proteome is supplied in Additional data file 13.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p>Flowchart detailing our <italic>P. trichocarpa </italic>candidate GPCR identification scheme. Numbers in parentheses include redundant protein sequences. A complete list of splice variants and redundant proteins for the <italic>Populus </italic>proteome is supplied in Additional data file 14.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p>Molecular evolutionary analyses of candidate GPCRs shown to physically interact with GPA1. The <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes were subjected to BLAST analyses (e-20 cutoff) using our positive interacting candidate GPCR protein sequences (filled triangles). Multiple sequence alignments were created in ClustalX and evolutionary relationships were estimated using the neighbor joining method with 1,000 bootstrap replicates. Sequences identified by our bioinformatic pipeline as candidate GPCRs are indicated with empty triangles, with upward pointing triangles indicating those found within our high ranking candidate sets and downward pointing triangles indicating those present in the second tier. Scale bars indicate evolutionary distance as measured by residue substitutions per site. <bold>(a) </bold>RGS1; <bold>(b) </bold>GCR1; <bold>(c) </bold>Cand2 and Cand8; <bold>(d) </bold>Cand1; <bold>(e) </bold>Cand3, 4, and 5; <bold>(f) </bold>Cand6 and Cand7; and <bold>(g) </bold>HHP2.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p>Multiple sequence alignment of the Cand2 (At3g05010.1) and Cand8 (At5g27210.1) family. The family is widely conserved beyond the <italic>Oryza </italic>and <italic>Populus </italic>proteomes; homologous sequences can be found in other dicotyledonous plants (grape [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"CAN61534.1\">CAN61534.1</ext-link>]), monocotyledonous plants (sorghum [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAM47585.1\">AAM47585.1</ext-link>]), insects (honeybee [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"XP_625021.2\">XP_625021.2</ext-link>]), and mammals (mouse GPR175 protein [GenBank:<ext-link ext-link-type=\"gen\" xlink:href=\"AAH10244.1\">AAH10244.1</ext-link>]). The long carboxy-terminal region of the honeybee and mouse protein sequences are truncated due to the lack of any meaningful alignment beyond that shown. Schematic above the alignment blocks indicates the 7TM topology of Cand2, as predicted by TMHMM. Blue lines, extracellular regions; blue blocks, TM domains; red lines, intracellular regions.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Characterization of our high ranking <italic>Arabidopsis </italic>candidate G-protein coupled receptors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"left\">ID</td><td align=\"center\">QFC</td><td align=\"center\">GPCRHMM</td><td align=\"left\">TMHMM</td><td align=\"left\">HMMTOP</td><td align=\"left\">Phobius</td><td align=\"left\">Pcut-T</td><td align=\"left\">Pcut-H</td></tr></thead><tbody><tr><td align=\"left\">Upper bin</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>At1g48270.1</bold><sup>‡</sup></td><td align=\"left\">GCR1</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At3g26090.1</bold><sup>†</sup></td><td align=\"left\">RGS1</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At1g57680.1</bold></td><td align=\"left\"><bold>Cand1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At3g05010.1</bold></td><td align=\"left\"><bold>Cand2</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At3g59090.1</bold></td><td align=\"left\"><bold>Cand3</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> At3g59090.2</td><td align=\"left\"><bold>Cand4</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At3g59090.3</bold></td><td align=\"left\"><bold>Cand5</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <italic>At4g21790.1</italic></td><td align=\"left\">TOM1</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">8 (in)</td><td/><td/></tr><tr><td align=\"left\"> At5g02630.1</td><td align=\"left\">Cand6</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">6 (out)</td><td align=\"left\">8 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (in)</td><td align=\"left\">7 (in)</td></tr><tr><td align=\"left\"> <bold>At5g18520.1</bold></td><td align=\"left\"><bold>Cand7</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">8 (in)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td></tr><tr><td align=\"left\"> <bold>At5g27210.1</bold></td><td align=\"left\"><bold>Cand8</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Lower bin</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> At2g02180.1</td><td align=\"left\">TOM3</td><td align=\"center\">*</td><td align=\"center\">-0.47</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>At4g30850.1</bold></td><td align=\"left\"><bold>HHP2</bold></td><td align=\"center\">*</td><td align=\"center\">-4.83</td><td align=\"left\">7 (in)</td><td align=\"left\">7 (in)</td><td align=\"left\">8 (out)</td><td/><td/></tr><tr><td align=\"left\"> At5g26740.1</td><td align=\"left\">Cand9</td><td align=\"center\">*</td><td align=\"center\">-7.95</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> At1g14530.1</td><td align=\"left\">THH1</td><td align=\"center\">*</td><td align=\"center\">-8.95</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> At3g05940.1</td><td align=\"left\">Cand10</td><td align=\"center\">*</td><td align=\"center\">-9.89</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Results for the split-ubiquitin system protein-protein interaction assays between candidate GPCR Nub fusion proteins and the GPA1-Cub-PLV fusion protein</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\" colspan=\"4\">Candidate GPCR Nub fusion orientations</td></tr><tr><td/><td/><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Locus tested</td><td align=\"left\">Name</td><td align=\"center\">Nub<sub>wt</sub>X</td><td align=\"center\">Nub<sub>G</sub>X</td><td align=\"center\">XNub<sub>wt</sub></td><td align=\"center\">XNub<sub>G</sub></td></tr></thead><tbody><tr><td align=\"left\">At1g57680.1</td><td align=\"left\">Cand1</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g05010.1</td><td align=\"left\">Cand2</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g59090.1</td><td align=\"left\">Cand3</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g59090.3</td><td align=\"left\">Cand5</td><td align=\"center\">++</td><td align=\"center\">+</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At5g18520.1</td><td align=\"left\">Cand7</td><td align=\"center\">++</td><td align=\"center\">+</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At5g27210.1</td><td align=\"left\">Cand8</td><td align=\"center\">++</td><td align=\"center\">++</td><td align=\"center\">+</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At4g21790.1</td><td align=\"left\">TOM1</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At4g30850.1</td><td align=\"left\">HHP2</td><td align=\"center\">++</td><td align=\"center\">+</td><td align=\"center\">-</td><td align=\"center\">-</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Results for the split-ubiquitin system protein-protein interaction assays between GPA1 Nub fusion proteins and the candidate GPCR-Cub-PLV fusion proteins</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td align=\"center\" colspan=\"4\">GPA1 Nub fusion orientations</td></tr><tr><td/><td/><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Locus tested (X-Cub-PLV)</td><td align=\"left\">Name</td><td align=\"center\">Nub<sub>wt</sub>-GPA1</td><td align=\"center\">Nub<sub>G</sub>-GPA1</td><td align=\"center\">GPA1-Nub<sub>wt</sub></td><td align=\"center\">GPA1-Nub<sub>G</sub></td></tr></thead><tbody><tr><td align=\"left\">At1g57680.1</td><td align=\"left\">Cand1</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g05010.1</td><td align=\"left\">Cand2</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">+</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g59090.1</td><td align=\"left\">Cand3</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At3g59090.3</td><td align=\"left\">Cand5</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At5g18520.1</td><td align=\"left\">Cand7</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At5g27210.1</td><td align=\"left\">Cand8</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">-</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At4g21790.1</td><td align=\"left\">TOM1</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">++</td><td align=\"center\">-</td></tr><tr><td align=\"left\">At4g30850.1</td><td align=\"left\">HHP2</td><td align=\"center\">++</td><td align=\"center\">-</td><td align=\"center\">+</td><td align=\"center\">-</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>GPCRsIdentifier classification of the high ranking candidate GPCRs in the <italic>Arabidopsis</italic>, <italic>Oryza</italic>, and <italic>Populus </italic>proteomes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Genus and locus</td><td align=\"left\">ID</td><td align=\"left\">Prediction</td><td align=\"left\">Family</td><td align=\"left\">Subfamily</td><td align=\"left\">Subfamily type</td></tr></thead><tbody><tr><td align=\"left\"><bold>\n <italic>Arabidopsis</italic>\n </bold></td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>At1g48270.1</bold><sup>†</sup></td><td align=\"left\">GCR1</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr><tr><td align=\"left\"> <bold>At3g26090.1</bold><sup>†</sup></td><td align=\"left\">RGS1</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> <bold>At1g57680.1</bold></td><td align=\"left\">Cand1</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Viral</td><td/></tr><tr><td align=\"left\"> <bold>At3g05010.1</bold></td><td align=\"left\">Cand2</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 4</td></tr><tr><td align=\"left\"> <bold>At3g59090.1</bold></td><td align=\"left\">Cand3</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory FOR-like</td></tr><tr><td align=\"left\"> At3g59090.2</td><td align=\"left\">Cand4</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory FOR-like</td></tr><tr><td align=\"left\"> <bold>At3g59090.3</bold></td><td align=\"left\">Cand5</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 8</td></tr><tr><td align=\"left\"> At4g21790.1*</td><td align=\"left\">TOM1</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 10</td></tr><tr><td align=\"left\"> At5g02630.1</td><td align=\"left\">Cand6</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 4</td></tr><tr><td align=\"left\"> <bold>At5g18520.1</bold></td><td align=\"left\">Cand7</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> <bold>At5g27210.1</bold></td><td align=\"left\">Cand8</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 13</td></tr><tr><td align=\"left\"> At2g02180.1</td><td align=\"left\">TOM3</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 5</td></tr><tr><td align=\"left\"> <bold>At4g30850.1</bold></td><td align=\"left\">HHP2</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 1</td></tr><tr><td align=\"left\"> At5g26740.1</td><td align=\"left\">Cand9</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 4</td></tr><tr><td align=\"left\"> At1g14530.1</td><td align=\"left\">THH1</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> At3g05940.1</td><td align=\"left\">Cand10</td><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam 2</td></tr><tr><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>\n <italic>Oryza</italic>\n </bold></td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <italic>Os01g54784.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> <italic>Os01g61970.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Rhodopsin</td><td/></tr><tr><td align=\"left\"> <italic>Os01g66190.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class C</td><td/><td/></tr><tr><td align=\"left\"> <italic>Os02g40550.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Rhodopsin</td><td/></tr><tr><td align=\"left\"> Os02g45870.1</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr><tr><td align=\"left\"> Os03g36790.1</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam5</td></tr><tr><td align=\"left\"> <italic>Os03g54920.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Rhodopsin</td><td/></tr><tr><td align=\"left\"> <italic>Os04g36630.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam10</td></tr><tr><td align=\"left\"> <italic>Os04g42960.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Rhodopsin</td><td/></tr><tr><td align=\"left\"> Os05g39730.1</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Lysosphingolipid</td><td/></tr><tr><td align=\"left\"> <italic>Os06g04130.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Rhodopsin</td><td/></tr><tr><td align=\"left\"> <italic>Os06g09930.1</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr><tr><td align=\"left\"> Os07g01250.1</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam4</td></tr><tr><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>\n <italic>Populus</italic>\n </bold></td><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <italic>Pop205267</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> <italic>Pop240991</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam2</td></tr><tr><td align=\"left\"> <italic>Pop241510</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam4</td></tr><tr><td align=\"left\"> Pop254437</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> <italic>Pop256636</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam10</td></tr><tr><td align=\"left\"> Pop272274</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Thyrotropin</td><td/></tr><tr><td align=\"left\"> Pop273474</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> Pop279432</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class C</td><td/><td/></tr><tr><td align=\"left\"> <italic>Pop294952</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam2</td></tr><tr><td align=\"left\"> <italic>Pop554569</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Nucleotide</td><td/></tr><tr><td align=\"left\"> <italic>Pop561523</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam4</td></tr><tr><td align=\"left\"> <italic>Pop569632</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr><tr><td align=\"left\"> Pop647588</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr><tr><td align=\"left\"> <italic>Pop742547</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam6</td></tr><tr><td align=\"left\"> Pop762585</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Peptide</td><td/></tr><tr><td align=\"left\"> Pop796139</td><td/><td align=\"left\">Globular</td><td/><td/><td/></tr><tr><td align=\"left\"> <italic>Pop797267</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Nucleotide</td><td/></tr><tr><td align=\"left\"> <italic>Pop820940</italic></td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam9</td></tr><tr><td align=\"left\"> Pop822025</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory II fam5</td></tr><tr><td align=\"left\"> Pop832788</td><td/><td align=\"left\">GPCRs</td><td align=\"left\">Class A</td><td align=\"left\">Olfactory</td><td align=\"left\">Olfactory I fam</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Characterization of our high ranking <italic>Oryza </italic>candidate G-protein coupled receptors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\">QFC</td><td align=\"center\">GPCRHMM</td><td align=\"left\">TMHMM</td><td align=\"left\">HMMTOP</td><td align=\"left\">Phobius</td><td align=\"left\">Pcut-T</td><td align=\"left\">Pcut-H</td><td align=\"left\">Query</td><td align=\"center\">e-value</td></tr></thead><tbody><tr><td align=\"left\"><bold>Upper bin</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Os01g54784.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand3,4,5</td><td align=\"center\">&lt;<italic>e</italic><sup>-85</sup></td></tr><tr><td align=\"left\"> <bold>Os01g61970.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">8 (in)</td><td align=\"left\">9 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-150</sup></td></tr><tr><td align=\"left\"> <bold>Os01g66190.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand1</td><td align=\"center\">e<sup>-65</sup></td></tr><tr><td align=\"left\"> <bold>Os04g36630.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand1</td><td align=\"center\">e<sup>-60</sup></td></tr><tr><td align=\"left\"> Os05g39730.1</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Os06g04130.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (in)</td><td align=\"left\">9 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">6 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-123</sup></td></tr><tr><td align=\"left\"> <bold>Os06g09930.1</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">GCR1</td><td align=\"center\">e<sup>-120</sup></td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Lower bin</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> Os03g36790.1</td><td align=\"center\">*</td><td align=\"center\">-0.81</td><td align=\"left\">8 (in)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> Os07g01250.1</td><td align=\"center\">*</td><td align=\"center\">-1.16</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Os02g40550.1</bold></td><td align=\"center\">*</td><td align=\"center\">-1.17</td><td align=\"left\">8 (in)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-111</sup></td></tr><tr><td align=\"left\"> <bold>Os04g42960.1</bold></td><td align=\"center\">*</td><td align=\"center\">-4.39</td><td align=\"left\">8 (in)</td><td align=\"left\">9 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">8 (in)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-120</sup></td></tr><tr><td align=\"left\"> <bold>Os03g54920.1</bold></td><td align=\"center\">*</td><td align=\"center\">-8.01</td><td align=\"left\">7 (in)</td><td align=\"left\">6 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand2,8</td><td align=\"center\">&lt;e<sup>-96</sup></td></tr><tr><td align=\"left\"> Os02g45870.1</td><td align=\"center\">*</td><td align=\"center\">-9.73</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T6\"><label>Table 6</label><caption><p>Characterization of our high ranking <italic>Populus </italic>candidate G-protein coupled receptors</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Locus</td><td align=\"center\">QFC</td><td align=\"center\">GPCRHMM</td><td align=\"left\">TMHMM</td><td align=\"left\">HMMTOP</td><td align=\"left\">Phobius</td><td align=\"left\">Pcut-T</td><td align=\"left\">Pcut-H</td><td align=\"left\">Query</td><td align=\"center\">e-value</td></tr></thead><tbody><tr><td align=\"left\"><bold>Upper bin</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Pop205267</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand3,4,5</td><td align=\"center\">&lt;e<sup>-110</sup></td></tr><tr><td align=\"left\"> <bold>Pop241510</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-130</sup></td></tr><tr><td align=\"left\"> Pop254437</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> <bold>Pop256636</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand7</td><td align=\"center\">0.0</td></tr><tr><td align=\"left\"> <bold>Pop561523</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">0.0</td></tr><tr><td align=\"left\"> <bold>Pop569632</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand1</td><td align=\"center\">e<sup>-32</sup></td></tr><tr><td align=\"left\"> <bold>Pop742547</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand1</td><td align=\"center\">e<sup>-110</sup></td></tr><tr><td align=\"left\"> <bold>Pop797267</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-129</sup></td></tr><tr><td align=\"left\"> <bold>Pop820940</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">GCR1</td><td align=\"center\">e<sup>-142</sup></td></tr><tr><td align=\"left\"> <bold>Pop272274</bold></td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand1</td><td align=\"center\">e<sup>-114</sup></td></tr><tr><td align=\"left\"> Pop554569</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">Cand7</td><td align=\"center\">e<sup>-130</sup></td></tr><tr><td align=\"left\"> Pop647588</td><td align=\"center\">*</td><td align=\"center\">*</td><td align=\"left\">6 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>Lower bin</bold></td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>Pop294952</bold></td><td align=\"center\">*</td><td align=\"center\">-1.05</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand2,8</td><td align=\"center\">&lt;e<sup>-115</sup></td></tr><tr><td align=\"left\"> <bold>Pop240991</bold></td><td align=\"center\">*</td><td align=\"center\">-2.37</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td align=\"left\">Cand2,8</td><td align=\"center\">&lt;e<sup>-117</sup></td></tr><tr><td align=\"left\"> Pop273474</td><td align=\"center\">*</td><td align=\"center\">-2.99</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/></tr><tr><td align=\"left\"> Pop822025</td><td align=\"center\">*</td><td align=\"center\">-3.67</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Pop279432</td><td align=\"center\">*</td><td align=\"center\">-4.84</td><td align=\"left\">7 (in)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Pop796139</td><td align=\"center\">*</td><td align=\"center\">-5.58</td><td align=\"left\">7 (in)</td><td align=\"left\">8 (in)</td><td align=\"left\">7 (in)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Pop762585</td><td align=\"center\">*</td><td align=\"center\">-6.23</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr><tr><td align=\"left\"> Pop832788</td><td align=\"center\">*</td><td align=\"center\">-8.32</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td align=\"left\">7 (out)</td><td/><td/><td/><td/></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[ "<supplementary-material content-type=\"local-data\" id=\"S1\"><caption><title>Additional data file 1</title><p>Bioinformatic characterization of our second tier candidate G-protein coupled receptors from the <italic>Arabidopsis </italic>proteome.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S2\"><caption><title>Additional data file 2</title><p>Bioinformatic characterization of our second tier candidate G-protein coupled receptors from the <italic>Oryza </italic>proteome.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S3\"><caption><title>Additional data file 3</title><p>Bioinformatic characterization of our second tier candidate G-protein coupled receptors from the <italic>Populus </italic>proteome.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S4\"><caption><title>Additional data file 4</title><p>Reconstructed phylogenetic tree of the Cand6/7 GPCR 'superfamily'.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S5\"><caption><title>Additional data file 5</title><p>Multiple sequence alignment of Cand7 (At5g18520) and its closest homologs.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S6\"><caption><title>Additional data file 6</title><p>Results from our Pfam A database analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S7\"><caption><title>Additional data file 7</title><p>Results from our Pfam B database analysis.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S8\"><caption><title>Additional data file 8</title><p><italic>Arabidopsis </italic>sequences removed from our analysis by our QFC ion channel filter.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S9\"><caption><title>Additional data file 9</title><p>PredCouple 2 predicted coupling specificities of our candidate GPCRs from all three proteomes.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S10\"><caption><title>Additional data file 10</title><p>Bioinformatic characterization of the <italic>Arabidopsis </italic>proteome (version 6) sequences predicted to be candidate GPCRs by PRED-GPCR.</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S11\"><caption><title>Additional data file 11</title><p>Venn diagram detailing the extent of overlap between candidate GPCRs predicted by our analysis and that of Moriyama <italic>et al</italic>. [##REF##17064408##49##].</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S12\"><caption><title>Additional data file 12</title><p>Also listed are <italic>Arabidopsis </italic>protein sequence identifiers, including splice variant identifiers, used in this study</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S13\"><caption><title>Additional data file 13</title><p>Also listed are <italic>Oryza </italic>protein sequence identifiers, including splice variant identifiers, used in this study</p></caption></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"S14\"><caption><title>Additional data file 14</title><p>Also listed are <italic>Populus </italic>protein sequence identifiers, including splice variant identifiers, used in this study</p></caption></supplementary-material>" ]
[ "<table-wrap-foot><p>Protein sequences predicted to be GPCRs by QFC and GPCRHMM are indicated by an asterisk and the GPCRHMM global score is provided for sequences we identify as GPCRs using a relaxed cutoff threshold score of -10. The predicted number of transmembrane domains and the intracellular (in) or extracellular (out) localization of the amino terminus are shown. Pcut-T and Pcut-H describe topology predictions of the mature proteins by TMHMM and HMMTOP, respectively, after <italic>in silico </italic>cleavage at the signal peptide cleavage site predicted by Phobius. Candidates shown to interact with GPA1 <italic>in vivo </italic>using the split-ubiquitin system are identified in bold while the sole negative result from that assay is shown in italics. <sup>‡</sup>GCR1 interaction with GPA1 in the split-ubiquitin system was previously described by Pandey and Assmann [##REF##15155892##60##]. <sup>†</sup>RGS1 interaction with GPA1 in the split-ubiquitin system was previously described by Chen <italic>et al</italic>. [##REF##14500984##20##]; the RGS1 sequence was truncated at the upstream border of the RGS box prior to analysis by GPCRHMM.</p></table-wrap-foot>", "<table-wrap-foot><p>All results were recorded after 3-5 of days of yeast growth on minimal media containing 1 mM methionine to identify proteins that interact specifically with GPA1 as indicated by the extent of yeast growth. (+) indicates moderate growth while (++) indicate heavy yeast growth. Empty vector control plates did not show growth (data not shown). Nub<sub>wt</sub>, wild type amino-terminal half of ubiquitin. NubG, reduced affinity mutant of Nub<sub>wt</sub>.</p></table-wrap-foot>", "<table-wrap-foot><p>Results were recorded after 3-5 days of yeast growth on minimal media containing 1 mM methionine to identify proteins that interact specifically with GPA1 as indicated by the extent of yeast growth. (+) indicates moderate growth while (++) indicate heavy yeast growth. Empty vector control plates did not show growth (data not shown). X-Cub-PLV, candidate GPCR-Cub-PLV fusions. Nub<sub>wt</sub>, wild type amino-terminal half of ubiquitin. NubG, reduced affinity mutant of Nub<sub>wt</sub>.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>Arabidopsis </italic>candidate GPCRs shown to interact with GPA1 <italic>in vivo </italic>in the split-ubiquitin fusion assays are identified in bold while the sole negative result is indicated with an asterisk. <italic>Oryza </italic>and <italic>Populus </italic>candidate GPCRs that are orthologous to one of the emboldened <italic>Arabidopsis </italic>candidates are identified in italic type (see Table 5 for orthology details). <sup>†</sup>GCR1 and RGS1 interaction with GPA1 in the split-ubiquitin system was previously described by Pandey and Assmann [##REF##15155892##60##] and Chen <italic>et al</italic>. [##REF##14500984##20##], respectively.</p></table-wrap-foot>", "<table-wrap-foot><p>High ranking <italic>Oryza </italic>candidate GPCRs that are orthologous to our <italic>Arabidopsis </italic>candidate GPCRs demonstrated to interact with the <italic>Arabidopsis </italic>Gα subunit are shown in bold and BLAST e-values are provided to support their identification (see Table 1 for additional details).</p></table-wrap-foot>", "<table-wrap-foot><p>Protein sequences orthologous to our <italic>Arabidopsis </italic>candidate GPCRs demonstrated to interact with the <italic>Arabidopsis </italic>Gα subunit are shown in bold and BLAST e-values are provided to support their identification (see Table 1 for additional details).</p></table-wrap-foot>" ]
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[{"article-title": ["The SEVENS Database."]}, {"surname": ["De Moraes", "Lewis", "Pare", "Alborn", "Tumlinson"], "given-names": ["CM", "WJ", "PW", "HT", "JH"], "article-title": ["Herbivore-infested plants selectively attract parasitoids."], "source": ["Nature"], "year": ["1998"], "volume": ["393"], "fpage": ["570"], "lpage": ["573"]}, {"article-title": ["The "], "italic": ["Arabidopsis "]}, {"article-title": ["The Institute for Genomic Resource (TIGR) "], "italic": ["Oryza sativa "]}, {"article-title": ["Joint Genome Initiative (JGI) "], "italic": ["Populus trichocarpa "]}, {"article-title": ["The Kim Lab Software Repository."]}, {"surname": ["Hall"], "given-names": ["TA"], "article-title": ["BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT."], "source": ["Nucleic Acids Symp Ser"], "year": ["1999"], "volume": ["41"], "fpage": ["95"], "lpage": ["98"]}]
{ "acronym": [], "definition": [] }
91
CC BY
no
2022-01-12 14:47:27
Genome Biol. 2008 Jul 31; 9(7):R120
oa_package/63/e3/PMC2530877.tar.gz
PMC2530895
18776956
[ "<title>BACKGROUND</title>", "<p>Screening for invasive anal squamous cell carcinoma and its precursors has been increasingly advocated in high risk populations, especially HIV infected men having sex with men. Although the most recent U.S. Public Health Service guidelines for prevention of opportunistic infections[##REF##12617574##1##] do not recommend routine screening, the New York State Department of Health AIDS institute recently recommended baseline and annual anal cytology examinations with referral for high resolution anoscopy and/or biopsy for cytology abnormalities for: men who have sex with men, any patient with a history of anogenital condylomas, and women with abnormal cervical/vulvar histology [##UREF##0##2##]. The present state of knowledge to justify such a screening program was recently reviewed [##REF##16779751##3##,##REF##16335300## 4##]. In 2001, we implemented a comprehensive screening program for anal cancer and its precursor lesions in the UCSD Owen Clinic, an academic multidisciplinary adult HIV clinic in San Diego and have previously presented our observations regarding the prevalence of detected\n abnormalities, their association with degree of immunosuppression, and the reproducibility of screening component measurements [##REF##15577418##5##, ####UREF##1##6##, ##REF##12717041##7####12717041##7##]. In the current work, we present preliminary findings regarding implementation of our screening program and discuss challenges to scientific evaluation of such a screening program using observational cohort data.</p>", "<p>Our specific research aims were: (1) to estimate the incidence of invasive anal cancer (IAC) and case-survival before (1995-2000) and after (2001-2005) screening program implementation and (2) to examine potential screening program quality indicators. We hypothesized that screening-prompted early surgical intervention for IAC would reduce the incidence of IAC requiring treatment with chemoradiation (IAC<sub>chemorad</sub>).</p>" ]
[ "<title>METHODS</title>", "<title>Incidence Analysis</title>", "<p>The study cohort included all patients under care for HIV infection between 1995-2005 at UCSD Owen Clinic, a multidisciplinary academic adult HIV clinic. Follow up time for each patient began on the date of the first clinic visit during the study period or on 1 January 1995 for those already under care. Follow up time ended on the date of the first</p>", "<p>diagnosis of invasive anal cancer (for those developing the outcome) or on the latest of either the date of the last clinic visit during the study period or the end of the study period (for those with visits after 31 December 2005). During the study period, treatment for high grade dysplasia was not routinely offered. All cases of biopsy confirmed invasive anal squamous cell carcinoma were ascertained by review of the clinic electronic medical record, review of surgical pathology records and the medical center cancer registry. Carcinoma in situ (CIS) was classified as a precursor lesion, not as an outcome. Routine anal cytologic screening of all patients under care was implemented as part of a comprehensive anal dysplasia screening program in 2001. Routine human papilloma virus (HPV) typing was not included among the screening procedures. Person-time incidence rates (IR) of IAC were estimated for the pre-screening (1995-2000) and post-screening (2001-2005) periods. Cases of IAC were further classified by primary treatment modality (surgical excision or chemoradiation). Because no treatment for high grade dysplasia was used during the study period, it would not be expected that screening <italic>per se</italic> would reduce the overall incidence of IAC. To estimate the potential impact of screening on IAC<sub>chemorad</sub>, the preventive fractions in the population and in those exposed to screening were estimated for the screening period 2001 – 2005. The preventive fraction for the population (PFp) is defined as the net proportion of all potential cases of IAC<sub>chemorad</sub> that would be prevented by screening-prompted early surgical intervention. The preventive fraction among the exposed (PFe) is the net proportion of all potential cases of IAC<sub>chemorad</sub> in the screened population that were prevented by screening [##REF##3057878##8##,##UREF##2## 9##]. Because of biases inherent in the estimation of preventive fractions from observational studies, we estimated them using two different reference rates of IAC<sub>chemorad</sub> in the absence of screening: (1) IR <sub>1995 – 2000 </sub>and (2) IR <sub>unscreened, 2001-2005</sub>.</p>", "<title>Case Survival Analysis</title>", "<p>Kaplan Meier survival was estimated using two alternative definitions of the origin of time at risk (t0). In one analysis, t0 was taken as the date of diagnosis of IAC. In the alternate analysis, t0 was taken as the date of clinic entry or the opening date of the study period (if visits occurred prior to that date), irrespective of when subsequently the patient was diagnosed with IAC. These analyses were chosen to illustrate the sensitivity of inference regarding case survival to <italic>lead time bias</italic> and <italic>length biased sampling </italic>[##REF##1530116##10##].</p>", "<title>Quality Indicator Analysis</title>", "<p>Five potential program quality indicators were examined: (1) screening coverage; (2) percent technically unsatisfactory anal cytology results; (3) cyto-histologic agreement at HRA; (4) time delay between first abnormal anal cytology and first HRA; (5) time between last clinic visit and last anal cytology. <italic>Screening coverage</italic> is defined in this study as the proportion of the target population screened at least once during the screening period [##REF##9893635##11##]. During the study period, cytologic specimens were obtained using the previously described “blind sampling” technique[##REF##16199742##12##] with a moistened Dacron swab and conventional formalin slide fixation. During the study period, the clinic guideline recommended annual anal cytologic examination for all patients[##REF##10340370##13##] and referral to HRA for any cytologic abnormality [##REF##12717041##7##]. Because of limited availability of trained HRA clinicians, patients were triaged to HRA according to severity of the antecedent cytologic abnormality. For example, a high grade (HSIL) or “atypical squamous cells, cannot rule out high grade” (ASC/H) result took priority in scheduling over either low grade squamous intraepithelial lesion (LSIL) or atypical squamous cells of uncertain significance (ASCUS) results. Quality of individual clinician performance of anal cytologic examination was estimated as the percent of technically unsatisfactory results as determined by the reading cytopathologist. Spearman rho was calculated to determine if there was an association between experience (number of cytologic specimens submitted) and the proportion of technically unsatisfactory cytology results. HRA operator accuracy as a measure of procedural quality was estimated by calculating kappa agreement[##UREF##3##14##] between the most severe histopathologic biopsy diagnosis and the concurrent cytology diagnosis obtained at HRA. For purposes of kappa agreement analysis, cytology results were binary coded as either high grade squamous intraepithelial lesion (HSIL) or lesser abnormality (including low grade SIL, ASCUS and “no atypical or malignant cells”), and histopathologic results were coded as either HSIL (including moderate or severe dysplasia or carcinoma) or lesser abnormality. Following revision of the Bethesda staging system for cervical cytology in 2001[##UREF##4##15##], an additional cytologic category was created: atypical squamous cells, cannot exclude HSIL (ASC/H). This cytologic abnormality was coded as HSIL for analysis of cyto-histologic agreement. In order to monitor the program so that patients with abnormal results are evaluated in a timely fashion, the two measures of procedure delay were examined overall and stratified according to severity of antecedent cytologic diagnosis. Differences in median procedure delay were evaluated using the Kruskal-Wallis test. The last examined potential quality indicator was program coverage, defined as the proportion of patients under care during the study period that underwent anal cytologic screening at least once.</p>", "<p>Statistical analyses were performed using Stata 9.2 (Stata Corporation, College Station, Texas). This study was approved by the UCSD Human Subjects Committee (Project No. 040394).</p>" ]
[ "<title>RESULTS</title>", "<title>Incidence Analysis</title>", "<p>The study cohort included 5,083 patients contributing 13,411 person-years (p-y) at risk between 1 January 1995 and 31 December 2005. Demographic and clinical characteristics of the study cohort have been previously published [##REF##15577418##5##]. The median (IQR) duration of follow up time was 1.8 (0.5 – 4.7) years. During this period, 28 cases of biopsy confirmed IAC were observed, of which 11 were diagnosed in the pre-screening period (1995-2000) and 17 in the screening period (2001-2005). Of the 17 cases diagnosed in the screening period, 10 (59 %) had undergone prior anal cytology screening. Of the 10 IAC patients who had undergone prior anal cytology screening, 2 underwent their first screening less than 6 weeks prior to the diagnosis of IAC. During the screening period, of the 17 IAC cases, the percent with IAC<sub>chemorad </sub>did not vary by screening status (66.7% [6/9] among the unscreened and 75% [6/8] among the screened, exact p=1.0). Table <bold>##TAB##0##1##</bold> presents the person-time incidence rates (per 100,000 person-years) of IAC overall and IAC<sub>chemorad</sub> for the pre-screening (1995-2000) and screening (2001 – 2005) periods. Also presented are estimated incidence rates among the screened patient population at risk. The IAC incidence rates in the pre-screening and screening periods were 199 and 216 per 100,000 person-years, respectively with an incidence rate ratio (IRR <sub>screening/pre-screening</sub>) of 1.1 (95% exact CI: 0.48 – 2.56). Of the 28 IAC cases, 22 (78.6%) received chemoradiation. The proportion receiving chemoradiation in the pre-screening period was 90.9 % (10/11) compared with 70.6 % (12/17) in the screening period (exact p=0.355). The incidence rates of IAC<sub>chemorad</sub> in the pre-screening and screening periods were 181 and 152 per 100,000 person-years, respectively, with a corresponding IRR <sub>screening/pre-screening</sub> of 0.84 (95% exact CI: 0.33 - 2.17). When incidence was estimated only among those who had undergone prior anal cytology screening between 2001 - 2005, the incidence rates were 126 and 94 for IAC overall and IAC<sub>chemorad</sub>, respectively.</p>", "<p>The potential impact of screening without treatment of HSIL lesions on incidence of IAC<sub>chemorad</sub> was estimated by calculating preventive fractions, comparing incidence among those screened to those not screened using two different reference rates for the unscreened population: (1) IR <sub>1995 – 2000 </sub>and (2) IR <sub>unscreened, 2001-2005</sub>. The IRR<sub> 2001-2005/1995-2000</sub> was 0.52 (95% exact CI: 0.16 – 1.58). The corresponding estimated preventive fractions among those exposed to screening (PFe) and in the population (PFp) were 0.48 (95% exact CI: -0.58 - +0.84) and 0.26, respectively. The IRR <sub>screened/not screened, 2001-2005</sub> was 0.24 (95% exact CI: 0.06 - 0.89). The corresponding estimated PFe and PFp were 0.76 (95% exact CI: 0.11 - 0.94) and 0.62, respectively.</p>", "<title>Case Survival Analysis</title>", "<p>Figs. (<bold>##FIG##0##1##</bold>,<bold>##FIG##1##2##</bold>) present Kaplan Meier survival estimates for the 28 IAC cases, stratified by screening period and screening status (1995-2000 <sub>pre-screening</sub>, 2001-2005 <sub>unscreened</sub>, 2001-2005 <sub>screened</sub>). In Fig. (<bold>##FIG##0##1##</bold>), time at risk (t0) was taken as the date of IAC diagnosis. The log rank p-value for equality of the three survival curves was 0.03. In Fig. (<bold>##FIG##1##2##</bold>), t0 was taken as the date of first clinic visit during the study period (or the opening of the study period if visits occurred prior to that date). The log rank p-value under this assumption was 0.015. Under either assumption of origin of risk time, those in the pre-screening period clearly faired the most poorly, while any suggestive difference between groups during the screen-</p>", "<p>ing period was attenuated by assuming t0 to be at clinic entry rather than at IAC diagnosis date.</p>", "<title>Quality Indicator Analysis</title>", "<p>Overall screening coverage during the screening period was 73%. Fourteen clinicians obtained specimens for anal cytologic analysis during the study period. The median number of specimens submitted per provider was 270, varying from 45 to 839. Among the 14 clinicians, the median percent of specimens read as technically unsatisfactory was 25% but varied from 0 – 62% (Fig. <bold>##FIG##2##3##</bold>) with no correlation between the number of cytologic specimens submitted by each clinician and the proportion of technically unsatisfactory results (Spearman rho =-0.0022, p=0.99).</p>", "<p>Six clinicians performed a total of 1763 high resolution anoscopies between 2001 - 2005. The median number of procedures per operator was 176, varying from 16 – 886. Overall chance-corrected cyto-histologic agreement (kappa) was 0.29, but varied among operators from 0.09 – 0.34. In contrast to what was observed for the technical unsatisfactory cytology indicator, there was a definite relationship (Fig. <bold>##FIG##3##4##</bold>) between operator experience and kappa cyto-histologic agreement (Spearman rho 0.89, p= 0.02).</p>", "<p>The median interval (range) between first anal cytologic examination and first HRA for those with any cytologic abnormality was 258 (1 – 1567) days. This interval varied according to the severity of the first reported anal cytology: 46 days (HSIL or ASC/H), 189 days (LSIL), and 503 days (ASCUS) (Kruskal-Wallis p = 0.0001). The median interval (range) between the last anal cytology and the last clinic visit was 207 (0 – 1639) days. This interval varied by severity of the antecedent anal cytology: 235 days( HSIL or ASC/H), 433 days (LSIL), 1305 days (ASCUS), and 393 days (no atypical or malignant cells) (Kruskal-Wallis p = 0.0001).</p>" ]
[ "<title>DISCUSSION</title>", "<title>Is Screening Justified?</title>", "<p>This description of selected early outcomes and process indicators of a comprehensive screening program for anal squamous cell carcinoma in a population of HIV infected adults under care should be viewed in the context of a generally accepted framework of screening for chronic diseases. Such a framework includes satisfaction of several requirements: (1) The disorder should be well defined with known</p>", "<p>prevalence; (2) its consequences should be medically important; (3) an effective remedy should be available; (4) the screening procedures should be simple and safe and should have known and acceptable operating characteristics; (5) the screening program should be cost-effective, (6) implementable in an equitable manner; and (7) the screening procedures should be acceptable to those screened [##REF##15825234##16##]. Of these criteria, there is convincing epidemiological evidence that among HIV-infected men having sex with men, the incidence of IAC is substantial and increasing [##REF##16280701##17##, ####REF##17344204##18##, ##REF##15849533##19##, ##REF##15241824##20####15241824##20##], and that its consequences in terms of morbidity and mortality are medically important [##REF##16779751##3##,##REF##15630846## 21##,##UREF##5## 22##]. The rates of IAC reported in the current study, spanning the first 10 years of potent antiretroviral therapy, are higher than that reported in a cohort of HIV infected patients observed during the period 1996-2003 (92 per 100,000) [##REF##15577408##23##] and comparable to a recent report of IAC incidence among patients with AIDS living in San Diego County between 1996-2000 (144 per 100,000) [##REF##15849533##19##]. There is also evidence that screening procedures thus far recommended [##REF##12384848##24##], based as they are on the model of cervical cancer screening, are relatively simple and safe, with operating characteristics not dissimilar from those reported for cervical cancer screening [##REF##15577418##5##]. Although the addition of HPV typing to cytology is increasingly associated with improved cervical cancer screening program characteristics [##REF##17942871##25##,##REF##17942872## 26##], its role in screening for anal cancer precursors, especially among HIV infected patients, is uncertain. There is some evidence for cost-effectiveness of screening for anal cancer precursors, although the results were sensitive to the assumed rate of progression from precursor lesions to IAC and to the effectiveness of treatment for pre-cancerous lesions [##REF##10340370##13##]. In the case of cervical carcinoma, the most analogous disease process for which screening is universally accepted, the link between precursor lesions and invasive cancer has been established, justifying these precursor lesions as legitimate intermediate targets for detection and intervention [##REF##16124170##27##, ####REF##10037103##28##, ##REF##9764690##29##, ##REF##7827594##30##, ##REF##8463044##31####8463044##31##]. However, the relationship between comparable precursor lesions and IAC, while highly likely based on biological similarities, is less well characterized [##REF##16607677##32##, ####REF##12073068##33##, ##REF##16044425##34####16044425##34##]. In addition, while several treatment modalities have been suggested for management of anal squamous intraepithelial lesions (ASIL), none have been demonstrated to alter natural history in a conclusive way [##REF##16779751##3##]. We are unaware of any published research regarding the acceptability and psychosocial consequences of procedures employed in anal cancer screening, but there are published models regarding how to address this issue in general and in the context of other disease processes [##REF##17210071##35##, ####UREF##6##36##, ##REF##15226053##37####15226053##37##]. Recent survey data suggest that knowledge of the importance of anal cancer, its association with HPV, and available screening modalities among those at risk may be quite limited [##REF##16837830##38##].</p>", "<p>The gold standard for evaluation of screening programs is the randomized controlled trial, but observational designs including both case-control and cohort study designs have contributed to evaluation of screening strategies [##REF##15316907##39##,##REF##3570404## 40##]. The efficacy of cervical cancer screening programs on incidence of and mortality from invasive cervical cancer was based on observational cohort and ecological studies [##REF##600908##41##,##REF##626255## 42##]. Because screening for IAC is increasingly practiced at centers treating patients at increased risk for IAC based on existing epidemiological studies, it is worthwhile to consider how such screening programs could be evaluated and what studies should be undertaken to evaluate screening efficacy [##REF##1530116##10##,##REF##4093187## 43##].</p>", "<title>For What Should We Screen?</title>", "<p>A distinction should be made between <italic>screening for precursor lesions</italic> to IAC and <italic>screening for early IAC</italic>. In the case of precursor lesions, using estimates from an overview of natural history studies of cervical dysplasia as a model for AIN, the probability of progression of CIN 3 to invasive cervical cancer (ICC) averaged 12 % with, however, a 33% probability of CIN 3 regressing to less severe lesions [##REF##8463044##31##]. A more recent meta-analysis estimated the 6 month transition probability of HSIL (including CIN 2 and CIN 3) to ICC to be 0.0037 (95% prediction interval: 0.00004 –0.03386) [##REF##16124170##27##]. Robust estimates of transition probabilities from HSIL to ICC for HIV infected women are not available although there is evidence that, relative to women without HIV infection, transition probabilities from lower grade to higher grade dysplasia are higher, especially for those with low CD4 count; and regression probabilities from higher to lower grade abnormalities were lower [##REF##12825181##44##]. In designing a screening program that targets identification and treatment of precursor lesions of ICC or IAC, the number needed to screen (NNS) [##REF##11129969##45##,##REF##9685274## 46##] to prevent one targeted outcome (e.g. death, IAC, advanced IAC) will vary as a function of transition probabilities, accuracy of screening procedures, effectiveness of treatment of precursor lesions (including recurrence rates and prognosis after treatment). In contrast to screening for precursor lesions, screening for early IAC without treatment of precursor lesions, although reducing the number of patients undergoing intervention who may never have progressed anyway, runs the risk of intervening too late if screening intervals are too long or screening procedures less than completely accurate.</p>", "<title>Direct and Indirect Measures of Screening Program Success</title>", "<p>Potential outcomes for evaluation of a screening program for either precursor lesions or early IAC include, among others[##REF##1530116##10##,##REF##16371251## 47##]: (1) overall mortality rate or mortality attributable to IAC in the population at risk; (2) incidence of all IAC or of advanced IAC; (3) metrics of quality-adjusted survival with IAC; (4) case survival rate; and (5) <italic>stage shift</italic> in presentation with IAC. Of these potential endpoints, Prorok concluded that “there is only one outcome variable known to be valid: the cancer mortality rate”, defined as “the number of cancer deaths per unit of time, per unit of population at risk.”[##REF##1530116##10##] The pretreatment prognosis for IAC is determined, in part, by TNM stage, location, cell differentiation, and comorbid conditions including HIV related immunosuppression [##REF##12780879##48##, ####REF##10219805##49##, ##REF##16396154##50##, ##REF##17522240##51####17522240##51##]. Practice guidelines of the National Comprehensive Cancer Network [##UREF##0##2##] recommend initial local excision for stage T1,N0 (≤ 2 cm diameter, no regional lymph node metastases) anal margin carcinomas and chemoradiation for T1-2, N0 disease for anal canal carcinomas or anal margin carcinomas with positive margins at resection. While it remains controversial whether ablative treatment for precursor ASILs should be routinely offered in the absence of randomized controlled trial evidence of efficacy in reducing the incidence of invasive disease[##REF##16283561##52##,##REF##16972138## 53##], we would argue that an acceptable outcome of a screening program for anal cancer could be detection of disease at a stage no higher than T1N0 if it would permit primary treatment by local resection and spare patients the morbidity associated with chemoradiation. Even if chemoradiation was required, detection of invasive disease at earlier stages should result in more favorable prognosis [##REF##15890590##54##,##REF##16165347## 55##]. Such an early stage endpoint could be viewed as an <italic>indirect measure</italic> or <italic>surrogate marker</italic> of screening efficacy, resulting in a “shift (toward less advanced disease) in the stage distribution of cases detected by screening compared with clinically detected cases.”[##REF##15825234##16##] Validation of stage shift as an indirect outcome measure, however, requires distinguishing prolongation of life due to early treatment from simply extending the <italic>lead time</italic> (the interval between diagnosis at screening and when it would have been detected due to symptoms[##REF##16371251##47##]) with no net gain in survival because treatment had no effect on stage-specific prognosis. Stage shift is additionally vulnerable as an endpoint to what has been termed <italic>overdiagnosis bias </italic>[##REF##1530116##10##] resulting from the identification of early stage invasive disease (e.g. microinvasive disease[##REF##16998682##56##]) that might not have progressed anyway.</p>", "<p>Does the early data from our screening program show any evidence of such a shift toward less advanced disease? Only a randomized control trial comparing screening to no screening can definitively address the question because of biases inherent in observational studies such as our own. It would be expected that initially the overall incidence of IAC might increase in the immediate post screening period due to earlier detection of prevalent cases and to progression of sub clinical to clinical disease among those observed in both the pre-screening and screening periods of our study. With regard to stage of disease at presentation, it is likely that screening would tend to detect preferentially early stage disease with longer pre-clinical durations rather than advanced disease that is more likely to present with symptoms. This phenomenon has been termed <italic>length-biased sampling</italic>[##REF##15825234##16##,##REF##2231057## 57##]. Using chemoradiation as a proxy of disease stage, although we observed no significant difference between the <underline>proportion</underline> of IAC<sub>chemorad</sub> cases comparing screened and not screened during the implementation phase of the screening program (2001 – 2005), there was a significant difference in the <underline>rates</underline> of IAC<sub>chemorad</sub> comparing the screened and not screened during the same period (94 <italic>vs</italic> 395 per 100,000 p-years). This consideration illustrates the extreme caution that must be used in interpreting the statistically significant although likely biased estimate of IRR <sub>screened/not screened, 2001-2005 </sub>of 0.24 reported above. The estimates of prevented fractions based on the same rates are similarly suspect. The estimates of program impact based on the reference rate of IAC<sub>chemorad,1995-2000</sub> = 181 per 100,000 p-years did not support a contention of shift in disease stage due to screening.</p>", "<p>Case survival has been considered a possible endpoint for studies of screening efficacy but its interpretation is subject to both <italic>lead time bias</italic> and <italic>length biased sampling </italic>[##REF##1530116##10##]. Prorok has maintained that distinguishing real increases in case survival attributable to screening from artifactual prolongations in apparent survival due to these biases is “virtually impossible”[##REF##1530116##10##]. However, considering survival time from study entry instead of from IAC diagnosis date would tend to reduce lead time bias by assigning all cases a comparable time at risk origin independent of both screening and diagnosis of IAC. However, if patient entry to the clinic was in any way associated with risk of having subclinical IAC, the comparability of risk at t0 would be compromised. The analyses presented in Figs. (<bold>##FIG##0##1##</bold>,<bold>##FIG##1##2##</bold>) illustrate that the decision regarding assignment of t0 in case survival analysis is nontrivial.</p>", "<title>What Should Be the Screening Interval?</title>", "<p>Because AIN, like CIN, is a dynamic process with incompletely defined natural history and because the sensitivity of a single cytology and HRA-directed biopsy is too low to preclude an important risk of false negative results, repeat screening at defined intervals is required. In the only published cost-effectiveness study of screening for AIN in HIV infected homosexual and bisexual men, Goldie <italic>et al</italic>. found screening annually or every 2 years to be cost-effective, although the results were sensitive to the rate of progression of ASIL to invasive cancer and to the effectiveness of treatment [##REF##10340370##13##]. It is important to note that one of the assumptions of their model was that there was no shift to earlier stage disease as a result of screening. After a negative baseline screening procedure, the incidence of IAC will increase due to false negative screening tests and development of de novo disease. In the case of cervical cancer screening programs, two baseline cytology examinations are recommended to reduce the false negative rate. The incidence curve after one or more negative baseline screenings will be a measure of the duration of the detectable preclinical phase of disease, the <italic>sojourn time </italic>[##UREF##7##58##]. Both case-control and cohort studies have been performed to estimate optimal re-screening interval [##REF##12410014##59##]. The definition of a negative screen is itself not straight forward when the screening test result can be viewed as either continuous (e.g. PSA test for prostate cancer) or ordinal (as in cervical and anal cancer screening). Taking into account the imperfect operating characteristics of both cytology and HRA as screening modalities and the cytology trigger used to refer for HRA (e.g. ASCUS or more abnormal), several combinations of results could define negative tests (e.g. 2 negative cytologies or ASCUS cytology with negative HRA). An additional complication, as in our study, is the limited duration of follow up of individual patients in a dynamic cohort. In our study, the median time at risk was 1.8 years, so estimating rates of incident cancer at increasing times after a negative baseline screen becomes less precise as fewer patients are under observation. The results we have presented, however, are based on being screened one or more times during the follow up period and therefore cannot directly address the important issue of optimal re-screening interval. Other programmatic concerns regarding re-screening relate to the follow up of patients already known to have abnormal cytology. In the same clinic population, we showed that the prevalence of AIN 3 at HRA-directed biopsy was 21% and 27% for ASCUS and LSIL cytology results [##REF##15577418##5##]. Evidence-based guidance regarding optimal frequency of interval examination for those with abnormal cytology is lacking.</p>", "<title>Metrics of Screening Program Quality</title>", "<p>Separate from consideration of screening program outcome indicators, the ultimate success of screening programs depends on how they are implemented and hence on process indicators of program quality. Such indicators include the achieved <italic>coverage rate</italic> of the target population, maintenance of accuracy and reproducibility of screening procedures, measures of delay and access to both screening and treatment modalities. Although we achieved an overall coverage of 73% for at least one anal cytology screening, close to the 80% benchmark accepted for cervical cytology screening[##UREF##8##60##], there was a substantial delay in access to high resolution anoscopy especially for those with lower degrees of cytologic abnormality. This delay was attributable both to limited availability of trained HRA operators and to high “no show” rates among scheduled patients. In addition, the interval between last anal cytology and last clinic visit can be interpreted as an additional indicator of program fidelity. The clinic guideline is annual cytology screening for all patients and this target was approximately achieved for all cytology result categories except for ASCUS (median interval 1305 days). Previous research from the study clinic estimated that the prevalence of AIN 3 at biopsy among those with ASCUS cytology was 21%[##REF##15577418##5##], both justifying referral for HRA and indicating the advisability of regular follow up of such patients. The optimal frequency for repeat screening to reduce important IAC-related endpoints has not been determined and likely depends on factors similar to those reported for cervical cancer: the duration of pre-clinical disease, the progression and regression rates of precursor lesions, the sensitivity of and costs associated with screening tests, and the stage-specific curability of detected disease [##REF##12517642##61##,##UREF##9## 62##]. In the case of HIV-infected patients, there is evidence that progression rates to high grade cervical and anal SIL are higher than among uninfected patients, and that among HIV infected patients, progression rates are higher among the immunosuppressed [##REF##16054967##63##,##REF##9525431## 64##]. In addition, there is evidence that the pathogenesis of the transition from AIN 3 to ICC may differ according to HIV infection status [##REF##16607677##32##]. Therefore optimal SIL stage-specific screening frequency will likely differ according to these risk factors for progression.</p>", "<p>With regard to ongoing assessment of clinician technical performance of screening procedures, we examined the percent technically unsatisfactory cytology results as a measure applicable to all primary care providers in the clinic and the agreement between cytology diagnostic category and histopathologic diagnosis as an indicator of high resolution anoscopist technical quality. As Fig. (<bold>##FIG##2##3##</bold>) demonstrates, there was substantial and clearly unacceptable variability in the proportion of technically unsatisfactory cytology specimens obtained by our fourteen primary care providers. Switching from conventional slide cytology preparation to liquid media offers a technology-based approach to reducing both the variability and rate of unsatisfactory results[##REF##10212646##65##,##REF##12821354## 66##], but ongoing monitoring and training is required to regain an overall technically unsatisfactory rate of 6%[##REF##15577418##5##], that observed during the early period of program implementation at our clinic.</p>", "<p>With regard to technical performance of high resolution anoscopists, we evaluated cyto-histologic agreement as a quality indicator and demonstrated a positive relationship between operator experience and kappa agreement. We believe this metric of chance-corrected agreement [##UREF##3##14##,##REF##15883903## 67##] can be used to compare performance of HRA operators whose patient populations may differ in prevalence of high grade lesions. An alternative indicator, agreement between visual impression and histology, has been evaluated in the context of cervical colposopic accuracy using the Reid index[##REF##16522401##68##], which has not been validated for use in high resolution anoscopy. We are aware of only one publication providing estimates of predictive value of high resolution anoscopic visual findings (e.g. punctation and mosaicism) for high grade dysplasia on biopsy [##REF##9269808##69##]. Standards for proficiency in HRA have not been established. However, based on precedent for training and evaluation of competency in the performance of colposcopy, formal didactic training followed by a clinical mentorship involving supervised performance of 25 – 50 procedures and including at least 10 HSIL cases would be reasonable [##REF##9597533##70##,##REF##11518918## 71##]. Recently the American Society for Colposcopy and Cervical Pathology (ASCCP) has offered courses in performance of HRA (http://www.asccp.org/index.html).</p>", "<p>A number of limitations of our analysis, particularly with regard to the potential biases in estimating IAC incidence rates in the two study periods and their associated preventive fractions, as well as the limited duration of follow up, have been discussed above. Additional limitations include: (1) incomplete case ascertainment as a result of loss to follow up; (2) possible selection bias in offering and accepting screening; and (3) possible overdiagnosis bias if some of the early stage IAC cases may not have progressed. The analyses were presented to illustrate approaches to evaluation of evolving screening programs for IAC and its precursor lesions in HIV-infected patient populations.</p>" ]
[ "<title>CONCLUSIONS</title>", "<p>We believe that there is insufficient evidence at the present time to recommend comprehensive screening with cytology followed by referral for HRA and then ablative treatment of high grade lesions as a general practice guideline. It must be recognized that such a comprehensive approach, modeled as it is on the highly successful cervical cancer screening paradigm, could not be widely implemented in the current environment because of very limited numbers of trained HRA operators who would have to split their time between diagnostic and therapeutic procedures. The utility of adjunctive reflex anal HPV testing as a screening component in HIV infected populations, while recommended for cervical ASCUS, is an open research question for which minimal data is available [##REF##16078257##72##]. However, while awaiting further evidence that treatment of precursor lesions favorably alters natural history at acceptable costs, a more limited screening program could be advocated in contrast to doing nothing to detect potentially curable IAC in populations at known high risk. Such a limited program might involve routine cytology screening accompanied by digital rectal examinations and referral either to HRA or a surgeon for any palpable lesions, bleeding, or other anorectal symptoms.</p>" ]
[ "<p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<uri xlink:type=\"simple\" xlink:href=\"http://creativecommons.org/licenses/by/2.5/\">http://creativecommons.org/licenses/by/2.5/</uri>), which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background:</title>", "<p> Screening for invasive anal cancer and its precursors is being increasingly advocated as a response to increasing incidence among HIV-infected persons. We implemented a comprehensive screening program in 2001 and report our early experience to inform monitoring and evaluation of such programs. Our research aims were: (1) to estimate incidence of and mortality from invasive anal cancer (IAC) before (1995-2000) and after (2001-2005) screening program implementation and (2) to examine potential screening program quality indicators.</p>", "<title>Methods:</title>", "<p> The study cohort included all patients under care for HIV infection at UCSD Owen Clinic between 1995-2005. Person-time incidence rates (IR) and case survival of IAC were estimated for the pre-screening (1995-2000) and post-screening (2001-2005) periods. High resolution anoscopy (HRA) operator accuracy was estimated by kappa agreement between cyto-histologic comparisons. Program quality indicators included: (1) screening coverage; (2) percent technically unsatisfactory cytology smears; (3) time between 1st abnormal cytology and 1st HRA; and (4) time between last clinic visit and last cytology.</p>", "<title>Results:</title>", "<p>28 cases of IAC and 13,411 person-years were observed between 1995-2005. IRs (95% CI) pre-screening and post-screening were 199 and 216 per 100,000 person-years, respectively. There was no routine treatment of high grade squamous intraepithelial lesions (HSIL) during the study period. The percent of patients with IAC requiring chemoradiation decreased from 90.9% to 70.6% (p=0.36). There was a significant improvement in cyto-histologic agreement at HRA with increasing operator experience (r=0.92, p=0.025). Screening coverage was 73% of the target population. Among 14 providers, the percent unsatisfactory cytology smears averaged 27% but varied from 0 – 62%. The median time from 1st abnormal cytology to 1st HRA was 258 days. The median interval between the last cytology and the last clinic visit was 207 days.</p>", "<title>Conclusion:</title>", "<p>(1) The overall IR of IAC did not decline in the screening era and was higher than previous estimates for HIV cohorts; (2) stage shift to IAC of more favorable prognosis is a reasonable screening goal; (3) HRA accuracy varied by provider experience; (4) because of delay in access to HRA, digital rectal exam should be combined with cytology screening to detect palpable disease.</p>", "<title>Keywords</title>" ]
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[ "<title>ACKNOWLEDGEMENTS</title>", "<p>This work was supported in part by the UCSD Center for AIDS Research (AI 36214) and the CFAR-Network of Inte-grated Clinical Sciences (AI067039).</p>", "<p>This work was presented in part at the 13<sup>th</sup> Conference on Retroviruses and Opportunistic Infections, Denver, Feb 5-8, 2006, Abstract 808</p>", "<p>CM was involved in all phases of the research project and wrote the first draft of the manuscript. EC conducted medical record review to determine outcomes of study patients. JC and BC performed medical and surgical procedures reported in the manuscript. All authors have reviewed and approved the final manuscript.</p>" ]
[ "<fig id=\"F1\" position=\"float\"><label>Fig. (1)</label><caption><p>Survival from invasive anal cancer (IAC) diagnosis date.</p></caption></fig>", "<fig id=\"F2\" position=\"float\"><label>Fig. (2)</label><caption><p>Survival from clinic entry.</p></caption></fig>", "<fig id=\"F3\" position=\"float\"><label>Fig. (3)</label><caption><p>Proportion of technically unsatisfactory anal cytology results.</p></caption></fig>", "<fig id=\"F4\" position=\"float\"><label>Fig. (4)</label><caption><p>Agreement between anal cytology and anal biopsy histopathology.</p></caption></fig>" ]
[ "<table-wrap id=\"T1\" position=\"float\"><label>Table 1.</label><caption><p>Person-Time Incidence Rates of Invasive Anal Cancer (IAC), by Study Period and by Treatment Modality</p></caption><table frame=\"border\" rules=\"all\" width=\"100%\"><thead><tr><th rowspan=\"2\" colspan=\"1\"/><th colspan=\"2\" rowspan=\"1\"><bold>Pre-Screening Period (1995 – 2000)</bold></th><th colspan=\"2\" rowspan=\"1\"><bold>Screening Period (2001 – 2005)</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"><bold>Incidence (Per 100,000 Person-Years)</bold></th><th rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></th><th rowspan=\"1\" colspan=\"1\"><bold>Incidence (Per 100,000 Person-Years)</bold></th><th rowspan=\"1\" colspan=\"1\"><bold>95% CI</bold></th></tr></thead><tbody><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Any IAC</bold></th><td align=\"center\" rowspan=\"1\" colspan=\"1\">199</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">110 - 359</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">216</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">134 - 347</td></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Any IAC (screened population only)</bold></th><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">126</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">63 - 251</td></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>IAC with chemoradiation</bold></th><td align=\"center\" rowspan=\"1\" colspan=\"1\">181</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">97 - 336</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">152</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">86 - 268</td></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>IAC with chemoradiation (screened population)</bold></th><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">94</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">42 - 210</td></tr><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>IAC with chemoradiation (unscreened population)</bold></th><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">395</td><td align=\"center\" rowspan=\"1\" colspan=\"1\">177 - 879</td></tr></tbody></table></table-wrap>" ]
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[ "<graphic xlink:href=\"TOAIDJ-1-11_F1\"/>", "<graphic xlink:href=\"TOAIDJ-1-11_F2\"/>", "<graphic xlink:href=\"TOAIDJ-1-11_F3\"/>", "<graphic xlink:href=\"TOAIDJ-1-11_F4\"/>" ]
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[{"label": ["2"], "article-title": ["Human Papilloma Virus [Guideline]: New York State Department of Health AIDS Institute; "], "source": ["Available from: "], "uri": ["http://www.hivguidelines.org/GuidelineDocuments/a-hpv.pdf\""], "year": ["2007"], "access-date": ["updated 2007 cited 2007 2 November"]}, {"label": ["6"], "surname": ["Mathews", "Mar-Tang", "Smith", "Saville", "Cosman"], "given-names": ["C", "M", "D", "W", "B"], "article-title": ["Reproducibility and outcomes of anal dysplasia screening in an HIV primary care clinic (Abstract 605-W)"], "source": ["Foundation for Retrovirology and Human Health"], "year": ["2002"], "conf-name": ["9th Conference on Retroviruses and Opportunistic Infections"], "conf-loc": ["Seattle, Wash"]}, {"label": ["9"], "article-title": ["Stata"], "source": ["Stata Reference Manual Relese 7, Volume 1 A-G. College Station"], "year": ["2001"], "publisher-loc": ["Texas"], "publisher-name": ["Stata Press"]}, {"label": ["14"], "surname": ["Cohen"], "given-names": ["J"], "article-title": ["A coefficient of agreement for nominal scales"], "source": ["Educational and Psychological Measurement"], "year": ["1960"], "volume": ["20"], "fpage": ["37"], "lpage": ["46"]}, {"label": ["15"], "surname": ["Solomon", "Nayar"], "given-names": ["D", "R"], "source": ["The Bethesda System for Reporting Cervical Cytology"], "year": ["2004"], "edition": ["Second ed"], "publisher-loc": ["New York"], "publisher-name": ["Springer"]}, {"label": ["22"], "surname": ["Pachler", "Wille-Jorgensen"], "given-names": ["J", "P"], "article-title": ["Quality of life after rectal resection for cancer, with or without permanent colostomy"], "source": ["Cochrane Database Syst Rev Rev 2004: CD004323"], "year": ["2004"]}, {"label": ["36"], "surname": ["Croyle"], "given-names": ["RT"], "article-title": ["Psychosocial Effects of Screening for Disease Prevention and Detection"], "year": ["1995"], "publisher-loc": ["New York"], "publisher-name": ["Oxford University Press US"]}, {"label": ["58"], "article-title": [" Screening for squamous cervical cancer: duration of low risk after negative results of cervical cytology and its implication for screening policies. IARC Working Group on evaluation of cervical cancer screening programmes"], "source": ["Br Med J Clin Res Ed"], "year": ["1986"], "volume": ["293"], "issue": ["6548"], "fpage": ["659"], "lpage": ["64"]}, {"label": ["60"], "collab": ["ACCP"], "source": ["Planning and Implementing Cervical Cancer Prevention and Control Programs: A Manual for Managers"], "year": ["2004"], "publisher-loc": ["Seattle"], "publisher-name": ["Alliance for Cervical Cancer Prevention"]}, {"label": ["62"], "surname": ["Morrison"], "given-names": ["AS"], "source": ["Screening in Chronic Disease"], "year": ["1992"], "edition": ["Second ed"], "publisher-loc": ["New York"], "publisher-name": ["Oxford University Press"]}]
{ "acronym": [], "definition": [] }
72
CC BY
no
2022-01-12 14:48:09
Open AIDS J. 2007 Nov 29; 1:11-20
oa_package/05/59/PMC2530895.tar.gz
PMC2531074
18700981
[ "<title>Background</title>", "<p>The Intracranial Hypertension Research Foundation (IHRF) in Vancouver, Washington, promotes research into the pathophysiological basis of chronic IH and the evolution of better treatment and, ultimately, a cure. IHRF is a multi-functional organization. It not only encourages research, but also facilitates understanding and management of chronic primary and secondary IH, through research, training and education programs worldwide.</p>", "<p>IHRF sponsors programs for researchers and clinicians, as well as educational conferences for patients and families. The Houston conference was attended by 108 patients and family members who heard presentations by nine clinicians and researchers.</p>", "<p>Patients asked many questions during extensive panel sessions. The conference also allowed patients to speak about personal experiences with the disorder. IHRF held the first conference on chronic IH in 2006 at the Oregon Health &amp; Science University (OHSU). A wide variety of subjects were presented to enhance patient knowledge and awareness. Patient education importantly improves patient insight about the disorder. It also facilitates active cooperation with their physicians. Understanding limitations of accepted medical and surgical treatment leads to realistic goals in management.</p>", "<p>Additionally, patient education is vital in controlling health care costs.</p>" ]
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[ "<title>Discussion</title>", "<p>The opening presentation by Conrad Johanson, Professor of Clinical Neuroscience at Brown University dealt with CSF production in the choroid plexus and its possible role in IH. Covered in the lecture was the structure of the choroid plexus, the dynamic turnover of ions and water in CSF production by the choroidal epithelium, the transport of CSF solute for the brain, and homeostasis of CNS extracellular fluid. A discussion of translational research goals involving the choroid plexus and CSF and possible directions for research in IH concluded the presentation.</p>", "<p>John McGregor, Associate Professor of Neurosurgery at Ohio State University, spoke on \"Neural Hydrodynamics Disorders: Hydrocephalus, Intracranial Hypertension and the Chiari Malformation. Relationships, Similarities and Differences\" which was followed by two lectures covering CSF shunt and valve technology. The latter two presentations discussed in depth the available technology with details of the features of each device.</p>", "<p>The conclusion was that despite the numerous designs, the overall success rate for the various shunts and valve equipment are similar. Better designed shunts are greatly needed.</p>", "<p>The next lecture by Conrad Johanson: \"Current Theories on Causation and Reduction of Elevated CSF Pressures: Implications for Intracranial Hypertension\", began with a discussion of CSF fluid dynamics centering on CSF reabsorption including the controversial arachnoid and CSF lymphatic drainage. The role of neuropeptides such as atrial natriuretic peptide (ANP) in CSF production and the role of growth factors like basic fibroblast growth factor (FGF2) in CSF reabsorption, were identified. The intriguing possibility of peptides as pharmacological agents in controlling intracranial pressure (ICP) was thought provoking and stimulating, especially since no specific drug is available to control production and egress of CSF.</p>", "<p>Steven Katz, neuro-ophthalmologist and Associate Professor of Ophthalmology at Ohio State University, lectured on the symptoms and signs in idiopathic intracranial hypertension (IIH) and the medical management options. He discussed approaches that work best in his practice. Steven Katz had previously demonstrated the somatostatin receptors 1 and 2 in normal human choroid plexus and arachnoid granulations and thus surmised that somatostatin is involved in CSF production and egress. His preliminary clinical use of octreotide, a peptide that mimics somatostatin, was discussed, including the potential complications of somatosatin analogs to control IH. Steven Katz described his techniques for optic nerve sheath decompression and demonstrated findings from many of his procedures. He attributed excellent outcomes to reduced surgery duration, i.e. especially the time of optic nerve stretching. He emphasized that short exposure and optimal surgical approach lead to minimal diplopia, ptosis and most importantly, minimal vision loss.</p>", "<p>Marc Criden, neuro-ophthalmologist and Assistant Professor of Ophthalmology at the University of Texas, Houston, presented an updated overview of pediatric IH. In children under 10 years, gender and weight are not the factors they are in adults. He pointed out that many physicians consider IH in children under 10 to be a different disorder because of these characteristic differences. Headaches associated with IH and how best to manage them were discussed by neurologist and neuro-ophthalmologist, Leonard Hershkowitz of Baylor University. He indicated his management techniques that worked well for him.</p>", "<p>A reception followed in which patients again communicated with the speakers.</p>", "<p>The 2nd conference day opened with a discussion of the mission and goals of IHRF and a review of IHRF-sponsored research by Emanuel Tanne, Clinical Assistant Professor of Ophthalmology, OHSU and president of IHRF. He pointed out that IHRF works to remove significant obstacles to research: under-funding, under-coordination of effort, incomplete recognition of the life-altering effects of IIH, and low recruitment of researchers in this area. IHRF funded animal model development projects at the University of Chicago and the University of Arkansas. Under investigation is a knockout mouse that develops IH shortly after weaning. Other research areas included joint testing at the University of Utah of a NASA-developed, non-invasive, closed loop ultrasound device to measure ICP in microgravity and an investigation of vitamin A receptors in arachnoid granulations at Ohio State University. IHRF also partners with the Casey Eye</p>", "<p>Institute, in regard to the Intracranial Hypertension Registry at the Oregon Health &amp; Science University, Portland. The IH Registry is a relational database management system designed for medical research. Emanuel Tanne discussed ongoing Registry research, including studies in genetics, economics, weight gain and pregnancy. Jessica Tanne, IHRF Director, Communications &amp; Development, discussed raising community awareness, fundraising and the importance of becoming an IH ambassador. Illustrating and discussing their fundraising creativity were Dori Clements and Jacque Tate, both parents of IH children.</p>", "<p>Clark Sitton, Assistant Professor of Radiology, University of Texas, Houston, discussed types of imaging studies and the goals of imaging in IH. He presented an extensive collection of studies demonstrating pathological findings associated with IH.</p>", "<p>Bariatric surgery as a possible option in the management of IH was presented by Erik Wilson, Assistant Professor of Surgery, University of Texas, Houston, who covered risks and benefits of bariatric surgery, using his extensive experience as a guide. He also discussed types of procedures available and his personal approach to follow-up and long term goals for patients. Marc Criden presented a new hypothesis to consider for clinical management of chronic IH: establishing target pressures for each patient with IH. The hypothesis arose out of his and Steven Katz's experience with patients at the last IHRF patient conference, where they were impressed by the variety of symptoms and patient reports contradictory to current teaching. They hypothesized that chronically elevated ICP is neurologically damaging, even in patients without significant visual dysfunction or intractable headache. They are presently investigating and evaluating this hypothesis. The final speaker, Kapil Kapoor, resident in ophthalmology at the University of Texas, Galveston, presented his research on hyposmia and IH. He found that patients with elevated ICP have a decreased sense of smell and concluded that other nerves such as the olfactory nerve may be functionally compromised in the setting of IH by a mechanism similar to that of optic nerve compression. Therefore, it may be appropriate to consider IH as a more global neuroanatomic insult of augmented CSF pressure than previously considered.</p>" ]
[ "<title>Conclusion</title>", "<p>While the intent of educating patients was admirably accomplished by this conference, the conference served as a unique experience for researchers and clinicians to hear from a large group of patients about the nature of their disorders. Therefore, not only did physicians from a variety of sub-specialties have an opportunity to exchange ideas and explore future collaborative projects they also left with a different perspective of chronic IH and a new appreciation of patient-centered conferences as a mechanism to expedite translational CSF research.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>This report highlights a conference designed for patient education on elevated cerebrospinal fluid (CSF) pressure. The conference centered on chronic intracranial hypertension (IH) including the latest research and clinical information. It was sponsored by the Intracranial Hypertension Research Foundation and held at the University of Texas Medical School, Houston, on June 21–22<sup>nd</sup>, 2008.</p>" ]
[ "<title>Competing interests</title>", "<p>The author declares he has no competing interests.</p>", "<title>Authors' contributions</title>", "<p>I am the sole author and have read and approved the final version of this paper.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We thank Codman &amp; Shurtleff and Medtronic, Inc. for financial support.</p>", "<p>We are grateful to Dr. Marc Criden and the University of Texas Ophthalmology</p>", "<p>Department Staff for their assistance, and for making available the facilities at the University of Texas Medical School (Houston). Appreciation is extended to Drs. Criden and Katz in helping with program design, and speaker selection.</p>" ]
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{ "acronym": [], "definition": [] }
0
CC BY
no
2022-01-12 14:47:28
Cerebrospinal Fluid Res. 2008 Aug 13; 5:13
oa_package/9f/11/PMC2531074.tar.gz
PMC2531075
18710521
[ "<title>Background</title>", "<p>Community-acquired (CAP) and nosocomial pneumonias represent a substantial burden to the US healthcare system. CAP alone is responsible for over 1 million hospital admissions annually [##UREF##0##1##] and $4.4 billion in hospitalization costs [##REF##9737840##2##]. In addition to CAP, there are nearly 130,000 cases of non-ICU hospital-acquired pneumonia (HAP) and over 100,000 cases of ICU-acquired pneumonia annually [##REF##17357358##3##], the incremental hospital costs of which have been reported to range from $5,800 to $20,000 per case [##REF##2043013##4##, ####REF##12475855##5##, ##REF##12771596##6####12771596##6##]. Because of pneumonia's staggering clinical and economic burden, it is important to identify and fully understand all modifiable factors that influence its hospital course.</p>", "<p>Hyponatremia is the most common electrolyte imbalance seen in clinical practice [##REF##3966753##7##]. It also frequently accompanies pulmonary diseases, both infectious and neoplastic [##REF##17507705##8##]. In mixed patient populations, others have documented that hyponatremia adversely affects clinical outcomes [##UREF##1##9##]. With respect to pneumonia, a recent single-center cohort study found the incidence of hyponatremia at hospital admission among CAP patients to be 28% [##REF##17356253##10##]. Importantly, the presence of hyponatremia was associated with not only prolongation of hospitalization (HLOS), but also an increase in hospital mortality.</p>", "<p>To extend our understanding of the epidemiology and outcomes of hyponatremia at the time of hospitalization among subjects with pneumonia, and to clarify the association of hyponatremia with the measures of hospital resource utilization, such as the need for mechanical ventilation (MV) and the need for and LOS in the ICU, we conducted a large retrospective cohort study. We hypothesized that hyponatremia at admission is associated with an increased risk of hospital death among patients with pneumonia and that this condition additionally adds significantly to HLOS and costs.</p>" ]
[ "<title>Methods</title>", "<p>No human subjects were prospectively enrolled in the study, and therefore an independent Institutional Review Board (Copernicus Group IRB, Research Triangle Park, NC, USA, #ICO1-08-252) granted an exemption and waived the informed consent requirement.</p>", "<title>Data source description</title>", "<p>Data were obtained from Solucient's ACTracker<sup>® </sup>database, a sample of hospitals that contribute general discharge-level information, in addition to specific drug and laboratory information. Approximately 27 hospitals contribute data at any point in time, with 39 unique hospitals represented over the two-year study period from January 2004 through December 2005. The data source is de-identified in compliance with HIPPA regulations.</p>", "<title>Subjects</title>", "<p>We identified persons with pneumonia based on the presence of the International Classification of Disease version 9, Clinical Modification (ICD-9-CM) 3-digit disease code for the principal discharge diagnosis of pneumonia (ICD-9-CM code 486) [##UREF##2##11##]. We required that patients have at least one laboratory value for serum sodium [Na<sup>+</sup>] available during their hospital admission. For those patients who did not meet the laboratory definition for hyponatremia (see below), we further required that s/he have no primary or secondary diagnosis of hyponatremia (ICD-9-CM code 276.1).</p>", "<title>Hyponatremia definition</title>", "<p>Hyponatremia discharges were required to have an admission [Na<sup>+</sup>] below 135 mEq/L on the first or second day of admission. To improve specificity for the diagnosis, we defined hyponatremia to be present if there was at least one additional [Na<sup>+</sup>] below 135 mEq/L within 24 hours of the admission value. We did not specifically seek to exclude pseudohyponatremia.</p>", "<title>Outcomes</title>", "<p>Hospital mortality served as the primary study outcome; vital status was determined based on the presence of a hospital discharge disposition of \"Died\". Because it may be related to severity of illness, we secondarily examined the relationship of admission hyponatremia and the need for MV and ICU in the first 48 hours, on LOS in the ICU, as well as on overall HLOS and incremental hospital costs. A patient's MV utilization was identified by an ICD-9-CM procedure code of 96.7 within 48 hours of admission. An ICU stay was identified by a laboratory test or drug utilization within the ICU in the first 48 hours of admission. By counting the distinct number of days on which a patient had a lab test or drug record in the ICU, we derived the ICU LOS. Costs were estimated using claim charges and adjusted to 2005 US dollars using the medical component of the Consumer Price Index. These adjusted charges were then multiplied by the hospital-specific cost-to-charge ratio, estimated from a Medicare Provider Analysis Review (MEDPAR) file, to yield hospital costs in 2005 US dollars.</p>", "<title>Covariates</title>", "<p>We examined multiple potential confounders of the outcomes of interest. In all models covariates were age, gender, race, geographic region, teaching status of the hospital, admission source, principal payer, and Deyo-Charlson Comorbidity Index (Deyo-CCI) score [##REF##3558716##12##,##REF##1607900##13##]. The CCI is a score derived from data abstracted from medical records to arrive at a measure of comorbidity burden [##REF##3558716##12##]. The Deyo-CCI is an adaptation of this method applied to administrative datasets using ICD-9 codes to generate a comorbidity score [##REF##1607900##13##]. The hospital cost and LOS models adjusted for the additional covariates of mortality and ICU and MV stay in the first 48 hours, while the ICU LOS model adjusted for mortality and MV stay in the first 48 hours.</p>", "<title>Statistical analyses</title>", "<p>All of the covariates were examined in univariate analyses comparing their prevalence among those with and without hyponatremia. We subsequently adjusted our estimates of the impact of hyponatremia on the outcomes of interest by entering them into multivariable regression models. All unadjusted between-group comparisons were performed using the Wilcoxon rank-sum test or Median score test for continuous variables, and the chi-square test for categorical variables. We used generalized linear models (GLM) to estimate the costs and LOS (both hospital and ICU) that were attributable to hyponatremia in this population, adjusted for covariates. Costs were log-transformed due to the skewed distribution of medical expenditures, and the negative binomial distribution was used to model LOS to account for overdispersion. Logistic regression modeling was performed to estimate the excess risk of MV and ICU need, as well as for hospital mortality, conferred by hyponatremia. Covariates were included in these models based on previous evidence of associations with the outcomes or on biologic or clinical plausibility of such an association. Goodness of fit for these models was assessed using the scaled deviance and Pearson's chi-square statistic. For all analyses, statistical significance was reached when a two-tailed p-value was less than 0.05.</p>", "<p>All analyses were performed using SAS 9.1.3 (SAS Institute, North Carolina).</p>" ]
[ "<title>Results</title>", "<p>Of the 198,281 patients in the database, 7,965 (4%) had been diagnosed with pneumonia and met the inclusion criteria, and of those 5,916 had complete cost data. Patients with pneumonia were 45% male, 85% Caucasian, had a mean age of 68.4 ± 21.6 years, and a mean Deyo-CCI (13) of 1.6 ± 1.6. Eight percent (n = 649) of the entire pneumonia population had evidence of hyponatremia.</p>", "<p>The baseline characteristics by hyponatremia status are shown in Table ##TAB##0##1##. Patients with hyponatremia were more likely to be older, and had a greater burden of comorbid illness as signified by a higher Deyo-CCI. There were no gender or racial differences between the groups. Patients with pneumonia who also had hyponatremia were more likely to be at a teaching hospital than at a non-teaching facility.</p>", "<p>Hospital mortality, though low in both groups, was greater among patients with hyponatremia (5.4% vs. 4.0%). This difference, however, only approached statistical significance (p = 0.099) (Table ##TAB##1##2##). The proportion of patients requiring MV (3.9% vs. 2.3%, p = 0.014) or any ICU admission (10.0% vs. 6.3%, p &lt; 0.001) was significantly higher in the hyponatremic than the normonatremic group. Hyponatremia also was associated with increased ICU LOS, and HLOS. Reflecting this, hospital costs were higher among those with hyponatremia (Table ##TAB##1##2##). Thus, aggregate median hospital costs exceeded $7,000 in the population with hyponatremia compared to $5,732 in persons with no hyponatremia at admission.</p>", "<p>When the relationship of hospital mortality with hyponatremia was examined in a multivariable logistic regression, adjusting for age, gender, race, geographic region, teaching status of the hospital, admission source, principal payer, and Deyo-CCI score, we observed that hyponatremia was associated with a trend towards an increase in the risk of hospital death (adjusted odds ratio (OR) 1.30, 95% confidence interval (95% CI) 0.90 to 1.87) (Figure ##FIG##0##1##). The adjusted risk of the need for MV (OR 1.75, 95% CI 1.13 to 2.69) and ICU care (OR 1.58, 95% CI 1.20 to 2.08) within 48 hours of hospital admission were elevated in the group with hyponatremia as well (Figure ##FIG##0##1##). Hyponatremia was also found to contribute on average 0.8 (95% CI -0.25 to 2.04) and 0.3 (95% CI 0.01 – 0.69) days excess in ICU and HLOS, respectively (Table ##TAB##2##3##). The marginal hospital costs associated with hyponatremia were $1,324 (95% CI $98 to $2,682) (Table ##TAB##2##3##). Both the scaled deviance and Pearson's chi-square statistic for these models were close to one, indicating an excellent model fit.</p>" ]
[ "<title>Discussion</title>", "<p>We have shown that hyponatremia frequently accompanies hospitalization for pneumonia. Our findings further confirm the independent influence of hyponatremia on hospital length of stay. In addition, we have shown that hyponatremia exerts a negative impact on multiple outcomes such as the need for MV and ICU care, as well as the duration of ICU stay. Financially, hyponatremia adds over $1,300 to the costs of care.</p>", "<p>Previous investigations found that hyponatremia in association with a severe Legionella pneumonia requiring an ICU stay is a strong independent predictor of mortality [##REF##9372662##14##]. However, only one prior cohort study from a single institution evaluated the relationship between hyponatremia and outcomes among all patients hospitalized with CAP [##REF##17356253##10##]. In this prospective cohort study, performed between 2002 and 2005, Nair and coworkers found the prevalence of hyponatremia, defined as [Na<sup>+</sup>] &lt; 135 mEq/L in the first hospital-obtained sample, to be 28% among 342 patients enrolled in the study. Although hyponatremia was mostly mild, the investigators found an increase in crude HLOS of 2.3 days and a near tripling of hospital mortality among hyponatremic patients when compared to those without hyponatremia. After adjusting for covariates, the presence of hyponatremia was associated with a 7% (p = 0.03) increase in the risk of hospital death.</p>", "<p>Our study, although finding a lower prevalence of hyponatremia, adds to this earlier work. First, by employing a more restrictive definition, we confirm the general impact of hyponatremia on multiple outcomes and help clarify the importance of this factor. We further extend the earlier work in that our data derive from a larger and more generalizable multi-center cohort of patients with pneumonia. Simply put, with a sample size more than ten-fold greater we were able to explore more precisely the impact of hyponatremia on economic outcomes and measures of resource use. The lack of effect of hyponatremia on mortality likely reflects the overall low rate of mortality in our cohort. Reliance on a more stringent definition of hyponatremia may have also contributed to this discordant finding regarding mortality. Since we required a second [Na<sup>+</sup>] measurement, patients with hyponatremia at admission who died prior to having a second [Na<sup>+</sup>] drawn are by definition not included in our mortality analysis. Additionally, to the best of our knowledge, ours is the first study to show an association of hyponatremia with such important components of the hospitalization as the need for MV and the need for and LOS in the ICU, as well as to derive hospital costs attributable to hyponatremia.</p>", "<p>Pneumonia is an important driver of healthcare costs. The full burden of hospitalization with pneumonia in the US approaches 1.5 million cases annually and its economic impact may be close to $8 billion [##UREF##0##1##,##REF##9737840##2##,##REF##2043013##4##, ####REF##12475855##5##, ##REF##12771596##6####12771596##6##] in hospital costs alone. Although the majority of patients with CAP are treated in the outpatient setting, hospital-based management is responsible for over 90% of the costs of care for this disease [##REF##9737840##2##]. Several risk stratification algorithms, such as CURB, CURB-65 and Pneumonia Severity Index (PSI), have been developed to help identify patients at high risk of CAP-related complications, and to make appropriate site-of-care decisions [##REF##11254821##15##, ####REF##12728155##16##, ##REF##8995086##17####8995086##17##]. The more detailed 20-point PSI containing points for hyponatremia ([Na<sup>+</sup>] &lt; 130 mEq/L) has been found to be better than CURB or CURB-65 at identifying low-risk CAP patients, and thus more helpful at avoiding potentially costly and unnecessary hospitalizations [##REF##15808136##18##]. However, the simplicity of both of the CURB instruments makes them attractive bedside clinical tools. The current study gives rise to the possibility that predictive abilities of the CURB instruments may benefit from the addition of the initial [Na<sup>+</sup>] value without compromising its simplicity.</p>", "<p>Along similar lines, and once the decision to admit to the hospital has been made, attention to modifiable determinants of hospital outcomes becomes a critical component of care in patients with pneumonia. As an illustration, a randomized controlled trial showed that a simple intervention consisting of making sure that a CAP patient is sitting out of bed or ambulating for at least 20 minutes during the first 24 hours of hospitalization cut the average HLOS fully by 1 day without an increase in adverse events [##REF##12970012##19##]. Approaches like this demonstrate that identification of and attention to important determinants of outcomes can result in substantial gains in those outcomes. By defining the marginal contribution of hyponatremia to the HLOS and associated costs, our study provides further evidence that hyponatremia needs to be evaluated as a potential target for intervention among hospitalized patients with pneumonia.</p>", "<p>Although somewhat novel among patients with pneumonia, hyponatremia has been identified as a predictor of hospital outcomes in other populations. For example, among patients with heart failure there is a well-recognized inverse relationship between admission [Na<sup>+</sup>] and hospital mortality [##REF##15867182##20##,##REF##15599835##21##]. A recent large cohort study of nearly 50,000 patients with acutely decompensated heart failure reported the prevalence of hyponatremia (defined as admission [Na<sup>+</sup>] &lt; 135 mEq/L) of 20% and noted that there is a 20% increase in the risk of hospital death for each 3 mEq/L decrease in [Na<sup>+</sup>] below 140 mEq/L; admission hyponatremia was also independently associated with increased HLOS [##REF##17309900##22##]. Chua et al., in a cohort of 103 geriatric hospitalized patients in the United Kingdom found an 18% prevalence of hyponatremia ([Na<sup>+</sup>] &lt; 135 mEq/L) and a similar association between hyponatremia and outcomes [##REF##17244514##23##]. Contrary to these observed associations, Brouwer and coworkers, while finding a high prevalence of admission hyponatremia (30%), did not uncover any influence on the outcomes among patients hospitalized with community-acquired bacterial meningitis; HLOS, however, was not examined [##REF##17178734##24##]. To sum up, the preponderance of evidence points to a significant association between the presence of hyponatremia at admission and worsened outcomes. The current study furthers this evidence base to the hospitalized population with pneumonia and additionally demonstrates that hyponatremia impacts every component of the aggregate hospital outcomes.</p>", "<p>Our study has several important limitations that should be acknowledged. Firstly, because of its observational nature, the crude associations between hyponatremia and outcomes are likely confounded. Notably, we have attempted to address this limitation by performing multivariable analyses. However, the possibility of residual confounding remains. Secondly, by virtue of its retrospective design, the study is prone to several forms of bias. Thirdly, the fact that the data source is not clinical but administrative in nature makes our case definition prone to misclassification. Although the presence of laboratory data and our stringent definition of hyponatremia eliminated that as a limitation of exposure classification, the definition of pneumonia was somewhat less precise, as it relied on the presence of the corresponding ICD-9-CM code. Conversely, our stringent definition of hyponatremia may have itself resulted in an immortal time bias, such that our current estimate of mortality rate among hyponatremic patients is an underestimate of the actual risk of death. That is, by excluding cases without a confirmatory [Na<sup>+</sup>] value we may have eliminated a substantial number of patients with hyponatremia who died prior to having the opportunity to have had the second [Na<sup>+</sup>] checked. As far as the diagnosis of pneumonia, misclassification remains a potential concern, though, if present, it is most likely non-differential leading the estimated associations to appear less strong. Along the same lines, it is also possible that at least some of the included patients were actually misclassified cases of congestive heart failure. Though our dataset precludes us from confirming or refuting its presence, this misclassification is not unusual in clinical practice, and thus should not detract from the relevance of our estimates. Finally, we have not attempted to separate our cohort by the origins of pneumonia (e.g., CAP vs. HAP). Although of importance for future investigations, our aim for the current analysis was to answer a more general question of what role hyponatremia may play in any patient with an infection of the lower respiratory tract, regardless of its etiology.</p>" ]
[ "<title>Conclusion</title>", "<p>In summary, we have shown that hyponatremia is common among hospitalized patients with pneumonia and independently associated with worsened clinical outcomes, as well as with an increase in the utilization of MV, ICU and hospital resources. Future research needs to focus not only on how hyponatremia may affect subpopulations of patients with pneumonia, but also how severity of hyponatremia impacts hospital outcomes. Most importantly, studies are needed to evaluate the role of currently available therapies aimed at correction of hyponatremia in improving the outcomes of patients with pneumonia.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Community-acquired (CAP) and nosocomial pneumonias contribute substantially to morbidity and hospital resource utilization. Hyponatremia, occurring in &gt;1/4 of patients with CAP, is associated with greater disease severity and worsened outcomes.</p>", "<title>Methods</title>", "<p>To explore how hyponatremia is associated with outcomes in hospitalized patients with pneumonia, we analyzed a large administrative database with laboratory component from January 2004 to December 2005. Hyponatremia was defined as at least two [Na<sup>+</sup>] &lt; 135 mEq/L within 24 hours of admission value.</p>", "<title>Results</title>", "<p>Of 7,965 patients with pneumonia, 649 (8.1%) with hyponatremia were older (72.4 ± 15.7 vs. 68.0 ± 22.0, p &lt; 0.01), had a higher mean Deyo-Charlson Comorbidity Index Score (1.7 ± 1.7 vs. 1.6 ± 1.6, p = 0.02), and higher rates of ICU (10.0% vs. 6.3%, p &lt; 0.001) and MV (3.9% vs. 2.3%, p = 0.01) in the first 48 hours of hospitalization than patients with normal sodium. Hyponatremia was associated with an increased ICU (6.3 ± 5.6 vs. 5.3 ± 5.1 days, p = 0.07) and hospital lengths of stay (LOS, 7.6 ± 5.3 vs. 7.0 ± 5.2 days, p &lt; 0.001) and a trend toward increased hospital mortality (5.4% vs. 4.0%, p = 0.1). After adjusting for confounders, hyponatremia was associated with an increased risk of ICU (OR 1.58, 95% CI 1.20–2.08), MV (OR 1.75 95% CI 1.13–2.69), and hospital death (OR 1.3, 95% CI 0.90–1.87) and with increases of 0.8 day to ICU and 0.3 day to hospital LOS, and over $1,300 to total hospital costs.</p>", "<title>Conclusion</title>", "<p>Hyponatremia is common among hospitalized patients with pneumonia and is associated with worsened clinical and economic outcomes. Studies in this large population are needed to explore whether prompt correction of [Na<sup>+</sup>] may impact these outcomes.</p>" ]
[ "<title>Competing interests</title>", "<p>This project was supported by a grant from Astellas Pharma US, Inc. Drs. Zilberberg and Shorr are consultants to Astellas Pharma US, Inc., who markets an arginine vasopressin antagonist. Drs. Exuzides and Colby, Ms. Foreman and Ms. Graves Jones are employees of ICON Clinical Research, which has received research funding from Astellas Pharma US, Inc. Dr. Spalding is an employee and a stock holder of Astellas Pharma US, Inc. The sponsor contributed to analysis planning but had no veto power over their performance or reporting of the data. All analyses were performed by ICON Clinical Research. No external medical writer was engaged to develop this manuscript.</p>", "<title>Authors' contributions</title>", "<p>MDZ participated in the design of the study, data interpretation, drafting and revision of the manuscript for important intellectual content. AE participated in the design of the study, data acquisition and analysis, and drafting of the manuscript. JS participated in the design of the study, data interpretation and revision of the manuscript for important intellectual content. AF participated in the design of the study, data acquisition and analysis, and drafting of the manuscript. AGJ participated in the design of the study, data acquisition and analysis, and drafting of the manuscript. CC participated in data acquisition and analysis, and drafting of the manuscript. AFS participated in the design of the study, data interpretation and revision of the manuscript for important intellectual content.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2466/8/16/prepub\"/></p>" ]
[]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Adjusted risk for hospital death, need for MV and need for ICU care among hyponatremic compared to normonatremic patients with pneumonia</bold>. Point estimates and 95% confidence intervals, depicting the individual contribution of hyponatremia to the respective outcomes, were derived from logistic regression models utilizing hospital mortality, need for MV and need for ICU as dependent variables, and adjusting for age, gender, race, region, teaching hospital, admission source, principal payer, and Deyo-CCI score.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Baseline characteristics of pneumonia patients by hyponatremia status</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Hyponatremia present <break/>(n = 649)</td><td align=\"left\">Hyponatremia absent <break/>(n = 7,316)</td></tr></thead><tbody><tr><td align=\"left\">Mean (SD) age (years)*</td><td align=\"left\">72.4 (15.7)</td><td align=\"left\">68.0 (22.0)</td></tr><tr><td align=\"left\">Proportion male (%)</td><td align=\"left\">48.2</td><td align=\"left\">44.9</td></tr><tr><td align=\"left\">Proportion Caucasian (%)</td><td align=\"left\">87.8</td><td align=\"left\">85.0</td></tr><tr><td align=\"left\">Mean (SD) Deyo-CCI*</td><td align=\"left\">1.7 (1.7)</td><td align=\"left\">1.6 (1.6)</td></tr><tr><td align=\"left\">Proportion in teaching hospitals<sup>†</sup></td><td align=\"left\">33.3</td><td align=\"left\">29.5</td></tr><tr><td align=\"left\">Source of admission (%)<sup>†</sup></td><td/><td/></tr><tr><td align=\"left\"> Acute Care Facility</td><td align=\"left\">0.2</td><td align=\"left\">0.7</td></tr><tr><td align=\"left\"> ER</td><td align=\"left\">77.3</td><td align=\"left\">71.8</td></tr><tr><td align=\"left\"> Physician referral</td><td align=\"left\">16.2</td><td align=\"left\">17.3</td></tr><tr><td align=\"left\"> Skilled Nursing Facility</td><td align=\"left\">1.4</td><td align=\"left\">2.0</td></tr><tr><td align=\"left\"> Transfer</td><td align=\"left\">1.1</td><td align=\"left\">1.1</td></tr><tr><td align=\"left\"> Other</td><td align=\"left\">0.5</td><td align=\"left\">0.7</td></tr><tr><td align=\"left\"> Missing/Unknown</td><td align=\"left\">3.4</td><td align=\"left\">6.4</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Hospital outcomes of pneumonia patients by hyponatremia status</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">Hyponatremia present <break/>(n = 649)</td><td align=\"left\">Hyponatremia absent <break/>(n = 7,316)</td><td align=\"left\">p value*<break/></td></tr></thead><tbody><tr><td align=\"left\">Hospital mortality (%)</td><td align=\"left\">5.4</td><td align=\"left\">4.0</td><td align=\"left\">0.099</td></tr><tr><td align=\"left\">Proportion on MV (%)</td><td align=\"left\">3.9</td><td align=\"left\">2.3</td><td align=\"left\">0.014</td></tr><tr><td align=\"left\">Proportion in ICU (%)</td><td align=\"left\">10.0</td><td align=\"left\">6.3</td><td align=\"left\">&lt;0.001</td></tr><tr><td align=\"left\">Mean (SD) ICU LOS (days)</td><td align=\"left\">6.3 (5.6)</td><td align=\"left\">5.3 (5.1)</td><td align=\"left\">0.069</td></tr><tr><td align=\"left\">Mean (SD) HLOS (days)</td><td align=\"left\">7.6 (5.3)</td><td align=\"left\">7.0 (5.2)</td><td align=\"left\">&lt;0.001</td></tr><tr><td align=\"left\">Median (95%CI) hospital costs</td><td align=\"left\">$7,086 ($3,765–$14,221)</td><td align=\"left\">$5,732 ($2,966–$12,290)</td><td align=\"left\">0.001</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Incremental impact of hyponatremia on components of hospital resource utilization and costs among patients with pneumonia*</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">Incremental increase</td><td align=\"center\">95% CI</td></tr></thead><tbody><tr><td align=\"left\">ICU LOS (days)</td><td align=\"center\">0.8</td><td align=\"center\">-0.25, 2.04</td></tr><tr><td align=\"left\">HLOS (days)</td><td align=\"center\">0.3</td><td align=\"center\">0.01, 0.69</td></tr><tr><td align=\"left\">Hospital costs</td><td align=\"center\">$1,324</td><td align=\"center\">$98, $2,682</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>SD = standard deviation; CCI = Charlson Comorbidity Index; ER = emergency room</p><p>*p &lt; 0.05 by Wilcoxon rank-sum test</p><p><sup>†</sup>p &lt; 0.05 by chi-square</p></table-wrap-foot>", "<table-wrap-foot><p>MV = mechanical ventilation; ICU = intensive care unit; SD = standard deviation; HLOS = hospital length of stay; CI = confidence interval</p><p>*P-values obtained using Wilcoxon rank-sum test or Median score test for continuous variables, and chi-square test for categorical variables</p></table-wrap-foot>", "<table-wrap-foot><p>MV = mechanical ventilation; ICU = intensive care unit; LOS = length of stay; HLOS = hospital length of stay *Derived from multivariable linear regression models adjusting for age, gender, race, region, teaching hospital, admission source, principal payer, and Deyo-CCI score, ICU and MV in first 48 hrs, and mortality</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1471-2466-8-16-1\"/>" ]
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[{"surname": ["Garibaldi"], "given-names": ["RA"], "article-title": ["Epidemiology of community acquired respiratory tract infections in adults: incidence, etiology, and impact"], "source": ["Am J Med"], "year": ["1985"], "volume": ["78"], "fpage": ["32S"], "lpage": ["37S"], "pub-id": ["10.1016/0002-9343(85)90361-4"]}, {"surname": ["Asadollahi", "Beeching", "Gill"], "given-names": ["K", "N"], "article-title": ["Hyponatraemia as a risk factor for hospital mortality"], "source": ["Q J Med"], "year": ["2006"], "volume": ["99"], "fpage": ["877"], "lpage": ["80"]}, {"collab": ["U.S. Department of Health and Human Services"], "source": ["International Classification of Diseases (ICD-9-CM)"], "year": ["2003"], "publisher-name": ["Los Angeles, Practice Management Information Corporation"]}]
{ "acronym": [], "definition": [] }
24
CC BY
no
2022-01-12 14:47:28
BMC Pulm Med. 2008 Aug 18; 8:16
oa_package/58/76/PMC2531075.tar.gz
PMC2531076
18700965
[ "<title>Background</title>", "<p>On March 19, 2008 a Symposium on Pathophysiology of Ageing and Age-Related diseases was held in Palermo, Italy. Here, the lectures of M. Racchi on History and future perspectives of Alzheimer Biomarkers and of G. Scapagnini on Cellular Stress Response and Brain Ageing are summarized.</p>", "<p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting aged people; AD prevalence is approximately 1% between 65 and 69 years and is higher than 50% in individuals above 95 years. It is characterized by irreversible cognitive and physical deterioration. With increasing life expectancy across the world, dementia is a rapidly growing socioeconomic and medical problem. The confirmatory diagnosis of AD is based on the recognition and quantification of senile plaques and neurofibrillary tangles, which are the hallmarks of the disease [##UREF##0##1##].</p>", "<p>AD prevention is an important goal of ongoing research. Two objectives must be accomplished to make prevention feasible: i) individuals at high risk of AD need to be identified before the earliest symptoms become evident, by which time extensive neurodegeneration has already occurred and intervention to prevent the disease is likely to be less successful and ii) safe and effective interventions need to be developed that either reduce or slow the accumulation of AD neuropathology or lead to a decrease in clinical expression of this pathology [##REF##18086003##2##].</p>", "<title>p53 as a putative peripheral marker for AD</title>", "<p>The treatment of AD remains a major challenge because of an incomplete understanding of the events that lead to the selective neurodegeneration typical of Alzheimer's brains. Nowadays the attention is focused on one side on the study of the β-amyloid (Aβ) precursor protein (APP) metabolism's pharmacological modulation, and on the other one to develop disease-modifying or -arresting compounds. The first purpose is that to reduce the development of Aβ in the hope of reducing the formation of a potentially neurotoxic peptide whereas examples of the second concern the use either of monoclonal antibodies direct to inflammatory mediators or of β-sheet breakers [##REF##18086003##2##, ####REF##15679923##3##, ##REF##18184433##4##, ##REF##15270201##5##, ##REF##15679924##6####15679924##6##].</p>", "<p>In view of existing and emerging therapeutic compounds, the focus has increasingly shifted to accurate detection of the earliest phase of illness and, to date, there is an increasing interest to develop techniques allowing an accurate detection of the earliest stages of the disease. Candidate biochemical markers for AD should be molecules representing some of the cerebral pathogenetic processes typical of AD or representing altered metabolic or cellular processes as shown by several studies performed either on brain or on peripheral tissues from affected patients. A wide variety of different proteins such as inflammatory markers, markers of oxidative stress, apolipoproteins, and markers of neuronal degeneration in blood and cerebrospinal fluid (CSF) have been examined [##REF##14505582##7##,##REF##16493230##8##]. Most of these studies have, however, yielded conflicting results. The cerebrospinal fluid has been the focus of research for diagnostic markers in AD pathology due to its direct contact with the extracellular space of the brain [##REF##14505582##7##]. The more encouraging results come from the studies on the measurement of different isoforms of Aβ in CSF, particularly Aβ 1–42 [##REF##18285532##9##], due to its role in the early pathogenesis of AD. Most studies showed that Aβ 1–42 concentrations are lower in the CSF of AD [##REF##14505582##7##,##REF##16493230##8##]. Unfortunately, plasma Aβ 1–40 and Aβ 1–42 did not correlate with the disease. In fact the results from these studies are often contradictory [##REF##16362769##10##].</p>", "<p>The biological markers can be classified as primary (specific), such as Aβ, or secondary to the disease, or they can simply be epiphenomenal in nature. In search of secondary markers, Uberti et al. demonstrated an intriguing correlation between p53 and AD by using cell lines derived from these patients [##REF##16165254##11##]. Fibroblasts of sporadic AD patients represent an important starting point in the research for novel biomarkers because of their various abnormalities in metabolic and biochemical processes, which reflect some of the events in the AD brain. They described and demonstrated an abnormal response of AD fibroblasts to an acute oxidative injury; in particular, fibroblasts from AD patients were found to be less vulnerable to the oxidative injury induced by H<sub>2</sub>O<sub>2 </sub>in comparison with fibroblasts from non-AD subjects. On the basis of immunoprecipitation studies with conformation-specific p53 antibodies, which discriminated folded vs. unfolded p53 tertiary structure, they found that in fibroblasts from AD patients a significant amount of total p53 assumes an unfolded tertiary structure in comparison with fibroblasts from control elderly subjects. Sequence analysis of the p53 gene allowed to exclude the possibility that the mutant p53 found in AD fibroblasts was the result of gene mutation. Thus, these data suggest that one of the peripheral events associated to the disease is responsible for generating such p53 isoform [##REF##16165254##11##,##REF##18322392##12##].</p>", "<p>In the attempt of investigating on the mechanism of such alteration, they assessed the contribution of APP metabolic products to the change in p53 conformational state. They found that the exposure to nanomolar concentrations of beta-amyloid (Aβ) 1–40 peptide induced the expression of an unfolded p53 protein isoform in fibroblasts derived from non-AD subjects. These data suggest that the tertiary structure of p53 and the sensitivity to p53-dependent apoptosis are influenced by low concentrations of soluble Aβ. On this basis, they hypothesised that low amounts of soluble Aβ induce early pathological changes at cellular level that may precede the amyloidogenic cascade. One of these changes is the induction of a novel conformational state of p53 [##REF##16165254##11##,##REF##17851197##13##].</p>", "<p>In addition and most importantly, Lanni et al. [##REF##17684496##14##] were able to develop a rapid, easy and quantitative flow cytometric approach for the discrimination of conformational mutant p53-bearing cells from AD patients compared to non-AD controls, using small volumes of blood. Using this technique, they processed 75 AD, 66 controls, 15 subjects affected by another neuroinflammatory disease, Parkinson's disease and 3 subjects affected with other types of dementia (2 vascular dementia; 1 progressive supranuclear palsy) and confirmed the previous findings: AD subjects expressed higher levels of unfolded p53 in comparison with controls and subjects with other neurological diseases. The levels of conformationally altered p53, both in controls and AD patients, correlated with age but not with the lenght of illness or with the Mini Mental State Examination value. Interestingly, the sensitivity and specificity within different age intervals were more significant in subjects up to 70 years of age compared with the corresponding values for individuals older than 70 years. Within this specific age interval (≤ 70 years), the Authors worked out a sensitivity of 90% to discriminate AD patients from nondemented aged individuals at a specificity value of 77%. A comparison of these sensitivity and specificity values with those published in several studies, which evaluated the diagnostic power of CSF markers for AD (Total-tau, Phospho-tau and Abeta 1–42), reveal that p53 measurement is more sensitive (90% compared to respectively 81.4%, 81.3% and 85.9%), but less specific (77% compared to respectively 91.5%, 91.2% and 88.5%) [##REF##14505582##7##].</p>", "<p>On the whole, these data strongly suggest that the measurement of conformationally altered p53 in blood cells has a high ability to discriminate AD cases from normal ageing, Parkinson's disease and other dementias. In spite of the fact that the method described in this study has a lower specificity value compared to CSF biomarkers, its high sensitivity in subjects up to 70 years and the non invasive nature of the test, permit its proposal as an adjunctive marker. Accordingly, p53 analysis may be used in the clinical evaluation of mild cognitive impairment cases or to improve a clinical diagnosis of AD, which should be based on cumulative information derived from clinical examination, brain neuroimageing techniques and biochemical markers either from CSF or blood. In a disease where therapeutic treatments are at most symptomatic, early treatment and therefore early prediction of future pathology is particularly important. Whether this different expression of conformationally altered p53 will be suitable as an adjunctive diagnostic tool in early stage AD in larger and independent populations of patients is matter of further investigations.</p>", "<p>On the other hand, p53 is a hot topic in AD research. Interestingly, it has been hypothesised that oxidative modification of p53 could be involved in the neuronal loss observed in neurodegenerative conditions [##REF##18494939##15##,##REF##18439434##16##].</p>", "<title>Cellular stress response</title>", "<p>Oxidative stress has been implicated in a variety of pathophysiological conditions, including neurodegenerative disorders. Irrespective of the source and mechanisms that lead to the generation of reactive oxygen species, mammalian cells have developed highly regulated inducible defensive systems, whose cytoprotective functions are essential in terms of cell survival. When appropriately activated, each one of these systems has the possibility to restore cellular homeostasis and rebalance redox equilibrium. Activation of antioxidant pathways is particularly important for tissue with relatively weak endogenous antioxidant defenses, such as the brain. Increasing evidences, in fact, support the notion that reduction of cellular expression and activity of antioxidant proteins and consequent augment of oxidative stress are fundamental causes for aging processes and neurodegenerative diseases [##REF##11519733##17##]. Among the molecules belonging to stress protein family, Heme oxygenase-1 (HO-1) has been the object of intensive studies in the brain for its potential role in protecting neurons against cell death. HO enzymes provide the first and rate-limiting step in heme degradation, to give biliverdin, gaseous carbon monoxide and free iron. All the byproducts of HO activity play a significant role in physiological cell functions [##UREF##1##18##]. In the CNS, the HO system has been reported to be very active [##REF##10872744##19##,##REF##12393232##20##] and its modulation seems to play a crucial role in the pathogenesis of neurodegenerative disorders. Deregulation of the HO system has been associated with the pathogenesis of Alzheimer's disease, multiple sclerosis and brain aging [##REF##10681514##21##,##REF##11053673##22##]. Many studies clearly demonstrate that activation of HO-1 in neurons is strongly protective against oxidative damage and cell death [##REF##10854275##23##]. Thus, modulation of HO-1 should represent a potential pharmaceutical strategy for the treatment of neurodegenerative disorders. A number of experimental and epidemiological studies have recently supported the beneficial effects of some commonly used natural products in preventing various pathologic conditions ranging from cardiovascular diseases to cancer. Spices and herbs often contain phenolic substances with potent antioxidative and chemopreventive properties [##REF##11237173##24##]. Scapagnini et al. have previously shown that curcumin (1,7-bis [4-Hydroxy-3-methoxyphenyl]-1,6-heptadiene-3,5-dione), a natural phenolic agent, extracted from the rhizome of <italic>Curcuma Longa</italic>, strongly induced HO-1 expression and activity in rat astrocytes [##REF##11854435##25##]. The Authors have then extended their findings demonstrating curcumin ability to induce HO-1 in cultured hippocampal neurons [##REF##16677086##26##]. The results indicate that curcumin activates HO-1 and phase II enzymes expression in astrocytes and neurons, probably by activation of transcription factor Nrf2, and this activation is able to effort a significant cytoprotection in cultured neurons exposed to oxidative stress. The involvement of curcumin in restoring cellular homeostasis and rebalancing redox equilibrium, suggests that it might be a useful adjunct also in the treatment of neurodegenerative illnesses characterized by inflammation, such as AD. This idea has been reinforced by epidemiological studies showing that, in India where this spice is widely used in daily diet, there is a reduced age-adjusted prevalence of AD (in patients between 70 and 79 years of age is 4.4-fold less than that of the United States) [##REF##11571321##27##]. Consistent with its possible use in neurodegenerative diseases, curcumin has been reported to decrease oxidative damage and amyloid deposition in a transgenic mouse model of Alzheimer's disease, and to reverse Aβ-induced cognitive deficits and neuropathology in rats [##REF##15590663##28##,##REF##11755008##29##]. Other plant-derived phenolic agents with analogous chemical structures to curcumin have been demonstrated to strongly activate HO-1 expression and to defend cells against oxidative stress. In particular, Scapagnini et al. have shown that ethyl ferulate, resveratrol (a phitoalexin derived from grape) and caffeic acid phenethyl ester (CAPE), are able to protect neurons via HO-1 induction [##REF##15345140##30##]. These and other studies identify a novel class of natural substances that could be used for therapeutic purposes as potent inducers of HO-1 in the protection of tissues against inflammatory and neurodegenerative conditions. It needs to be emphasized that curcumin, and other plant constituents eventually become part of the human diet and can be consumed daily as herbal supplements. Further in vitro and in vivo studies using curcumin-like molecules will give important information on the feasibility of developing new pharmacological strategies for maximizing heme oxygenase activity in targeted tissues as an alternative to or in combination with HO-1 gene therapy.</p>", "<p>However, curcumin studies are a growing area in AD research [##REF##18568016##31##] as well as in other pathological conditions. Various preclinical cell culture and animal studies suggest that curcumin has potential as an antiproliferative, anti-invasive, and antiangiogenic agent; as a mediator of chemoresistance and radioresistance; as a chemopreventive agent; and as a therapeutic agent in wound healing, diabetes, AD, Parkinson disease, cardiovascular disease, pulmonary disease, and arthritis [##REF##17900536##32##].</p>" ]
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[ "<title>Conclusion</title>", "<p>A major goal of ongoing research in AD is to improve early detection by developing tools to move diagnosis backward in disease temporal course, i.e. before the clinical manifestation of the disease, where a treatment might play a decisive role in preventing or significantly retarding the manifestation of the disease [##REF##18086003##2##,##UREF##2##33##]. On the whole, data here reviewed strongly suggest that the measurement of conformationally altered p53 in blood cells has a high ability to discriminate AD cases from normal ageing, Parkinson's disease and other dementias. On the other hand, available data on the involvement of curcumin in restoring cellular homeostasis and rebalancing redox equilibrium, suggest that curcumin might be an useful adjunct in the treatment of neurodegenerative illnesses characterized by inflammation, such as AD.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>On March 19, 2008 a Symposium on Pathophysiology of Ageing and Age-Related diseases was held in Palermo, Italy. Here, the lectures of M. Racchi on History and future perspectives of Alzheimer Biomarkers and of G. Scapagnini on Cellular Stress Response and Brain Ageing are summarized. Alzheimer's disease (AD) is a heterogeneous and progressive neurodegenerative disease, which in Western society mainly accounts for clinica dementia. AD prevention is an important goal of ongoing research. Two objectives must be accomplished to make prevention feasible: i) individuals at high risk of AD need to be identified before the earliest symptoms become evident, by which time extensive neurodegeneration has already occurred and intervention to prevent the disease is likely to be less successful and ii) safe and effective interventions need to be developed that lead to a decrease in expression of this pathology. On the whole, data here reviewed strongly suggest that the measurement of conformationally altered p53 in blood cells has a high ability to discriminate AD cases from normal ageing, Parkinson's disease and other dementias. On the other hand, available data on the involvement of curcumin in restoring cellular homeostasis and rebalancing redox equilibrium, suggest that curcumin might be a useful adjunct in the treatment of neurodegenerative illnesses characterized by inflammation, such as AD.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MR, DU, SG, MM, CL carried out all the studied on conformational p53, SV, GC, CC took care of pharmacogenomic approach and drafted the manuscript, LR, GS carried out the curcumin experiments. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The meeting organizer Prof. C. Caruso is deeply indebted to all the speakers and chairpersons of the meeting (Frans Claas, Biagio Agostaro, Daniela Mari, Marco Racchi, Giovanni Scapagnini, Vittorio Nicita Mauro, Mario Barbagallo, Giuseppina Candore, Giuseppina Colonna-Romano, Domenico Lio) who contributed to the scientific success of the symposium. In addition, the same day of the meeting the defence of PhD thesis of students belonging to the Pathobiology PhD course directed by CC was held. Prof. Caruso is proud of the hard and challenging work of his students which motivation and enthusiasm, with the management of Drs. Giuseppina Candore, Giuseppina Colonna-Romano and Prof. Domenico Lio have permitted to the whole Immunosenescence Unit to grow in the field of immunosenescence.</p>" ]
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[{"surname": ["Vasto", "Candore", "List\u00ec", "Balistreri", "Colonna-Romano", "Malavolta", "Lio", "Nuzzo", "Mocchegiani", "Di Bona", "Caruso"], "given-names": ["S", "G", "F", "CR", "G", "M", "D", "D", "E", "D", "C"], "article-title": ["Inflammation, Genes and Zinc in Alzheimer's disease"], "source": ["Brain Research Review"], "year": ["2008"], "volume": ["58"], "fpage": ["96"], "lpage": ["105"], "pub-id": ["10.1016/j.brainresrev.2007.12.001"]}, {"surname": ["Abraham", "Drummond", "Lutton", "Kappas"], "given-names": ["NG", "GS", "JD", "A"], "article-title": ["The biological significance and physiological role of heme oxygenase"], "source": ["Cell Physiol Biochem"], "year": ["1996"], "volume": ["6"], "fpage": ["129"], "lpage": ["168"]}, {"surname": ["Mortimer", "Borenstein"], "given-names": ["JA", "AR"], "article-title": ["Early-life risk factors for Alzheimer's disease"], "source": ["Research and Practice in Alzheimer's Disease"], "year": ["2007"], "volume": ["12"], "fpage": ["76"], "lpage": ["80"]}]
{ "acronym": [], "definition": [] }
33
CC BY
no
2022-01-12 14:47:28
Immun Ageing. 2008 Aug 13; 5:7
oa_package/33/bc/PMC2531076.tar.gz
PMC2531077
18706104
[ "<title>Background</title>", "<p>The debilitating nature of untreated diabetic retinopathy promotes the need for cost-effective screening methods. Various studies have shown that cost effective screening can reduce blind registration due to diabetes [##REF##2513049##1##, ####REF##17627978##2##, ##REF##16430383##3####16430383##3##]. Although seven field 30 degree stereo colour fundus photographs are the gold standard for diabetic screening, both remain relatively expensive and difficult to obtain [##REF##1546868##4##,##REF##2656572##5##]. In the UK, the National Screening Program for Diabetic Retinopathy utilises non-stereo digital photography as this meets the Diabetes UK standards for sensitivity and specificity.</p>", "<p>Non-stereo fundus imaging is easier to obtain but has limitations in establishing macular oedema [##REF##7580708##6##]. There is evidence that tritan colour vision is diminished in patients with diabetic maculopathy, but testing with the FM100 hue and Farnsworth-Lanthony D-15 test are labour intensive and time consuming [##REF##4038123##7##]. Colour vision testing with a computer graphics system is an effective alternative [##UREF##0##8##]. This study assesses the ability of an automated, digital colour contrast sensitivity program in investigating diabetic maculopathy.</p>" ]
[ "<title>Methods</title>", "<p>Patients from either the Diabetic Eye Screening Service or patients returning for their follow-up appointment in the Medical Retina Service were recruited for this study. Inclusion criteria included Type 2 diabetic patients with untreated non-proliferative diabetic retinopathy (NPDR) and untreated clinically significant macular oedema (CSMO). Exclusion criteria included Type 1 diabetes, proliferative diabetic retinopathy, previous laser photocoagulation, and concurrent ocular pathology including infection, trauma, amblyopia, glaucoma, and/or vascular occlusion.</p>", "<p>Medical history including duration of diabetes, hypertension, renal disease, recent HbA1c, and smoking were recorded. Concurrent eye disease and previous treatment were also recorded. Examination of best corrected logMar visual acuities (BCVA) was followed by colour contrast sensitivity testing of each eye by occluding the fellow eye and using the diabetic module of ChromaTest, a software program analyzing the age-corrected tritan (TCCT) and protan color contrast thresholds (PCCT). A brief explanation of what the patient is expected to see and their expected response was made prior to the test. The right eye was tested first followed by the left.</p>", "<p>For the Chromatest, the subject is seated at a fixed distance from the monitor so the alphabetical letter displayed on the computer screen subtends a constant angle on the retina. The letter size creates an image that tests the central 6.5 degrees of the retina. The letters are displayed on a background of equiluminance. The operator has no influence on the contrast of the test letter given. The computer finds the endpoint of the test by a Modified Binary Search method; if response is correct, on the next presentation the colour difference between letter and background is halved. If response is incorrect, the colour -contrast is doubled. Incorrect responses prolong the test, but do not influence the final threshold. This method of determining thresholds leads to finite steps which reach a plateau at the colour contrast sensitivity threshold. The reproducibility of this measurement is 1%, which is the sensitivity of the test. The Chromatest has been further described in various articles [##UREF##0##8##, ####UREF##1##9##, ##REF##3168720##10####3168720##10##]. Control data was obtained from unpublished data collected by G.B. Arden from diabetic patients without any diabetic retinopathy prior to this study (Table ##TAB##0##1##). Test and training sets are both from the group studied in this report.</p>", "<p>Dilated fundoscopy with slit lamp biomicroscopy and 78 D lens was performed by a specialist registrar (RW) to confirm the grading of CSMO according to the Early Treatment Diabetic Retinopathy Study extension of the modified Airlie House classification [##REF##2062513##11##]. CSMO is defined as any retinal thickening within 500 microns of the centre of the fovea; hard, yellow exudates within 500 microns of the centre of the fovea with adjacent retinal thickening; or at least 1 disc area of retinal thickening, any part of which is within 1 disc are of the centre of the fovea.</p>", "<p>Each age group (eg. 30–49 years old, 50–69, 70–89) separated by 2 decades was assigned pass-fail criterion for TCCT as previous data suggests age related change in threshold for tritan colour. Since this is the first study of NPDR using the Chromatest, threshold levels were derived using the same data set for both training and testing the effectiveness. Pass-fail criterion for each age group was chosen piecewise and sensitivity/specificity calculations were made according to these arbitrarily assigned levels.</p>", "<p>Sensitivity, specificity, confidence intervals, and χ<sup>2 </sup>test were calculated by web-based statistical calculator made available by Professor Lowry at Vassar College, New York <ext-link ext-link-type=\"uri\" xlink:href=\"http://faculty.vassar.edu/lowry/VassarStats.html\"/>. Wilcoxon Rank Sum Test for non-parametric statistical analysis was performed using web software <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.fon.hum.uva.nl/Service/Statistics/Wilcoxon_Test.html\"/>.</p>" ]
[ "<title>Results</title>", "<p>150 eyes of 150 patients were included in this study. Of the 150 eyes, 115 eyes had untreated NPDR (Table ##TAB##1##2##) and 35 eyes had untreated CSMO (Table ##TAB##2##3##). Median age was 60 years. Median duration of diabetes was 16.0 years.</p>", "<p>Median LogMar BCVA for NPDR patients was 0.20 and for CSMO patients was 0.20. Interquartile range for VA NPDR and CSMO was 0.20 and 0.30, respectively. Median PCCT for NPDR was 3.9% and for CSMO patients was 5.6%. Wilcoxon Rank Sum Test analysis revealed statistical significant difference between CSMO and NPDR eyes for PCCT (p = 0.01). When compared to controls with sample size N = 30 (Table ##TAB##0##1##), PCCT for NPDR had no statistical significance (p = 0.15) whereas PCCT for CSMO was significant (p = 0.002). Median TCCT for NPDR was 15.4% and for CSME patients was 29.6%. Statistical significance was found between CSMO and NPDR eyes for TCCT (p = 0.0002). Both were also statistically significant when compared to controls (p &lt; 0.001)</p>", "<p>The piecewise pass/fail criterion for TCCT for each age group was as follows: 11.0 (30–49 year old); 23.0 (50–69 year old); 32.0 (70–89 year old). Sensitivity and specificity for screening of CSMO using the above pass-fail criterion for age matched TCCT results achieved 71% (95% confidence interval: 53–85%) and 70% (95% confidence interval: 60–78%), respectively (Table ##TAB##3##4##).</p>", "<p>When repeating the analysis in Table ##TAB##3##4## for only subjects with logMar BCVA &gt; = 0.1, sensitivity to detect CSMO improves to 75% (CI: 47–91%) and specificity to 85% (CI: 67–89%) p = 0.0002. Similarly, when repeating the analysis in Table ##TAB##3##4## for only subjects with CSMO with central macular thickening, sensitivity to detect CSMO improves to 83.3% (CI: 58–96%) p &lt; 0.0001.</p>" ]
[ "<title>Discussion</title>", "<p>Cost effective screening for chronic and debilitating disorders such as diabetic retinopathy is not only important to the well being of the patient, but these healthy adults contribute to the economy of a nation. With the rise in type 2 diabetes in obese adolescents due to dietary and lifestyle changes, the need for an optimal method of screening for sight threatening diabetic retinopathy becomes a critical essential [##REF##10609117##12##].</p>", "<p>Abnormal protan and especially tritan colour vision is associated with diabetic retinopathy [##UREF##2##13##]. Blue-yellow defect has also been described in both diabetic retinopathy and glaucoma [##REF##16963854##14##]. In contrast to the optotype used for testing macular function, the Chromatest has a separate glaucoma module for which it is designed to measure peripheral colour sensitivity changes in an arcuate manner using a central fixation point. This study did not cross examine patients with glaucoma and diabetic retinopathy using both glaucoma and macular modules, but it is feasible that further testing may reveal an overlap in colour defect for these patients. Although the mechanism of altered colour vision is unknown, there is evidence that reduced retinal oxygen saturation is associated with impaired colour vision in diabetics [##REF##9135404##15##]. Error scores in colour vision have been found to be directly correlated to severity of macular oedema [##REF##6979252##16##]. This may be similar to the effects of retinal detachment where photoreceptors are shifted obliquely [##REF##6979252##16##]. Correlation between selective loss of short wavelength pathway sensitivity and the severity of diabetic macular oedema has been demonstrated [##REF##1390531##17##,##REF##2354907##18##]. Therefore, we have concentrated on the study of untreated CSMO to ascertain the viability of such a screening method. The use of smaller letters (1.5 degree; Chromatest module for age related macular degeneration) might give better results for CSMO as it may test macular function better than the larger 6.5 degree optotype.</p>", "<p>This study included only patients with type 2 diabetes to reduce the possible variability in pathogenesis. Although the mechanism of diabetic retinopathy is likely to be identical in both type 1 and type 2 diabetes, previous studies such as the Early Treatment Diabetic Retinopathy Study and Diabetic Retinopathy Study have investigated each type of diabetes separately. Laser photocoagulation was an exclusion criterion as it affects tritan colour vision [##REF##8172263##19##]. Cataract and pseudophakia were not excluded as both are more common in diabetics and exclusion would have limited the usefulness of the Chromatest in screening. It is understood that lens-yellowing effects due to cataract may cause pre-retinal absorption of short-wavelength light resulting in tritan deficits. This may have influenced the overall sensitivity and specificity of the study, but it was a representation of the realistic setting clinicians experience in their practice.</p>", "<p>In colour contrast testing, the higher the TCCT or PCCT score, the more abnormal the result compared to age-matched normal levels. 30% (35 of 115) patients with NPDR had TCCT above normal levels. 12 male patients were suspected to have congenital colour blindness as their PCCT were considerably worse than normal and not corresponding to their visual acuity or their fundus appearance. This was not confirmed with any other mode of investigation as the study was aimed at mimicking realistic clinical setting where high volume testing can be conducted without further time consuming tests. 16 cases had severe NPDR and may have contributed to the poor results whereas the remaining 7 had results not corresponding to their fundus appearance. We postulate that these 7 eyes may have had concurrent disease indistinguishable by indirect biomicroscopy such as more advanced ischaemia. Ultimately, fluorescein angiography may have further elucidated the true pathology.</p>", "<p>29% (10 of 35) CSMO patients had TCCT better than normal levels. 8 eyes had CSMO qualified as 1 disc area of retinal thickening within 1 disc area of the fovea. 2 eyes had exudates with associated retinal thickening within 500 microns of the fovea, but both were left eyes and it is possible that the patients were able to perform educated guesses because they had been conditioned following testing with their right eye.</p>", "<p>Unfortunately, we were forced to obtain normal threshold levels through the same dataset. These levels were obtained through analysis of cases without CSMO. Therefore, the results may be biased. However, because this device is relatively new and the limited availability of further data from diabetics, we are limited to using this dataset to obtain \"normal\" threshold values. Further data will strengthen our case of the power of this diagnostic tool.</p>", "<p>The Chromatest is unable to successfully screen those patients with congenital blindness and performs less well for patients without foveal pathology. Conditioning following testing with the right eye may also allow patients to perform better on their left eye. From anecdotal evidence, time for testing of the second eye was observed by the investigators to be shorter than the first eye. Repeated testing which was not done in our study may alleviate this problem. This study has studied more untreated CSMO eyes with colour vision than any other that have been published, but it requires more data to solidify our findings. Colour contrast analysis may become a useful tool for defining the need for laser treatment, but so far our experience fails the Exeter Standards of the British Diabetic Association (Diabetes UK), which established screening levels of at least 80% sensitivity and 95% specificity [##UREF##3##20##].</p>", "<p>Despite the limitations of the results, there was no discrimination for age and visual acuity due to the ease of the test. All patients were able to perform this test unlike the 1.5% of patients failing to perform another automated TCCT test [##REF##12770974##21##]. Average test time was fast at 5 minutes and requires no mydriasis unlike fluorescein angiography and fundus photography. Conditioning after repeated testing is an issue for reliability, but this study was aimed at mimicking realistic clinical settings where patients have no experience of colour contrast testing. Further studies to distinguish repeatability and data for classifying normal results from abnormals are planned. The equipment required is relatively cheap and readily available compared to those required for optical coherence tomography or stereomacular photographs. It is also a non-invasive procedure and less labour intensive compared to fluorescein angiography.</p>" ]
[ "<title>Conclusion</title>", "<p>Non-ophthalmic doctors can have a retinopathy detection rate of 49% compared to 96% for ophthalmologists [##REF##7087063##22##]. Therefore, a cost effective method for screening is essential for diabetic retinopathy. Screening by digital photography proposed under the National Service Framework is offered to all patients with diabetes in the United Kingdom. It is supplemented by biomicroscopy by the ophthalmologists in monitoring and treating sight threatening disease. Furthermore, optical coherence tomography has become a powerful tool in screening and monitoring CSMO with sensitivity and specificity rates of near 80% and 90%, respectively [##REF##17962446##23##]. Perhaps with further investigation, TCCT testing may become a supplement for detecting and monitoring sight threatening pathology without much equipment or trained technicians. However, with current data, all forms of TCCT testing including the Chromatest do not qualify for use in screening for CSMO.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>To assess the ability of the Chromatest in investigating diabetic maculopathy.</p>", "<title>Method</title>", "<p>Patients with Type 2 diabetes and no concurrent ocular pathology or previous laser photocoagulation were recruited. Visual acuities were assessed followed by colour contrast sensitivity testing of each eye using Chromatest. Dilated fundoscopy with slit lamp biomicroscopy with 78 D lens was then performed to confirm the stage of diabetic retinopathy according to the Early Treatment Diabetic Retinopathy Study.</p>", "<title>Results</title>", "<p>150 eyes in 150 patients were recruited into this study. 35 eyes with no previous laser photocoagulation were shown to have clinically significant macular oedema (CSMO) and 115 eyes with untreated non-proliferative diabetic retinopathy (NPDR) on fundus biomicroscopy. Statistical significant difference was found between CSMO and NPDR eyes for protan colour contrast threshold (p = 0.01). Statistical significance was found between CSMO and NPDR eyes for tritan colour contrast threshold (p = 0.0002). Sensitivity and specificity for screening of CSMO using pass-fail criterion for age matched TCCT results achieved 71% (95% confidence interval: 53–85%) and 70% (95% confidence interval: 60–78%), respectively. However, threshold levels were derived using the same data set for both training and testing the effectiveness since this was the first study of NPDR using the Chromatest</p>", "<title>Conclusion</title>", "<p>The ChromaTest is a simple, cheap, easy to use, and quick test for colour contrast sensitivity. This study did not achieve results to justify use of the Chromatest for screening, but it reinforced the changes seen in tritan colour vision in diabetic retinopathy.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>RW examined patients, conducted investigation, conceived, drafted the manuscript. TA performed the statistical analysis. JK compiled patient list and conducted investigation. SS compiled patient list and conducted investigation. GA performed the statistical analysis. VC conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.</p>", "<title>Pre-publication history</title>", "<p>The pre-publication history for this paper can be accessed here:</p>", "<p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.biomedcentral.com/1471-2415/8/15/prepub\"/></p>" ]
[ "<title>Acknowledgements</title>", "<p>No funding was obtained for this study.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Colour Contrast Sensitivity in Patients with Diabetes and No Clinical Retinopathy (N = 30)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Age</td><td align=\"left\">Tritan</td><td align=\"left\">Protan</td></tr></thead><tbody><tr><td align=\"left\">37</td><td align=\"left\">12.4</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">44</td><td align=\"left\">9.4</td><td align=\"left\">3.1</td></tr><tr><td align=\"left\">48</td><td align=\"left\">4.2</td><td align=\"left\">4.2</td></tr><tr><td align=\"left\">48</td><td align=\"left\">4.1</td><td align=\"left\">2.9</td></tr><tr><td align=\"left\">48</td><td align=\"left\">4.2</td><td align=\"left\">2.4</td></tr><tr><td align=\"left\">51</td><td align=\"left\">11.3</td><td align=\"left\">5.9</td></tr><tr><td align=\"left\">51</td><td align=\"left\">4.2</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">51</td><td align=\"left\">5.9</td><td align=\"left\">4.7</td></tr><tr><td align=\"left\">54</td><td align=\"left\">6.9</td><td align=\"left\">6.6</td></tr><tr><td align=\"left\">54</td><td align=\"left\">4.1</td><td align=\"left\">4.8</td></tr><tr><td align=\"left\">54</td><td align=\"left\">7.9</td><td align=\"left\">3.7</td></tr><tr><td align=\"left\">57</td><td align=\"left\">6.8</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">59</td><td align=\"left\">8.6</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\">59</td><td align=\"left\">9.4</td><td align=\"left\">2.4</td></tr><tr><td align=\"left\">60</td><td align=\"left\">15.7</td><td align=\"left\">2.6</td></tr><tr><td align=\"left\">60</td><td align=\"left\">6.2</td><td align=\"left\">5.4</td></tr><tr><td align=\"left\">61</td><td align=\"left\">15.7</td><td align=\"left\">11.6</td></tr><tr><td align=\"left\">62</td><td align=\"left\">7.1</td><td align=\"left\">2.7</td></tr><tr><td align=\"left\">62</td><td align=\"left\">8.6</td><td align=\"left\">11.4</td></tr><tr><td align=\"left\">64</td><td align=\"left\">7.9</td><td align=\"left\">3.7</td></tr><tr><td align=\"left\">67</td><td align=\"left\">9.4</td><td align=\"left\">5.1</td></tr><tr><td align=\"left\">67</td><td align=\"left\">13.6</td><td align=\"left\">5.4</td></tr><tr><td align=\"left\">68</td><td align=\"left\">17.3</td><td align=\"left\">5.4</td></tr><tr><td align=\"left\">68</td><td align=\"left\">11.7</td><td align=\"left\">5.71</td></tr><tr><td align=\"left\">69</td><td align=\"left\">6.8</td><td align=\"left\">6.8</td></tr><tr><td align=\"left\">69</td><td align=\"left\">13.9</td><td align=\"left\">4.7</td></tr><tr><td align=\"left\">70</td><td align=\"left\">17.3</td><td align=\"left\">4.7</td></tr><tr><td align=\"left\">70</td><td align=\"left\">12.4</td><td align=\"left\">5</td></tr><tr><td align=\"left\">71</td><td align=\"left\">6.7</td><td align=\"left\">3.8</td></tr><tr><td align=\"left\">72</td><td align=\"left\">21.7</td><td align=\"left\">5.4</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Colour Contrast Sensitivity in Patients with NPDR (N = 115)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Age</td><td align=\"right\">Log Mar VA</td><td align=\"right\">Tritan</td><td align=\"right\">Protan</td></tr></thead><tbody><tr><td align=\"right\">31</td><td align=\"right\">0</td><td align=\"right\">13.6</td><td align=\"right\">3.4</td></tr><tr><td align=\"right\">32</td><td align=\"right\">0</td><td align=\"right\">5.2</td><td align=\"right\">3.2</td></tr><tr><td align=\"right\">32</td><td align=\"right\">0</td><td align=\"right\">6.7</td><td align=\"right\">2</td></tr><tr><td align=\"right\">32</td><td align=\"right\">0.2</td><td align=\"right\">15.4</td><td align=\"right\">3.2</td></tr><tr><td align=\"right\">41</td><td align=\"right\">0</td><td align=\"right\">16.1</td><td align=\"right\">15.4</td></tr><tr><td align=\"right\">41</td><td align=\"right\">0</td><td align=\"right\">6.1</td><td align=\"right\">2.1</td></tr><tr><td align=\"right\">41</td><td align=\"right\">0</td><td align=\"right\">6.2</td><td align=\"right\">2.1</td></tr><tr><td align=\"right\">41</td><td align=\"right\">0</td><td align=\"right\">6</td><td align=\"right\">1.7</td></tr><tr><td align=\"right\">41</td><td align=\"right\">0</td><td align=\"right\">8.4</td><td align=\"right\">3.9</td></tr><tr><td align=\"right\">42</td><td align=\"right\">0</td><td align=\"right\">11.4</td><td align=\"right\">3</td></tr><tr><td align=\"right\">44</td><td align=\"right\">0.2</td><td align=\"right\">9.6</td><td align=\"right\">4.8</td></tr><tr><td align=\"right\">44</td><td align=\"right\">0.2</td><td align=\"right\">13.3</td><td align=\"right\">8.1</td></tr><tr><td align=\"right\">45</td><td align=\"right\">0.2</td><td align=\"right\">16.1</td><td align=\"right\">4.2</td></tr><tr><td align=\"right\">45</td><td align=\"right\">0.2</td><td align=\"right\">22.1</td><td align=\"right\">5.5</td></tr><tr><td align=\"right\">45</td><td align=\"right\">0.4</td><td align=\"right\">19.9</td><td align=\"right\">5.8</td></tr><tr><td align=\"right\">48</td><td align=\"right\">0</td><td align=\"right\">5.6</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">48</td><td align=\"right\">0.5</td><td align=\"right\">20.6</td><td align=\"right\">3.8</td></tr><tr><td align=\"right\">48</td><td align=\"right\">0.6</td><td align=\"right\">29.5</td><td align=\"right\">5</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">7.4</td><td align=\"right\">3.4</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">6.3</td><td align=\"right\">2.2</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">8.4</td><td align=\"right\">3.9</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">8.4</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">9.4</td><td align=\"right\">3.1</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">9.9</td><td align=\"right\">3.4</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">10.3</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">30.5</td><td align=\"right\">6.1</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0</td><td align=\"right\">34.5</td><td align=\"right\">4</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0.1</td><td align=\"right\">33.6</td><td align=\"right\">6</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0.7</td><td align=\"right\">9.2</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">49</td><td align=\"right\">0.7</td><td align=\"right\">12.2</td><td align=\"right\">3.6</td></tr><tr><td align=\"right\">51</td><td align=\"right\">0</td><td align=\"right\">13.6</td><td align=\"right\">4.4</td></tr><tr><td align=\"right\">51</td><td align=\"right\">0.1</td><td align=\"right\">18</td><td align=\"right\">5.8</td></tr><tr><td align=\"right\">51</td><td align=\"right\">0.2</td><td align=\"right\">19.1</td><td align=\"right\">7</td></tr><tr><td align=\"right\">52</td><td align=\"right\">0</td><td align=\"right\">10.8</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">52</td><td align=\"right\">0.2</td><td align=\"right\">82.4</td><td align=\"right\">9.3</td></tr><tr><td align=\"right\">54</td><td align=\"right\">0</td><td align=\"right\">9</td><td align=\"right\">3.1</td></tr><tr><td align=\"right\">54</td><td align=\"right\">0</td><td align=\"right\">22.1</td><td align=\"right\">4.6</td></tr><tr><td align=\"right\">54</td><td align=\"right\">0.2</td><td align=\"right\">23.6</td><td align=\"right\">4.3</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0</td><td align=\"right\">14.4</td><td align=\"right\">3.1</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0</td><td align=\"right\">20.2</td><td align=\"right\">5.4</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.2</td><td align=\"right\">18.4</td><td align=\"right\">3.5</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.2</td><td align=\"right\">17.6</td><td align=\"right\">2.1</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.3</td><td align=\"right\">19.6</td><td align=\"right\">4.4</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.3</td><td align=\"right\">85.9</td><td align=\"right\">7.7</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.4</td><td align=\"right\">22.1</td><td align=\"right\">7.7</td></tr><tr><td align=\"right\">56</td><td align=\"right\">0</td><td align=\"right\">8.1</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">56</td><td align=\"right\">0</td><td align=\"right\">11.1</td><td align=\"right\">2.5</td></tr><tr><td align=\"right\">56</td><td align=\"right\">0.1</td><td align=\"right\">6.6</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">57</td><td align=\"right\">0</td><td align=\"right\">10.3</td><td align=\"right\">3.6</td></tr><tr><td align=\"right\">57</td><td align=\"right\">0.1</td><td align=\"right\">6.7</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">57</td><td align=\"right\">0.1</td><td align=\"right\">7.2</td><td align=\"right\">2.1</td></tr><tr><td align=\"right\">57</td><td align=\"right\">0.2</td><td align=\"right\">14.9</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.1</td><td align=\"right\">13.9</td><td align=\"right\">3.8</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.2</td><td align=\"right\">11</td><td align=\"right\">3.3</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.2</td><td align=\"right\">21.4</td><td align=\"right\">2.8</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.2</td><td align=\"right\">38</td><td align=\"right\">3.8</td></tr><tr><td align=\"right\">59</td><td align=\"right\">0.2</td><td align=\"right\">6.8</td><td align=\"right\">2.1</td></tr><tr><td align=\"right\">59</td><td align=\"right\">0.2</td><td align=\"right\">6.3</td><td align=\"right\">1.4</td></tr><tr><td align=\"right\">59</td><td align=\"right\">0.2</td><td align=\"right\">10.1</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">60</td><td align=\"right\">0.2</td><td align=\"right\">8</td><td align=\"right\">3.1</td></tr><tr><td align=\"right\">60</td><td align=\"right\">0.2</td><td align=\"right\">12.2</td><td align=\"right\">4.4</td></tr><tr><td align=\"right\">61</td><td align=\"right\">0</td><td align=\"right\">5.7</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">61</td><td align=\"right\">0</td><td align=\"right\">7.5</td><td align=\"right\">2.5</td></tr><tr><td align=\"right\">61</td><td align=\"right\">0.2</td><td align=\"right\">8.6</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">61</td><td align=\"right\">0.2</td><td align=\"right\">13.4</td><td align=\"right\">2.8</td></tr><tr><td align=\"right\">62</td><td align=\"right\">0</td><td align=\"right\">10.4</td><td align=\"right\">2.8</td></tr><tr><td align=\"right\">62</td><td align=\"right\">0.3</td><td align=\"right\">98.7</td><td align=\"right\">78.2</td></tr><tr><td align=\"right\">62</td><td align=\"right\">0.3</td><td align=\"right\">98.7</td><td align=\"right\">75.7</td></tr><tr><td align=\"right\">63</td><td align=\"right\">0</td><td align=\"right\">9.9</td><td align=\"right\">4</td></tr><tr><td align=\"right\">63</td><td align=\"right\">0.1</td><td align=\"right\">15.4</td><td align=\"right\">5</td></tr><tr><td align=\"right\">63</td><td align=\"right\">0.1</td><td align=\"right\">25.3</td><td align=\"right\">6.5</td></tr><tr><td align=\"right\">64</td><td align=\"right\">0</td><td align=\"right\">18.5</td><td align=\"right\">3.7</td></tr><tr><td align=\"right\">64</td><td align=\"right\">0.2</td><td align=\"right\">20.2</td><td align=\"right\">4</td></tr><tr><td align=\"right\">64</td><td align=\"right\">0.2</td><td align=\"right\">75.7</td><td align=\"right\">21.4</td></tr><tr><td align=\"right\">65</td><td align=\"right\">0.3</td><td align=\"right\">15.4</td><td align=\"right\">6.3</td></tr><tr><td align=\"right\">65</td><td align=\"right\">0.3</td><td align=\"right\">37.9</td><td align=\"right\">19.9</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0</td><td align=\"right\">18.3</td><td align=\"right\">7.7</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0</td><td align=\"right\">20.6</td><td align=\"right\">6.7</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.1</td><td align=\"right\">19.9</td><td align=\"right\">4.6</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.1</td><td align=\"right\">57.7</td><td align=\"right\">3.8</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.2</td><td align=\"right\">8.1</td><td align=\"right\">2.5</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.3</td><td align=\"right\">20</td><td align=\"right\">6.5</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.3</td><td align=\"right\">50.4</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.5</td><td align=\"right\">52.4</td><td align=\"right\">8.4</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.6</td><td align=\"right\">18.1</td><td align=\"right\">6.7</td></tr><tr><td align=\"right\">68</td><td align=\"right\">0.1</td><td align=\"right\">32.7</td><td align=\"right\">6</td></tr><tr><td align=\"right\">68</td><td align=\"right\">0.2</td><td align=\"right\">10.6</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">68</td><td align=\"right\">0.2</td><td align=\"right\">31.5</td><td align=\"right\">3.9</td></tr><tr><td align=\"right\">69</td><td align=\"right\">0</td><td align=\"right\">14.4</td><td align=\"right\">4.4</td></tr><tr><td align=\"right\">69</td><td align=\"right\">0.1</td><td align=\"right\">49.6</td><td align=\"right\">6.2</td></tr><tr><td align=\"right\">69</td><td align=\"right\">0.5</td><td align=\"right\">19.9</td><td align=\"right\">5.2</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0</td><td align=\"right\">9.2</td><td align=\"right\">13.3</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0</td><td align=\"right\">11.1</td><td align=\"right\">3.8</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0.1</td><td align=\"right\">7.2</td><td align=\"right\">13.7</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0.2</td><td align=\"right\">9.6</td><td align=\"right\">2.5</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.2</td><td align=\"right\">21.5</td><td align=\"right\">5.7</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.4</td><td align=\"right\">5.5</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.4</td><td align=\"right\">60.3</td><td align=\"right\">6.1</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.5</td><td align=\"right\">34.8</td><td align=\"right\">6.4</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.6</td><td align=\"right\">18.6</td><td align=\"right\">3.3</td></tr><tr><td align=\"right\">75</td><td align=\"right\">0</td><td align=\"right\">12.9</td><td align=\"right\">2.2</td></tr><tr><td align=\"right\">75</td><td align=\"right\">0.1</td><td align=\"right\">19.9</td><td align=\"right\">4</td></tr><tr><td align=\"right\">75</td><td align=\"right\">0.3</td><td align=\"right\">40.4</td><td align=\"right\">3.6</td></tr><tr><td align=\"right\">76</td><td align=\"right\">0.3</td><td align=\"right\">27.6</td><td align=\"right\">4.4</td></tr><tr><td align=\"right\">76</td><td align=\"right\">0.3</td><td align=\"right\">70.5</td><td align=\"right\">9.6</td></tr><tr><td align=\"right\">77</td><td align=\"right\">0.1</td><td align=\"right\">11.9</td><td align=\"right\">3.6</td></tr><tr><td align=\"right\">78</td><td align=\"right\">0</td><td align=\"right\">24</td><td align=\"right\">5.2</td></tr><tr><td align=\"right\">78</td><td align=\"right\">0.2</td><td align=\"right\">17.6</td><td align=\"right\">4</td></tr><tr><td align=\"right\">78</td><td align=\"right\">0.2</td><td align=\"right\">20.9</td><td align=\"right\">7.1</td></tr><tr><td align=\"right\">78</td><td align=\"right\">0.3</td><td align=\"right\">22.4</td><td align=\"right\">12.9</td></tr><tr><td align=\"right\">79</td><td align=\"right\">0.5</td><td align=\"right\">52.6</td><td align=\"right\">21.7</td></tr><tr><td align=\"right\">79</td><td align=\"right\">0.5</td><td align=\"right\">98.7</td><td align=\"right\">67.6</td></tr><tr><td align=\"right\">82</td><td align=\"right\">0</td><td align=\"right\">13.5</td><td align=\"right\">5.2</td></tr><tr><td align=\"right\">82</td><td align=\"right\">0.2</td><td align=\"right\">23.6</td><td align=\"right\">6.8</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Colour Contrast Sensitivity in Patients with CSMO (N = 35)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\">Age</td><td align=\"right\">LogMar VA</td><td align=\"right\">Tritan</td><td align=\"right\">Protan</td></tr></thead><tbody><tr><td align=\"right\">31</td><td align=\"right\">0</td><td align=\"right\">8.5</td><td align=\"right\">3.6</td></tr><tr><td align=\"right\">31</td><td align=\"right\">0</td><td align=\"right\">11.1</td><td align=\"right\">4</td></tr><tr><td align=\"right\">42</td><td align=\"right\">0.2</td><td align=\"right\">14.1</td><td align=\"right\">4.5</td></tr><tr><td align=\"right\">44</td><td align=\"right\">0</td><td align=\"right\">7</td><td align=\"right\">1.9</td></tr><tr><td align=\"right\">44</td><td align=\"right\">0</td><td align=\"right\">18.8</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">51</td><td align=\"right\">0.2</td><td align=\"right\">8.8</td><td align=\"right\">2.6</td></tr><tr><td align=\"right\">52</td><td align=\"right\">0</td><td align=\"right\">29.6</td><td align=\"right\">3.5</td></tr><tr><td align=\"right\">52</td><td align=\"right\">0.3</td><td align=\"right\">72.3</td><td align=\"right\">10.7</td></tr><tr><td align=\"right\">55</td><td align=\"right\">0.2</td><td align=\"right\">18.4</td><td align=\"right\">3.5</td></tr><tr><td align=\"right\">56</td><td align=\"right\">0.3</td><td align=\"right\">18.4</td><td align=\"right\">2.9</td></tr><tr><td align=\"right\">56</td><td align=\"right\">0.5</td><td align=\"right\">36</td><td align=\"right\">5.6</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.1</td><td align=\"right\">7.7</td><td align=\"right\">2.7</td></tr><tr><td align=\"right\">58</td><td align=\"right\">0.3</td><td align=\"right\">78.2</td><td align=\"right\">13.7</td></tr><tr><td align=\"right\">59</td><td align=\"right\">0.2</td><td align=\"right\">23.6</td><td align=\"right\">3</td></tr><tr><td align=\"right\">62</td><td align=\"right\">0</td><td align=\"right\">70.5</td><td align=\"right\">7.7</td></tr><tr><td align=\"right\">62</td><td align=\"right\">0.1</td><td align=\"right\">49.9</td><td align=\"right\">11.4</td></tr><tr><td align=\"right\">63</td><td align=\"right\">0.4</td><td align=\"right\">27.3</td><td align=\"right\">6.7</td></tr><tr><td align=\"right\">65</td><td align=\"right\">0.1</td><td align=\"right\">85.9</td><td align=\"right\">14.4</td></tr><tr><td align=\"right\">65</td><td align=\"right\">0.3</td><td align=\"right\">98.7</td><td align=\"right\">16.9</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.1</td><td align=\"right\">16.1</td><td align=\"right\">3.2</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.2</td><td align=\"right\">11.8</td><td align=\"right\">3</td></tr><tr><td align=\"right\">67</td><td align=\"right\">0.3</td><td align=\"right\">80.8</td><td align=\"right\">12.4</td></tr><tr><td align=\"right\">68</td><td align=\"right\">0.2</td><td align=\"right\">13.3</td><td align=\"right\">3.2</td></tr><tr><td align=\"right\">69</td><td align=\"right\">0.1</td><td align=\"right\">23.3</td><td align=\"right\">5.3</td></tr><tr><td align=\"right\">69</td><td align=\"right\">0.5</td><td align=\"right\">30.3</td><td align=\"right\">16.1</td></tr><tr><td align=\"right\">70</td><td align=\"right\">0</td><td align=\"right\">21.5</td><td align=\"right\">6.8</td></tr><tr><td align=\"right\">70</td><td align=\"right\">0</td><td align=\"right\">35.4</td><td align=\"right\">5.6</td></tr><tr><td align=\"right\">70</td><td align=\"right\">0</td><td align=\"right\">32.7</td><td align=\"right\">5.5</td></tr><tr><td align=\"right\">70</td><td align=\"right\">0</td><td align=\"right\">62.8</td><td align=\"right\">9</td></tr><tr><td align=\"right\">70</td><td align=\"right\">0.5</td><td align=\"right\">98.7</td><td align=\"right\">20.8</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0</td><td align=\"right\">98.7</td><td align=\"right\">14.7</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0.2</td><td align=\"right\">64.8</td><td align=\"right\">20</td></tr><tr><td align=\"right\">71</td><td align=\"right\">0.3</td><td align=\"right\">98.7</td><td align=\"right\">42.3</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.7</td><td align=\"right\">68</td><td align=\"right\">18.4</td></tr><tr><td align=\"right\">72</td><td align=\"right\">0.9</td><td align=\"right\">57.7</td><td align=\"right\">16.9</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>χ<sup>2 </sup>test for TCCT detection of CSMO</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\">True Positive</td><td align=\"left\">True Negative</td><td align=\"left\">Total</td></tr></thead><tbody><tr><td align=\"left\">Test Positive</td><td align=\"left\">25</td><td align=\"left\">35</td><td align=\"left\">60</td></tr><tr><td align=\"left\">Test Negative</td><td align=\"left\">10</td><td align=\"left\">80</td><td align=\"left\">90</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">Total</td><td align=\"left\">35</td><td align=\"left\">115</td><td align=\"left\">150</td></tr></tbody></table></table-wrap>" ]
[]
[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>Control: Age, TCCT, PCCT</p></table-wrap-foot>", "<table-wrap-foot><p>NPDR patients: Age, VA, TCCT, PCCT</p></table-wrap-foot>", "<table-wrap-foot><p>CSMO patients: Age, VA, TCCT, PCCT</p></table-wrap-foot>", "<table-wrap-foot><p>Sensitivity = 71% (CI: 53–85%), Specificity: 70% (CI: 60–78%); χ<sup>2 </sup>test: p &lt; 0.0001 comparing proportions of true positives among the test positive versus test negative subjects</p></table-wrap-foot>" ]
[]
[]
[{"surname": ["Arden", "Gunduz", "Perry"], "given-names": ["GB", "K", "S"], "article-title": ["Colour vision testing with a computer graphics system"], "source": ["Clin Vis Sci"], "year": ["1988"], "volume": ["2"], "fpage": ["303"], "lpage": ["20"]}, {"surname": ["Arden"], "given-names": ["GB"], "article-title": ["Testing contrast sensitivity in clinical practice"], "source": ["Clin Vis Sci"], "year": ["1987"], "volume": ["2"], "fpage": ["213"], "lpage": ["24"]}, {"surname": ["Treager", "Knowles", "De Alwys", "Reffin", "Ripley", "Casswell"], "given-names": ["SD", "PI", "DV", "JP", "LG", "AG"], "article-title": ["Colour vision deficits predict the development of sight-threatening disease with background retinopathy"], "source": ["Invest Ophthalmol Vis Sci"], "year": ["1993"], "volume": ["34"], "fpage": ["719"], "comment": ["(ARVO Abstracts no 81)"]}, {"collab": ["British Diabetic Association"], "source": ["Retinal photography screening for diabetic eye disease"], "year": ["1997"], "publisher-name": ["London: British Diabetic Association Report"]}]
{ "acronym": [], "definition": [] }
23
CC BY
no
2022-01-12 14:47:28
BMC Ophthalmol. 2008 Aug 17; 8:15
oa_package/86/05/PMC2531077.tar.gz
PMC2531078
18700004
[ "<title>Background</title>", "<p>In January of 2002, the United States Congress passed the Best Pharmaceuticals for Children Act (BPCA) [##UREF##0##1##] into law with the intent of improving the safety and efficacy of medications in pediatric populations. Ultimately, the initial goal of the BPCA was to establish a process for studying on-patent and off-patent drugs for use in pediatric populations. The legislation also calls for the scientific investigation of pediatric therapeutics through the conducting of pediatric studies and research to learn more about the efficacy and safety of medications in children. This occurs through a partnership of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) [##UREF##1##2##]. The Director of the NIH has delegated the authority to implement the drug development program to the Director of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), and the NICHD administers the research program through the Obstetric and Pediatric Pharmacology Branch of the Center for Research for Mothers and Children, working in cooperation with the other NIH Institutes and Centers with other significant pediatric research portfolios.</p>", "<p>To identify off-patent medications in need of further study, the BPCA asks the NICHD, in consultation with the FDA and experts in pediatric drug development, to develop a process for prioritizing needs in pediatric therapeutics by publishing a priority list. As a result, the U.S. FDA and NICHD began collaborations to identify and prioritize medications that were to be studied in pediatric populations [##UREF##1##2##]. In 2003, the first list of drugs for which pediatric studies were needed was generated. It consisted of 20 medications, including lithium [##UREF##1##2##].</p>", "<p>Lithium is a benchmark treatment for adult patients with bipolar disorder (BD). Lithium has been found to be efficacious in alleviating acute mania and preventing manic and mixed mood relapses in adults [##REF##11719917##3##, ####REF##14754766##4##, ##UREF##2##5##, ##REF##17176493##6####17176493##6##]. As a result, lithium is indicated in the United States for the acute and maintenance treatment of mania in BD in adults. Unfortunately, definitive randomized controlled trials of lithium have not been performed in pediatric populations that would lead to labeling of lithium for children and/or adolescents suffering from mania or mixed states in BD.</p>", "<p>Despite the paucity of data, it should be noted that preliminary studies have found that open label treatment with lithium may be effective in the treatment of children and adolescents with bipolar disorders [##REF##12960703##7##,##UREF##3##8##]. In addition, lithium is a recommended treatment for manic or mixed states for youth with BD according to published treatment guidelines for pediatric BD [##REF##15725966##9##]. A major consideration regarding the study of lithium as a treatment for youths suffering from BD is the untested assumption that lithium dosing procedures and therapeutic drug level monitoring that are used in adults are applicable to children and adolescents.</p>" ]
[ "<title>Methods</title>", "<p>In an effort to better characterize lithium's use and efficacy in children as well as develop pediatric labeling under the auspices of the BPCA, a Written Request (WR) pertaining to lithium was issued by the FDA to the NICHD in 2004. A Written Request is a letter issued by FDA to the holder of the New Drug Application (NDA) that outlines how a pediatric study should be conducted and includes the study population, numbers of patients, study design, outcome measures, format, and time line of submission.</p>", "<p>The study design outlined in the WR was informed by recommendations included in a published consensus paper regarding the study of mania in pediatric patients [##REF##12804123##10##]. The WR for lithium noted that three studies should be executed in children and adolescents ages 7–17 years with acute mania in order to inform the labeling of lithium for this population. These three studies included a Pediatric Pharmacokinetic and Tolerability study, a Pediatric Efficacy and Safety study, and a Pediatric Long-term Safety study.</p>", "<p>In the Pediatric Pharmacokinetic and Tolerability study, the pharmacokinetics of lithium would be examined. The WR mandated that at least 18 pediatric patients (9 males and 9 females) be enrolled in this study. In addition, an evidence-based dosing paradigm would be developed that would achieve target serum levels but also minimize toxicity. Moreover, the dosing schedule results from this study would then be utilized in the subsequent Efficacy and Long-term Safety treatment trials.</p>", "<p>According to the WR, following the completion of the Pediatric Pharmacokinetic and Tolerability study, the Pediatric Efficacy and Safety study was to be initiated. This trial would last for a minimum of 6 to 8 weeks, and would consist of a randomized, double-blind, parallel-group, placebo-controlled acute study. As directed by the WR, this study would have a sufficient number of male and female patients to detect a difference between lithium and placebo, equivalent to the median effect size seen in adult trials. After this second trial was completed, the Pediatric Long-term Safety Study would commence so that long-term safety data could be collected. It was required that at least 100 patients be exposed to lithium for no less than 6 months for this study. Specific areas of attention for both the Efficacy and Long-term studies included safety assessments with special emphasis being placed on the examination of putative short- and long-term effects of lithium on cognition, growth, thyroid, and renal function.</p>", "<p>Accordingly, the NICHD issued a Request for Proposals (RFP) on February 10, 2005 soliciting submissions for the study of lithium as described in the WR. The RFP indicated that the key purposes of the lithium studies were to: (1) establish evidence-based dosing strategies for lithium in children and adolescents; (2) characterize the pharmacokinetics and biodisposition of lithium in youth; (3) examine the acute efficacy of lithium in pediatric bipolarity; (4) investigate the long-term effectiveness of lithium treatment; and (5) comprehensively and meticulously characterize the short- and long-term safety of lithium in children and adolescents. RFPs are peer-reviewed and all proposals submitted are scored based upon technical merit in response to the criteria set forth in the RFP. The offerors who submit the proposal with the highest technical score and business proposals are then awarded a contract to perform the clinical studies. The NICHD then submits an Investigational New Drug Application (IND) to the FDA for the proposed studies. The data generated from these trials will be submitted to the FDA and it is anticipated that the label of lithium will be changed to reflect the outcome of these important clinical trials.</p>" ]
[ "<title>Results</title>", "<title>Submission and Development of Studies</title>", "<p>In order to respond to this RFP, the <bold>Co</bold>llaborative <bold>L</bold>ithium <bold>T</bold>rials (<bold>CoLT</bold>) group was formed. The current CoLT team, which is lead by investigators from Case Western Reserve University (P.I. Findling), also includes investigators from Cincinnati Children's Hospital Medical Center/University of Cincinnati (P.I. Kowatch), Cambridge Health Alliance (P.I. Frazier), Children's Hospital &amp; Regional Medical Center Seattle Washington (P.I. McClellan), University of North Carolina (P.I. Sikich), University of Illinois at Chicago (P.I. Pavuluri), and The Feinstein Institute for Medical Research of the North Shore–Long Island Health System (P.I. Kafantaris). These sites were selected specifically based upon the sites' investigators' established scientific expertise in pediatric bipolar disorder as well as clear evidence of being able to consistently, successfully, and safely recruit youths into prospective pediatric bipolar treatment studies. Proposals were submitted for competitive review in April of 2005.</p>", "<p>As a result of the CoLT group's submission, these investigators were subsequently awarded this government contract to study lithium in juvenile mania. As part of the work that was to be conducted under the auspices of this contract from the NICHD, collaboration with the Best Pharmaceuticals for Children Act-Coordinating Center (BPCA-CC; Premier Research; Medical Director, B. Brownstein, M.D.) and the CoLT team was established. In addition to the BPCA-CC, the CoLT team also began to collaborate with the NICHD Project Officer (P. Taylor-Zapata, M.D.) in order to propose final study designs to the FDA prior to initiating the requisite clinical trials.</p>", "<p>During this protocol refinement process, the CoLT team integrated feedback and input from the NICHD and the BPCA-CC into the study protocols. Although it was originally indicated that three distinct studies were to be performed to meet the goals of the WR, the CoLT team, BPCA-CC, and NICHD collaboratively created two multi-phase trials that would both: (1) ensure that the data that were outlined in the WR were obtained, and (2) allow feasibility of implementation to be enhanced. Each of these two multi-phase studies consists of four phases. The designs of both of these clinical trials were subsequently reviewed by the FDA in February, 2006. Enrollment into the first of these studies began in December of 2006.</p>", "<title>Lithium Formulations and Daily Dosing</title>", "<p>It should be noted that throughout these studies, immediate release lithium carbonate will be used due to its availability as a generic formulation. In addition, patients will receive treatment in 300 mg dose increments and for doses of 900 mg or greater, lithium will be given in thrice daily divided doses.</p>", "<title>Ethical Approval and Informed Consent</title>", "<p>These studies will be conducted in full accordance with the principles of the Declaration of Helsinki (52nd WMA General Assembly, Edinburgh, Scotland, October 2000). Additionally, prior to enrollment, these studies will be approved by all sites' Institutional Review Board for Human Investigation, and an independent Data Safety Monitoring Board (DSMB) will monitor the studies.</p>", "<p>Written informed consent will be acquired from all participants' legal guardians. Additionally, all participating youths will provide written assent prior to the initiation of any study related procedures.</p>", "<title>Inclusion and Exclusion Criteria</title>", "<p>Similar entry criteria for each of these outpatient clinical trials will be employed. In short, medically healthy children and adolescents (ages 7–17 years) with bipolar I disorder experiencing a manic or mixed episode may be eligible to enroll. These inclusion and exclusion criteria were developed in order to permit many youths suffering from mania to enroll. However, it was felt that the participation of some youths with selected comorbidities might confound the results of this work. For that reason, a limitation of the CoLT trials is that the data collected may not be applicable to all patients with bipolar I disorder. The inclusion and exclusion criteria for both studies are shown in Tables ##TAB##0##1## and ##TAB##1##2##.</p>", "<title>Overview of Studies 1 and 2</title>", "<p>Due to their anticipated sample size of approximately 260 patients and their methodological rigor, when completed, the CoLT studies should provide definitive data about the acute efficacy and long-term treatment with lithium in children and teenagers with bipolar mania/mixed states. A brief description of each of these two studies is presented below.</p>", "<p>Study 1 includes an initial open-label phase lasting 8 weeks during which a variety of dosing paradigms will be explored and data for pharmacokinetic analyses will be obtained. This phase is then followed by a 16 week open-label, long-term stabilization phase and a subsequent double-blind discontinuation phase. During this discontinuation phase, eligible patients are randomly assigned to either continue with lithium treatment or receive placebo for up to 28 weeks. Subsequently, the final phase of this first study includes a restabilization phase that allows subjects who suffer a mood symptom relapse during the discontinuation phase to re-initiate open-label lithium therapy. It is planned that this first study will enroll 60 subjects who will receive up to a maximum of 52 weeks of treatment.</p>", "<p>Study 2, which will begin subsequent to completion of the first study, begins with an 8-week, double-blind, placebo-controlled phase where 200 subjects will be randomized to receive either lithium or placebo. Similar to the first study, this initial phase will be followed by an open-label long-term phase. However, unlike the analogous phase in the first study, participation in this open-label long term phase in this trial will be of either 16 or 24 weeks in duration depending on the treatment that the patient received during the double-blind phase of this protocol. The long-term treatment phase will be followed by two subsequent phases: a discontinuation phase and a restabilization phase, identical to those described in Study 1. As in Study 1, this study will allow patients to receive treatment with lithium for up to 52 weeks in duration. These studies are described in more detail below. Our hypothesis in Study 1 is that rational dosing strategies for lithium in children and adolescents will be able to be developed. Our hypotheses in Study 2 are (1) that lithium will reduce manic symptoms to a greater extent than placebo acutely and (2) that few participants treated with lithium will withdraw because of adverse effects. Both studies will address the hypotheses (1) that lithium will have long-term efficacy for reducing bipolar symptoms and (2) that lithium will be safe and generally well-tolerated for up to one year.</p>", "<title>Study 1 (see Figure ##FIG##0##1##)</title>", "<p>As noted above, Study 1 is comprised of four phases that will address the following key components of the WR and the RFP: (1) establishing evidence-based dosing strategies for lithium in youth; (2) characterizing the pharmacokinetics and biodisposition of lithium in children and adolescents; (3) investigating the long-term effectiveness of lithium treatment; and (4) comprehensively characterizing the short- and long-term safety of lithium in children and adolescents. Patients' continuation into subsequent phases of this study is dependent upon their response to their current treatment in the phase of study in which they are participating. Throughout both studies, the <italic>a priori </italic>response criteria found in Table ##TAB##2##3## will be used in order to determine eligibility to participate in subsequent study phases.</p>", "<title>Pharmacokinetic Phase</title>", "<p>The Pharmacokinetic Phase of Study 1 is an 8-week, open-label, randomized, escalating dose clinical trial that has two key objectives. The first is to characterize the pharmacokinetic profile of lithium. The second is to develop evidence-based dosing strategies for lithium in children and adolescents with bipolar I disorder. Three different starting doses of lithium carbonate will be explored: 300 mg, 600 mg, and 900 mg. Additionally, two different dose escalation strategies will be examined. In one, the dose of lithium will be increased weekly by 300 mg. In the other, the dose of lithium will be increased by 300 mg twice weekly.</p>", "<p>In this initial phase of Study 1, subjects will be assigned to one of three treatment arms. A total of approximately 60 subjects, in which approximately 20 subjects will be assigned to each of the three study arms, will be enrolled and dosed. Initially, subjects will be assigned to one of two different treatment groups: \"Arm I\" or \"Arm II\".</p>", "<p>In Arm I of this initial phase, subjects will be given a starting dose of 300 mg or 600 mg, depending on their weight. All subjects that weigh less than 30 kg will be assigned to Arm I and will receive a starting dose of 300 mg/day. All initial subjects weighing greater than or equal to (≥) 30 kg, after being stratified by age and sex, will be randomly assigned to Arms I and II in approximately equal numbers. All subjects whose weight is ≥ 30 kg and are assigned to Arm I will have a starting dose of 600 mg/day (divided twice daily). In Arm II, subjects who are randomized to this treatment arm will receive a starting dose of 900 mg (divided thrice daily). An evaluation of data collected from subjects who are treated in Arm I and Arm II will provide information about the appropriate starting dose of lithium in children/adolescents. Subjects randomized to Arms I and II may have their dose increased by 300 mg weekly, based upon response and tolerability. After 10 subjects are enrolled in Arm II and have completed the 8 weeks of treatment, and if at least 8 of the first 10 subjects dosed have tolerated the study drug for at least 8 weeks, enrollment into the third arm (Arm III) may begin. Only subjects weighing ≥ 30 kg will be permitted to enter into Arm III. Subjects enrolled into Arm III will have a starting dose of 900 mg, divided thrice daily.</p>", "<p>In order to examine a second dose escalation strategy, subjects in Arm III will have their lithium dose increased twice weekly depending upon the effectiveness and tolerability of lithium in this cohort. As a result of this study arm, the speed that the lithium dose may be increased (weekly vs. twice weekly) in children and adolescents will be determined.</p>", "<p>Final dosing for subjects will be determined based upon both response and side effect profile for Arms I-III. For the purposes of this study, subjects will continue to have their dose of lithium increased until any of the following criteria are met: (1) a subject meets response criteria (Clinical Global Improvement Scale (CGI-I) [##UREF##4##11##] of ≤ 2 and ≥ 50% decrease in the Young Mania Rating Scale (YMRS) [##REF##728692##12##]; (2) the patient experiences side effects that significantly impact functioning; (3) the serum lithium level is &gt; 1.4 mEq/L [##REF##6762704##13##]; or (4) if the dose exceeds 40 mg/kg/day (with the exception of subjects weighing less than 23 kg who may receive up to 900 mg/day). Based upon the Pharmacokinetic Phase, information will be obtained about the most appropriate starting dose and the speed by which the lithium dose can be increased. This dosing strategy will then be employed in the acute randomized controlled trial that is to be performed under the auspices of Study 2 (below).</p>", "<title>Pharmacokinetic Sampling</title>", "<p>In addition to these dosing procedures, subjects in Arms I and II will undergo blood sampling procedures in order to characterize first-dose pharmacokinetic (PK) parameters for lithium. Blood samples will be obtained prior to dosing and at 0.5, 1.5, 1, 1.5, 2, 4, 8, 12, and 24 hours post-dose. Additionally, one half of the subjects will provide a single blood sample for PK analyses at 48 hours post-dose, and one half will provide a single blood sample at 72 hours post-dose.</p>", "<p>Furthermore, subjects in Arms I and II will have additional PK samples collected at 2 more time points over the next 16 weeks. The time points for these samples to be collected will be determined by random assignment. These additional samples are collected over a 12 hour period, including a 0 and 12 hour sample and 3 randomly assigned additional samples at the following possible time points: 0.5, 1, 1.5, 2, 4, or 8 hours post-dose.</p>", "<title>Long-Term Effectiveness Phase</title>", "<p>Once the Pharmacokinetic Phase has ended, subjects who continue to be eligible and who demonstrate at least a partial response (reduction in YMRS score of ≥ 25% and a CGI-I score ≤ 3) and are able to tolerate at least 600 mg lithium/day will be eligible to continue with their current dose for 16 weeks in a Long-Term Effectiveness Phase (LTE). Following a standardized algorithm, adjunctive medications may be added during this phase. Of note, a maximum of only 2 adjunctive medications are allowed to be prescribed at the same time to study subjects once participation in this study phase begins. Patients who are prescribed other agents besides lithium with therapeutic serum concentration levels will have their medication levels monitored throughout their participation in this study.</p>", "<p>The standardized algorithm includes a sequence of medications to treat residual symptoms of psychosis, mania and hypomania, depression, anxiety, and ADHD (prioritized in that order). These treatment algorithms were developed in order to limit variability across subjects and to provide reasonably interpretable preliminary information regarding adjunctive pharmacotherapy in patients treated with lithium. The rationale concerning the choice of adjunctive medications for residual symptom treatment was informed by various adult data in bipolar I and II disorder, as well as limited data that exist in children and adolescents with bipolar disorder (for a review, see Smarty and Findling 2007) [##UREF##5##14##]. When no adult or juvenile data existed, algorithms were derived by investigators' consensus, based upon their clinical experiences and consideration of which widely used treatments lacked study to support or refute their use.</p>", "<p>Psychotic symptoms are to be treated with risperidone, and if needed, followed by a trial of quetiapine, and then aripiprazole. Furthermore, unresponsive manic and hypomanic symptoms will be initially treated with valproate. If the manic or hypomanic symptoms do not respond to valproate, then quetiapine will be started, followed by a trial of aripiprazole.</p>", "<p>To address residual depressive symptoms, lamotrigine will be the first line treatment followed by a trial of quetiapine. If the patient is non-responsive to treatment with quetiapine, concomitant treatment with citalopram will be initiated to address the depressive symptoms. Additionally, patients who experience anxiety symptoms will initially be treated with valproate. Subsequently, unresponsive anxiety symptoms will be addressed with concomitant treatment with quetiapine followed by lamotrigine.</p>", "<p>Finally, adjunctive ADHD treatment will be begin with a long acting methylphenidate compound. If it is necessary to initiate another ADHD treatment due to unresponsiveness or intolerance to this initial treatment, a long acting mixed amphetamine salt preparation may be started. The final treatment option for comorbid ADHD symptoms is atomoxetine. These adjunctive interventions will provide additional treatment options for youth with BD for which lithium monotherapy does not address residual mood and other psychiatric symptoms.</p>", "<p>At the end of this phase, subjects will be categorized as \"responders,\" \"partial responders,\" and \"non-responders\" based upon <italic>a priori </italic>criteria (Table ##TAB##2##3##). Subjects who have 6 out of the last 8 consecutive weeks starting at week 8 (the last two weeks must be final 2 weeks of participation in the LTE Phase) without symptom relapse and who have therapeutic lithium levels are eligible for continuation into the Discontinuation Phase.</p>", "<title>Discontinuation and Restabilization Phases</title>", "<p>The third phase, Discontinuation Phase, is a 28-week, double-blind phase where subjects are randomized to receive either continued treatment with lithium or placebo. Subjects who are randomized to receive placebo will have their dose of lithium gradually discontinued. It should be noted that the ethical issues associated with a medication discontinuation paradigm were carefully reviewed. However, in the absence of maintenance treatment data for pediatric bipolar disorder, the investigators believed that the discontinuation phase of the study provided clinical equipoise between the potential risks of side effects related to long-term lithium exposure, and the potential risks for relapse in patients randomized to placebo. While patients are enrolled in Discontinuation Phase, patients may continue to receive the adjunctive medication at the same dose that was prescribed during the Long-Term Effectiveness Phase. During the Discontinuation Phase, if subjects experience a significant deterioration in clinical status, they are offered 8 weeks of open-label lithium treatment re-initiated in a Restabilization Phase.</p>", "<title>Study 2 (see Figure ##FIG##1##2##)</title>", "<p>Like Study 1, Study 2 is comprised of four phases that will investigate the acute efficacy of lithium in pediatric bipolarity, examine the long-term effectiveness of lithium treatment, and allow for both the short- and long-term safety of lithium in youth as outlined in the WR. As in Study 1, patients will continue into subsequent phases of the study based upon their response to their response to their current phase of treatment.</p>", "<title>Efficacy Phase</title>", "<p>The first phase of Study 2 is the 8-week, double-blind, parallel-group, placebo-controlled Efficacy Phase. During the Efficacy Phase, approximately 200 subjects will be randomized to receive 8 weeks of treatment with either lithium carbonate or placebo. The starting lithium dose and the rate at which lithium will be titrated upwards will be based upon the results of the Pharmacokinetic Phase in Study 1. At the end of 8 weeks of treatment, response status will be evaluated in all participating subjects.</p>", "<title>Long-Term Effectiveness Phase</title>", "<p>Similar to Study 1, at the end of the first 8 weeks of the study and depending on their response to blinded treatment, the patient may proceed to the Long-Term Effectiveness Phase. However, subjects who are responders to placebo or non-responders to lithium treatment will not continue into the Long-Term Effectiveness Phase. In Study 2, the length of participation in this phase will be dependent upon the treatment and response status of subjects in the Efficacy Phase. This is done in order to ensure all subjects receive open label lithium for 24 weeks prior to possible participation in the Discontinuation Phase. As a result, eligible subjects will continue into two parallel treatment arms of different lengths of time in the Long-Term Effectiveness Phase. The two arms include a 16-week arm and a 24-week arm. Those subjects who received lithium in the Efficacy Phase and showed partial or full response will be eligible to continue in the 16-week arm. During the 16-week arm, adjunctive medications may be added following the standardized algorithm noted above as needed.</p>", "<p>Those subjects who received placebo during the Efficacy Phase and are either partial responders or non-responders (YMRS reduction of &lt; 25% or a CGI-I score ≥ 4) will receive open-label lithium treatment in the 24-week treatment arm of the Long-Term Effectiveness Phase. After an initial 8 weeks of treatment with open-label lithium, these subjects will be assessed; if subjects are at least partial responders, they will continue treatment with lithium for the remaining 16 weeks. During the final 16 weeks of the 24-week arm, adjunctive psychotropic agents will be permitted per the aforesaid treatment algorithms. If subjects are non-responders at the end of 8 weeks of treatment in the 24-week arm, they will be discontinued from the study.</p>", "<p>Furthermore, subjects who received placebo during the Efficacy Phase will be randomized to possibly receive psychosocial treatment at the onset of the 24-week Long-Term Effectiveness Phase in order to explore whether additional benefit to open-label lithium initiation occurs when combined with psychotherapy. In addition, in order to explore the neurological effects of lithium in juveniles, electro-encephalograms (EEG) will be obtained in the 24-week treatment arm for a randomly chosen subset at baseline, end of week 8, and the end of the Long-Term Effectiveness Phase participation.</p>", "<p>As in Study 1, patients will be eligible for participation into the Discontinuation Phase if they have had at least 6 of the last 8 weeks (including the last 2 weeks) of the LTE Phase without symptom relapse and have therapeutic lithium levels.</p>", "<title>Discontinuation and Restabilization Phases</title>", "<p>The Discontinuation and Restabilization phases in Study 2 are identical to these phases in Study 1. Half of the responders will remain on lithium maintenance treatment, and the other half will undergo gradual tapering of their lithium dose by the substitution of placebo capsules for active lithium capsules for 28 weeks. Subjects who experience significant deterioration in clinical status during the Discontinuation Phase will be offered 8 weeks of treatment with open-label lithium in the Restabilization Phase. Data from these final study phases (Discontinuation and Restabilization) will be combined from both Study 1 and Study 2 for statistical analyses.</p>", "<title>Study Teams and Maintaining the Blind</title>", "<p>In order to maintain the blind throughout the two trials, two different study teams will be assembled at each of the sites analogous to the study design implemented by the RUPP research group [##REF##11838820##15##]. At each CoLT site, there will be two groups of clinicians and coordinators that compose the \"blinded\" team and an \"unblinded\" team. At a minimum, each team will include a child and adolescent psychiatrist and a study coordinator. The blinded study team will manage all aspects of study enrollment with the exception of reviewing lithium levels and making lithium concentration-based dose adjustment decisions during placebo-controlled phases. The unblinded teams will be responsible for reviewing the lithium levels and making dosing adjustments in the blinded phases of the two studies.</p>", "<title>Patient Assessments</title>", "<p>As requested by the WR, the YMRS will be the primary outcome measure owing to its ability to detect the effects of medication treatment of mania [##REF##17450054##16##]. Patient diagnoses will be based upon results of the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Episode (K-SADS-PL) [##REF##9204677##17##]. In addition, the WR indicated that the secondary measures should assess attention-deficit/hyperactivity disorder (ADHD) symptomatology, aggressive behavior, irritability, substance abuse, clinical global improvement, and family, school, peer relationships and quality of life. The measures that will be used to assess these domains during the trials are shown in Table ##TAB##3##4##.</p>", "<title>Neurocognitve Testing</title>", "<p>The WR also required that possible cognitive and neurological effects of lithium be evaluated. Lithium can potentially improve certain cognitive functions, but can be deleterious in other domains. The purpose of this adjunctive testing is to provide an evidence-based understanding of the neurocognitive effects of lithium pre- and post-acute trial and post-maintenance trial. The data collected will provide a comprehensive characterization of lithium-specific effects on neurocognitive function that has not been available to date. Therefore, in Study 2, all subjects will undergo a neurocognitve battery at baseline prior to receiving lithium or placebo, at week 8, and after 24 weeks of lithium treatment.</p>", "<p>The goal of this testing is to determine the integrity of fine-motor, attention, verbal memory, and selected executive function domains pre- and post-acute and maintenance lithium trials. It is hypothesized that positive improvement will be noted in domains of attention, verbal memory, visual memory, and selected executive functions (e.g., set-shirting, inhibition) post-treatment. In contrast, based upon data from adult studies, it is hypothesized that lithium may negatively affect fine-motor speed and control and cognitive processing speed, but results may vary based upon response to lithium and serum levels. In addition, testing will help to determine the integrity of affective regulation, including affective inhibition, pre- and post-acute and maintenance trials. It is hypothesized that the affective regulation of this sample will improve from baseline to the proposed post-acute and maintenance trial time points.</p>", "<p>Given the available literature on the pathophysiology of bipolar disorder, these assessment domains were selected to coincide with brain regions where the effects of this disorder would be most expected to occur (i.e., hippocampal and pre/frontal brain regions). In addition, tasks were selected to: (1) be age-appropriate and child friendly; (2) have adequate statistical applicability to the various ages and ability levels of this population; (3) be psychometrically sound (i.e., reliable, valid); and (4) theoretically driven by the available literature that has examined lithium usage in children and adults, as well as the extant literature on pediatric bipolar disorder. Further, given the 8-week differential between some of the tasks, measures that evidenced minimal practice effects over this time frame were selected. The neurocognitive tests that will be used in this trial are shown in Table ##TAB##4##5##.</p>", "<title>Safety Assessments</title>", "<title>Adverse Event Monitoring</title>", "<p>Subjects and their guardians will be directly queried about the presence of adverse events throughout the CoLT trials. In addition, to facilitate the careful monitoring of adverse events, multiple assessments will be utilized throughout both studies. These include the Neurological Examination for Lithium (NELi), a modified Side Effects Form for Children and Adolescents (SEFCA) [##REF##8084982##18##], and the Neurological Rating Scale (NRS) [##UREF##6##19##].</p>", "<p>The NeLi, which was developed specifically for this trial, includes an examination of neurological events that have been associated with Li treatment. These neurological symptoms include: (1) Tremor; (2) a Finger-nose Test; (3) Tandem Walk; (4) Gait; (5) Grip Strength; and (6) the Romberg Test.</p>", "<p>The SEFCA is a 54-item scale that rates both the frequency and severity of adverse events. In addition, the SEFCA used in the CoLT studies will be supplemented by selected UKU Side Effect Rating Scale [##UREF##7##20##] items including queries regarding concentration difficulties, increased fatigability, sleepiness/sedation, reduced salivation, and memory difficulties. Furthermore, items that pertain to acne, motor in-coordination, muscle weakness, and confusion will be added to the SEFCA from the Safety Monitoring and Uniform Report Form (SMURF) [##UREF##8##21##].</p>", "<title>Laboratory and Electrocardiogram (ECG) Monitoring</title>", "<p>Over the course of the CoLT trials, laboratory and ECG testing will be performed periodically. The chemistry profile that will be used throughout the CoLT trials will measure blood concentrations of sodium, potassium, chlorine, carbon dioxide, blood urea nitrogen (BUN), creatinine, calcium and glucose. Furthermore, blood concentrations of total protein, albumin, alkaline phosphate, alkaline transferase, alkaline aspartate, and total bilirubin will be obtained.</p>", "<p>To study prospectively the effects of lithium on metabolism and lipid profile, fasting total cholesterol, triglycerides, high density lipo-proteins (HDL), low density lipo-proteins (LDL), and cholesterol/HDL ratio will be acquired on all subjects at specified times during the CoLT trials. Additionally, a Complete Blood Count (CBC) with differential will be performed periodically. A urinalysis and urine drug toxicology screen will be assessed at various time points during both CoLT trials.</p>", "<p>Lithium has been found to interfere with the production of thyroid hormones including the inhibition of the thyroid stimulating hormone (TSH)-responsive adenylate cyclase and PKC in thyroid cells [##REF##2948990##22##, ####UREF##9##23##, ##REF##14726729##24####14726729##24##]. Therefore, thyroid function tests, including TSH (thyroid stimulating hormone), triiodothyronine, and thyroxine will be regularly obtained in theses studies. It should be noted that the CoLT trials also incorporate an algorithm for the assessment and management of TSH elevation/hypothyroidism should either event occur during the auspices of these trials.</p>", "<p>In addition, research indicates that about 5% of subjects treated with lithium may develop kidney dysfunction as indicated by impaired renal function tests [##REF##8376615##25##]. For this reason, creatinine clearance will be measured at various time points throughout the CoLT trials in order to further assess renal function.</p>", "<title>Lithium Serum Levels</title>", "<p>Monitoring lithium serum concentrations is critical for the safe use of this agent. It has been suggested that the therapeutic serum concentration range for treatment with lithium lies between 0.3 and 1.3 mEq/L, with 1.5 mEq/L representing the lower limit for intoxication [##REF##6762704##13##]. Additionally, it has been recognized that lithium has a narrow therapeutic index and near-toxic doses are required to achieve the optimal therapeutic effect [##REF##324690##26##,##UREF##10##27##]. Therefore, the chosen maximum lithium level after which dose increases would not be permitted was set at 1.4 mEq/L. Lithium serum concentrations will be obtained weekly during the first 8 weeks of treatment and will be monitored regularly thereafter.</p>", "<title>Electrocardiogram (ECG)</title>", "<p>The cardiovascular effects of orally administered lithium have been reported as being generally benign. However, lithium has been shown to prolong sinus node recovery time [##REF##8217448##28##,##REF##15591709##29##]. Therefore, an ECG will be utilized prior to patients receiving lithium and throughout the CoLT trials in order to monitor cardiac function.</p>", "<title>Electroencephalogram (EEG)</title>", "<p>As stated above, an EEG will be obtained from a randomly assigned subset of subjects who participate in the 24-week long Long-Term Effectiveness Phase at baseline, at the end of week 8, and at the 24-week time point. In addition, if a subject experiences significant deterioration in neurological or cognitive status, an EEG will also be obtained. Furthermore, if a patient experiences moderate or severe headaches that are temporally related to medication/placebo, dysarthria, ataxia, cognitive dulling, or confusion that are possibly or probably related to the study medication an EEG will be obtained.</p>", "<title>RBC/Plasma Lithium (Li<sup>+</sup>) Ratio</title>", "<p>RBC/plasma Li<sup>+ </sup>ratio is considered to have clinical implications in: (1) predicting clinical response; (2) risk for toxicity; (3) possibly, time course for response; and (4) optimal dosage for long-term prophylaxis. Researchers have found that high <italic>in vivo </italic>RBC/plasma Li<sup>+ </sup>ratios are a result of a RBC membrane defect that causes a deficiency of Li<sup>+ </sup>- Na<sup>+ </sup>counter flow [##REF##690597##30##]. In addition, there is preliminary evidence that this membrane defect is autosomal dominant in transmission, and leads to high intracellular Li<sup>+ </sup>level compared to serum level [##REF##690597##30##,##REF##696928##31##]. A high Li<sup>+ </sup>ratio is considered to be predictive of a positive lithium response [##REF##36253##32##].</p>", "<p>Therefore, during the 24-week Long-Term Effectiveness arm, a subset of 24 randomly subjects will have weekly serum and whole blood samples to allow for a Li<sup>+ </sup>ratio to be computed. In addition, subjects who experience ataxia, dysarthria, reduced motor coordination, listlessness/sedation, slurred speech, tremors, confusion, or delirium that is both possibly or probably related to the study medication, and is noted to be of moderate or severe intensity, will have a serum and a whole blood sample in order to assess the RBC/plasma Li<sup>+ </sup>ratios.</p>", "<p>Throughout the trials, a Data Safety Monitoring Board (DSMB) will be involved in monitoring the trials' progress.</p>" ]
[]
[ "<title>Conclusion</title>", "<p>In summary, the innovative and multidisciplinary CoLT studies will provide the data to allow for evidence based dosing of lithium in the children and adolescents with bipolar disorder. In addition, if lithium is shown to have an acceptable acute- and long-term efficacy and safety profile in children and adolescents, this knowledge could substantively influence the treatment choices of clinicians who provide care to those vulnerable children and teenagers suffering from bipolar disorder.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Lithium is a benchmark treatment for bipolar illness in adults. However, there has been relatively little methodologically stringent research regarding the use of lithium in youth suffering from bipolarity.</p>", "<title>Methods</title>", "<p>Under the auspices of the Best Pharmaceuticals for Children Act (BPCA), a Written Request (WR) pertaining to the study of lithium in pediatric mania was issued by the United States Food and Drug Administration (FDA) to the National Institute of Child Health and Human Development (NICHD) in 2004. Accordingly, the NICHD issued a Request for Proposals (RFP) soliciting submissions to pursue this research. Subsequently, the NICHD awarded a contract to a group of investigators in order to conduct these studies.</p>", "<title>Results</title>", "<p>The Collaborative Lithium Trials (CoLT) investigators, the BPCA-Coordinating Center, and the NICHD developed protocols to provide data that will: (1) establish evidence-based dosing strategies for lithium; (2) characterize the pharmacokinetics and biodisposition of lithium; (3) examine the acute efficacy of lithium in pediatric bipolarity; (4) investigate the long-term effectiveness of lithium treatment; and (5) characterize the short- and long-term safety of lithium. By undertaking two multi-phase trials rather than multiple single-phase studies (as was described in the WR), the feasibility of the research to be undertaken was enhanced while ensuring all the data outlined in the WR would be obtained. The first study consists of: (1) an 8-week open-label, randomized, escalating dose Pharmacokinetic Phase; (2) a 16-week Long-Term Effectiveness Phase; (3) a 28-week double-blind Discontinuation Phase; and (4) an 8-week open-label Restabilization Phase. The second study consists of: (1) an 8-week, double-blind, parallel-group, placebo-controlled Efficacy Phase; (2) an open-label Long-Term Effectiveness lasting either 16 or 24 weeks (depending upon blinded treatment assignment during the Efficacy Phase); (3) a 28-week double-blind Discontinuation Phase; and (4) an 8-week open-label Restabilization Phase. In December of 2006, enrollment into the first of these studies began across seven sites.</p>", "<title>Conclusion</title>", "<p>These innovative studies will not only provide data to inform the labeling of lithium in children and adolescents with bipolar disorder, but will also enhance clinical decision-making regarding the use of lithium treatment in pediatric bipolar illness.</p>", "<title>Trial Registration</title>", "<p>NCT00442039</p>" ]
[ "<title>Competing interests</title>", "<p>Dr. Findling receives or has received research support, acted as a consultant and/or served on a speaker's bureau for Abbott, Addrenex, AstraZeneca, Bristol-Myers Squibb, Forest, GlaxoSmithKline, Johnson &amp; Johnson, Lilly, Neuropharm, Novartis, Organon, Otsuka, Pfizer, Sanofi-Aventis, Sepracore, Shire, Solvay, Supernus Pharmaceuticals, and Wyeth. Dr. Frazier receives or has received research support from Bristol Myers Squibb, GlaxoSmithKline, Eli Lilly and Company, Johnson and Johnson, Neuropharm, Otsuka, and Pfizer. Dr. Kowatch receives or has received research support, acted as a consultant, served on an advisory board, and/or served on a speaker's bureau for Abbott, Astra-Zeneca, Bristol-Myers Squibb, CABF, Creative Educational Concepts, GlaxoSmithKline, Medscape, NICHD, NIMH, Sanofi-Aventis, and the Stanley Research Foundation. Dr. Pavuluri's work unrelated to this manuscript is supported by NIH/NCRR K23 RR018638-01, NIMH MH077852, NIMH P50 HD055751, DANA Foundation, NARSAD, American Foundation for Suicide Prevention, Colbeth Foundation, GlaxoSmithKline- NeuroHealth, Abbott Pharmaceuticals and Janssen Research Foundation. Dr. Sikich receives or has received research support from Eli Lilly, Janssen, Pfizer, Bristol Myers Squibb, Otsuka and Neuropharm. Dr. Hooper has acted as a consultant to Lilly. Dr. Taylor-Zapata is the project officer for the funding institute, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), for this study. The other authors have no financial ties to disclose.</p>", "<title>Authors' contributions</title>", "<p>All authors have made substantial contribution to the conception and design of the study, have been involved in the drafting and/or critical revising of this manuscript, and all authors have given final approval of this manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Ms. Brieana Rowles for her technical assistance in drafting this manuscript. The authors' and Ms. Rowles' efforts on this project were supported by a contract to Case Western Reserve University from the NICHD.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Study 1 of CoLT.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Study 2 of CoLT.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Inclusion Criteria</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Inclusion Criteria</bold></td></tr></thead><tbody><tr><td align=\"left\">1. Subjects aged 7 years to 17 years, 11 months old at time of first dose</td></tr><tr><td align=\"left\">2. Patients must meet DSM-IV diagnostic criteria, as assessed by a semi -structured assessment (KSADS-PL) and a separate clinical interview with a child/adolescent psychiatrist for manic or mixed episodes in bipolar I disorder</td></tr><tr><td align=\"left\">3. Score of &gt; 20 on the YMRS at screening and baseline</td></tr><tr><td align=\"left\">4. The patient and legal guardian must understand the nature of the study and be able to comply with protocol requirements. The legal guardian must give written informed consent and the youth, written assent.</td></tr><tr><td align=\"left\">5. Patients with comorbid conditions [attention deficit hyperactivity disorder (ADHD), conduct disorder] may participate.</td></tr><tr><td align=\"left\">6. If female: is premenarchal, or is incapable of pregnancy because of a hysterectomy, tubal ligation, or spousal/partner sterility. If sexually active and capable of pregnancy, has been using an acceptable method of contraception (hormonal contraceptives, intrauterine device, spermicide and barrier) for at least one month prior to study entry and agrees to continue to use one of these for the duration of the study. If sexually abstinent and capable of pregnancy, agrees to continued abstinence or to use of an acceptable method of birth control (either intrauterine device or spermicide and barrier) should sexual activity commence</td></tr><tr><td align=\"left\">7. Has a negative quantitative serum ß-human chorionic gonadotrophin hormone pregnancy test at screening and a negative qualitative urine pregnancy test at baseline, if female</td></tr><tr><td align=\"left\">8. Patients with a history of substance abuse may participate if they agree to abstain from drugs during the trial and have a negative drug screen at screening or prior to baseline.</td></tr><tr><td align=\"left\">9. The subject is willing and clinically able to wash out of exclusionary medication during the screening period. Prior to the administration of lithium, patients will not have used any of the following mediations: antipsychotics, monoamine oxidase inhibitors, antidepressants within the preceding two weeks; stimulants within the preceding week; or fluoxetine or depot antipsychotics in the past month (no stable patients will be asked to discontinue medications)</td></tr><tr><td align=\"left\">10. ECG and blood work including CBC, prothrombin/partial thromboplastin time, fibrinogen, and thyroid function showing no clinically significant abnormalities</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Exclusion Criteria</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Exclusion Criteria</td></tr></thead><tbody><tr><td align=\"left\">1. Patient who is clinically stable on current medication regimen for bipolar disorder.</td></tr><tr><td align=\"left\">2. A current or lifetime diagnosis of Schizophrenia or Schizoaffective Disorder, a Pervasive Developmental Disorder, Anorexia Nervosa, Bulimia Nervosa, or Obsessive-Compulsive Disorder</td></tr><tr><td align=\"left\">3. Current DSM-IV diagnosis of Substance Dependence</td></tr><tr><td align=\"left\">4. Positive drug screen at screening and on retest 1–3 weeks later</td></tr><tr><td align=\"left\">5. Patients with symptoms of mania that may be attributable to a general medical condition, or secondary to use of medications (e.g., corticosteroids)</td></tr><tr><td align=\"left\">6. Evidence of any serious and/or unstable neurological illness for which treatment under the auspices of this study would be contra-indicated</td></tr><tr><td align=\"left\">7. Any serious, unstable medical illness or clinically significant abnormal laboratory assessments that would adversely impact the scientific interpretability or unduly increase the risks of the protocol</td></tr><tr><td align=\"left\">8. Current general medical condition including neurological disease, diabetes mellitus, thyroid dysfunction, or renal dysfunction that might be affected adversely by lithium, could influence the efficacy or safety of lithium, or would complicate interpretation of study results</td></tr><tr><td align=\"left\">9. Evidence of current serious homicidal/suicidal ideation such that in the treating physician's opinion it would not be appropriately safe for the subject to participate in this study</td></tr><tr><td align=\"left\">10. Evidence of current active hallucinations and delusions such that in the treating physician's opinion it would not be appropriately safe for the subject to participate in this study</td></tr><tr><td align=\"left\">11. Concomitant prescription of over-the-counter medication or nutritional supplements that would interact with lithium or the subject's physical or mental status</td></tr><tr><td align=\"left\">12. Concurrent psychotherapy treatments provided outside the study initiated within 4 weeks prior to screening</td></tr><tr><td align=\"left\">13. Previous adequate trial with lithium (at least 4 weeks with lithium serum levels between 0.8–1.2 mEq/L)</td></tr><tr><td align=\"left\">14. History of allergy to lithium</td></tr><tr><td align=\"left\">15. Psychiatric hospitalization within 1 month of screening</td></tr><tr><td align=\"left\">16. Clinician's judgment that subject is not likely to be able to complete the study as an outpatient due to psychiatric reasons</td></tr><tr><td align=\"left\">17. History of lithium intolerance</td></tr><tr><td align=\"left\">18. Females who are currently pregnant or lactating</td></tr><tr><td align=\"left\">19. Sexually active females who, in the investigators' opinion, are not using an adequate form of birth control.</td></tr><tr><td align=\"left\">20. Subjects who are unable to swallow the study medication</td></tr><tr><td align=\"left\">21. Subjects for whom a baseline YMRS score of &lt; 20 is anticipated</td></tr><tr><td align=\"left\">22. Subjects with an IQ less than 70 (determined using the Wechsler Abbreviated Scales of Intelligence {WASI}Vocabulary and Matrix Reasoning Subscales) [##UREF##11##33##]</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p><italic>A priori </italic>response criteria used throughout both studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Response Criteria</bold></td><td/><td/></tr></thead><tbody><tr><td align=\"center\"><bold>Non-Response</bold></td><td align=\"center\"><bold>Partial Response</bold></td><td align=\"center\"><bold>Response</bold></td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"center\">&lt; 25% reduction in baseline YMRS</td><td align=\"center\">25–49% reduction in baseline YMRS</td><td align=\"center\">≥ 50% reduction in baseline YMRS</td></tr><tr><td align=\"center\">OR</td><td align=\"center\">AND</td><td align=\"center\">AND</td></tr><tr><td align=\"center\">CGI-I ≥ 4</td><td align=\"center\">CGI-I ≤ 3</td><td align=\"center\">CGI-I = 1 or 2</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Mood Symptomatology and Life Satisfaction Measures obtained in the CoLT trials</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\"><bold>Measure</bold></td><td align=\"center\"><bold>Domain</bold></td><td align=\"center\"><bold>Reference</bold></td></tr></thead><tbody><tr><td align=\"center\" colspan=\"3\"><bold>Interview with parents &amp; child/adolescent</bold></td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">Young Mania Rating Scale (YMRS)</td><td align=\"center\">Manic Symptoms</td><td align=\"center\">Young et al. [##REF##728692##12##]</td></tr><tr><td align=\"left\">Children Depression Rating Scale (CDRS-R)</td><td align=\"center\">Depression Symptoms</td><td align=\"center\">Overholser et al. [##UREF##12##34##]</td></tr><tr><td align=\"left\">Brief Psychiatric Rating Scale – for Children (BPRS-C)</td><td align=\"center\">Psychosis</td><td align=\"center\">Hughes et al. [##REF##11322749##35##]</td></tr><tr><td align=\"left\">Children's Global Assessment Scale (CGAS)</td><td align=\"center\">Global Outcome</td><td align=\"center\">Shaffer et al. [##REF##6639293##36##]</td></tr><tr><td align=\"left\">Clinical Global Impressions Scale–Severity (CGI-S)</td><td align=\"center\">Severity of illness</td><td align=\"center\">NIMH [##UREF##4##11##]</td></tr><tr><td align=\"left\">Clinical Global Impressions Scale–Improvement (CGI-I)</td><td align=\"center\">Improvement of illness</td><td align=\"center\">NIMH [##UREF##4##11##]</td></tr><tr><td align=\"left\">Drug Use Severity Inventory (DUSI)</td><td align=\"center\">Substance Use</td><td align=\"center\">Tarter et al. [##UREF##13##37##]</td></tr><tr><td align=\"left\">Irritability, Depression, and Anxiety (IDA) (selected items)</td><td align=\"center\">Irritability</td><td align=\"center\">Snaith et al. [##REF##623950##38##]</td></tr><tr><td align=\"left\">The Pediatric Anxiety Rating Scale (PARS)</td><td align=\"center\">Anxiety</td><td align=\"center\">The Research Units on Pediatric Psychopharmacology Anxiety Study Group [##REF##12218427##39##]</td></tr><tr><td align=\"left\">Social Adjustment Inventory for Children &amp; Adolescents (SAICA)</td><td align=\"center\">Social Development, Academic Achievement</td><td align=\"center\">John et al. [##REF##3429410##40##]</td></tr><tr><td align=\"left\">Suicide Severity Rating Scale (SSRS)-Lifetime</td><td align=\"center\">Lifetime suicidal ideation and behavior</td><td align=\"center\">Posner et al. [##REF##17277716##41##]</td></tr><tr><td align=\"left\">Suicide Severity Rating Scale (SSRS)</td><td align=\"center\">Suicidal ideation and behavior</td><td align=\"center\">Posner et al. [##REF##17277716##41##]</td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"center\" colspan=\"3\"><bold>Parent Report</bold></td></tr><tr><td colspan=\"3\"><hr/></td></tr><tr><td align=\"left\">General Behavior Inventory – Parent Report Mania and Depression Short Form</td><td align=\"center\">Manic and Depression Symptoms</td><td align=\"center\">Youngstrom et al. [##REF##11433802##42##]</td></tr><tr><td align=\"left\">AD/HD Rating Scale-IV (ARS-IV)</td><td align=\"center\">ADHD symptoms</td><td align=\"center\">DuPaul et al. [##UREF##14##43##]</td></tr><tr><td align=\"left\">Child Mania Rating Scale-Parent (CMRS-P)</td><td align=\"center\">Manic symptoms</td><td align=\"center\">Pavuluri et al. [##REF##16601399##44##]</td></tr><tr><td align=\"left\">Nisonger Child Behavior Rating Form (NCBRF) Parent Version</td><td align=\"center\">Aggression</td><td align=\"center\">Aman et al. [##REF##8750075##45##]</td></tr><tr><td align=\"left\">Caregiver Strain Questionnaire (CSQ)</td><td align=\"center\">Parental Stress</td><td align=\"center\">Brannan et al. [##UREF##15##46##]</td></tr><tr><td align=\"left\">Family Environment Scale (FES)</td><td align=\"center\">Family Relationships</td><td align=\"center\">Moos &amp; Moos [##UREF##16##47##]</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Neurocognitive measures to be collected in the CoLT study</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Domain</td><td/><td/><td/><td/><td/></tr></thead><tbody><tr><td align=\"center\">Intellectual</td><td align=\"center\">Fine Motor</td><td align=\"center\">Attention</td><td align=\"center\">Memory</td><td align=\"center\">Executive</td><td align=\"center\">Affective Processing</td></tr><tr><td colspan=\"6\"><hr/></td></tr><tr><td align=\"center\">WASI 2 Subtest (WASI) [##UREF##11##33##]</td><td align=\"center\">Grooved Pegboard [##UREF##17##48##, ####UREF##18##49##, ##REF##3235651##50####3235651##50##]</td><td align=\"center\">Vigil Auditory CPT [##UREF##20##53##]</td><td align=\"center\">WRAML-2 Verbal Memory (2 Subtests) [##REF##17523889##54##]</td><td align=\"center\">D-KEF Verbal Fluency (Conditions 1–3) [##UREF##19##51##,##REF##15012851##52##]</td><td align=\"center\">Affective Stroop Task [##REF##8711015##55##]</td></tr><tr><td/><td align=\"center\">Delis-Kaplan Executive Function System</td><td align=\"center\">Vigil Visual CPT [##UREF##20##53##]</td><td align=\"center\">WRAML-2 Delayed Verbal Memory [##REF##17523889##54##]</td><td align=\"center\">D-KEF Figural Fluency (Condition 1) [##UREF##19##51##,##REF##15012851##52##]</td><td align=\"center\">Affective N Back Memory Task [##UREF##21##56##]</td></tr><tr><td/><td align=\"center\">(D-KEFS) Trail Making (Condition 4) [##UREF##19##51##,##REF##15012851##52##]</td><td/><td/><td align=\"center\">D-KEF Color-Word [##UREF##19##51##,##REF##15012851##52##]</td><td/></tr></tbody></table></table-wrap>" ]
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[ "<graphic xlink:href=\"1753-2000-2-21-1\"/>", "<graphic xlink:href=\"1753-2000-2-21-2\"/>" ]
[]
[{"article-title": ["U.S. Food and Drug Administration: Best Pharmaceuticals for Children Act"]}, {"article-title": ["Best Pharmaceuticals for Children Act"]}, {"surname": ["Muzina", "Calabrese"], "given-names": ["DJ", "JR"], "article-title": ["Maintenance therapies in bipolar disorder: focus on randomized controlled trials"], "source": ["Aust NZ J Psychiatry"], "year": ["2005"], "volume": ["39"], "fpage": ["652"], "lpage": ["661"], "pub-id": ["10.1111/j.1440-1614.2005.01649.x"]}, {"surname": ["Findling", "Pavuluri", "Geller B, DelBello MP"], "given-names": ["RL", "MN"], "article-title": ["Lithium"], "source": ["Treatment of Bipolar Disorder in Children & Adolescents"], "publisher-name": ["New York, NY: Guilford Press"]}, {"collab": ["National Institute of Mental Health"], "article-title": ["Clinical Global Impressions Scale"], "source": ["Psychopharmacol Bull"], "year": ["1985"], "volume": ["21"], "fpage": ["839"], "lpage": ["843"]}, {"surname": ["Smarty", "Findling"], "given-names": ["S", "RL"], "article-title": ["Psychopharmacology of pediatric bipolar disorder: a review"], "source": ["Psychopharmacol"], "year": ["2007"], "volume": ["191"], "fpage": ["39"], "lpage": ["54"], "pub-id": ["10.1007/s00213-006-0569-y"]}, {"surname": ["Simpson", "Angus"], "given-names": ["GM", "JW"], "article-title": ["A rating scale for extrapyramidal side effects"], "source": ["Acta Psychiatr Scand"], "year": ["1970"], "fpage": ["11"], "lpage": ["19"], "pub-id": ["10.1111/j.1600-0447.1970.tb02066.x"]}, {"surname": ["Lingjaerde", "Ahlfors", "Bech", "Dencker", "Elgen"], "given-names": ["O", "UG", "P", "SJ", "K"], "article-title": ["The UKU side effect rating scale. A new comprehensive rating scale for psychotropic drugs and a cross-sectional study of side effects in neuroleptic-treated patients"], "source": ["Acta Psychiatr Scand"], "year": ["1987"], "volume": ["334"], "fpage": ["1"], "lpage": ["100"], "pub-id": ["10.1111/j.1600-0447.1987.tb10566.x"]}, {"surname": ["Greenhill", "Vitiello", "Fisher", "Levine", "Davies", "Abikoff", "Chrisman", "Chuang", "Findling", "March", "Scahill", "Walkup", "Riddle"], "given-names": ["LL", "B", "P", "J", "M", "H", "AK", "S", "RL", "J", "L", "J", "MA"], "article-title": ["Comparison of increasingly detailed elicitation methods for the assessment of adverse events in pediatric psychopharmacology"], "source": ["J Am Acad Adolesc Psychiatry"], "year": ["2004"], "volume": ["43"], "fpage": ["1488"], "lpage": ["1496"], "pub-id": ["10.1097/01.chi.0000142668.29191.13"]}, {"surname": ["Johnson"], "given-names": ["GF"], "article-title": ["Lithium in depression: a review of the antidepressant and prophylactic effects of lithium"], "source": ["Aust NZ J Psychiatry"], "year": ["1987"], "volume": ["21"], "fpage": ["356"], "lpage": ["365"]}, {"surname": ["Carson", "Evans WE, Schentag JJ, Jusko WJ"], "given-names": ["SW"], "article-title": ["Lithium"], "source": ["Applied Pharmacokinetics: Principles of Therapeutic Drug Monitoring"], "year": ["1992"], "edition": ["3"], "publisher-name": ["Vancouver, WA: Applied Therapeutics"]}, {"surname": ["Wechsler"], "given-names": ["DA"], "source": ["Wechsler Abbreviated Scale of Intelligence Manual"], "year": ["1999"], "publisher-name": ["San Antonio, TX: The Psychological Corporation"]}, {"surname": ["Overholser", "Brinkman", "Lehnert", "Ricciardi"], "given-names": ["JC", "DC", "KL", "AM"], "article-title": ["Children's Depression Rating Scale\u2013Revised: Development of a short form"], "source": ["J Clin Child Psychol"], "year": ["1995"], "volume": ["24"], "fpage": ["443"], "lpage": ["452"], "pub-id": ["10.1207/s15374424jccp2404_8"]}, {"surname": ["Tarter", "Hegedus"], "given-names": ["RE", "AM"], "article-title": ["The drug use screening inventory: its applications in the evaluation and treatment of alcohol and other drug abuse"], "source": ["Alcohol Health Res World"], "year": ["1991"], "volume": ["15"], "fpage": ["65"], "lpage": ["75"]}, {"surname": ["DuPaul", "Power", "Anastopoulos", "Reid"], "given-names": ["GJ", "TJ", "AD", "R"], "source": ["ADHD Rating Scale \u2013 IV: Checklists, norms, and clinical interpretation"], "year": ["1998"], "publisher-name": ["New York: The Guilford Press"]}, {"surname": ["Brannan", "Heflinger", "Bickman"], "given-names": ["AM", "CA", "L"], "article-title": ["The caregiver strain questionnaire: measuring the impact of the family of living with a child with serious emotional disturbance"], "source": ["Journal of Emotional and Behavioral Disorders"], "year": ["1997"], "volume": ["5"], "fpage": ["212"], "lpage": ["222"]}, {"surname": ["Moos", "Moos"], "given-names": ["R", "E"], "source": ["Family Environment Scale Manual"], "year": ["1984"], "publisher-name": ["Palo Alto, CA: Consulting Psychologist Press"]}, {"surname": ["Matthews", "Klove"], "given-names": ["CG", "H"], "source": ["Instruction manual for the adult neuropsychology test battery"], "year": ["1964"], "publisher-name": ["Madison, WI: University of Wisconsin Medical School"]}, {"surname": ["Matthews", "Kl\u00f8ve"], "given-names": ["CG", "H"], "source": ["Wisconsin Motor Steadiness Battery Administration manual for child neuropsychology battery"], "year": ["1978"], "publisher-name": ["Madison: University of Wisconsin Medical School, Neuropsychology Laboratory"]}, {"surname": ["Delis", "Kaplan", "Kramer"], "given-names": ["DC", "E", "JH"], "source": ["Delis-Kaplan Executive Function System"], "year": ["2001"], "publisher-name": ["San Antonio: The Psychological Corporation"]}, {"surname": ["Vigil"], "given-names": ["W"], "source": ["Continuous Performance Test: User and Technical Manual"], "year": ["1996"], "publisher-name": ["NH: For Thought Ltd"]}, {"surname": ["Kane", "Conway", "Miura", "Colflesh"], "given-names": ["MJ", "ARA", "TK", "GJH"], "article-title": ["Working memory, attention control, and the "], "italic": ["n"], "source": ["J Exp Psychol Learn Mem Cog"], "year": ["2007"], "volume": ["33"], "fpage": ["615"], "lpage": ["622"], "pub-id": ["10.1037/0278-7393.33.3.615"]}]
{ "acronym": [], "definition": [] }
56
CC BY
no
2022-01-12 14:47:28
Child Adolesc Psychiatry Ment Health. 2008 Aug 12; 2:21
oa_package/b3/41/PMC2531078.tar.gz
PMC2531079
18700957
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>" ]
[ "<title>Editorial</title>", "<p>It was about one year ago, in June 2007, that the first 4 manuscripts were published in CAPMH, thus marking the birth of a new, open-access, international journal in the field of child and adolescent psychiatry and mental health. Now, at the first birthday, seems the right time for both looking back at the accomplishments of the last year and considering further development of CAPMH.</p>", "<p>Since June 2007, we have published 29 peer reviewed articles from all over the world. The average time from initial submission to first editorial decision was 7 weeks, and from initial submission to publication was 5 months. Considering that all submissions undergo rigorous peer-review and that those that are ultimately accepted often had to be revised multiple times, theses figures indicate that CAPMH has been successful in providing researchers with an opportunity to share their work in a timely manner. On a 5-point Likert scale, where 5 indicates maximum satisfaction, the authors' rated the submission process an average of 4.1, the peer review process 4.2, and the production process 4.1. The author overall satisfaction with CAPMH was 4.4. Of the respondents, 86% indicated that they would recommend the journal to a colleague and 100% would publish again in CAPMH.</p>", "<p>All manuscripts published in CAPMH are immediately accessible for free in PubMed. In summer 2008 the American Psychological Association has accepted CAPMH in their indexing service PsycINFO. Since the beginning of 2008, CAPMH registered approximately 5,000 accesses per month. Among the highly accessed articles, there were the papers by Basker et al. from India [##REF##17688697##1##], more than 5,000 accesses), by Ginicola from the U.S. [##REF##17714590##2##], more than 2,600 accesses), and by Hammerlynck et al. from the Netherlands [##REF##17683633##3##], more than 2,600 accesses), thus indicating the international scope of CAPMH. Our editorial team is especially committed to providing a broad, worldwide perspective on child and adolescent mental health by encouraging and facilitating submissions from a geographically and culturally diverse pool of contributors. In particular, authors from countries with traditionally limited access to research resources have been encouraged to submit manuscripts and given all possible editorial support throughout the review process. This strategy has allowed the Journal to offer our readers a truly international \"menu\" of scientific reports.</p>", "<p>After this successful start, we would like to thank the authors, the reviewers, the entire production team, and all those who have supported CAPMH during its first year of life. The road ahead looks both promising and challenging. The two main aims for the next two years are to achieve registration in Medline and at Thompson Reuters (in order to receive an impact factor as early as possible). In addition to regular submissions, there will be special sections with invited authors devoted to specific themes of high relevance to child and adolescent mental health. A series of articles on regulatory and ethical issues of child and adolescent psychopharmacological research in the U.S. and Europe is being prepared, and another focused on psychotherapy of posttraumatic stress disorders is under consideration. We welcome suggestions and nominations of high interest topics from the editorial board and all readers. We also have a special interest in case reports with reference to cultural backgrounds and respective mental health systems and would like to encourage relevant submissions from all countries of the world.</p>", "<p>We remain aware that one of the challenges for authors is the publication cost which is required in order preserve the open-access nature of the Journal and its complete independence from commercial entities. Still, this \"cost of freedom\" may be a barrier and discourage submissions. We are examining possible ways of reducing this burden as much as it can be possibly achieved while preserving the open-access, independent characteristics of CAPMH.</p>", "<p>We would like to invite all clinicians and researchers in the field, of mental health to contribute to the further development of CAPMH!</p>" ]
[ "<title>Acknowledgements</title>", "<p>The opinions and assertions contained in this report are the private views of the authors and are not to be construed as official or as reflecting the views of the Department of Health and Human Services, the National Institutes of Health, or the National Institute of Mental Health.</p>" ]
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{ "acronym": [], "definition": [] }
3
CC BY
no
2022-01-12 14:47:28
Child Adolesc Psychiatry Ment Health. 2008 Aug 13; 2:22
oa_package/41/ab/PMC2531079.tar.gz
PMC2531080
18706096
[ "<title>Background</title>", "<p>The diagnosis of breast cancer has changed over the last years. Previously, about 50 to 70% of breast cancers were diagnosed through physical examination [##REF##12713496##1##]. The detection of subclinical lesions has increased with screening mammography [##UREF##0##2##]. Thus, the need arose to develop minimally invasive techniques of locating and histological confirming small alterations [##REF##12413920##3##,##REF##11597011##4##]. Wire localization (WL) is a well-known technique in breast surgery where a malleable needle with a spear at its distal extremity is used to locate a lesion. Under mammography or ultrasound visualization, a needle is placed directly into an area suspicious as per the nature of the lesion [##REF##12413920##3##,##REF##11597011##4##]. There is a risk of needle displacement during the period between its positioning and retreat, mainly in breasts with a predominant fatty component [##UREF##0##2##,##REF##11597011##4##]. This can represent an important complication in patients with mammary prosthesis, for example. In dense breasts, difficulty in positioning the needle localization device can occur. Cases of transected needles, pneumothorax, and other accidents have been described [##UREF##0##2##,##REF##15505875##5##]. Needle localization of occult lesions is usually done under general anesthesia due to patient discomfort when the needle localization device is manipulated [##REF##15505875##5##, ####REF##16038695##6##, ##REF##15985370##7####15985370##7##].</p>", "<p>Radioguided occult lesion localization (ROLL) is a method that has been used since 1996 [##UREF##1##8##]. It was developed at the European Institute of Oncology in Milan, and is currently the standard of care in many breast surgery services. In this procedure, a radioactive labeling substance is used at the suspect site (under ultrasound or mammography guidance). The gamma-detecting probe guides the localization of a suspicious opacity or microcalcification cluster during the surgical procedure [##REF##16038695##6##,##REF##10215829##9##]. The cutaneous incision can be planned with better aesthetic results. In this method, a spear is not used; instead, a small portion of liquid makes the process less traumatic for patients. Local anesthesias for ROLL and patients' opinions as to the pain and postoperative aesthetic results have not been previously studied for effectiveness and patient acceptability [##REF##15985370##7##,##REF##10215829##9##].</p>", "<p>The goal of this paper is to show the feasibility of performing the ROLL technique in an ambulatory setting, with shorter operative time and less patient morbidity, through careful surgical planning and the extraction of a smaller mammary sample. Therefore, these advantages make it the preferred method for occult breast lesion localization with diagnostic intention.</p>" ]
[ "<title>Methods</title>", "<p>One hundred and twenty patients with suspicious breast opacity or microcalcification cluster requiring diagnostic excision were randomized and submitted to guided surgical biopsy. WL was performed in 59 patients (49.2%) with standard techniques [##UREF##0##2##]. For ROLL (61 patients), 0.15 mCi (5.55 MBq) of <sup>99m</sup>Tc-labeled macro albumin aggregate in 0.2 mL of saline was used. On the day of surgery, this solution was injected into the non-palpable lesion under mammography or ultrasound guidance. In addition, 0.1 mL of water-soluble non-ionic iodinated contrast medium was administered to check the exact position of the radiotracer at the time of injection. One hour later, the patient was submitted to front and lateral view planar scintigraphic images using a <sup>99m</sup>TcO<sub>4 </sub>flood to check the radiographic correlation (Figure ##FIG##0##1##). The patient was taken to the operating room for excision of the lesion. Localization of the area of highest radioactivity was performed with a hand-held gamma probe (Navigator GPS™ – United States Surgical/Tyco Healthcare) to choose the most cosmetically acceptable site to incise. The specimen was excised after locating the highest radioactivity point and this hot spot was removed. It was located in the center of the specimen with a resection margin, with no excessive removal of normal breast parenchyma. The parenchyma bed was verified with the probe to rule out residual areas of high radioactivity. During surgery, a radiological study was performed to confirm total resection of cases previously demarcated by mammography. The use of local anesthesia for the ROLL procedure was proposed, considering that, contrary to the WL method, the wire is not maintained in the breast during the procedure. In all ROLL cases, local anesthesia (mean of 16 mL/patient of lidocaine with epinephrine-1:200000) was used in the skin and breast parenchyma close to the lesion. The hospital stay considered the period (in hours) between the beginning of the surgery and the discharge from the hospital. The procedure time was considered as the mean time of surgery in minutes. The patients were requested to score the cosmetic appearance of their breast as excellent, good, or poor in the first month after surgery. In addition, a numerical rating scale was used to measure pain on the firs postoperative day, considering a variation between 0 (no pain) and 10 (worst pain) [##REF##16000093##10##,##REF##14992058##11##]. Different statistical tests were used, when appropriate, in order to test the significance between the two groups, considering a P value &lt; 0.05 as statistically significant. Clearance margin was considered as ≥ 10 mm for invasive cancer, ≥ 5 mm for ductal carcinoma <italic>in situ</italic>, and ≥ 1 mm for benign disease. All specimens were included with transversal serial cuts, with the margin size defined as the distance between the lesion and closest margin. Patients were subsequently treated according to the definitive histology result. This study was performed as per a protocol approved by the Ethical Review Board of Chapeco University.</p>" ]
[ "<title>Results</title>", "<p>Fifty-nine patients were randomized to WL and 61 to ROLL. The mean age of the two groups was 51.3 and 49.6 years, respectively. All procedures (in both groups) were done with diagnostic intention. The clinical and radiological characteristics of the two groups are summarized in Tables ##TAB##0##1## and ##TAB##1##2##. There were no significant differences in the lesion sites and in the auxiliary localization technique in both groups. The ROLL technique did not increase the number of aesthetic incisions. The hospital stay was significantly longer in the WL group due to the use of general anesthesia (mean of 19.82 h vs. 2.04 h). This difference was statistically significant, but the result presents the bias that all the patients submitted to the WL technique needed a period of recovery from the anesthetic, i.e., a longer hospital stay. Procedure time was significantly shorter in the ROLL group (37.2 min vs. 26.06 min, respectively). The mean volume of the excised specimen was significantly smaller in the ROLL group than in WL group (8.70 cm<sup>3 </sup>vs. 23.15 cm<sup>3</sup>, respectively). There was a difference in the margin status of the surgical specimens (P &lt; 0.05), but these findings were not significant when considering only malignant lesions (Table ##TAB##1##2##). Postoperative wound infections between the groups were not significant. The mean of the numerical rating pain scale was different in both groups (2.20 for WL vs 1.62 for ROLL), and according to patients' opinions, there were better cosmetic outcomes with the ROLL technique (Table ##TAB##2##3##). In two cases of WL, there was wire dislodgement and the procedure needed repositioning.</p>" ]
[ "<title>Discussion</title>", "<p>Nowadays, the diagnosis of subclinical breast lesions is very common due to easy access to standard mammography in most places. Many techniques, such as core biopsy, fine needle aspiration, and mammotomy are used for the histological study of clinically occult breast lesions. Sometimes it is necessary to excise all occult lesions in order to choose the adequate treatment. WL is a method used in many places as standard preoperative localization of non-palpable lesions. However, the problems reported with this technique are well known: wire transection, difficulties in wire repositioning in dense or fatty breasts, dislodgement, interference with the surgical approach, and patient discomfort during wire positioning and during patient transportation from the radiological center to the operating room [##UREF##0##2##,##REF##10215829##9##,##REF##12722097##12##].</p>", "<p>Since 1996, when the first paper presented the advantages of ROLL, other authors have reported the same findings and have documented some characteristics of this technique: it is a radiologically and surgically easier procedure to perform, and the lesion can be identified in three dimensions affording greater flexibility in making a cosmetic incision [##REF##18365547##13##]. ROLL is also appropriate for combination with sentinel lymph node mapping in which the occult breast cancer and sentinel lymph node can be excised in the same procedure [##REF##10215829##9##,##REF##15498634##14##,##REF##15230788##15##]. Until now, the methodology for evaluation of postoperative pain has not mentioned the ROLL procedure, despite some works reporting postoperative pain when the WL is carried out [##REF##16038695##6##,##UREF##1##8##,##REF##15498634##14##,##UREF##2##16##]. The evaluation of pain on the first postoperative day and of the cosmetic outcomes was used as a parameter for comparing patients' opinions about both procedures, and there was a difference between the two groups. This is due to a better choice of an incision site (radioguided) and the fact that the size of the ROLL specimen is smaller. Hospital stay was shorter in the ROLL group due to the ambulatory characteristics of this procedure. Moreover, with the WL procedure, the patient needed a time to recover from general anesthesia. The failure rate of the wire guided technique (<italic>i.e</italic>. incomplete cancer resection) has been reported in the range of 40–50% [##REF##9158186##17##]. The duration of the procedure was shorter in the ROLL procedure, which could be explained by better radioguided planning of the method; however, there were no significant findings (p &gt; 0.05). The specimen size (mean in cm<sup>3</sup>) was smaller in patients submitted to ROLL and there were more cases with compromised margins with the WL procedure (p &lt; 0.05), which again reflects better planning to include all lesions at the same time, and the specimen excised is the smallest possible. This difference, however, could affect the results for rates of cases with involved margins due to the different criteria of histological categories. To date, there have been no descriptions of a comparison between the use of local anesthesia in the ROLL procedure and use of general anesthesia in WL, especially comparing them as to aesthetic results and pain measurement.</p>" ]
[ "<title>Conclusion</title>", "<p>ROLL can provide diagnosis or treatment of the breast lesion with a shorter hospital stay, shorter operative period, less breast tissue excised, and consequently, better aesthetic outcomes and fewer procedure-related symptoms. It can result in lower costs and a better acceptance on the part of patients.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>The detection of sub-clinical breast lesions has increased with screening mammography. Biopsy techniques can offer precision and agility in its execution, as well as patient comfort. This trial compares radioguided occult lesion localization (ROLL) and wire-guided localization (WL) of breast lesions. We investigate if a procedure at the ambulatorial level (ROLL) could lead to a better aesthetic result and less postoperative pain. In addition, we intend to demonstrate the efficacy of radioguided localization and removal of occult breast lesions using radiopharmaceuticals injected directly into the lesions and correlate radiological and histopathological findings.</p>", "<title>Methods</title>", "<p>One hundred and twenty patients were randomized into two groups (59 WL and 61 ROLL). The patients were requested to score the cosmetic appearance of their breast after surgery, and a numerical rating scale was used to measure pain on the first postoperative day. Clearance margins were considered at ≥ 10 mm for invasive cancer, ≥ 5 mm for ductal carcinoma <italic>in situ</italic>, and ≥ 1 mm for benign disease. Patients were subsequently treated according to the definitive histological result. When appropriate, different statistical tests were used in order to test the significance between the two groups, considering a P value &lt; 0.05 as statistically significant.</p>", "<title>Results</title>", "<p>WL and ROLL located all the occult breast lesions successfully. In the ROLL group, the specimen volume was smaller and there were more cases with clear margins (P &lt; 0.05). There were significant differences in mean time of hospital stay between WL and ROLL (21.42 vs. 2.56 hours), but not in operative time (39.4 vs. 29.9 minutes). There were significant differences in the subjective ease of the procedures as rated by the patients (cosmetic outcomes and postoperative pain).</p>", "<title>Conclusion</title>", "<p>ROLL is an effective method for the excision of non-palpable breast lesions. It enables more careful planning of the cutaneous incision, leading to better aesthetic results, less postoperative symptoms, and smaller volumes of excised tissue.</p>" ]
[ "<title>List of Abbreviations</title>", "<p>ROLL: Radioguided Occult Lesion Localization; WL: Wire-guided Localization; US: Ultrassonography.</p>", "<title>Conflict of interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MM carried out all surgeries, participated in the interpretation of the study and drafted the manuscript. JEW participated in the execution of all ROLLs procedures by US or estereotaxy. BB participated in the execution and in the interpretation of the study. RLS participated in the design of study and performed the statistical analysis. BG aided to write the manuscript. LMBF conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Lateral (A) and front (B) view planar scintigraphic images to check the radiographic correlation of a breast lesion (arrow).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Clinical and radiological characteristics of WL and ROLL groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\">WL</td><td align=\"center\">ROLL</td><td align=\"center\">P</td></tr></thead><tbody><tr><td align=\"center\">Number of patients</td><td align=\"center\">59</td><td align=\"center\">61</td><td/></tr><tr><td align=\"center\">Mean of age (yr)</td><td align=\"center\">49.9 (35–77)</td><td align=\"center\">50.7 (32–76)</td><td align=\"center\"><sup>£ </sup>0.540</td></tr><tr><td align=\"center\">Micro-calcifications</td><td align=\"center\">40</td><td align=\"center\">49</td><td/></tr><tr><td align=\"center\">Micro-Calcifications + Stromal deformity</td><td align=\"center\">2</td><td align=\"center\">3</td><td/></tr><tr><td align=\"center\">Stromal deformity</td><td align=\"center\">3</td><td align=\"center\">0</td><td/></tr><tr><td align=\"center\">Nodule</td><td align=\"center\">14</td><td align=\"center\">9</td><td align=\"center\"><sup>£ </sup>0.080</td></tr><tr><td align=\"center\">Right Breast</td><td align=\"center\">17</td><td align=\"center\">20</td><td/></tr><tr><td align=\"center\">Left Breast</td><td align=\"center\">42</td><td align=\"center\">41</td><td align=\"center\"><sup>£</sup>0.351</td></tr><tr><td align=\"center\">SL Quadrant</td><td align=\"center\">22</td><td align=\"center\">27</td><td/></tr><tr><td align=\"center\">SM Quadrant</td><td align=\"center\">21</td><td align=\"center\">19</td><td/></tr><tr><td align=\"center\">IM Quadrant</td><td align=\"center\">6</td><td align=\"center\">4</td><td/></tr><tr><td align=\"center\">IL Quadrant</td><td align=\"center\">8</td><td align=\"center\">10</td><td/></tr><tr><td align=\"center\">Central</td><td align=\"center\">2</td><td align=\"center\">1</td><td align=\"center\"><sup>£</sup>0.948</td></tr><tr><td align=\"center\">Estereotaxy Localization</td><td align=\"center\">31</td><td align=\"center\">27</td><td/></tr><tr><td align=\"center\">US Localization</td><td align=\"center\">28</td><td align=\"center\">24</td><td align=\"center\"><sup>£</sup>0.130</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Comparison of surgical and pathological features of WL and ROLL groups.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\"><bold>WL</bold></td><td align=\"left\"><bold>ROLL</bold></td><td align=\"left\"><bold>P</bold></td></tr></thead><tbody><tr><td align=\"left\">Local of cutaneous incision</td><td/><td/><td/></tr><tr><td align=\"left\"> Peri-areolar</td><td align=\"left\">21</td><td align=\"left\">31</td><td/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">38</td><td align=\"left\">30</td><td align=\"left\"><sup>§ </sup>0.022</td></tr><tr><td align=\"left\">Histopathological Diagnosis</td><td/><td/><td/></tr><tr><td align=\"left\"> Fibrocystic changes</td><td align=\"left\">25</td><td align=\"left\">34</td><td/></tr><tr><td align=\"left\"> Fibroadenoma</td><td align=\"left\">12</td><td align=\"left\">14</td><td/></tr><tr><td align=\"left\"> Invasive carcinoma</td><td align=\"left\">6</td><td align=\"left\">2</td><td/></tr><tr><td align=\"left\"> Carcinoma <italic>in situ</italic></td><td align=\"left\">10</td><td align=\"left\">8</td><td/></tr><tr><td align=\"left\"> Others</td><td align=\"left\">6</td><td align=\"left\">3</td><td align=\"left\"><sup>£</sup>0.047</td></tr><tr><td align=\"left\">Size of specimen (mean in cm<sup>3</sup>)</td><td/><td/><td/></tr><tr><td align=\"left\"> All lesions</td><td align=\"left\">23.15</td><td align=\"left\">9.70</td><td align=\"left\"><sup>¥</sup>0.001</td></tr><tr><td align=\"left\"> Benign lesion</td><td align=\"left\">14.80</td><td align=\"left\">8.70</td><td align=\"left\"><sup>¥</sup>0.002</td></tr><tr><td align=\"left\">Invasive and not invasive carcinomas</td><td align=\"left\">20.30</td><td align=\"left\">9.45</td><td align=\"left\"><sup>¥</sup>0.170</td></tr><tr><td align=\"left\">Period of hospital stay (mean in hours)</td><td align=\"left\">18.7</td><td align=\"left\">3.06</td><td align=\"left\"><sup>¥</sup>&lt;0.001</td></tr><tr><td align=\"left\">Anesthesia</td><td align=\"left\">General</td><td align=\"left\">Local</td><td/></tr><tr><td align=\"left\">Time of procedure (mean – min)</td><td align=\"left\">37.2</td><td align=\"left\">26.06</td><td align=\"left\"><sup>¥</sup>0.719</td></tr><tr><td align=\"left\">Margins of all lesions</td><td/><td/><td/></tr><tr><td align=\"left\"> Clear</td><td align=\"left\">51</td><td align=\"left\">57</td><td/></tr><tr><td align=\"left\"> Involved</td><td align=\"left\">8</td><td align=\"left\">4</td><td align=\"left\"><sup>£</sup>0.010</td></tr><tr><td align=\"left\">Carcinoma Margins</td><td/><td/><td/></tr><tr><td align=\"left\"> Clear</td><td align=\"left\">14</td><td align=\"left\">9</td><td/></tr><tr><td align=\"left\"> Involved</td><td align=\"left\">2</td><td align=\"left\">1</td><td align=\"left\"><sup>#</sup>0.670</td></tr><tr><td align=\"left\"> Postoperative wound infection</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\"><sup>£</sup>0.270</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Dependent variables on the patient opinion.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\">Techniques</td><td align=\"left\">WL</td><td align=\"left\">ROLL</td><td align=\"left\">P</td></tr></thead><tbody><tr><td align=\"left\">Cosmetic outcome</td><td/><td/><td/></tr><tr><td align=\"left\"> Excellent</td><td align=\"left\">49</td><td align=\"left\">57</td><td/></tr><tr><td align=\"left\"> Good</td><td align=\"left\">10</td><td align=\"left\">04</td><td/></tr><tr><td align=\"left\"> Poor</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\"><sup>§ </sup>&lt;0.001</td></tr><tr><td align=\"left\">Pain (mean of numerical scale)</td><td align=\"left\">2.20</td><td align=\"left\">1.62</td><td align=\"left\"><sup>¥ </sup>0.021</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p><sup>£ </sup>Chi-Square Test with Yates' correction</p></table-wrap-foot>", "<table-wrap-foot><p><sup>§ </sup>Chi Square Test. <sup>£ </sup>Chi-Square Test with Yates' correction. <sup>¥ </sup>T-test. <sup># </sup>Fisher exact</p></table-wrap-foot>", "<table-wrap-foot><p><sup>§ </sup>Chi Square Test. <sup>¥ </sup>T – test</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1756-9966-27-29-1\"/>" ]
[]
[{"surname": ["Kopans", "Kopans DB"], "given-names": ["DB"], "source": ["Imaging-guided needle placement for biopsy and the preoperative localization of clinically occult lesions"], "year": ["1997"], "publisher-name": ["Breast Imaging, United States of America, LIPPINCOTT Williams & Wilkins"], "fpage": ["637"], "lpage": ["720"]}, {"surname": ["Zurrida", "Galimberti", "Monti", "Luini"], "given-names": ["S", "V", "S", "A"], "article-title": ["Radioguided localization of occult breast lesions"], "source": ["Breast"], "year": ["1998"], "volume": ["7"], "fpage": ["11"], "lpage": ["13"], "pub-id": ["10.1016/S0960-9776(98)90044-3"]}, {"surname": ["Geissler", "De Freitas", "Cachin", "Mestas", "Lebouedec", "Maublant"], "given-names": ["B", "D", "F", "D", "G", "G"], "article-title": ["Radioguided occult lesion localization: better delineation of the injection site with a high-resolution collimator"], "source": ["Nuclear Instruments and Methods in Physics Research A"], "year": ["2004"], "volume": ["527"], "fpage": ["216"], "lpage": ["219"], "pub-id": ["10.1016/j.nima.2004.03.123"]}]
{ "acronym": [], "definition": [] }
17
CC BY
no
2022-01-12 14:47:28
J Exp Clin Cancer Res. 2008 Aug 15; 27(1):29
oa_package/4a/ae/PMC2531080.tar.gz
PMC2531081
18702805
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Deaths after percutaneous ethanol injection (PEI) into hepatocellular carcinoma (HCC) may occur within a few hours to a few days following the procedure because of hemoperitoneum and haemorrhage from oesophageal varices or hepatic insufficiency. Pancreatitis has been recently reported as a rare lethal complication of intra-arterial PEI, another modality for treating HCCs. In this minireview, we analyze the literature concerning the development of acute pancreatitis after PEI. Pathogenesis of pancreatitis from opioids and ethanol is also addressed. Treatment with opioids to reduce the patient's abdominal pain after PEI in combination with the PEI itself may lead to direct toxic effects, thus favouring the development of pancreatitis.</p>" ]
[ "<title>Review</title>", "<p>Percutaneous ethanol injection (PEI) is a widely used procedure for the treatment of hepatocellular carcinoma (HCC), and may be performed via conventional, \"one shot\" or intra-arterial modalities.</p>", "<p>While conventional PEI is performed under localized anaesthesia and the amount of ethanol injected into the HCC generally does not exceed 10 ml/session, \"one shot\" PEI is performed under general anaesthesia and the amount of administered ethanol is higher, ranging from 20 to 60 ml/session. Intra-arterial PEI is also performed under general anaesthesia, but ethanol (up to 50 ml) is directly injected, through a percutaneous route, into the artery that supplies the HCC after visualizing and puncturing this artery by using colour Doppler and B-mode ultrasound guidance. Interestingly, as demonstrated in a cell culture experimental study on malignant and liver cell lines, the cytotoxic effect of ethanol is dependent upon both its concentration and the exposure time [##REF##8995467##1##].</p>", "<p>Since at present, the concentration of ethanol is standardized to 95% and the exposure time of the HCC is considered to be practically identical in the two PEI procedures, our opinion is that the development of complications may only depend on the high total dosage of ethanol injected and the patient's clinical conditions. However, according to some authors, no difference in complications (pain and fever excluded) has been reported when using larger doses of ethanol [##REF##8390070##2##].</p>", "<p>The most frequently reported complication of these three PEI modalities is abdominal pain that may be observed in up to 48% of cases [##REF##8915270##3##].</p>", "<p>If pain is not tolerated, especially when the doses of injected ethanol are high, the administration of nonopioid or mild opioid analgesics may be required [##REF##8915270##3##]. Since cases of acute pancreatitis after opioid administration have been reported [##REF##8884856##4##, ####REF##9414983##5##, ##REF##9598820##6##, ##REF##11095359##7##, ##REF##12594378##8##, ##REF##15235508##9##, ##REF##15800821##10##, ##UREF##0##11##, ##REF##16082282##12##, ##REF##6153371##13##, ##REF##6479537##14##, ##REF##3197985##15##, ##REF##11316181##16##, ##REF##14607663##17####14607663##17##], we believe that more attention must be given when such drugs are administered. In fact, it is ascertained that there is a close temporal relationship (ranging from 1 to 3 hours) between opioid administration and the development of pancreatitis [##REF##11095359##7##,##REF##17728850##18##].</p>", "<p>A number of physiopathological studies have elucidated the mechanism through which opioids may induce pancreatitis. These studies most often implicate direct constriction of the sphincter of Oddi [##REF##17728850##18##]; in fact, it has been demonstrated that intravenous morphine increases the intrabiliary pressure by enhancing sphincter of Oddi pressure [##REF##6479537##14##]. It has also been shown that, after biliary sphincterotomy, pancreatitis may occur due to the sphincter spasm [##REF##11095359##7##]. Taking into consideration that sphincter of Oddi dysfunction, a clinical syndrome due to a dyskinesia resulting from a functional alteration of sphincter motility or to stenosis, may occur at any age [##REF##16842450##19##], our opinion is that it should be excluded before giving opioids after PEI in patients with HCC. This caution is very important considering that in patients with idiopathic recurrent pancreatitis, manometric evidence of sphincter of Oddi dysfunction was found to vary between 39 and 90% [##REF##2028949##20##]. Furthermore, most cirrhotic patients with HCC suffer from cholelithiasis, and acute pancreatitis has been reported to occur in association with secondary sphincter of Oddi dysfunction, which is related to biliary calculi in 90% of cases [##UREF##1##21##]. According to some authors, since cholecistectomy would seem to favour the development of acute pancreatitis after ingestion of therapeutic doses of opioids [##REF##11095359##7##], we believe that pain management with opioids after PEI treatment in cholecistectomized cirrhotics with HCC should be performed with great caution.</p>", "<p>Furthermore, interesting animal studies have demonstrated that ethanol may have direct effects on the pancreas, such as microcirculatory changes and direct toxic damage to the pancreatic acini [##REF##9841996##22##, ####REF##12828957##23##, ##REF##14576492##24####14576492##24##]. Moreover, the mechanism of ethanol-induced pancreatitis has been well-studied in an interesting animal model in which it was demonstrated that sphincter of Oddi dysfunction was implicated in several forms of acute and chronic pancreatitis [##REF##17509022##25##]. In fact, according to the authors, since trans-sphincteric flow, regulated by the sphincter of Oddi which acts as a pump, is a direct measure of sphincter of Oddi function, an alteration of this trans-sphincteric flow after intragastric or i.v. ethanol may indicate Oddi dysfunction; the authors also investigated whether neural mechanisms and gastric mucosal damage might play a role in this process [##REF##17509022##25##], demonstrating that both intragastric and i.v. ethanol administration altered the Oddi trans-sphincteric flow. They also suggested, in accordance with other studies [##REF##1252737##26##, ####REF##6320728##27##, ##REF##12846727##28####12846727##28##], that the fall in Oddi trans-sphincteric flow might be due to the direct effects of ethanol, its metabolites (acetaldehyde) and/or other humoural agents (superoxide, endothelin-1) on sphincter of Oddi motility. Furthermore, an effect of ethanol and/or its metabolites on sphincter of Oddi nitrergic innervations was observed [##REF##17509022##25##]. The authors thus concluded that reduced sphincter of Oddi function might contribute to elevated pancreatic duct pressure, which is one of the events required for the onset of acute pancreatitis [##REF##17509022##25##].</p>", "<p>There are no reports in the literature of acute pancreatitis after treatment of HCC with conventional PEI; in contrast, a case of lethal acute pancreatitis is described as a complication of intra-arterial PEI [##REF##15564388##29##]. This technique can only be performed after the superselective puncture of HCC-supplying arteries, and the extreme technical difficulty of this method provides the major reason for the frequent failures of intra-arterial PEI [##REF##9655292##30##].</p>", "<p>In an interesting study on large infiltrative HCC treated with intra-arterial PEI, the volume of ethanol intra-arterially injected ranged from 12 to 50 mL [mean, 25 mL ± 13 (63% of total volume injected into tumour)] in a single session and from 0 mL to 50 mL [mean: 15 mL ± 19 (37% of total volume injected)] in the subsequent sessions [##REF##15564388##29##].</p>", "<p>A higher survival rate compared with that obtained after one-shot PEI [##REF##9655292##30##] was observed with this intra-arterial PEI procedure [##REF##15564388##29##]. However, the authors found that the main specific complication of this procedure, which caused the death of one of their patients, was ethanol reflux into the pancreaticoduodenal artery, a condition that can occur when the arterial branch of the HCC, in which ethanol is injected, originates from a short left hepatic artery close to the origin of the pancreaticoduodenal trunk [##REF##12147854##31##].</p>", "<p>It is obvious that in this case, the reflux of ethanol in the pancreaticoduodenal trunk was the initial cause of pancreatitis through direct induction of a toxic necrosis of the pancreas. However, we cannot rule out the possibility that also opioids may have contributed to the development of pancreatitis and that the alteration of the Oddi trans-sphincteric flow induced by ethanol may have played a role, although the authors did not mention this possibility [##REF##8915270##3##].</p>", "<p>Quite recently, we performed a \"one shot\" PEI (a total dose of 50 ml) into two HCC nodules of 4,6 and 3,1 cm respectively, in a patient with Child A cirrhosis. Pain management after the procedure was applied with morphine (10 mg i.v. and 10 mg s.c.), and with paravertebral block (right side) of D3-D5 by means of naropine 0,75 60 mg (total dose). On the next day, the patient developed oedematous head pancreatitis. In order to reduce his abdominal pain, treatment with opioids (morphine 8 mg/i.v. and tramadol 50 mg/i.v.) was maintained until two days after PEI; then, only tramadol 50 mg/i.v b.i.d. was continued until nine days after PEI. Despite an appropriate medical treatment of oedematous head pancreatitis and paralytic ileus (with octreotide, subcutaneous longastatine, hydration infusion and antibiotics), the patient's clinical condition further worsened and free subdiaphragmatic airways, mild abdominal fluid collection and necrosis of the head of the pancreas were observed on a contrast CT. Surgical intervention was mandatory and histological examination of the resected organs showed necrosis of the gallbladder, chronic steatophagic inflammation of the omentum, steatonecrosis of the gastric antrum with microerosive gastritis, haemorrhagic necrosis of the appendix and steatonecrosis of both the pancreatic head and the duodenum. After a few weeks, the patient fell into a hepatic coma and died of multiorgan failure and end-stage hepatic insufficiency.</p>", "<p>Based on the data available in the literature, our opinion is that acute pancreatitis may develop in cirrhotics with HCC treated with opioids to alleviate their pain after PEI. The mechanism through which ethanol may induce pancreatitis is partially known. After PEI, ethanol cannot easily diffuse into the surrounding non-tumoural tissue, since that tissue is firmer than the tumour structure. Therefore, in this case, the development of pancreatitis may have been favoured by the ensuing treatment with opioids although it cannot be ruled out that ethanol may have played a role; in fact, possible mechanisms of ethanol-induced pancreatitis may be pancreatic duct constriction, Oddi trans-sphincteric flow alteration, metabolic effects, direct cellular toxicity, all of which have been previously discussed [##REF##9841996##22##, ####REF##12828957##23##, ##REF##14576492##24##, ##REF##17509022##25####17509022##25##].</p>", "<p>An experimental animal study on rats with BW7756 hepatoma, performed to compare efficacy and safety of two percutaneous ablation methods [PEI and PAI (percutaneous acetic acid)], showed that PEI had a lower mortality rate for complications than PAI, and that none of the complications from either procedure was due to pancreatitis [##REF##18078519##32##]. In fact, autopsies revealed that the deaths of the rats were due to massive liver necrosis (about 40%) with diaphragma involvement, or to complete inferior vena cava thrombosis with extension to the right atrium.</p>", "<p>In this experiment, PEI was performed under general anaesthesia and opioid analgesics were not administered: this might be the reason why no evidence of pancreatitis was observed [##REF##18078519##32##].</p>", "<p>It is true that pancreatitis after treatment with PEI of cirrhotics with HCC is a very rare complication, but these data, taken together, show that both opioids and ethanol may induce acute pancreatitis.</p>", "<p>It is well established that opioids can favour the development of pancreatitis through a constriction of the Oddi's sphincter. The fact that i.v. ethanol may alter the function of the Oddi's sphincter [##REF##17509022##25##] suggests that both in intra-arterial PEI and in \"one shot\" PEI, the pathogenesis of pancreatitis may have also been due to mechanisms of motility dysfunction of the Oddi's sphincter.</p>", "<p>Therefore, the combined administration of ethanol and opioids may greatly favour the development of pancreatitis in both procedures.</p>", "<p>According to Beger et al., mortality after acute pancreatitis is 7.6% when less than 30% of the pancreas is necrotic and 24% when up to 50% of the pancreas is necrotic. However, mortality is 34.3% when there are additional extrapancreatic fluid effusions [##REF##1855779##33##]. According to Rau et al. and Hartwig et al., the mortality rate after acute pancreatitis varies from 20 to 30% [##REF##8995071##34##,##REF##12483264##35##].</p>", "<p>In animal models of severe necrotizing pancreatitis, mortality is promoted by sepsis and by the development of a systemic inflammatory response syndrome, which, in turn, causes lethal multiorgan failure [##REF##9895386##36##,##REF##10922996##37##].</p>", "<p>Therefore, given the elevated mortality rate of pancreatitis, more attention is necessary when pain is treated with opioids in cirrhotics with HCC after PEI.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>" ]
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[{"surname": ["Moreno Escobosa", "Amat Lopez", "Cruz Granados", "Moya Quesada"], "given-names": ["MC", "J", "S", "MC"], "article-title": ["Pancreatitis due to codeine"], "source": ["Allergol Immunopathol"], "year": ["2005"], "volume": ["33"], "fpage": ["175"], "lpage": ["177"], "pub-id": ["10.1157/13075703"]}, {"surname": ["White", "Delmont J"], "given-names": ["TT"], "article-title": ["The part that the sphincter of Oddi plays in the etiology of pancreatitis"], "source": ["The sphincter of Oddi"], "year": ["1977"], "publisher-name": ["Paris: Karger"], "fpage": ["175"], "lpage": ["179"]}]
{ "acronym": [], "definition": [] }
37
CC BY
no
2022-01-12 14:47:28
J Exp Clin Cancer Res. 2008 Aug 14; 27(1):28
oa_package/dc/14/PMC2531081.tar.gz
PMC2531082
18706117
[ "<title>Background</title>", "<p>Venous thromboembolism (VTE), usually manifested as deep vein thrombosis (DVT) or pulmonary embolism (PE), is usually considered to be a distinct entity from the thromboembolic arterial diseases, such as myocardial infarction (MI), peripheral artery disease and ischaemic stroke. However, both VTE and thromboembolic arterial diseases involve the formation of clots within blood vessels, and so may be linked. Recent studies have investigated the incidence of arterial events in patients with VTE, and have reported data suggesting that there may be a positive association between VTE and thromboembolic arterial disease [##REF##16961597##1##, ####REF##15615803##2##, ##REF##16634738##3####16634738##3##]. However, these studies either lacked a suitable control group or involved relatively small numbers of patients.</p>", "<p>To determine whether a diagnosis of VTE is associated with an increased risk of arterial disease, the incidence of arterial disease in patients with a prior VTE should be compared with that in matched controls without a history of VTE. In this study we aimed to perform such an analysis by assessing the risk of MI in patients with a VTE diagnosis and control patients without VTE using data recorded prospectively from the UK General Practice Research Database. We also aimed to estimate the overall long-term mortality among patients with VTE during a long-term follow-up period in comparison with that of the control population without VTE.</p>" ]
[ "<title>Methods</title>", "<title>Data source</title>", "<p>The GPRD contains computerized information entered by primary care physicians (PCPs) in the UK. The vast majority of the UK population is registered with a PCP. About 1500 PCPs participate in the GPRD, covering a population of around 3 million individuals, who are broadly representative of the UK population. The PCPs hold the complete medical record of registered individuals, including demographic data, all medical diagnoses, consultant and hospital referrals, and a record of all prescriptions issued. Prescriptions are generated directly from the PCP's computer and entered into the patient's computerized file. All the information is recorded by PCPs during consultations in a standard fashion and practices regularly anonymize and send these data to the Medicines and Healthcare Products Regulatory Agency (MHRA), which is in charge of quality control and management of the data for use in research projects. Several validation studies have shown the accuracy and completeness of data in the GPRD [##REF##2021768##4##,##REF##12741446##5##]. Previous studies have also confirmed the validity of using the GPRD for epidemiological research in the field of DVT and PE [##REF##10848723##6##, ####REF##7500750##7##, ##REF##10560680##8##, ##REF##9081000##9##, ##REF##9663819##10####9663819##10##].</p>", "<title>Study cohorts</title>", "<p>The source population included individuals aged 20–79 years enrolled with a participating PCP for more than 2 years during 1 January 1994 to 31 December 2000, without a previously recorded diagnosis of VTE, as described in a previous cohort study of the natural history of VTE [##UREF##0##11##]. The resulting source population consisted of 1 856 206 patients, and the first day of meeting these eligibility criteria was used as each individual's start date. Of this source population, 6550 patients had a first recorded diagnosis of VTE from the individual's start date until 31 December 2000. The validation (positive predictive value) of a VTE diagnosis in the GPRD has been described previously: a questionnaire was sent to PCPs for a random sample of 5% of patients with a record of VTE and, after reviewing these questionnaires, the diagnosis of VTE was confirmed in 94% of cases [##UREF##0##11##]. Moreover, we previously reported that the overall incidence rate of VTE in the study cohort was 74.5 per 100 000 person-years [##UREF##0##11##]. In other epidemiological studies, the incidence of VTE ranged from 71 to 117 case per 100 000 of the population per year (standardized for age and sex) [##REF##12814979##12##].</p>", "<p>We classified VTE episodes as idiopathic if they occurred in the absence of the following transient risk factors: fracture, surgery, pregnancy or childbirth, or any hospitalization (all occurring in the 3-month period before VTE); cancer in the year before VTE; or use of hormone replacement therapy or oral contraceptives in the 6 months before the date of the VTE episode. We considered all other cases of VTE to be secondary, in a similar manner to other studies [##REF##16961597##1##,##REF##16894454##13##].</p>", "<p>A control cohort was also identified. For this, 50 000 individuals without a VTE diagnosis were randomly sampled from the source population and matched by age, sex and calendar year to the VTE cohort.</p>", "<title>Definition of clinical endpoints</title>", "<title>Myocardial infarction</title>", "<p>Patients with a history of ischaemic heart disease prior to their start date were excluded from both VTE and control cohorts for the analysis of the risk of MI following a VTE, as a history of ischaemic heart disease could mask the influence of VTE on a subsequent MI. Each individual's start date was the date of diagnosis of VTE in the VTE cohort and a random date within the study period for the control cohort. Follow up started a month after the start date in order to exclude patients dying due to the initial episode of VTE. Finally, there were 4890 patients in the VTE cohort and 43 382 patients in the control cohort.</p>", "<p>Patients in both cohorts were then followed up until one of the following endpoints was reached: a recorded code of MI, age of 80 years, death or the end of the study period (31 December 2002). We manually reviewed the computerized profiles of all patients identified with a code of MI, and all deaths. We considered MI cases to be patients whose diagnosis was confirmed by a letter from a consultant cardiologist or on hospital discharge. We also considered as cases: those who died from coronary heart disease (CHD); patients with post-mortem evidence of a recent MI or a recent coronary artery occlusion; patients with ante-mortem evidence of CHD in the absence of another cause of death; and patients for whom CHD was recorded as the underlying cause of death. We did not contact PCPs for further confirmation of the diagnosis of MI, as our experience from a previous study of MI in the GPRD has shown that case ascertainment after manual review of the computerized information supports our case definition in more than 90% of instances [##REF##15197149##14##].</p>", "<title>Mortality</title>", "<p>For the mortality analysis, individuals with a history of ischaemic heart disease prior to the start date were not excluded, but follow-up was started 1 month after the episode of VTE as before. (Data for patients who died within the first month of the VTE diagnosis have been presented elsewhere [##UREF##0##11##].) The VTE cohort consisted of 5801 patients and the control cohort consisted of 48 399 patients. Patients were followed until death, age of 80 years or the end of the study period (31 December 2002).</p>", "<title>Analysis of MI risk</title>", "<title>Relative risk and Kaplan-Meier survival analysis</title>", "<p>Estimates of MI occurrence (with 95% CI) were calculated for the VTE and control cohorts. Individuals alive at the end of the study period were regarded as censored from that date, while individuals with their last practice visit before the end of the study were regarded as censored from the date of their last practice visit. The cumulative hazard of MI was calculated using a Kaplan-Meier survival analysis. Cox proportional hazards regression was used to estimate the relative risk (RR) and 95% confidence intervals of MI in the VTE cohort compared with the control cohort (overall and according to type of VTE). Variables included in the multivariate model were the presence of heart failure and hypertension, as well as frequency-matched variables (age, sex, and calendar year).</p>", "<title>Analysis of mortality</title>", "<p>Deaths from any cause during the follow-up period were analysed using Kaplan-Meier life-tables to compare survival between patients with or without VTE. Cox proportional hazards regression was used to estimate the RR and 95% CI of death in the VTE cohort compared with the comparison cohort. Variables included in the multivariate model were the presence of cancer or heart failure as well as the frequency-matched variables (age, sex and calendar year). All statistical analyses were conducted using STATA (version 8.2; Stata Corporation, College Station, Texas, USA).</p>" ]
[ "<title>Results</title>", "<title>Risk of myocardial infarction</title>", "<p>The incidence and risk of MI were determined for the cohort of VTE patients (n = 4890) and the control cohort (n = 43 382), in order to provide an estimate of the RR of MI following VTE. The age distribution in the two cohorts was successfully matched, with 12.3% of both cohorts aged 20–39 years, 33.3% aged 40–59 years, 27.1% aged 60–69 years and 27.3% aged 70 years or older. During the mean follow-up period of 3 years (range: 3–8 years; median: 3 years), MI occurred in 55 patients from the VTE cohort and 472 patients from the control cohort. Thus the incidence rate (IR) of MI per 1000 person-years was 4.1 (95% CI: 3.1–5.3) for the VTE cohort and 3.5 (95% CI: 3.2–3.8) for the control cohort. The difference between the two groups was not significant, as shown by the RR of MI (RR: 1.2; 95% CI: 0.9–1.6). The incidence rates of MI were within the range reported in previous population-based studies in the UK (2.73–8.23 and 0.66–2.56 per 1000 person-years in men and women, respectively [##REF##9764057##15##,##REF##8026046##16##]).</p>", "<p>The IR of MI increased with age in both cohorts (see Figure ##FIG##0##1##). Although the IR of MI was numerically greater in the VTE cohort than the control cohort for those aged 60–69 years or at least 70 years, determination of RR indicated that these differences were not significant (RR: 1.3; 95% CI: 0.8–2.0 for patients aged 60–69 years, RR: 1.4; 95% CI: 0.9–2.0 for those aged 70 years or more). The cumulative proportion of patients diagnosed with MI over time for the two cohorts is shown in Figure ##FIG##1##2##. This analysis showed that the cumulative proportion of patients with MI was slightly greater in the VTE cohort than the control cohort in the first year, but this difference narrowed in years 2–4. Indeed, the increased risk of MI in the VTE cohort compared with the control cohort in the first year was of borderline significance (adjusted RR: 1.6; 95% CI: 1.0–2.5). After the first year, the adjusted RR of MI associated with VTE fell to 1.0 (95% CI: 0.7–1.5).</p>", "<p>Further analysis of the risk of MI in the VTE cohort compared with the control cohort for the first year showed that the excess risk was of borderline significance in patients aged between 60 and 69 years (RR: 2.0; 95% CI: 1.0–4.0) and insignificant in the younger age group (40–59 years of age, RR: 0.70; 95% CI: 0.1–5.3) and older age group (≥70 years, RR: 1.5; 95% CI: 0.8–2.9). The risk of MI in the VTE cohort, however, was similar for patients who had had DVT (n = 33) and for patients who had had PE with or without DVT (n = 22) (Table ##TAB##0##1##). The risk of MI was also similar regardless of whether the VTE was idiopathic or secondary (Table ##TAB##0##1##).</p>", "<title>Mortality</title>", "<p>During the total follow-up period of 8 years, 3088 patients died: 2266 of 48 399 in the control cohort and 822 of 5801 in the VTE cohort. Overall mortality was, therefore, higher in the VTE cohort (14.2%; 49.5 per 1000 person-years) than the control cohort (4.7%; 14.5 per 1000 person-years) (Figure ##FIG##2##3##). After adjustment for the presence of cancer, ischaemic heart disease and heart failure, the RR of death during this 8-year period in the VTE cohort compared with the control cohort was 2.4 (95% CI: 2.2–2.6). When we re-analyzed the mortality findings after excluding patients with a history of ischaemic heart disease to allow comparison with the incidence of MI in these cohorts, the mortality rates dropped to 47.4 and 12.1 per 1000 person-years in the VTE and control cohorts, respectively.</p>", "<p>The increased risk of death associated with VTE was much greater in the first year after VTE diagnosis (RR: 3.8; 95% CI: 3.4–4.3) than in subsequent years (RR: 1.6; 95% CI: 1.8–1.4) (Table ##TAB##1##2##). The risk of death in the first year was also greater in patients with a diagnosis of DVT (RR: 4.4; 95% CI: 3.9–5.1) than in those with a diagnosis of PE (RR: 2.9; 95% CI: 2.5–3.5) (Table ##TAB##1##2##). Compared with the control group, mortality was increased in patients aged 20–59 years (RR: 10.5; 95% CI: 7.3–15.1) and at least 60 years (RR: 3.1; 95% CI: 2.7–3.6).</p>", "<p>Causes of death for patients dying within the first year of follow up are shown in Table ##TAB##2##3##. The main cause of death was cancer in both groups, but the percentage of patients dying from cancer was almost two-fold higher in the VTE group (56.0 vs. 29.6%). Conversely, the proportion of patients dying from CHD was approximately two-fold greater in the control cohort than the VTE cohort.</p>" ]
[ "<title>Discussion</title>", "<p>The results of this study suggest that a first VTE episode does not increase the risk of MI. These results were similar, regardless of VTE type (DVT or PE), or whether VTE was idiopathic or secondary. However, while we did not observe a significant increase in the risk of MI following VTE (RR: 1.2 with a lower 95% CI below 1.0), the upper 95% confidence interval of 1.6 means that an increased risk of MI following VTE cannot be safely excluded on the basis of our results.</p>", "<p>These results contrast with several, much smaller, studies that have pointed towards an association between VTE and thromboembolic arterial disease. Case-control studies have reported a significantly higher prevalence of carotid plaques in patients with DVT (n = 299) [##REF##12686699##17##] and a significantly higher incidence of coronary artery calcification in patients with idiopathic VTE (n = 89) compared with matched controls lacking VTE [##REF##15939424##18##]. Moreover, Bova and colleagues found a significantly higher risk of arterial events in 151 patients with VTE compared with 151 controls (HR 2.9; 95% CI: 1.1–7.6) [##REF##16894454##13##]. Recently, a cohort study reported the risk of MI to be increased by 60% in the first year after an episode of VTE, with a progressive decline during the subsequent 20 years [##REF##18037081##19##]. As the present study includes nearly 5000 patients with VTE and a control cohort without prior VTE (n = 43 382), our conclusion that VTE is not associated with a major increased risk of subsequent MI is likely to be authoritative.</p>", "<p>The conclusions of our study contradict those of Bova and colleagues [##REF##16894454##13##], but this may reflect, in part, differences in the criteria used to identify patients with VTE and the arterial events used as endpoints. Moreover the latter study was comparatively small, though to our knowledge it is the only study other than ours and the aforementioned study by Hong et al. in 89 patients with VTE [##REF##15939424##18##] to compare patients with VTE with controls taken from a general population without VTE. Other studies investigating VTE as a risk factor for cardiovascular events did not include a control group without VTE. For example, two studies compared patients with idiopathic VTE with patients diagnosed with secondary VTE [##REF##16961597##1##,##REF##15615803##2##], and one investigated the long-term effects of 6 weeks vs 6 months of anticoagulation treatment on patients with VTE [##REF##16634738##3##]. Given the large number of patients with first VTE (n = 4890) in our study, it seems unlikely that VTE is associated with a subsequent MI for patients without a history of ischaemic heart disease.</p>", "<p>In contrast to the conflicting results surrounding the association between DVT and cardiovascular disease, it is well recognised that there is an increased mortality after VTE [##REF##16634738##3##]. The present study showed that a first diagnosis of VTE was associated with significantly increased mortality in those who survived the first month following the VTE event, particularly in the subsequent 11 months after VTE diagnosis. This risk was greatest in patients with DVT rather than PE, and in younger patients rather than the elderly. Previous studies have shown that mortality is highest immediately after VTE in patients with PE, and then decreases over the following year [##REF##10074952##20##,##REF##10077516##21##]. However, to our knowledge, few studies have investigated mortality in patients for prolonged periods after VTE. Our study therefore provides important data on long-term mortality following a VTE event in a large number of patients (n = 5801). These findings complement those from a previous study in the same cohort of patients with VTE which reported patient mortality in the first month after VTE [##UREF##0##11##].</p>", "<p>The present study showed that, during the first year after VTE, cancer was the most frequent cause of death in VTE patients surviving the first month. Cancer is a well-known risk factor for VTE and death from VTE [##REF##16634738##3##,##UREF##0##11##,##REF##12885687##22##, ####REF##8644983##23##, ##REF##12814981##24####12814981##24##], with the mortality seemingly independent of whether the cancer diagnosis is made before or after VTE diagnosis [##REF##12814981##24##]. As such, cancer (and death because of cancer) is likely to be more common in the VTE cohort. Secondly, the data for this period may be skewed somewhat, as the 1-month period after VTE is omitted. During this 1-month period there were more deaths due to cardiovascular causes than cancer, as has been reported previously [##UREF##0##11##]. Deaths during the 1-month period after VTE were largely due to PE, rather than DVT, with the 1-month death rate of 1.4% after an episode of DVT and 22.6% after PE with or without DVT [##UREF##0##11##].</p>", "<p>The 8-year risk of death in patients surviving the first month after VTE is higher than that in patients without VTE, even after adjustment for cancer, heart failure and ischaemic heart disease. Because of the likely multiple comorbid diseases and risk factors in the predominantly elderly population with VTE in this study, we should be cautious about the reasons for this excess mortality. Thus, although the 8-year risk of death in patients surviving 1-month after VTE is higher than that in patients without VTE, it is difficult to say whether this increased risk arises from VTE itself or other underlying conditions or risk factors.</p>", "<p>This study has a number of important strengths. Patients were drawn from a large primary care database representative of the UK population and spanning a wide age range, and representing a study population that is an order of magnitude larger than previous studies investigating potential risk factors and complications of VTE [##REF##16894454##13##,##REF##12686699##17##,##REF##15939424##18##,##REF##16836659##25##, ####REF##9039882##26##, ##REF##12020191##27##, ##REF##16961598##28##, ##REF##16848878##29####16848878##29##]. Cases of VTE were classified according to whether they were DVT or PE, and also if they were idiopathic or secondary, allowing analysis of possible differences between the types of VTE. VTE cases in a random sample were also identified and validated with a confirmation rate of over 94% [##UREF##0##11##]. We excluded information bias since information was collected in the same manner for VTE and comparison patients, and information collection in VTE cases was blinded to the later occurrence of MI. Using MI, a major clinical event, as the cardiovascular endpoint was advantageous as previous studies using the GPRD have shown the validity of using codes for MI [##REF##15197149##14##,##REF##8726591##30##]. Limitations of the study include the fact that it only involved a UK population sample and that patients included in the analysis of MI risk may have had subclinical cardiovascular disease prior to the start date of the study. Patients may also have had VTE or MI prior to enrolment in the GPRD, which would not have been systematically recorded. In addition, the limited number of MI cases in the VTE cohort (55) means that the study is not powered to detect modest but potentially clinically important elevations in the incidence rate of MI.</p>" ]
[ "<title>Conclusion</title>", "<p>In conclusion, our data show that a diagnosis of VTE does not increase the risk of MI in comparison with a control cohort drawn from the general population of the UK. There was some suggestion that the risk of MI may be increased in elderly patients with VTE during the first year following the diagnosis of VTE, but this increase was not statistically significant. While the risk of MI is not increased, patients who survive the first month after a VTE event were significantly more likely than controls to die in the subsequent 8 years. This increased risk is particularly marked over the first year following a VTE. Further studies may thus be required to investigate the causes of death following VTE in more detail, and thus determine how best to reduce mortality in patients after a first VTE event.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Venous thromboembolism (VTE) and thromboembolic arterial diseases are usually considered to be distinct entities, but there is evidence to suggest that these disorders may be linked. The aim of this study was to determine whether a diagnosis of VTE increases the long-term risk of myocardial infarction (MI).</p>", "<title>Methods</title>", "<p>The incidence rate (IR) and relative risk (RR) of MI in a cohort of patients with a diagnosis of VTE (n = 4890) compared with that of a control cohort without prior VTE (n = 43 382) were evaluated in the UK General Practice Research Database (GPRD). Death during follow-up was also determined. Patients were followed for up to 8 years (mean of 3 years).</p>", "<title>Results</title>", "<p>The IR of MI per 1000 person-years was 4.1 (95% CI: 3.1–5.3) for the VTE cohort and 3.5 (95% CI: 3.2–3.8) for the control cohort. The IR of MI was highest in the first year after the VTE episode, but overall differences between the two cohorts were not significant (RR of MI associated with VTE: 1.2; 95% CI: 0.9–1.6). The risk of death was higher in the VTE cohort than the control cohort, even after adjustment for cancer, heart failure and ischaemic heart disease (RR: 2.4; 95% CI: 2.2–2.6), particularly during the first year after VTE (RR: 3.8; 95% CI: 3.4–4.3).</p>", "<title>Conclusion</title>", "<p>A VTE episode does not significantly increase the risk of MI, but does increase the risk of death, particularly in the first year following VTE diagnosis.</p>" ]
[ "<title>Competing interests</title>", "<p>This study was funded by a research grant from AstraZeneca R&amp;D Mölndal, Sweden. SJ is an employee of AstraZeneca R&amp;D Mölndal, Sweden, and MAW was an employee of AstraZeneca R&amp;D Mölndal, Sweden at the time of the study. The corresponding author had full access to all the data in the study, and takes responsibility for the integrity of the data and the accuracy of the data analysis.</p>", "<title>Authors' contributions</title>", "<p>CH participated in the design of the study, carried out the statistical analysis, interpreted the data and helped to draft the manuscript. SJ and MAW participated in the design of the study and interpretation of the data. LAGR conceived of the study, participated in its design, analysis and interpretation and helped to draft the manuscript. All authors critically revised the manuscript for important intellectual content, and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We thank the primary care physicians participating in the General Practice Research Database for their excellent collaboration. We also thank Christopher Winchester, DPhil, from Oxford PharmaGenesis Ltd, who provided editorial assistance, funded by AstraZeneca R&amp;D Mölndal, Sweden. This study was funded by AstraZeneca R&amp;D Mölndal, Sweden.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Incidence rate (IR) and relative risk (RR) of myocardial infarction in the venous thromboembolism (VTE) cohort and control cohort according to age</bold>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Cumulative proportion of patients diagnosed with myocardial infarction (MI) in the venous thromboembolism (VTE) cohort and control cohort over time (log-rank test &gt; 0.05).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Kaplan-Meier survival curves for venous thromboembolism (VTE) cohort and control cohort (*log-rank = 1005.25; p &lt; 0.005).</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Incidence rate and relative risk of myocardial infarction in the venous thromboembolism cohort compared with the control cohort in the first year of follow up.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Myocardial</bold><break/><bold>infarction</bold><break/><bold> cases (n = 159)</bold></td><td align=\"center\"><bold>Incidence</bold><break/><bold>rate per 1000</bold><break/><bold> person-years</bold></td><td align=\"center\"><bold>Relative risk</bold><break/><bold> (95% CI)<sup>a</sup></bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Control cohort (n = 43 382)</bold></td><td align=\"center\">136</td><td align=\"center\">3.4 (2.9–4.0)</td><td align=\"center\">1</td></tr><tr><td align=\"left\"><bold>Venous thromboembolism cohort (n = 4890)</bold></td><td align=\"center\">23</td><td/><td/></tr><tr><td align=\"left\">Deep vein thrombosis</td><td align=\"center\">15</td><td align=\"center\">5.7 (3.4–9.4)</td><td align=\"center\">1.7 (1.0–2.9)</td></tr><tr><td align=\"left\">Pulmonary embolism</td><td align=\"center\">8</td><td align=\"center\">4.8 (2.4–9.6)</td><td align=\"center\">1.5 (0.7–3.1)</td></tr><tr><td align=\"left\">Secondary venous thromboembolism</td><td align=\"center\">13</td><td align=\"center\">5.2 (3.0–8.9)</td><td align=\"center\">1.6 (1.0–2.8)</td></tr><tr><td align=\"left\">Idiopathic venous thromboembolism</td><td align=\"center\">10</td><td align=\"center\">5.6 (3.0–10.4)</td><td align=\"center\">1.6 (0.8–3.2)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Mortality and relative risk of MI in the venous thromboembolism cohort compared with the control cohort, according to year of follow up.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"3\"><bold>First year</bold></td><td align=\"center\" colspan=\"3\"><bold>After 1 year</bold></td></tr></thead><tbody><tr><td/><td align=\"center\"><bold>Deaths</bold><break/><bold> (n = 1216)</bold></td><td align=\"center\"><bold>Mortality/1000</bold><break/><bold>person-years</bold><break/><bold> (95% CI)</bold></td><td align=\"center\"><bold>RR (95%</bold><break/><bold> CI)</bold><sup>a</sup></td><td align=\"center\"><bold>Deaths</bold><break/><bold> (n = 1872)</bold></td><td align=\"center\"><bold>Mortality/1000</bold><break/><bold>person-years</bold><break/><bold> (95% CI)</bold></td><td align=\"center\"><bold>RR (95% CI)</bold><sup>a</sup></td></tr><tr><td colspan=\"7\"><hr/></td></tr><tr><td align=\"left\"><bold>Control cohort</bold></td><td align=\"center\">716</td><td align=\"center\">16.0 (14.9–17.1)</td><td align=\"center\">1</td><td align=\"center\">1550</td><td align=\"center\">13.9 (13.2–14.6)</td><td align=\"center\">1</td></tr><tr><td align=\"left\"><bold>All VTE cases</bold></td><td align=\"center\">500</td><td align=\"center\">97.8 (89.6–106.8)</td><td align=\"center\">3.8 (3.4–4.3)</td><td align=\"center\">322</td><td align=\"center\">28.1 (25.2–31.3)</td><td align=\"center\">1.6 (1.4–1.8)</td></tr><tr><td align=\"left\"> DVT</td><td align=\"center\">344</td><td align=\"center\">113.4 (102.0–126.1)</td><td align=\"center\">4.4 (3.9–5.1)</td><td align=\"center\">186</td><td align=\"center\">27.7 (24.0–32.0)</td><td align=\"center\">1.6 (1.4–1.9)</td></tr><tr><td align=\"left\"> PE</td><td align=\"center\">156</td><td align=\"center\">75.1 (64.2–87.9)</td><td align=\"center\">2.9 (2.5–3.5)</td><td align=\"center\">136</td><td align=\"center\">28.7 (24.2–33.9)</td><td align=\"center\">1.6 (1.3–1.9)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Distribution of causes of death in patients dying during the 11 months of follow up in patients with VTE surviving the first month after VTE diagnosis in comparison with control cohort.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Cause of death</bold></td><td align=\"center\"><bold>VTE cohort (n = 500)</bold><break/><bold> n (%)</bold></td><td align=\"center\"><bold>Control cohort (n = 716)</bold><break/><bold> n (%)</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Coronary heart disease (CHD)</bold></td><td align=\"center\">49 (9.8)</td><td align=\"center\">153 (21.4)</td></tr><tr><td align=\"left\"><bold>Other cardiovascular and cerebrovascular diseases</bold></td><td align=\"center\">63 (12.6)</td><td align=\"center\">107 (14.9)</td></tr><tr><td align=\"left\"><bold>Cancer</bold></td><td align=\"center\">280 (56.0)</td><td align=\"center\">212 (29.6)</td></tr><tr><td align=\"left\"><bold>Other non-cardiovascular diseases (respiratory, digestive, urinary, other)</bold></td><td align=\"center\">47 (9.4)</td><td align=\"center\">129 (18.0)</td></tr><tr><td align=\"left\"><bold>Unknown</bold></td><td align=\"center\">61 (12.2)</td><td align=\"center\">115 (16.1)</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p><sup>a</sup>Adjusted relative risk derived from Cox regression models including sex, age, calendar year, heart failure, hypertension, and smoking.</p></table-wrap-foot>", "<table-wrap-foot><p>CI, confidence interval; DVT, deep vein thrombosis; PE, pulmonary embolism; RR, relative risk; VTE, venous thromboembolism.</p><p><sup>a</sup>Relative risk estimated by Cox regression adjusted for age, sex, calendar year, consultations in the previous year, cancer, heart failure and ischaemic heart disease.</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1477-9560-6-10-1\"/>", "<graphic xlink:href=\"1477-9560-6-10-2\"/>", "<graphic xlink:href=\"1477-9560-6-10-3\"/>" ]
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[{"surname": ["Huerta", "Wallander", "Johansson", "Garcia Rodriguez"], "given-names": ["C", "MA", "S", "LA"], "article-title": ["Natural history of venous thromboembolism diagnosed in UK primary care"], "source": ["Arch Int Med"]}]
{ "acronym": [], "definition": [] }
30
CC BY
no
2022-01-12 14:47:28
Thromb J. 2008 Aug 18; 6:10
oa_package/11/28/PMC2531082.tar.gz
PMC2531083
18680575
[ "<title>Introduction</title>", "<p>The annual Breastfeeding and Feminism Symposia were initiated with the aim to reposition breastfeeding as a valued part of women's reproductive rights and lives. Each year, the effort is made to raise the profile of breastfeeding within women's advocacy and feminist studies' communities. These symposia are also designed to increase recognition among breastfeeding supporters that breastfeeding promotion could receive more socio-political support by partnering with those concerned with women's reproductive health, rights and justice, women's economic advancement, and the elimination of social, economic and health inequities.</p>", "<p>The Third Annual Breastfeeding and Feminism Symposium, September 24 and 25, 2007, was held in Chapel Hill, North Carolina and was co-hosted by the Center for Women's Health and Wellness, University of North Carolina, Greensboro (founding organization), and Center for Infant and Young Child Feeding and Care, School of Public Health, University of North Carolina at Chapel Hill. This event included both presentations and working groups to build dialogue and increase communications between and among feminists, policymakers, researchers, breastfeeding advocates and practitioners to help promote breastfeeding as a woman's reproductive health, rights and justice concern.</p>", "<p>Although research on breastfeeding has established that it is a maternal and child health imperative, yielding optimal short and long term health outcomes for both mother and child, breastfeeding is not fully recognized as a feminist, women's rights or women's reproductive health concern. Most second wave feminist scholarship and activism has presented breastfeeding as an \"option\" or a \"choice\" that is generally presented as not very different from formula feeding. A limited number within the feminist community has recognized breastfeeding as a women's health issue or a reproductive right. In fact, global support for women's rights generally ignores the rights and importance associated with all of women's roles as mothers, opting instead to concentrate primarily on other important issues such as employment and reproductive freedom.</p>", "<p>The 2007 symposium on Breastfeeding and Feminism focused on reproductive health, rights and justice, and was intended to stimulate consideration and identification of areas of mutual interest across diverse groups, including feminists, health workers, public health planners, community members, mothers and breastfeeding specialists. It was also designed to serve as a catalyst to strengthen coalitions and synergy to further breastfeeding as a reproductive right, and to create the first steps in action planning.</p>", "<p>The presentations and working group discussions were based on the following principles:</p>", "<p>• Breastfeeding is a social <italic>and </italic>biological process wherein women must have the right of self-determination;</p>", "<p>• Breastfeeding is a maternal and child health imperative and reproductive right;</p>", "<p>• It is important to re-orient the paradigm from the current view that breastfeeding is a \"lifestyle choice,\" to a paradigm that views breastfeeding as a reproductive health, rights and social justice issue so as to ensure the social, economic and political conditions necessary to promote success;</p>", "<p>• Women's decisions to breastfeed should not result in the loss of their economic security or any rights or privileges to which they are otherwise entitled.</p>", "<p>The nine articles presented in this thematic series were selected by the journal editors from among the proceedings of this symposium and workshop, and cover the major areas of discussion. This series of articles provides a basis to understand the momentum that evolved among participants, and are presented to stimulate involvement among individuals and organizations not in attendance. The full proceedings of the symposium and working sessions are available for citation on the websites of the co-hosts [##UREF##0##1##,##UREF##1##2##].</p>" ]
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[ "<title>Outcomes and actions: transdisciplinary identification of common goals and multi-disciplinary discussion of feasible actions</title>", "<p>Two working group sessions were held, the first to explore areas of synergy and the second to begin the development of strategies for action. The first session grouped participants across disciplines, but with an interest in a) reproductive health, b) reproductive rights, or c) reproductive justice. During this session, groups were tasked with discovering areas of synergy between or among: a) breastfeeding, b) feminism, and c) reproductive health, rights or justice, in order to identify goals and areas of need.</p>", "<p>Working group outcomes and other discussions at the conference indicate that participants recognize the following goals:</p>", "<p>1) the economic, political and cultural connections between women's rights to have children, <italic>and </italic>their rights not to have children;</p>", "<p>2) that women place importance and value on being able to mother in ways that are consistent with their own values, <italic>and </italic>on being creatively and productively engaged in the labor force; and</p>", "<p>3) that the decision to breastfeed is not as yet a realistic \"choice\" for many women if not supported by policies and programs that provide <italic>all </italic>women, regardless of their socio-economic status, with education, opportunity, health system and socio-political support, and control over their bodies and lives.</p>", "<p>Conference participants identified the need for political, health care and cultural changes that support women's full participation in society as reproductive and productive beings, increase the value placed on women's reproductive abilities and secure their rights across a reproductive continuum that includes a full range of family planning services, clinical abortion, pregnancy and birth treated as part of a health continuum and breastfeeding.</p>", "<p>Four areas related to women's reproductive health emerged in this transdisciplinary discussion that could contribute toward these goals. These include the need to:</p>", "<p>(1) create a mother and breastfeeding friendly health care system that re-enforces breastfeeding as the normative approach to infant feeding;</p>", "<p>(2) train all health workers to support reproduction as health, not illness;</p>", "<p>(3) secure adequate regulation of infant formula marketing to health workers and to the public, and of pharmaceutical labeling as concerns the mother-child dyad; and</p>", "<p>(4) promote a culture and civil society that value women as whole beings.</p>", "<p>Four areas emerged from discussion that relates to women's reproductive rights, including the need to:</p>", "<p>(1) recognize the importance and value of women's rights across the reproductive continuum;</p>", "<p>(2) recognize the importance and value of women's reproductive and productive roles;</p>", "<p>(3) create mother-friendly workplaces, including paid maternity leave with guarantee of return; and</p>", "<p>(4) engage the US as a global partner in human rights efforts.</p>", "<p>The two identified areas related to women's reproductive justice were the need to: (1) secure economic justice for women; and (2) secure racial and ethnic equality.</p>", "<p>These areas of need that emerged as a result of transdisciplinary discussion, rather than sector-based discussion, serve as a starting point for formation of partnerships and coalitions for action to work together toward common goals.</p>", "<p>Participants self-selected by professional discipline into one of three groupings for the second set of working groups: a) academic/researchers, b) policy/legal expertise, or c) practitioners/service providers. These groups started with a brief review of the outcomes of the earlier discussions and went on to identify short and long term strategies for action.</p>", "<p>The working group participants identified numerous strategic actions intended to create change at different levels of the socio-ecological model. (See Labbok article in this thematic series for a discussion of the socio-ecological model).</p>", "<p>• <bold>Individual/family level: </bold>Support recognition of the cost-benefits of mother/child attentive reproductive health support and breastfeeding for the family, as well as for the mother and child.</p>", "<p>• <bold>Community/cultural level: </bold>The suggested actions include: (1) engage in broad-based breastfeeding education (targeting the public, students, clinicians, employers, unions and policymakers); (2) raise public awareness of infant formula companies' marketing strategies and influence on all sectors; (3) increasing all women's access to economic resources and opportunities to achieve reproductive goals based on unbiased information.</p>", "<p>• <bold>Organizational level: </bold>Overall, everyone interested in reproductive health, rights or justice must work together to secure the respect, rights and opportunities for women from \"the birthplace to the workplace\". Such actions related to health care organizations include the need to: (1) support the Baby Friendly Hospital Initiative; (2) improve women's access to midwifery care and other sources of woman-centered, holistic, integrative models of care. Necessary actions related to workplaces include: (1) secure workplace support including: paid maternity leave; onsite child care; flexible hours and home work; mothers' rooms; and separate paid infant feeding breaks in addition to other breaks; (2) dissemination of available documents related to costs and benefits of breastfeeding, and the costs and risks of alternate feeding, to appropriate policymakers, employers, consumers (e.g. the dissemination of the <italic>Business Case for Breastfeeding </italic>by the US Department of Health and Human Services). Actions suggested for universities and other research settings include: (1) reducing academic and professional \"silos\" in favor of transdisciplinary identification of problems and gaps, multi-disciplinary research to address these issues, evidence creation through translational and applied studies, knowledge dissemination, and professional practice; (2) examining and increasing understanding of the \"lived realities\" of diverse populations of women as related to mothering, breastfeeding and employment; (3) developing a scientific working group to clarify defensible, ethical and safe methods for research on the effects of medications on lactation, and to further understand the risks of not breastfeeding; and (4) supporting family practice within the health fields to increase attention to the reality of the mother/child dyad.</p>", "<p>• <bold>Policy level: </bold>There is a need to seek passage of currently proposed legislation in the U.S. such as the Breastfeeding Promotion Act, the Global Childhood Survival Act and the Emergency Contraceptive Education Act, and to work toward the development and passage of legislation to reduce aggressive and misleading infant formula industry marketing, to regulate and support growth of donor milk banking, to create equitable health insurance that is not employment-based, and to expand the Family and Medical Leave Act to address all maternal/child needs. To be part of the global community, work toward the US ratification of United Nations Convention on the Rights of the Child and the Convention to Eliminate Discrimination Against Women is needed.</p>", "<p>Table ##TAB##0##1## summarizes the areas of synergy and strategies for action that emerged from these working sessions.</p>" ]
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>The annual Breastfeeding and Feminism Symposia aim to reposition breastfeeding as a valued part of women's (re)productive lives and rights. The symposia are designed to raise the profile of breastfeeding within the women's advocacy and feminist studies' communities, and to increase recognition among breastfeeding supporters that breastfeeding promotion could receive more socio-political support by partnering with those concerned with women's reproductive health, rights and justice, women's economic advancement, and the elimination of social, economic and health inequities. The third symposium (2007) sought to build dialogue and increase communications between and among these diverse communities. The nine articles presented in this thematic series were selected by the journal editors, and represent the core discussions at the symposium. This editorial presents the areas of synergy and strategies for action that emerged from the discussions. These strategies and this thematic issue are intended to reassert the momentum that evolved among participants, and to stimulate involvement among individuals and organizations not in attendance in promoting breastfeeding as a women's reproductive health, rights and justice concern.</p>" ]
[ "<title>Tracking progress</title>", "<p>In the months that followed the Breastfeeding and Feminism Symposium, organizers continued to hear from enthusiastic participants. Some emails came simply in the form of high praise for the event, and expressions of high hopes for future partnerships, while others were searching for additional information to bring back to their organizations and communities. Many reported on progress made toward the goals and action steps identified at the meeting. Still others reported on progress made toward new objectives inspired by the ideas exchanged and the group work during the symposium.</p>", "<p>The following is a brief summary of the reports received by conference organizers:</p>", "<title>Actions taken in support of the individual and family</title>", "<p>• Local advocates and professionals working in family planning, pregnancy, childbirth, and breastfeeding gathered in Chapel Hill to celebrate the common location of their individual \"issues\" on the reproductive continuum. A listserv was created for sharing information between groups, in hopes of \"breaking down silos.\"</p>", "<p>• Many Lactation Consultants and nurses expressed a heightened awareness of the barriers to breastfeeding, leading them to be more supportive of mothers trying to overcome obstacles to breastfeeding, predominantly by counseling on possible barriers that they may face, and by supporting the actions needed and the strength and self-efficacy to overcome them.</p>", "<title>Actions taken in support of the community/societal/cultural level change</title>", "<title>Academic</title>", "<p>• Four participants created and presented, \"Disturbing the 'Public': Risks, Rights, Breastfeeding, and Feminism\" at the Southeastern Women's Studies Association at University of North Carolina, Charlotte.</p>", "<title>Health care</title>", "<p>• Two hospitals in North Carolina report being \"close to BFHI certification.\"</p>", "<p>• The North Carolina Breastfeeding Coalition launched a campaign to reduce infant formula companies' advertising through maternity centers. As of July 1, 2008, five hospitals will be acknowledged with \"Golden Bow Awards\" for eliminating all such advertising.</p>", "<p>• The Durham County Health Department has enhanced its programs to include breastfeeding goal setting and additional support for breastfeeding pairs with the objective of increasing breastfeeding rates among minority and low-income clients.</p>", "<title>Workplace</title>", "<p>• Two breakfast table discussions about employer recognition awards (ERAs) were held at the National Conference of State Breastfeeding Coalitions in January 2008. This was seen as an opportunity to find out what State Breastfeeding Coalitions are doing in this area, with the hope of developing replicable models for use in other states.</p>", "<p>• Participants participated in evaluating \"The Business Case for Breastfeeding,\" a toolkit developed by the Maternal and Child Health Bureau for training breastfeeding advocates to reach out to employers in support of women in the workplace.</p>", "<title>Actions taken in support of governmental/policy change</title>", "<p>• One group provided ongoing consultation to a state task force to ensure inclusion of breastfeeding in legislated Safe Sleep messaging in North Carolina.</p>", "<p>• Many individuals are engaging in grass roots advocacy for Carolyn Maloney's Family and Medical Leave Act.</p>", "<p>• The New Jersey Breastfeeding Task Force is participating in the Time to Care Coalition, a broad-based coalition campaigning for The Family and Medical Leave Act.</p>", "<p>• The New Jersey Breastfeeding Task Force is partnering with the Rutgers Center for Women and Work, New Jersey National Organization for Women (NOW) and Mothering-NOW, and mothers' organizations such as \"Mothers and More\" to advance The Family and Medical Leave Act.</p>", "<p>• Individuals and organizations in North Carolina, Pennsylvania, and Vermont report working on creating and advancing state-level legislation to support, promote and protect breastfeeding.</p>", "<p>• For the 2008 meeting of the UN's Commission on the Status of Women (CSW), UN Breastfeeding Action Team (UNBAT), the UN breastfeeding advocacy team made up of representatives from La Leche League International, International Lactation Consultants Association, Academy of Breastfeeding Medicine and World Alliance for Breastfeeding Action, prepared a statement on the theme for 2008, \"Financing for gender equality and the empowerment of women\". The purpose of the statement was to demonstrate, in a one-page format, the value of breastfeeding as food, health care and childcare. It was entitled, \"The Breastfeeding Budget,\" and was based on the publications of Australian economist Julie Smith and her colleagues. In order to increase attention to the statement, UNBAT placed an advertisement about it in the CSW program book.</p>", "<p>This list of actions is quite impressive, both in its content, and in the fact that it reflects a commitment to synergy across the reproductive health continuum and among the individuals and groups who came together at the Breastfeeding and Feminism Symposium. While Symposium objectives were achieved, there is still much work to be done. Plans are underway for the Fourth Breastfeeding and Feminism Symposium to be held in March 2009 at University of North Carolina, Greensboro, where our theme will be \"sustaining breastfeeding from the birthplace to the workplace in support of healthy mothers, healthy babies and healthy environments.\"</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>All three authors shared in conceptualizing the themes and working group agendae for the symposium. PHS was responsible for writing the abstract and introduction, and created Table ##TAB##0##1##. ECT organized and reported on working group outcomes and progress reports from symposium participants. MHL edited the near-complete draft for clarity and uniformity of style.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The organizers of the Breastfeeding and Feminism Symposium \"A focus on reproductive health, rights and justice\" were the Center for Infant and Young Child Feeding and Care at the UNC School of Public Health and the Center for Women's Health and Wellness at UNC Greensboro. Additional funding was provided by the United States Department of Health and Human Services, Office of Women's Health.</p>" ]
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[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Areas of synergy and strategies for action to improve women's reproductive health, rights and justice</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"center\" colspan=\"4\"><bold><italic>Seeking Synergy through Transdisciplinary Discussion of Gaps and Needs: </italic>Overarching themes that emerged, connecting breastfeeding to women's reproductive health, rights and justice</bold></td></tr></thead><tbody><tr><td align=\"left\" colspan=\"4\">• There are the economic, political and cultural connections between women's rights to have children, <italic>and </italic>their rights not to have children</td></tr><tr><td align=\"left\" colspan=\"4\">• Women place importance and value on being able to mother in ways that are consistent with their own values, <italic>and </italic>on being creatively and productively engaged in the labor force</td></tr><tr><td align=\"left\" colspan=\"4\">• The decision to breastfeed is not a \"real choice\" for many women if not supported by policies and programs that provide <italic>all </italic>women, regardless of their social position, with education, opportunity, and control over their bodies and lives.</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"center\" colspan=\"4\"><bold><italic>Goals and Areas of Need</italic></bold></td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\"><bold><underline>Reproductive Health</underline></bold></td><td align=\"left\" colspan=\"2\"><bold><underline>Reproductive Rights</underline></bold></td><td align=\"left\"><bold><underline>Reproductive Justice</underline></bold></td></tr><tr><td align=\"left\">• Create a mother-friendly health care system</td><td align=\"left\" colspan=\"2\">• Recognize the importance and value of women's reproductive and productive roles.</td><td align=\"left\">• Secure economic justice for women</td></tr><tr><td align=\"left\">• Value women as whole beings</td><td align=\"left\" colspan=\"2\">• Create a mother-friendly workplace</td><td align=\"left\">• Secure racial and ethnic equality</td></tr><tr><td/><td align=\"left\" colspan=\"2\">• Secure better governmental oversight over pharmaceutical labeling and infant formula</td><td/></tr><tr><td/><td align=\"left\" colspan=\"2\">• Engage the US as a global partner in human rights efforts</td><td/></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"center\" colspan=\"4\"><bold><italic>Strategies for Action across the Socio-ecological Levels</italic></bold></td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\"><bold><italic>Individual/Family</italic></bold></td><td align=\"center\"><bold><italic>Socio-Cultural</italic></bold></td><td align=\"center\"><bold><italic>Organizational, by Sector</italic></bold></td><td align=\"center\"><bold><italic>Policy</italic></bold></td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\">• Support recognition by individuals and by families of the costs and benefits of mother/child attentive support throughout the reproductive health continuum.</td><td align=\"left\">• Provide broad based education on benefits and practice of breastfeeding for:</td><td align=\"left\">Health care system:</td><td align=\"left\">Initiate and approve legislation to enable breastfeeding, including:</td></tr><tr><td align=\"left\">• Ensure that families understand the potential impact of less than optimal infant feeding on the health and welfare of mothers, children and families.</td><td align=\"left\">• Public</td><td align=\"left\">• Baby Friendly Hospital Initiative</td><td align=\"left\">• Breastfeeding promotion and incentives</td></tr><tr><td/><td align=\"left\">• Clinicians</td><td align=\"left\">• Access to midwives and holistic care</td><td align=\"left\">• Family planning access</td></tr><tr><td/><td align=\"left\">• Employers</td><td align=\"left\">Worksites:</td><td align=\"left\">• Donor milk banking</td></tr><tr><td/><td align=\"left\">• Unions</td><td align=\"left\">• Maternity leave</td><td align=\"left\">• Health insurance coverage for lactation services</td></tr><tr><td/><td align=\"left\">• Policymakers</td><td align=\"left\">• Onsite child care</td><td align=\"left\">• Paid maternity, family and medical leave</td></tr><tr><td/><td align=\"left\">• Raise public awareness of infant formula companies' practices</td><td align=\"left\">• Support for breastfeeding and pumping</td><td align=\"left\">Encourage US Government ratification of UN Conventions:</td></tr><tr><td/><td align=\"left\">• Increase women's access to resources and opportunities</td><td align=\"left\">• Flexible hours and home work</td><td align=\"left\">• Convention on the Rights of the Child</td></tr><tr><td/><td align=\"left\">• Continue Breastfeeding and Feminism Symposia</td><td align=\"left\">• Part-time work with benefits</td><td align=\"left\">• Convention for the Elimination of Discrimination against Women</td></tr><tr><td/><td/><td align=\"left\">Universities/Research Centers:</td><td/></tr><tr><td/><td/><td align=\"left\">• Reduce academic \"silos\"</td><td/></tr><tr><td/><td/><td align=\"left\">• Collaborative partners</td><td/></tr><tr><td/><td/><td align=\"left\">• Evidence and knowledge based dissemination</td><td/></tr><tr><td/><td/><td align=\"left\">• Research on \"lived realities\" of diverse populations</td><td/></tr><tr><td/><td/><td align=\"left\">• Scientific working group on ethical and safe methods for researching impact of medications on lactation</td><td/></tr></tbody></table></table-wrap>" ]
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[{"article-title": ["Third Annual Symposium on Breastfeeding and Feminism: Proceedings; 2007 Focus: Reproductive Health, Rights and Justice"]}, {"collab": ["The Center for Women's Health and Wellness"], "article-title": ["Proceedings of Breastfeeding and Feminism: A focus on reproductive health, rights and justice"]}]
{ "acronym": [], "definition": [] }
2
CC BY
no
2022-01-12 14:47:28
Int Breastfeed J. 2008 Aug 4; 3:8
oa_package/f2/60/PMC2531083.tar.gz
PMC2531084
18700953
[ "<title>Background</title>", "<p>Breast milk is recognised as the ideal nutrition for the human infant. Research continues to explore supportive strategies to enhance women's initiation and prevalence of breastfeeding. Although Western Australia had initiation rates as high as 84% in the mid 1990s [##REF##8799098##1##], evidence suggests that initiation rates have continued to increase to 88% [##REF##10342541##2##] and more recently to 94% [##REF##16028655##3##]. Prevalence rates, however, are not as encouraging as rates decrease to 62% at 3 months and 50% at 6 months [##REF##10342541##2##]. Western Australian data is comparable to national figures from the 2001 National Health Survey with 87% of infants receiving some breast milk, 48% being breastfed to six months, and 23% to 12 months [##UREF##0##4##]. Early cessation has been attributed to ineffective support from health care professionals and informal networks such as family and friends, unrealistic expectations, physical concerns with breastfeeding and faltering commitment by mothers [##REF##11847848##5##]. The most common reason cited for stopping breastfeeding within the first two weeks post birth was an unsettled baby, which mothers interpreted as indicating insufficient milk supply [##REF##12227559##6##].</p>", "<p>Adequate support for the breastfeeding woman is essential to promote initiation [##UREF##1##7##]. Support during breastfeeding initiation focuses upon ensuring that the woman can achieve correct attachment, understands the principles of supply and demand, and receives prompt treatment for any problems. Therefore, the support of a skilled, competent midwife during the initiation phase of breastfeeding cannot be underestimated.</p>", "<p>Snoezelen is a concept whereby an indoor environment using controllable stimuli is created to provide comfort. The specially designed room exposes the user to multiple sensory stimulations combining vision, touch, sounds and aromas. These rooms are credited with providing positive therapeutic or educational effects and positive emotions such as well-being, rest, satisfaction, poise and joy [##UREF##2##8##]. The original purpose of the Snoezelen concept was as a leisure activity for severely disabled people. Its popularity has grown since the 1980s beyond the original counties of Germany and the Netherlands, to Britain, Canada, the United States, Spain and Australia with use extending beyond mentally impaired clients. This environment has been used with adults with cerebral palsy [##UREF##3##9##]; multiple handicap clients [##UREF##4##10##]; nursing home and psychogeriatric clients [##UREF##5##11##, ####REF##15050851##12##, ##REF##15022825##13##, ##UREF##6##14##, ##REF##14964045##15##, ##UREF##7##16####7##16##]; palliative day-care clients [##REF##12682575##17##]; end-stage Alzheimer's clients [##REF##11913515##18##]; neonates [##UREF##8##19##]; critically ill children [##REF##9306849##20##]; and chronic pain sufferers [##REF##9439265##21##, ####UREF##9##22##, ##REF##9830944##23####9830944##23##].</p>", "<p>There is limited evidence on the use of the Snoezelen concept with maternity clients. One study has reported how using the Snoezelen room enhanced the labour experience of Western Australian women by providing distraction, environmental control, comfort, relaxation, choice of complementary therapy features and safety in a non-clinical atmosphere [##UREF##10##24##]. Consequently, this research adds to a new body of knowledge by providing insight into the experience of using a Snoelezen room with maternity clients and specifically, breastfeeding women.</p>" ]
[ "<title>Methods</title>", "<title>Research aim</title>", "<p>The aim of this study was to provide insight into the experience of using a Snoezelen room for breastfeeding women in the early postpartum period.</p>", "<title>Research design</title>", "<p>A qualitative exploratory design was employed to obtain a rich description of the experience of using the Snoezelen room. Therefore, a small cohort of breastfeeding women was invited to participate in in-depth interviews after using this environment. The average length of hospital stay for maternity clients is 2.7 days and to conduct an interview during this limited hospital stay when clients are in the acute phase of recuperation was not feasible. Therefore a time period of 4 to 6 weeks was chosen to balance the need for initial recuperation from the birth process and potential concern with recall bias.</p>", "<title>Setting and context</title>", "<p>Osborne Park Hospital (OPH), the setting for this study is the second largest public provider of obstetric services in Western Australia. The maternity setting at OPH is considered a low to moderate risk maternity unit with approximately 1600 births per year. Healthy women qualify to birth at OPH after they have reached 35 completed weeks gestation. The Snoezelen room at Osborne Park Hospital was designed especially for the maternity setting (Figure ##FIG##0##1##). The midwives, who introduced the concept into the maternity setting, took initial guidance from the website of the International Snoezelen Association [##UREF##2##8##] where the concept definition and aim is provided. However, Snoezelen rooms are unique as they are based upon a target audience. The room has polished floorboards and soft earthy colours of green, brown, terracotta and yellow. These colours were chosen as the midwives felt they represent the link between birthing and nature. The room provides a three-seater lounge with a chaise where a woman can lie and relax with a chair with a wrap-around backrest. There is a large soft rug and three-square ottomans for women to put their feet up while sitting on the lounge or chair. The main features of the room are the wheel projection that slowly rotates to display patterns on the wall and fibre optic lights that can be draped across a person or the room as it gradually changes colour. Finally, a tropical fish tank, music and aromatherapy were chosen to complete the room's ambience.</p>", "<title>Recruitment</title>", "<p>The sample for this study was drawn from breastfeeding women at Osborne Park Hospital (OPH) who chose to use the Snoezelen room for breastfeeding reasons during their hospital stay. Information about the Snoezelen room and its aim to promote relaxation is provided during hospital tours, antenatal classes, and via posters displayed in every birth room, the patient lounge and all postpartum rooms. All client requests to use the room are respected depending upon the room's availability. Midwives tend to encourage use of the room for relaxation for breastfeeding women, women in early labour, tired and anxious women needing a sleep and pregnant women with elevated blood pressure who are awaiting a blood pressure profile assessment. Only one client is able to use the room at a time and a midwife must unlock the door to provide access.</p>", "<p>All midwives on the postpartum ward at Osborne Park Hospital who put a woman in the Snoezelen room for breastfeeding issues were asked to enter the woman's name and date of the visit in a book. Three weeks after using the Snoezelen room an information letter was posted to 44 women who used the room for breastfeeding between March 2006 and March 2007. Potential participants replied to the letter by returning an expression of interest form to the researcher in a reply paid envelope. Twelve women replied to the invitation and eleven were interviewed. One woman had not used the room for breastfeeding but to gain some privacy and much needed sleep and was therefore not interviewed. The researcher contacted interested women to arrange an interview. This was to avoid perceptions of coercion should hospital midwives approach the women. Arrangements were made to conduct the interview in the privacy of the woman's home. The researcher who is a midwife but not involved in client care at the hospital conducted the interviews. This purposeful sampling method ensured that participants selected were appropriate and able to share perceptions of using the Snoezelen environment for breastfeeding issues [##UREF##11##25##]. Participants received an information letter and provided informed consent while being assured they could withdraw from the study at any time. An interview guide with five open-ended questions was used to encourage the women to share their experience: \"tell me about your breastfeeding while in hospital\"; \"tell me about using the Snoezelen room for breastfeeding\"; \"what did you like/dislike about the Snoezelen room\" and \"would you recommend the room to other mothers\". The interviews lasted between 45 minutes and 90 minutes (average of 60 minutes) as the questions facilitated a \"story telling\" atmosphere. Prompt questions were also used such as \"can you elaborate on that\" or \"tell me more about\". All interviews were audio-taped and transcribed verbatim. A demographic form was also completed to provide a brief profile of the participants (i.e. age, type of birth, education level, current breastfeeding pattern, number of children breastfed, breastfeeding intention). The final sample size of 11 participants was determined by data saturation as ongoing analysis revealed redundancy of information and no further concepts or themes were apparent [##UREF##11##25##].</p>", "<title>Data analysis</title>", "<p>The constant comparison method modified from the grounded theory methodology was used to analyse the interview transcripts [##UREF##11##25##]. Categories drawn from the data were compared to reveal commonalities and variations in the experience [##REF##12212430##26##]. Data analysis identified patterns or themes relevant to the mothers' experience of using the Snoezelen room. Initially team members conducted a separate data analysis with the transcripts and then came together for discussions to clarify, negotiate and refine the findings. Disagreements on interpretation were negotiated and although major disagreement did not occur, transcripts were referred back to for refinement of final themes. An audit trail was kept by the first author to provide transparency of the decisions and allow evaluation of how data were categorised into themes and subthemes [##REF##12212430##26##]. To ensure trustworthiness by confirming validity of the findings, a summary of the identified themes was posted to participants inviting comment and discussion [##REF##12212430##26##]. Due to changes in postal addresses and phone numbers over the course of the study period, four participants could not be contacted. The remaining seven participants confirmed that the themes were an accurate reflection of what the Snoezelen room offered a breastfeeding woman.</p>", "<title>Ethical considerations</title>", "<p>Ethical approval was obtained from the hospital and university human ethics and review committees. Data were coded with numbers to ensure confidentiality. Transcripts and demographic data forms were stored in a locked cabinet at the university. The person employed to transcribe the cassettes signed a confidentiality agreement to ensure they would not breach confidentiality and were aware of the seriousness of this issue. If participants become distressed during the interview process, a referral system was prearranged with appropriate counselling services. Referral was not undertaken as no women experienced any distress during the interviews.</p>" ]
[ "<title>Results</title>", "<p>It must be acknowledged that the sample was self-selecting and not representative as not all breastfeeding women chose to use the room. In addition, only 11 out of the 44 women invited agreed to participate in the study. All participants interviewed felt they benefited from using the Snoezelen room; however, we have no information regarding the women who chose not to participate. They may not have found the room helpful or had ceased breastfeeding when the invitations were posted, although the information letter stressed that their current infant feeding pattern was not relevant to the study. Finally, conducting an interview six weeks post birth could have resulted in recall bias, however, periods of less than three years for recall with infant feeding are considered acceptable [##REF##15869124##27##].</p>", "<p>Eleven women shared their experience of using the Snoezelen room during the early stages of their breastfeeding. All women had a positive experience in the Snoezelen room and indicated they would not only use it again but would recommend it to other mothers. Seven women were first time breastfeeding mothers, with three women breastfeeding their second child and one breastfeeding her third. The age of the mothers ranged from 23 to 39 years with a mean of 31 years. The length of hospital stay during the postpartum period ranged from 1 to 8 days with a mean of 4 days. All participants were living with a partner. The time of the interview ranged from 4 to 11 weeks post birth with a mean of 6.5 weeks. Ten of the eleven participants were still breastfeeding their infant at the time of interview. The mother who was completely formula feeding at her 11-week interview had planned to breastfeed for between 4 to 6 months. Seven mothers used the Snoezelen room once during their hospitalisation, two used it twice and two used the room for three or more times. The time spent in the room ranged from 30 minutes to 8 hours with the majority of women using the room for 1 to 2 hours for each visit. Four mothers specifically asked to use the room and three of these women had used the Snoezelen room during their labour. The remaining women used the room on their midwife's recommendation. The majority (n = 9) used the room for an unsettled baby and/or breastfeeding issues. One woman, who used the room during labour and did not have an unsettled baby but enjoyed the room so much she used it for several visits during her hospital stay and for up to 8 hours for one visit. Finally, one woman was not having breastfeeding difficulties but was unwell after experiencing a postpartum haemorrhage. Her midwife encouraged use of the room for relaxation and the woman did use this opportunity for breastfeeding. Further demographic data are provided in Table ##TAB##0##1##.</p>", "<p>The majority of women who chose to use the Snoezelen room by asking or following their midwife's recommendation were experiencing anxiety due to breastfeeding issues. Most were day two or three after their birth and were tired, emotional and having breastfeeding difficulties with attachment or pain with feeding. The majority of mothers had infants they described as unsettled. The three women who had used the Snoezelen room during labour asked to use the room earlier than the other participants. Although all postpartum rooms have a poster of the Snoezelen room only one mother who had not used the room in labour asked to use the room. The majority of women indicated that they would have never thought to ask about the room even though they were aware of it.</p>", "<p>All participants indicated that they were able to achieve some relaxation while in the room. Two key themes with seven subthemes were revealed during data analysis highlighting how the Snoezelen room enhanced relaxation for these breastfeeding mothers (Table ##TAB##1##2##).</p>", "<p>Participant quotes will be provided to illustrate each of these themes. Participants are identified by a code P1 to P11 to demonstrate the depth and variety of experiences and illustrate how all women are represented. The first theme was \"Finding Relaxation for the Breastfeeding Mother\" which incorporated three subthemes: Time out for mother, Control in own personal space and Quiet/calm environment with a homelike atmosphere.</p>", "<title>Time out for mother</title>", "<p>Being in the Snoezelen room was an escape from the ward environment and mothers appreciated being able to get away from the noise and activity. The room provided the opportunity to have a break from other mothers and their crying babies, visitors, and the hospital staff, no matter if they were doctors and midwives or the domestic staff doing their cleaning duties. Having private time meant having the freedom to do what she wanted such as put her feet up, listen to music or even have a cry in private. As one mother stated, \"<italic>I did sit there for a little while and have a good cry. I thought that I was doing it [breastfeeding] wrong, I thought it was a problem with me. I couldn't do it, I did sit there and have a little time out (P7)</italic><italic>.\"</italic> Having time out promoted relaxation for the women. Although all of the participants had their babies with them and were therefore not alone, they perceived that being in the room provided a haven or escape from being in the traditional maternity setting. One woman described how the room was not only a break for her but how her daughter enjoyed the lights in addition to breastfeeding well: \"<italic>She was just eyes wide open watching all the colours change; just really still and quiet and loved it; I fed her in there and I found it much nicer and just a break for me as well (P3).\"</italic></p>", "<p>Three women had a midwife come in the room for some of the time to assist with breastfeeding. Four women had their partners in the room, but only one mother encouraged visitors to come into the room. Having time out for the mother illustrated how being in the room was seen to be an indulgence: <italic>\"Nice to just have that little bit of peace, no I don't think I would have tried with anybody else in there, maybe my mum but I don't think I would have taken the kids or anyone else, selfish, I save that for me (P8).\"</italic></p>", "<title>Control in your own personal space</title>", "<p>The second theme highlighting how relaxation was enhanced focused upon having control of personal space and who entered that space. Even being in a private room did not afford women control. Health care professionals and hospital domestic staff continuously entered women's rooms. Closed doors and drawn curtains were no guarantee of privacy. <italic>\"There were a few occasions where I was feeding and obviously the people would go 'Oh no don't mind me', I'm thinking you might not mind but actually this is quite a personal thing for me and I'm not really comfortable with it (P6).\"</italic> The Snoezelen room door requires a key to enter or the visitor to knock to be allowed in. Most women indicated that it was rare for anyone to disturb them while in the room and the sign on the door indicating the Snoezelen room was in use also contributed to respect for privacy. <italic>\"The room was a safe haven, the privacy to do what I needed to do and to not have to worry about conforming to what they wanted me to be like in the ward (P7).\"</italic></p>", "<p>The modifiable features in the room such as movable furniture, music and lighting options were also a desirable feature that promoted control and therefore, relaxation. Traditional hospital rooms do not allow the flexibility of dimming lights or chairs with comfortable arms and footstools that can be moved to personal preferences. <italic>\"I was stunned because I think I just felt that as soon as I got in there and sat down and knew that I wasn't going to be bothered by anything, I wouldn't hear anybody else around me, I could set the environment how I wanted it, I just automatically felt myself relax and just chill out (P10).\"</italic></p>", "<title>Quiet/calm environment with homelike atmosphere</title>", "<p>The quiet and calming atmosphere in the Snoezelen room created feelings of safety plus the comfortable furniture was seen to provide a more homelike ambience. <italic>\"It was just so nice to have our own little room where you're warm and cozy and feel safe, I did feel safe in there (P7).\"</italic> The mothers commented on how their emotional and physical state of relaxation transferred to their unsettled babies whose behaviour improved. <italic>\"He [baby] seemed a lot more settled in there, I assume purely cause I was and I could get comfortable, I found the beds very uncomfortable for feeding and the chairs are ok, but it [Snoezelen room] was just really nice, you feel like you're at home (P5).\"</italic> The mean hospital stay for these women was 4 days, which is longer than the setting average of 2.7 days. Having the opportunity to go into a quiet and calm environment was welcomed by these women who accepted their need to stay in hospital for individual reasons but appreciated having a sanctuary from the ward. <italic>\"The privacy as well was a huge bonus to know that you didn't have the kids peeking in through the curtains or the person delivering your meal or the nurse coming in to check your bits and pieces, so it was good to be in that home environment (P11).\"</italic></p>", "<p>The second key theme \"Enabling Focus on Breastfeeding\" occurred after the mother was able to achieve some relaxation and encompasses four subthemes: Able to get one-on-one attention; Not physically exposed to others; Away from prying, judgemental eyes; and Able to safely attempt breastfeeding alone knowing help is nearby.</p>", "<title>Able to get one-on-one focused attention</title>", "<p>Those mothers who did require the support of a midwife in the Snoezelen room with breastfeeding commented how being in the room afforded them one-on-one focused attention. They felt that in a shared maternity room, they were not able to achieve this level of attention, due to the presence of other mothers and babies, partners, families and visitors. When a midwife joined the mother in the Snoezelen room there were no bells or buzzers around to distract the midwife or take her away and even if the time wasn't long, the undivided attention was valued. As one mother shared: \"<italic>It was really nice to go into a separate space and sit down with someone to really focus on trying to get the breastfeeding right... really good to have that special attention (P2).\"</italic> Being more relaxed in the room due to the privacy and atmosphere also contributed to the mother being better able to calmly listen and take in the advice being offered. <italic>\"If you needed one of the midwives to get them to come in there which was nice, cause you just sit and relax on the sofa as you would at home instead of sitting on the end of a bed or on a chair or something, so it was a lot easier and a lot calmer (P4).\"</italic></p>", "<title>Not being physically exposed to others</title>", "<p>Not all women feel comfortable having their breasts exposed as may occur when trying to initiate breastfeeding in a hospital setting. The women using the Snoezelen room indicated how they appreciated the privacy and not having to worry about <italic>\"covering up\" </italic>while attempting to breastfeed. The door to the Snoezelen room was locked and midwives tended to knock before they entered the room, even though they had a key. This respect for privacy was noted as being different from the traditional hospital room, even if the woman was in a private room. <italic>\"When the person next door came actually into our section, it was like 'Oh my God, I'm sitting here with my breast hanging out', which wasn't very nice (P4).\"</italic> The mothers commented that they felt confident that no one was going to come in unannounced while they were trying to breastfeed. This privacy was like being in your own home and was appreciated. <italic>\"The privacy of the breastfeeding which is a very intimate experience that I'm not that sort of outgoing that I would enjoy exposing myself publicly (P6).\"</italic></p>", "<title>Away from prying judgemental eyes</title>", "<p>Many women commented upon their feelings of being judged by others regarding either difficulties with breastfeeding or just the fact that their baby was crying, unsettled and disturbing others. <italic>\"I did find that the room took away my tension and the stress of not being able to do it, it did relax me, there was no one to watch me and see how bad a job I'm doing (P7).\"</italic> The sensitivity to being seen and judged was not just with regards to other mothers, partners and visitors, it also included the evaluating and watchful gaze of health professionals: <italic>\"You don't want to be judged and it sounds a bit strange as helpful as the midwives were when you are trying even when I was feeding they'd come in and go 'Is she attached properly?' and then come and have a look and check (P6).\"</italic> Feeling like they had to perform in front of others placed added stress to an already anxious woman whose breastfeeding expectations were not being realised. Being away or not being under the scrutiny of others, did help to ameliorate some of this anxiety: <italic>\"Completely away from everyone else, because I found it very stressful trying to do things even though everybody's baby was crying and everybody's having issues you still think you're the only one and you think you're the problem and it's just nice to be able to go away and not have anyone listening to you (P9).\"</italic> The Snoezelen room provided a safe retreat: <italic>\"Somewhere where eyes aren't going to be looking at you and asking 'How are you going, can you do it right or can you not', it's just your little space (P1).\"</italic></p>", "<title>Able to safely attempt breastfeeding alone knowing help is nearby</title>", "<p>Having an unsettled baby as a consequence of breastfeeding difficulties, meant that many of these women commented how their confidence was slowly eroding away. <italic>\"I'd gotten to a point where I was saying to my husband 'No that was it. I am not doing this. It's not worth it'. Thank goodness I didn't but I was losing confidence (P10).\"</italic> To regain confidence it was important to have a safe environment to try breastfeeding strategies for attachment as one example and know that help was available. Ultimately, the mothers wanted to be able to achieve a level of competency with their breastfeeding that could be transferred to their hospital room and home once discharged. The Snoezelen room provided this opportunity: <italic>\"I wanted to experiment and explore it on my own because I'm not a complete idiot, sometimes you just need a chance to try it for yourself. You want some time out to yourself to see if you can do it yourself and I knew that in that space I was just going to have some time to find out for myself and not have any interruptions (P6).\"</italic></p>", "<title>Consequences</title>", "<p>Using the Snoezelen room was described as a turning point for a number of participants during their early breastfeeding experience. The opportunity to use the room while initiating breastfeeding was described as a positive experience. Being able to achieve a degree of relaxation assisted most of the women in being able to have a positive breastfeeding experience. For some women, this one affirming experience contributed to a journey toward breastfeeding success. <italic>\"I just think knowing that I could do it and knowing what it felt like to have that right positioning and that feeling so that if she wasn't on properly I knew it (P11).\"</italic> A number of mothers said the positive experience increased their confidence in their ability to breastfeed and reinforced the idea that: <italic>\"I can do this.\"</italic> Prior to coming into the room, many women were emotionally and physically distressed. Their baby was unsettled and breastfeeding problems compounded the situation. <italic>\"I was losing confidence. I was definitely not doing it right. It was a turning point for me. I quite enjoy breastfeeding now. I am glad I didn't give it up (P10).\"</italic></p>", "<p>As the mothers relaxed, so did their babies and the majority of women were able to breastfeed successfully in the room. One woman's baby was not interested in feeding in the room even though she tried but her previously unsettled baby did settle and was content in the room, which reinforced to the woman how important it was to relax. Still another woman who breastfed with continuing nipple pain was satisfied that the experience was worthwhile and has continued to receive specialist support from a community lactation consultant at six weeks postpartum. Having a relaxed baby was a key issue for these women who noted a difference in their baby's behaviour. <italic>\"Every time we were in there he was really calm, slept really well and breastfed really well and when we went down to the [hospital] room a few times he'd start howling so we went back down to the [Snoezelen] room and he'd calm down, we think that everybody should have a Snoezelen room at home (P4).\"</italic> All babies settled while in the room and some mothers and babies even managed to have a nap in the Snoezelen room before returning to their hospital room. Women commented how having a positive breastfeeding experience left them feeling refreshed and rejuvenated and most importantly better able to cope with the challenges that lay ahead. One woman's comment aptly highlights her summary of the Snoezelen room: <italic>\"It was just such a blissful place, just missing chocolate really (P9).\"</italic></p>" ]
[ "<title>Discussion</title>", "<p>Our findings highlight what assisted breastfeeding mothers to achieve relaxation within a Snoezelen environment, which ultimately facilitated their early breastfeeding experience. It is anticipated that the rich description of these qualitative findings will enable the reader to determine the transferability of the findings to their own context [##UREF##11##25##,##REF##12212430##26##]. Although limited maternity settings may actually have a Snoezelen room, application of how the room enhanced relaxation can be considered and addressed in other settings. A woman who is anxious, in considerable pain or distressed due to physical concerns may find it difficult to relax during breastfeeding. The milk ejection reflex is enhanced when the woman is comfortable, relaxed, and not experiencing undue pain or anxiety [##UREF##12##28##]. Measures to enhance relaxation such as adequate pain relief and the provision of a calming environment may be a complementary strategy to midwifery support for breastfeeding women. The provision of a calming environment that addresses issues such as ensuring privacy and a space for the mother to have \"time out\" and control, feeling safe from prying, judgemental eyes, and having the opportunity of one-on-one focused support could be creatively considered in different maternity settings.</p>", "<p>Findings revealed factors that were perceived as stressors for new mothers such as limited access to privacy, wanting to do their best by breastfeeding and being a 'good mother' but feeling vulnerable to being judged. The link between breastfeeding and being seen as a 'good mother' has been noted [##REF##12746032##29##,##UREF##13##30##]. \"Heading toward the new normal\" has been described as the process women undergo in the early postpartum period while reorganising life as a mother [##REF##11572530##31##]. Becoming competent and developing confidence are two components of the settling in to this \"new normal\". Dykes' [##REF##15748676##32##] study also confirmed that gaining confidence in the skill of breastfeeding was regarded as a mother's primary goal and how having a discontented newborn resulted in the mother becoming anxious and doubtful of her abilities. Ideally, a focus of postpartum care should be to foster the confidence that new mothers are struggling to achieve in those early days of breastfeeding and not present obstacles that undermine this developing confidence.</p>", "<p>Participants indicated how their breastfeeding challenges were threatening their confidence but how having a positive breastfeeding experience assisted in boosting their faltering confidence. Uncertainty and threats to maternal confidence have been regarded as key concepts in women's breastfeeding experience [##REF##17681409##33##]. However, building confidence and reducing uncertainty in being a 'good mother' by successfully breastfeeding is not a simple process. Uncertainty and vulnerability have been noted as key issues for women who encounter initial breastfeeding difficulties and reality isn't meeting expectations [##REF##17681409##33##,##UREF##14##34##]. However, most women are not prepared to experience difficulties with breastfeeding, when in fact the evidence suggests that 83% of Perth women experience one or more problems during the early stage of their breastfeeding [##REF##12227559##6##]. Therefore, encouraging women to have realistic expectations regarding initial difficulties while ensuring appropriate support is available to overcome these difficulties is recommended for health professionals advocating breastfeeding.</p>", "<p>Most participants in this study entered the Snoezelen room with an unsettled baby. Given the most common reason for stopping breastfeeding within two weeks post birth was an unsettled baby, interpreted as an inadequate milk supply, anxiety over supply is a serious issue as it is associated with early cessation of breastfeeding [##REF##12227559##6##]. Postpartum anxiety has been associated with reducing breastfeeding confidence [##REF##17879829##35##]. Physical challenges have also been noted as affecting a woman's relationship with her newborn with some women being reluctant to continue breastfeeding due to the feelings of physical vulnerability, pain and discomfort [##REF##16879904##36##].</p>", "<p>Breastfeeding self-efficacy involves the mother's perception of her ability to breastfeed with higher self-efficacy being associated with longer duration [##REF##14974698##37##,##UREF##15##38##]. The tendency to experience negative emotions, such as anxiety, depression and irritability, can impact cortisol regulation and vulnerability to stress [##UREF##16##39##]. In fact, breastfed infants exposed to higher cortisol levels in breast milk demonstrated temperament changes such as increased fear behaviours [##UREF##17##40##]. Therefore, strategies that focus upon addressing maternal anxiety, enhancing confidence and promoting breastfeeding self-efficacy have potential benefits to both mother and infant.</p>" ]
[ "<title>Conclusion</title>", "<p>Health professionals make a difference to breastfeeding. Their encouragement and support is associated with longer duration and greater exclusive breastfeeding rates [##UREF##15##38##]. Awareness of how early breastfeeding issues can influence maternal anxiety and breastfeeding confidence allows the health professional to better support vulnerable women experiencing anxiety due to early breastfeeding issues. Fostering maternal relaxation in an environment like a Snoezelen room is just one example that may be considered to accompany the support already provided by midwives working in postpartum settings. A comfortable, relaxed mother with adequate midwifery support is more likely to successfully initiate breastfeeding, have a settled infant and develop the confidence she needs to continue breastfeeding after leaving hospital.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>There is limited evidence on the use of the Snoezelen concept for maternity clients. Snoezelen, a Dutch concept, initiated in the 1970s as a leisure activity for severely disabled people, involves creating an indoor environment using controllable stimuli to enhance comfort and relaxation. These specially designed rooms expose the user to multiple sensory stimulations combining vision, touch, sounds and aromas. The aim of this study was to provide insight into breastfeeding women's experience of using a Snoezelen room during hospitalisation.</p>", "<title>Methods</title>", "<p>A qualitative exploratory design was chosen to reveal women's perceptions of using the Snoezelen room. Osborne Park Hospital, the study setting is the second largest public provider of obstetric services in Western Australia. A purposive sample was drawn from breastfeeding women who used the Snoezelen room during their postpartum stay from March 2006 to March 2007. Saturation was achieved after eleven breastfeeding women were interviewed six weeks post discharge. Data analysis involved the constant comparison method.</p>", "<title>Results</title>", "<p>Participants entered the room feeling tired and emotional with an unsettled baby and breastfeeding issues aggravated by maternal stress and anxiety. All women indicated they were able to achieve relaxation while in the room and would recommend its use to other breastfeeding mothers. Two key themes revealed how the Snoezelen room facilitated maternal relaxation, which ultimately enhanced the breastfeeding experience. The first theme, \"Finding Relaxation for the Breastfeeding Mother\" incorporates three subthemes: 'Time out' for mother; Control in own personal space; and a Quiet/calm environment with homelike atmosphere. The second theme, \"Enabling Focus on Breastfeeding\", occurred after relaxation was achieved and involved four subthemes: Able to get one-on-one attention; Not physically exposed to others; Away from prying, judgemental eyes and Able to safely attempt breastfeeding alone knowing help is nearby.</p>", "<title>Conclusion</title>", "<p>Insight into how the Snoezelen room promoted relaxation also highlights what contributes to maternal anxiety during breastfeeding experiences in hospital. The findings offer health professionals the opportunity to consider adopting strategies such as a Snoezelen room in their hospital or being innovative in modifying the postpartum setting to promote relaxation for breastfeeding women.</p>" ]
[ "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>YH designed the study, recruited and interviewed participants, analysed data and drafted the manuscript. LS, EW and CJ assisted in analysis of the data and provided constructive feedback during revisions of the manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We would like to acknowledge the Western Australian Nurses Memorial Charitable Trust for funding this project and thank the eleven mothers who graciously shared their experiences of using the Snoezelen room.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Snoezelen room at Osborne Park Hospital.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Demographic profile</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Demographic variables</bold></td><td align=\"center\"><bold>Participants</bold><break/><bold> (n = 11)</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Type of birth</bold></td><td/></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"> Spontaneous vaginal birth</td><td align=\"center\">6</td></tr><tr><td align=\"left\"> Forceps or vacuum birth</td><td align=\"center\">1</td></tr><tr><td align=\"left\"> Caesarean birth</td><td align=\"center\">4</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"><bold>Educational level</bold></td><td/></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"> University</td><td align=\"center\">7</td></tr><tr><td align=\"left\"> Technical and further education</td><td align=\"center\">1</td></tr><tr><td align=\"left\"> High school certificate</td><td align=\"center\">2</td></tr><tr><td align=\"left\"> Completed year 10 in high school</td><td align=\"center\">1</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"><bold>Feeding pattern during interview</bold></td><td/></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"> Completely breastfeeding</td><td align=\"center\">8</td></tr><tr><td align=\"left\"> Breastfeeding with occasional/regular bottle of expressed breast milk</td><td align=\"center\">1</td></tr><tr><td align=\"left\"> Breastfeeding with occasional/regular bottle of formula</td><td align=\"center\">1</td></tr><tr><td align=\"left\"> Completely formula feeding</td><td align=\"center\">1</td></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"><bold>Plans for breastfeeding</bold></td><td/></tr><tr><td colspan=\"2\"><hr/></td></tr><tr><td align=\"left\"> &lt; 1 month</td><td/></tr><tr><td align=\"left\"> 1 to 3</td><td/></tr><tr><td align=\"left\"> 4 to 6</td><td align=\"center\">3</td></tr><tr><td align=\"left\"> 7 to 9</td><td align=\"center\">1</td></tr><tr><td align=\"left\"> 10 to 12</td><td align=\"center\">3</td></tr><tr><td align=\"left\"> &gt; 12</td><td align=\"center\">3</td></tr><tr><td align=\"left\"> Uncertain</td><td align=\"center\">1</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Key themes and subthemes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>THEME: Finding Relaxation for the Breastfeeding Mother</bold></td></tr></thead><tbody><tr><td align=\"left\">\"Time out\" for the mother</td></tr><tr><td align=\"left\">Control in own personal space</td></tr><tr><td align=\"left\">Quiet/calm environment with homelike atmosphere</td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\"><bold>THEME: Enabling Focus on Breastfeeding</bold></td></tr><tr><td colspan=\"1\"><hr/></td></tr><tr><td align=\"left\">Able to get one-on-one attention</td></tr><tr><td align=\"left\">Not physically exposed to others</td></tr><tr><td align=\"left\">Away from prying, judgemental eyes</td></tr><tr><td align=\"left\">Able to safely attempt breastfeeding alone knowing help is nearby</td></tr></tbody></table></table-wrap>" ]
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{ "acronym": [], "definition": [] }
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2022-01-12 14:47:28
Int Breastfeed J. 2008 Aug 13; 3:20
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PMC2531085
18727816
[ "<title>Introduction</title>", "<p>Measurement of carotid atherosclerosis burden and progression is an important tool for research and patient management [##REF##15017019##1##]. 2-dimensional (2D) B-mode ultrasound (US) has been shown to be a sensitive and reproducible method to detect pre intrusive thickening of artery walls and to measure intima-media thickness (IMT) in the carotid artery [##REF##10626622##2##,##REF##12182261##3##]. Ultrasonographically determined IMT has been used as a marker of atherosclerosis elsewhere in the arterial system and randomised imaging studies have examined the effects of blood pressure- and lipid-lowering therapies on carotid IMT changes [##REF##17242284##4##]. The atherogenic nature of IMT, however, is uncertain, since 2D US does not measure medial and intimal thickness separately [##REF##8305418##5##]. An increase in IMT may be the result of an adapted response of medial layer (remodelling) to tensile (hypertensive) stress or intimal thickening reflecting early atherosclerosis [##REF##12417537##6##]. Atherosclerotic plaque volume assessed by 3-dimensional (3D) US [##UREF##0##7##] may represent a more reliable measure of atherosclerosis than IMT [##REF##11300468##8##] and more recently non-invasive 3D US imaging has ermerged as the predominant approach for evaluating the progression of carotid atherosclerosis [##REF##15017019##1##,##REF##17057748##9##, ####UREF##1##10##, ##REF##10751112##11##, ##REF##11879661##12####11879661##12##]. 3D US provides a precise and reproducible method for determining the change in atherosclerotic plaque volume during treatment [##REF##15017019##1##,##REF##11300468##8##,##UREF##1##10##]. A recent randomised trial in hypertensive patients using carotid 3D US has successfully investigated the effects of two different classes of antihypertensive agents on plaque volume changes and demonstrated the suitability of the 3D US method for tracking progression or regression of plaque volume over time [##UREF##2##13##].</p>", "<p>3D US measurement of PV within a multicentre clinical trial setting typically involves processing of data obtained at more than one investigational site. It is important to ensure that analysis of the data, and re-reading of the 3D US images of plaque is carried out in a centralised, standardised and reproducible manner. The primary aim of this study was to assess reliability of a central re-reading procedure implemented in a multinational 3D US trial by determining intra- and inter-reader variabilities and major factors influencing reading precision.</p>" ]
[ "<title>Materials and methods</title>", "<p>The study was performed in accordance with the principles of the Declaration of Helsinki, and the regulatory requirements of the International Conference on Harmonisation Guidelines on Good Clinical Practice.</p>", "<p>The protocol was approved by the appropriate Institutional Review Board or Ethics Committee at each centre involved.</p>", "<p>All patients gave written informed consent for publication of this report and accompanying images.</p>", "<title>Patients</title>", "<p>Two data sets of 45 and 60 3D US carotid plaque images were obtained from 45 patients (32 men; mean [SD] age, 62.2 [6.9] years) and 60 patients (50 men; mean [SD] age, 61.3 [8.1] years), respectively, who had been followed up in the ACAT Inhibition Plaque Regression Study (APRES). The APRES was a multicentre European ultrasound trial, and participants were recruited at 31 clinical centers throughout the Czech Republic, Germany, Italy and Poland. The study assessed the effect of an ACAT-inhibitor as compared to placebo on carotid plaque volume (Data on file at Daiichi Sankyo, Munich, Germany). To be eligible for inclusion in the APRES, patients with defined cardiovascular risk had to have an increased common carotid artery (CCA) intima-media thickness (IMT) of &gt; 0.8 mm, and at least one atherosclerotic plaque in the CCA or the carotid bulb without marked mineralisation (plaque volume between 20 and 500 μl).</p>", "<title>Ultrasound measurement of plaque volume (PV)</title>", "<p>Ultrasound measurements of PV had been carried out by trained and certified sonographers at 14 ultrasound referral centers using a high-resolution Voluson 530 D MT, 2D-/3D CFM-Ultrasound System (Kretz-Technik AG, Zipf, Austria; <italic>the equipment is commercially available from General Electric, GE Ultraschall Deutschland GmbH/D-42655 Solingen and named Logiq 7 BT07</italic>) equipped with a mechanical 10 MHz sector motor-driven 3D-probe that provided an axial resolution of 0.1 mm. An integrated 6.9 MHz Doppler system was used to determine the grade of possible stenoses.</p>", "<p>Small depth of Sweep Box for 3D image acquisition with an angle of 45° and slow acquisition time of volume scan were additional features of the ultrasound system. To measure PV, a longitudinal scan of the common carotid artery or bulb was carried out followed by volume acquisition. Volume acquisition used 3 orthogonal sectional planes (A, B, C) and started with the sectional image (A), which gave a 2D image showing the longitudinal view of the vessel. Images B and C showed the transverse and horizontal axes, respectively. The volume sweep was saved as raw data and stored on MODs and sent to the Reading Center for volume calculations. The volume of analysed plaque was calculated by delineating the plaque boundaries and the transverse plane (B) using the firmware of Kretz Ultrasound apparates. This \"manual\" method required the object to be manually traced by using the track ball of the Kretz device. In order to achieve volume measurement a manual trace was displayed on different planes of the plaque (Figure ##FIG##0##1##). The methodology for PV measurement has been validated previously [##UREF##3##14##, ####UREF##4##15##, ##UREF##5##16####5##16##].</p>", "<title>Quality assessment of central reading</title>", "<p>The first set of data consisted of 45 3D US plaque images with PV ranging from 21 to 240 μl. This data set was used to compare the results of plaque volume measurements obtained by 3 trained and certified readers at the European Ultrasound Teaching and Reading Center (EUTARC; Feldafing, Germany) and to investigate intra- and inter-reader variability of the reading process. The second data set consisted of 60 plaque images (PV between 20 and 60 μl) and was used to explore the effect of plaque size and the number of slices (S) (5 vs. 10 S) in manual planimetry on the quality of the central reading procedure by a randomly assigned reader.</p>", "<title>Study protocol and statistical analysis</title>", "<p>Sonographic images of the 45 plaques of the first data set were recorded on video tapes and MODs, and 6 identical blinded copies were prepared by an independent institution (Medizinische Software GbR [MESO], Mittweida, Germany) and named A to F. Three sets of the 45 MODs were then sent to the Reading Center EUTARC and randomly assigned to 3 appointed readers for subsequent evaluation. After the measurements from the first round of reading procedure had been returned to MESO, the remaining 3 sets of MODs were delivered to the 3 readers for a second round of readings. Thus, each reader performed two independent evaluations of the 45 MODs according to a randomized, pre-defined procedure. The mean of 3 individual readings was used for analysis.</p>", "<p>Inter-reader variability was evaluated by comparing the mean of the first and the second measurement of each reader between all 3 readers. Assessment of intra-reader variability was based on comparison between the first and the second set of measurements of each reader. The intraclass correlation coefficient (ICC) was applied to assess inter- and intra-reader variabilities [##UREF##6##17##]. The ICC is a measure of the proportion of variance that is attributable to the objects of measurements, and has emerged as a universal and widely accepted reliability index [##UREF##7##18##]. Furthermore, it allows the evaluation of the variability between more than 2 methods and readers, respectively. Calculation of the ICC was carried out by means of analysis of covariance (ANCOVA) using the SAS program (version 9.1). The three ICC values between the first and second set of measurements of each reader provide an estimation of intra-reader variability, and the ICC between all 3 readers is a measure of inter-reader variability. An additional analysis was performed after stratification by plaque size (PV &lt; 60 μl versus PV &gt; 60 μl) as determined in the APRES.</p>", "<p>The second data set was used to investigate the effect of the number of slices in manual planimetry on the quality of PV measurement. The results of the 5 S- and 10 S- method were compared by means of the repeatability coefficient (RC) based on the mean of 3 individual readings per plaque. The RC was calculated as follows [##REF##2868172##19##]: RC = 1.96 SD<sub>Diff</sub>, where SD<sub>Diff </sub>denotes the standard deviation of the differences in PV measurements between both methods. If the differences follow a normal distribution, approximately 5 percent of differences between measurements are expected to lie outside the limits + RC. Variability of measurements was investigated by calculating the coefficient of variation (CV) for 3 individual assessments of each of the 60 plaques by either method. In order to analyse the effect of plaque size (PV) on the precision of measurement, CVs and RC were calculated separately for smaller plaques (PV between 20 and &lt; 40 μl) and larger plaques (PV between 40 and 60 μl).</p>" ]
[ "<title>Results</title>", "<title>Intra- and inter-reader variability</title>", "<p>The results of the PV measurements obtained by the 3 readers for 45 plaques (first data set) are summarised in Table ##TAB##0##1##. Mean PV values ranged between 67.8 and 71.8 μl. The ICC for intra-reader variability were close to 1 (the highest possible value) for each of the 3 readers with values of 0.985, 0.967 and 0.969 for the first, second and third reader, respectively. The ICC value generated between the 3 readers was 0.964 indicating that inter-reader variability was small, too (Table ##TAB##0##1##). Intra- and inter-reader variabilities were smaller, i.e. ICCs were higher, for plaques with PV &gt; 60 μl than for plaques with PV &lt; 60 μl (Table ##TAB##1##2##).</p>", "<title>Effect of number of slices</title>", "<p>Mean (SD) PV of the 60 plaques included in the second data set was 39.8 (11.3) μl for the 5 S-method and 40.1 (11.2) μl for the 10 S-method. Mean (SD) CV calculated from the 3 individual PV measurements per plaque were 3.4 (1.9)% and 3.1 (1.6)% for the 5 S- and 10 S-methods, respectively. The RC was 4.7 μl (Table ##TAB##2##3##). The corresponding differences in PV measurements between the two methods are depicted by means of a Bland-Altman plot in Figure ##FIG##1##2##. A stratified analysis of the 30 plaques with PV between 20 and &lt; 40 μl and for the 30 plaques between 40 and 60 μl showed that the mean CVs for both the 5 S- and the 10 S-methods were lower for larger plaques (2.4% and 2.4%) than for smaller plaques (4.3% and 3.8%).</p>" ]
[ "<title>Discussion</title>", "<p>Because carotid plaque progression is not limited to changes in one direction, it is important to measure progression in three dimensions. Plaques grow and regress circumferentially as well as in length and thickness. As a non-invasive technique, 3D imaging allows:</p>", "<p>- direct plaque visualization</p>", "<p>- quantification of plaque features</p>", "<p>- the possibility of investigating volume changes that occur in multiple dimensions, such as plaque surface morphology, plaque geometry, and plaque distribution.</p>", "<p>For these reasons 3D-ultrasound is becoming more important in serial monitoring of disease progression or regression. Sample sizes which are required to test the effects of new therapies might be smaller for measurements of plaque volume than for traditional 2D measurements.</p>", "<p>The present study aimed to assess the quality of a centralised reading procedure of 3D US recordings of carotid PV measured in a multinational clinical trial. The small intra- and inter-reader variabilities reported in the study validate the reproducibility and reliability of the centralised PV measurement reading technique. Any slight differences observed between the 3 appointed readers may have reflected a minor subjective element due to differences in practice; however, these differences were small and randomly distributed, and would not be expected to have a significant effect on results generated in a clinical trial setting. Individual deviations between the first and second measurement made by the same reader, and between participating readers, were comparable with the occurrence of outliers in other methods of measurement applied in clinical trials, with respect to magnitude and frequency.</p>", "<p>In the present study, variability of the re-reading procedure was dependent on plaque size. Both inter- and intra-reader variabilities were lower for larger than for smaller plaques. This finding is in agreement with previous PV variability studies which utilised 3D ultrasound [##REF##15017019##1##,##UREF##3##14##,##REF##16844159##20##,##REF##9707205##21##] showing that the CV in the measurement of PV decreased with plaque size. Due to irregular shape of some of the atheromatic plaques an increase in the number of slices used for calculation in manual planimetry method by decreasing the distance between slices could possibly increase the accuracy of volume determinations. Therefore, in the present study additional analyses were undertaken on a sample of 60 plaques to assess the variability depending on plaque size and the number of slices used during the determination of plaque volume. Half of plaques were between 20 and &lt; 40 μl and half were between 40 and &lt; 60 μl. The results indicate that the 5S- and the 10S-method provided similar results in volume calculation. Subgroup analyses of the two tracing techniques demonstrated that for the smaller plaques (PV between 20 and &lt; 40 μl), the 10S-method offered slightly greater reliability as indicated by a lower coefficient of variation. In contrast, there was no difference between reliability of the 5S- or 10S-method in the larger plaque group (PV between 40 and &lt; 60 μl). Therefore, the reliability of measurements of smaller plaques (&lt; 40 μl) may be improved by using the 10S-method.</p>", "<p>The small variability in 3D US PV measurements and re-readings is an important finding, given the fact that this approach may be useful for evaluating the progression of carotid atherosclerosis and its response to treatment and for targeting preventive therapy [##REF##11879661##12##]. Poor reproducibility could lead to inappropriate clinical management of individual patients, particularly given that the ratio of random measurement error to the variability among progression rates is large, and repeated measures or longer follow-up would be required to sufficiently reduce the contributions of random error to allow individual diagnoses [##REF##8610317##22##].</p>", "<p>In contrast to disease areas such as cancer, large-scale screening programs to identify at-risk individuals with atherosclerotic disease have not yet been introduced, despite the higher burden of associated morbidity and mortality, as highlighted by the recent SHAPE Task Force Report [##UREF##8##23##]. The present study has confirmed that a reliable, non-invasive technique for monitoring the progression or regression (Fig. ##FIG##2##3##) of carotid atherosclerosis is now available.</p>", "<p>It should be noted that a number of limitations can affect the 3D technique for measuring plaque volume. In a small number of cases, plaque boundaries might not be well defined due to dropouts and shadowing owing to attenuation of the US beam, and these might be present in the reconstructed 3D US images. Calcified plaques with marked shadowing are not measureable, and plaque identification at the carotid bifurcation and in areas of poor image resolution may in a few cases also create some difficulty in plaque identification. In addition, there is a higher variability in smaller plaques (&lt; 40 μl), and the reliability of measurements of smaller plaques (&lt; 40 μl) may be improved by using an increased number of slices (10 instead of 5 slices).</p>", "<p>In summary, the centralized reading procedure investigated in this study has been shown to be reliable and reproducible. Variability in the reading process increases with decreasing plaque volumes, and the 10S-method may offer greater reproducibility than the 5S-method for volume assessment of small plaques.</p>" ]
[ "<title>Conclusion</title>", "<p>The 3D US techniques combined with a well controlled centralised reading procedure described in this study are appropriate tools for monitoring progression of carotid atherosclerosis and its response to treatment. By implementing standardised central 3D US reading protocols and strict quality control procedures highly reliable ultrasonic re-readings of plaques images can be achieved in large multicentre trials.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Non-invasive 3-dimensional (3D) ultrasound (US) has emerged as the predominant approach for evaluating the progression of carotid atherosclerosis and its response to treatment. The aim of this study was to investigate the quality of a central reading procedure concerning plaque volume (PV), measured by 3D US in a multinational US trial.</p>", "<title>Methods</title>", "<p>Two data sets of 45 and 60 3D US patient images of plaques (mean PV, 71.8 and 39.8 μl, respectively) were used. PV was assessed by means of manual planimetry. The intraclass correlation coefficient (ICC) was applied to determine reader variabilities. The repeatability coefficient (RC) and the coefficient of variation (CV) were used to investigate the effect of number of slices (S) in manual planimetry and plaque size on measurement variability.</p>", "<title>Results</title>", "<p>Intra-reader variability was small as reflected by ICCs of 0.985, 0.967 and 0.969 for 3 appointed readers. The ICC value generated between the 3 readers was 0.964, indicating that inter-reader variability was small, too. Subgroup analyses showed that both intra- and inter-reader variabilities were lower for larger than for smaller plaques. Mean CVs were similar for the 5S- and 10S-methods with a RC of 4.7 μl. The RC between both methods as well as the CVs were comparatively lower for larger plaques.</p>", "<title>Conclusion</title>", "<p>By implementing standardised central 3D US reading protocols and strict quality control procedures highly reliable ultrasonic re-readings of plaque images can be achieved in large multicentre trials.</p>" ]
[ "<title>Competing interests</title>", "<p>Prof. Dr. M. Ludwig – consultant for Daiichi Sankyo, Munich, Germany.</p>", "<p>Prof. Dr. K.O. Stumpe has received research grants and lecture honoraria from Daiichi-Sankyo during the last five years.</p>", "<p>Prof. Dr. Tomasz Zielinski – consultant for Daiichi Sankyo, Munich, Germany.</p>", "<title>Authors' contributions</title>", "<p>Carotid artery scans were carried out by TZ. Reading of scans was carried out by the reading centre EUTARC/Feldafing, Germany. Statistical analyses had been carried out by DS. ML and TZ provided expert input during the analysis and interpretation of the data. All authors provided input during the writing and editing of the manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>Daiichi Sankyo, Munich, Germany provided funding for and were involved in the design, development and management of the APRES study. The analyses described in this paper were initiated and carried out independently without funding support from Daiichi Sankyo.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Carotid plaque images obtained with 3D US.</bold> The plaque is presented in longitudinal and cross-sectional views. The 3D image has been sliced 6 times from one end to the other along the vessel axis in the scan direction.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Difference against average of PV measurements using the 5S- and 10S-methods (second data set, n = 60)</bold>.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Carotid plaque images obtained with 3D US showing an example of plaque regression under therapy with a daily dose of 40 mg olmesartan:</bold> a) A fibrous non-calcified plaque of the carotid artery (bulb) presented in longitudinal and cross sectional views at the start of therapy (Mean volume: 384 μL). b) The same plaque during therapy six months later (Mean volume: 250 μL). c) The same plaque (no longer visible) 16 months later.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Inter- and intra-reader variabilities for re-reading plaque volume (PV) of 45 plaques</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Reader 1</bold></td><td align=\"center\"><bold>Reader 2</bold></td><td align=\"center\"><bold>Reader 3</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Intra-reader variability</bold></td><td/><td/><td/></tr><tr><td align=\"left\">PV reading 1 (μl)</td><td align=\"center\">71.8 (42.5)</td><td align=\"center\">67.8 (42.8)</td><td align=\"center\">70.1 (45.4)</td></tr><tr><td align=\"left\">PV reading 2 (μl)</td><td align=\"center\">71.6 (43.3)</td><td align=\"center\">70.0 (43.0)</td><td align=\"center\">71.5 (44.0)</td></tr><tr><td align=\"left\">ICC</td><td align=\"center\">0.985</td><td align=\"center\">0.967</td><td align=\"center\">0.969</td></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\"><bold>Inter-reader variability</bold></td><td/><td/><td/></tr><tr><td align=\"left\">PV both readings (μl)</td><td align=\"center\">71.7 (42.7)</td><td align=\"center\">68.9 (42.2)</td><td align=\"center\">70.8 (44.4)</td></tr><tr><td align=\"left\">ICC</td><td/><td align=\"center\">0.964</td><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Intra-class correlation coefficients for re-reading plaque volume (PV) of 21 plaques with PV &lt; 60 μl and 24 plaques with PV ≥ 60 μl</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\"><bold>Reader 1</bold></td><td align=\"center\"><bold>Reader 2</bold></td><td align=\"center\"><bold>Reader 3</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>PV &lt; 60 μl</bold></td><td/><td/><td/></tr><tr><td align=\"left\"><bold>Intra-reader variability</bold></td><td align=\"center\">0.929</td><td align=\"center\">0.783</td><td align=\"center\">0.689</td></tr><tr><td align=\"left\"><bold>Inter-reader variability</bold></td><td/><td align=\"center\">0.805</td><td/></tr><tr><td colspan=\"4\"><hr/></td></tr><tr><td align=\"left\"><bold>PV ≥ 60 μl</bold></td><td/><td/><td/></tr><tr><td align=\"left\"><bold>Intra-reader variability</bold></td><td align=\"center\">0.976</td><td align=\"center\">0.960</td><td align=\"center\">0.960</td></tr><tr><td align=\"left\"><bold>Inter-reader variability</bold></td><td/><td align=\"center\">0.949</td><td/></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Coefficients of variation (CV) and repeatability coefficients (RC) for 60 plaques comparing the 5S- and 10S- methods</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>PV (μL)</bold></td><td align=\"left\"><bold>N</bold></td><td align=\"center\" colspan=\"2\"><bold>CV (%)</bold></td><td align=\"center\"><bold>RC (μl)</bold></td></tr><tr><td/><td/><td colspan=\"3\"><hr/></td></tr><tr><td/><td/><td align=\"center\"><bold>5S method</bold></td><td align=\"center\"><bold>10S method</bold></td><td align=\"center\"><bold>5S vs. 10S</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>20 to &lt; 40</bold></td><td align=\"left\">30</td><td align=\"center\">4.3 (2.0)</td><td align=\"center\">3.8 (1.9)</td><td align=\"center\">3.9</td></tr><tr><td align=\"left\"><bold>40 to &lt; 60</bold></td><td align=\"left\">30</td><td align=\"center\">2.4 (1.1)</td><td align=\"center\">2.4 (0.9)</td><td align=\"center\">5.3</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\"><bold>20 to &lt; 60</bold></td><td align=\"left\">60</td><td align=\"center\">3.4 (1.9)</td><td align=\"center\">3.1 (1.6)</td><td align=\"center\">4.7</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>Values are mean (standard deviation); ICC, intra-class correlation coefficient</p></table-wrap-foot>", "<table-wrap-foot><p>PV, plaque volume</p><p>CV was calculated from 3 individual measurements, values are mean (standard deviation)</p><p>RC was calculated for mean of 3 individual measurements comparing 5S and 10S</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1476-7120-6-42-1\"/>", "<graphic xlink:href=\"1476-7120-6-42-2\"/>", "<graphic xlink:href=\"1476-7120-6-42-3\"/>" ]
[]
[{"surname": ["Fenster", "Downey", "Cardinal"], "given-names": ["A", "DB", "HN"], "article-title": ["Three-dimensional ultrasound imaging"], "source": ["Phys Med Biol"], "year": ["2001"], "volume": ["46"], "fpage": ["67"], "lpage": ["99"], "pub-id": ["10.1088/0031-9155/46/5/201"]}, {"surname": ["Ludwig", "Willinek", "von Buquoy", "J\u00f6rger", "Stumpe"], "given-names": ["M", "WA", "M", "U", "KO"], "article-title": ["Limitations of 2-dimensional (D)-ultrasound imaging for the quantitative assessment of common carotid artery atherosclerosis: superiority of high-resolution 3-D-ultrasonography"], "source": ["J Hypertens"], "year": ["1998"], "volume": ["16"], "fpage": ["S104"]}, {"surname": ["Stumpe", "Agabiti-Rosei", "Zielinski", "Schremmer", "Scholze", "Laeis", "Schwandt", "Ludwig"], "given-names": ["KO", "E", "T", "D", "J", "P", "P", "M"], "article-title": ["Carotid intima-media thickness and plaque volume changes following 2-year angiotensin II-receptor blockade. The Multicentre Olmesartan atherosclerosis Regression Evaluation (MORE) study"], "source": ["Ther Adv Cardiovasc Dis"], "year": ["2007"], "volume": ["1"], "fpage": ["97"], "lpage": ["106"], "pub-id": ["10.1177/1753944707085982"]}, {"surname": ["Ludwig", "J\u00f6rger", "Willinek", "Stumpe"], "given-names": ["M", "U", "WA", "KO"], "article-title": ["Simultaneous quantitative assessment of wall thickness and size and volume of soft plaques in superficial large arteries by a new 3-dimensional ultrasound method"], "source": ["J Hypertens"], "year": ["1996"], "volume": ["14"], "fpage": ["S263(P1231)"]}, {"surname": ["Ludwig"], "given-names": ["M"], "article-title": ["3D-Sonographie"], "source": ["Klinische Angiologie"], "year": ["1997"], "volume": ["4.6"], "fpage": ["137"], "lpage": ["175"]}, {"surname": ["Ludwig", "von Buquoy", "von Petzinger-Kruthoff", "Stumpe", "Schremmer"], "given-names": ["M", "M", "A", "KO", "D"], "article-title": ["Reproducibility of 3D-ultrasound assessment of carotid plaque in patients with cardiovascular risk"], "source": ["Atherosclerosis"], "year": ["2007"], "volume": ["8"], "fpage": ["S134"]}, {"surname": ["Shrout", "Fleiss"], "given-names": ["PE", "JL"], "article-title": ["Intraclass correlations: Uses in assessing rater reliability"], "source": ["Psychological Bulletin"], "year": ["1979"], "volume": ["86"], "fpage": ["420"], "lpage": ["428"], "pub-id": ["10.1037/0033-2909.86.2.420"]}, {"surname": ["Shoukri", "Shoukri MM"], "given-names": ["MM"], "article-title": ["Reliability for continuous scale measurements"], "source": ["Measures of interobserver agreement"], "year": ["2004"], "publisher-name": ["Boca Raton : Chapman & Hall/CRC"]}, {"surname": ["Naghavi", "Falk", "Hecht", "Jamieson", "Kaul", "Berman", "Fayad", "Budoff", "Rumberger", "Naqvi", "Shaw", "Faergeman", "Cohn", "Bahr", "Koenig", "Demirovic", "Arking", "Herrera", "Badimon", "Goldstein", "Rudy", "Airaksinen", "Schwartz", "Riley", "Mendes", "Douglas", "Shah"], "given-names": ["M", "E", "HS", "MJ", "S", "D", "Z", "MJ", "J", "TZ", "LJ", "O", "J", "R", "W", "J", "D", "VL", "J", "JA", "Y", "J", "RS", "WA", "RA", "P", "PK"], "collab": ["SHAPE Task Force"], "article-title": ["From vulnerable plaque to vulnerable patient-part III: executive summary of the Screening for Heart Attack Prevention and Education (SHAPE) Task Force Report"], "source": ["Am J Cardiol"], "year": ["2006"], "volume": ["98"], "fpage": ["2"], "lpage": ["15"], "pub-id": ["10.1016/j.amjcard.2006.03.002"]}]
{ "acronym": [], "definition": [] }
23
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2022-01-12 14:47:28
Cardiovasc Ultrasound. 2008 Aug 26; 6:42
oa_package/58/f7/PMC2531085.tar.gz
PMC2531086
18680574
[ "<title>Background</title>", "<p>Missing data are a common occurrence in any area of research, and are especially problematic in quality of life (QoL) studies. Data may be missing for a variety of reasons. If these reasons relate to the QoL of the patient, the missingness is informative. Simply excluding those with missing data from the analysis (\"complete case analysis\"), will bias the results if those who did not respond had significantly lower (or higher) QoL scores than those who did respond.</p>", "<p>Rubin [##UREF##0##1##] defines three main mechanisms of missing data: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). MCAR requires very strong assumptions. An observation is said to be MCAR if the missingness is independent of all observed and unobserved (i.e. previous, current and future) QoL assessments [##REF##9549814##2##]. For example a patient may simply forget to post the questionnaire back. Observations can also be MCAR if the missingness only depends on values of fixed covariates that are measured prior to treatment assignment – often termed covariate-dependent dropout. For example, if elderly patients were less likely to respond, missingness would be dependent on age group.</p>", "<p>A more relaxed assumption about the missing data mechanism is missing at random (MAR), where missingness is independent of all unobserved (missing or future) QoL values, although it may be dependent on the observed values. The \"observed values\" may comprise a baseline measure of QoL or a previous assessment and any appropriate covariates.</p>", "<p>A process that is neither MCAR nor MAR is called missing not at random (MNAR). MNAR occurs if missingness depends not only on the observed data but also on the unobserved (missing) values. An example is that a person with reduced QoL due to side effects of treatment may be less likely to return the questionnaire. The missing value depends on the unobserved QoL scores and the missingness mechanism is informative.</p>", "<p>Many investigators have explored approaches to determine the mechanism of missingness. They have either generated artificial datasets using simulation techniques [##REF##12428897##3##], or have made use of existing datasets in which missing data were then artificially created [##UREF##1##4##]. These procedures are potentially misleading: the missing patterns are predetermined and pre-specified, and usually the performance of the various tests can be anticipated through the known mechanism that was used to generate the samples.</p>", "<p>One approach to deal with missing data is simple imputation, which is the process whereby a single estimated value for the missing observation is obtained, thereby enabling standard statistical methods to be applied to the augmented data set. Various methods can be implemented to impute the missing data. However, the accuracy of imputation cannot normally be determined, as the true values are not known. Various authors have explored the potential accuracy of imputation methods by artificially removing data from a dataset and treating it as missing [##REF##12428897##3##, ####UREF##1##4##, ##REF##11927199##5####11927199##5##]. This is a circular argument, as noted above, because the data are either removed at random or according to some known and pre-specified pattern. In practice, the major analytical problem is that one does not know the exact missing mechanism.</p>", "<p>Engels and Diehr [##REF##14568628##6##] noted the need to use data with real missing patterns, and attempted to overcome these problems by using a dataset where a value was observed after one or more missing values had occurred; the observed value was treated as the true value for the missing data at the preceding time points. Various imputation methods were applied for the missing values, and the results compared against the observed value to assess accuracy of the imputation methods. As Engels and Diehr [##REF##14568628##6##] comment, \"this analysis hinges on the similarity of a known value following a string of missing values to other observations that are missing at that same time.\"</p>", "<p>Poor compliance with collecting QoL data is a well-recognised problem in clinical trials. In an attempt to minimise the level of missing data, the Health Services Research Unit (HSRU) at the University of Aberdeen makes strenuous efforts to recover QoL data. When QoL questionnaires are not returned, HSRU not only issues repeated reminders (including telephone contact), but in addition offers to interview patients by telephone. Therefore, a proportion of patients who initially had missing data – as would have been the case in most clinical trials – then have \"true\" values which were subsequently recovered. This provided a unique opportunity to investigate the performance of tests for identifying missing data mechanisms and methods of imputation, because the results could be evaluated against the data that was later recovered.</p>" ]
[ "<title>Methods</title>", "<title>The dataset</title>", "<p>The RECORD trial was a randomised placebo-controlled trial of daily oral vitamin D and calcium in the secondary prevention of osteoporosis-related fractures in older people [##REF##15885294##7##]. Patients' QoL was assessed by postal questionnaire at 4, 12, 24, 36 and 48 months. The four month data were considered the \"baseline\" measure as QoL for many patients at entry to the trial would be artificially low while they were being treated in hospital for their primary fracture. The questionnaire included the five items of the EuroQoL EQ5D [##REF##10158943##8##], and the 12-item SF12 questionnaire [##UREF##2##9##]. The EQ5D produces a single QoL score, and the SF12 gives two summary scores, the physical and mental component scores (PCS and MCS). The results for EQ5D data are presented here. At each occasion, if a participant did not return the questionnaire within two weeks, up to two reminders were issued (two weeks apart). Patients who returned the questionnaire without needing a reminder were considered 'immediate-responders', while those that returned a questionnaire after one or two reminders provided 'missing yet known' data, and were termed 'reminder-responders'. In the analyses that follow, the scores obtained for reminder-responders were regarded as missing – what they would have been in some clinical studies.</p>", "<title>Identifying the missing data mechanism</title>", "<title>Hypothesis tests</title>", "<p>The pattern of missing data can be described as either \"terminal\", when no further observations were made on a patient after a set of complete observations, or \"intermittent\", in which case one or more observations for a patient were missing before a subsequent observation was observed. It was possible for a patient to have a mixed pattern, with a period of intermittent dropout followed by terminal dropout.</p>", "<p>There are a number of hypothesis tests that can be carried out to test the assumption of MCAR. Little [##UREF##3##10##] developed a test based on the means of the variable of interest under the different missing data patterns (including intermittent and terminal missingness). Alternative hypothesis tests have been suggested by Diggle [##UREF##4##11##], Ridout [##REF##1786335##12##] and Listing and Schlittgen [##UREF##5##13##], all requiring terminal missingness. Diggle [##UREF##4##11##] used an approach which tests whether the subset about to dropout are a random sample of the whole population. Ridout [##REF##1786335##12##] adopted a similar approach to Diggle by utilising logistic regression. Listing and Schlittgen [##UREF##5##13##] proposed a test based on means. These alternatives to Little [##UREF##3##10##], will be less optimal in a situation where intermittent missingness is evident. Restricting the analysis to only those showing a terminal missingness pattern would cause a loss of information. Since RECORD contained intermittent missingness, Little's test was used to illustrate a hypothesis test for MCAR.</p>", "<p>Little's test of MCAR versus MAR [##UREF##3##10##] is based on the rationale that if the data are MCAR then at each time point the calculated means of the observed data should be the same irrespective of the pattern of missingness. For example, it should not matter whether the previous assessment was observed or not, nor whether the one before that was observed. If the data are not MCAR, the mean scores will vary across the patterns. Consider a study with <italic>J </italic>measurements of QoL. Let <italic>P </italic>be the number of distinct missing data patterns (<italic>R</italic><sub><italic>i</italic></sub>) where <italic>J</italic><sup>{<italic>p</italic>} </sup>is the number of observed variables. <italic>n</italic><sup>{<italic>p</italic>} </sup>is the number of cases with the <italic>p</italic><sup>th </sup>pattern and ∑<italic>n</italic><sup>{<italic>p</italic>} </sup>= <italic>N</italic>. Let <italic>M</italic><sup>{<italic>p</italic>} </sup>be a <italic>J</italic><sup>{<italic>p</italic>} </sup><italic>x J </italic>matrix of indicators of the observed variables in pattern <italic>P</italic>. The matrix has one row for each measure present consisting of (J-1) zero's and one 1 identifying the observed measure.</p>", "<p> is the <italic>J</italic><sup>{<italic>p</italic>} </sup><italic>x</italic>1 vector of means of the observed variables for pattern <italic>p</italic>, is the maximum likelihood (ML) estimate of the mean of <italic>Y</italic><sub><italic>i </italic></sub>and is the maximum likelihood estimate of the covariance of <italic>Y</italic><sub><italic>i</italic></sub>. The ML estimates assume the missing data mechanism is ignorable. is the <italic>J</italic><sup>{<italic>p</italic>} </sup><italic>x</italic>1 vector of ML estimates corresponding to the <italic>p</italic><sup><italic>th </italic></sup>pattern and is the corresponding <italic>J</italic><sup>{<italic>p</italic>} </sup><italic>x J</italic><sup>{<italic>p</italic>} </sup>covariance, matrix with a correction for degrees of freedom. Little's proposed test statistic when Σ is unknown, takes the form</p>", "<p></p>", "<p>This test statistic is asymptotically chi-squared with (Σ <italic>J</italic><sup>{<italic>p</italic>} </sup>- <italic>J</italic>) degrees of freedom.</p>", "<title>Logistic regression</title>", "<p>Fairclough [##UREF##6##14##] described an approach to determine the missing data mechanism using logistic regression. The process investigates the missingness mechanism from a cross-sectional standpoint, each time point assessed in turn. Those people who did not respond were excluded from these analyses. An indicator variable was created to identify those patients who responded without the need for a reminder (immediate-responders) and those which were reminder-responders. The first step identified covariates that predict the occurrence of missing observations (reminder-response). Differences between the two groups with respect to a number of covariates were explored with t-tests and chi-squared tests. Logistic regression analyses were used to model the probability of missing an assessment. Identified covariates were forced into the model and the observed QoL scores tested as to whether they also contributed to the prediction of missingness [##UREF##6##14##], as indicated by a reduction in deviance (change in -2*log likelihood). The statistical significance of this reduction in deviance was assessed by comparing it to an appropriate chi-squared distribution (χ<sup>2</sup><sub>1</sub>).</p>", "<p>The advantage of this approach in our setting was the incorporation of the reminder data. A subset of data containing only responders was utilised. The data obtained by reminder was regarded as missing. Initially the process outlined above was carried out assessing whether the covariates and observed QoL were significant predictors of missingness (reminder response). Since the current QoL scores were known, the significance of these to predict missingness (reminder-response) could be assessed. If these scores were found to be statistically significant, the process suggests that data were potentially MNAR.</p>", "<title>Simple imputation</title>", "<title>Methods of imputation</title>", "<p>Simple imputation methods use information from other people (cross-sectional), or information pertaining to the person whose QoL data were missing (longitudinal) [##UREF##7##15##]. Longitudinal methods include last value carried forwards (LVCF), next value carried backwards (NVCB), last-and-next (LaN – average of last value and next value), average available (Avg), average of previous (prev) and average of future (post). Regression can also be carried out utilising other observed QoL scores (regP) or suitable covariates (RegC) or both together (regP2). Some of these methods cannot be utilised at every time point, e.g. <italic>LaN </italic>cannot be used to impute the 48 month scores since there is no 'next' value. Cross sectional methods include mean imputation, regression and hot-decking (random selection from those observed). A disadvantage of regression methods is that people with the same covariate set will have an identical imputed value. This can lead to the variance of the imputed data being artificially small, producing inappropriate standard errors, leading to inflated test statistics and falsely narrow confidence intervals and inappropriate <italic>p</italic>-values in any subsequent analysis [##UREF##6##14##,##UREF##7##15##].</p>", "<p>A newer method not considered here is that of multiple imputation [##UREF##6##14##]. This procedure imputes a number of values for the missing data incorporating both the variability of the QoL measure and the uncertainty surrounding the missing observation. Each dataset is then analysed and the results combined. The focus of this paper however, is the adequacy of simple imputation.</p>", "<title>Assessing accuracy of methods</title>", "<p>The reminder-responses were regarded as missing and imputed using the methods explained above. The accuracy of these methods was then assessed by comparing imputed scores to the actual observed scores (of the reminder-responders), using a bias measure and proportionate variance (PV):</p>", "<p></p>", "<p>Where is the imputed value, <italic>y </italic>is the actual value and <italic>m </italic>is the number of missing values. A positive <italic>Bias </italic>indicated that on average the imputed value underestimated the true QoL value. The <italic>PV </italic>is the ratio of the observed variance to the true variance and assesses the under-dispersion for each method. A <italic>PV </italic>of one indicates that the variance of the imputed values is equal to that of the true values. A <italic>PV </italic>of less than one implies underestimation of the true variance. The bias and PV were calculated for each patient and then an average was taken across all patients. To produce confidence intervals (CIs) for each of the accuracy estimates, the bootstrapping technique [##UREF##8##16##] was used within the statistical package STATA.</p>" ]
[ "<title>Results</title>", "<title>Description of dataset</title>", "<p>The RECORD trial recruited 5,292 patients, with characteristics shown in Table ##TAB##0##1##. The majority were female (85%), and most lived in their own home prior to (88%) and after (86%) the index fracture. The recruiting fracture was less than 90 days before recruitment for 82%, and 94% could walk outdoors unaccompanied. Recruiting fractures were in the arm (62%) or leg/hip (38%). Patients aged over 70 were eligible and 13% of those recruited were 85 and over. At four months, the proportion of deaths was larger in the older age group (85+).</p>", "<p>Table ##TAB##1##2## shows the number of EQ5D assessments at each time point. The number of questionnaires sent at each assessment reduces for two reasons. Firstly, not all patients were followed up after two years. Only those which were recruited early on in the trial were followed up for longer. These patients continued to be followed up until those recruited later had reached the two year assessment. Once all recruited patients were followed up for at two years, follow up stopped and no further data were collected. At 36 months, only 3,663 patients were followed up and this reduced further to 1,629 patients at 48 months. Secondly, some patients withdrew from the trial or died. The proportion of those sent questionnaires that provided valid QoL scores with or without reminder varied from 79% at 4 months to 86% at 48 months. Of those completing forms, 20% to 26% were reminder-responders. Overall, more than half of the data initially missing were recovered by the reminder system.</p>", "<title>Identifying the missing data mechanism</title>", "<title>Hypothesis tests of MCAR</title>", "<p>Considering data from the first three time points, Little's test statistic was X<sup>2 </sup>= 133.75 (9 df) with p &lt; 0.001. The data were restricted to those patients who responded at each of the first three time points (N = 2606) and data collected by reminder was set to missing. In this situation Little's test statistic was X<sup>2 </sup>= 39.6 (9 df) with p &lt; 0.001. Therefore, there was evidence against MCAR, suggesting that QoL impacted on whether or not a patient responded with or without the need for reminder.</p>", "<title>Logistic regression</title>", "<p>This section deals with responders only and the reminder-responders were regarded as missing. Using logistic regression at 12 months the covariates found to be significant predictors of missingness were gender, locomotor ability, residence type prior to fracture and marital status; at 24 months -gender, age group, locomotor ability and type of recruiting fracture; while at 36 months – age group and marital status; finally at 48 months – locomotor ability and time since recruiting fracture.</p>", "<p>The change in deviance was used to determine whether the previous QoL score was a significant predictor having adjusted for covariates (Table ##TAB##2##3##). Previous QoL was defined as the most recent known QoL score prior to the time point of interest. The change in deviance was significant at 12 and 24 months. This indicated that, after adjusting for covariates, previous QoL remained important in modelling the probability of missing assessment. The null hypothesis of MCAR was rejected at 12 and 24 months. At 36 and 48 months there was insufficient evidence to reject the possibility that missingness was MCAR.</p>", "<p>In normal circumstances the investigation would stop at this point, because in most trials the true current score, <italic>x</italic><sub><italic>c</italic></sub>, is not available for the \"missing\" group. However, using data collected by reminder the process was continued. Table ##TAB##2##3## shows the log-likelihoods for model 3 (covariates + current QoL) and model 4 (covariates + previous and current QoL). After adjusting for both covariates and previous QoL, at 12, 24 and 36 months the current QoL was significant in the model, suggesting there was evidence of MNAR data. At 48 months there was no evidence that current or previous QoL were important in the model – but, at this time our sample size was substantially depleted.</p>", "<p>Another question of interest was whether the non-responders were in any way different to the reminder-responders. A similar process was undertaken as above. The non-responders differed in one or two covariates at each time point but having adjusted for this, their previous score was not a significant predictor. Thus, there was no evidence that the previous QoL experience differed between the non-responders and the reminder-responders at a given assessment. This gave confidence that the reminder-responders were perhaps similar to the non-responders.</p>", "<title>Imputation of reminder-responder scores</title>", "<p>Results for the imputed data were compared with the actual data and the 24 month data are presented in Figures ##FIG##0##1## and ##FIG##1##2##. Figure ##FIG##0##1## shows that at 24 months the smallest bias occurred with the <italic>post </italic>method (b = -0.002), while second smallest was <italic>NVCB </italic>(b = -0.014). The bias was significantly greater for the regression and cross-sectional approaches. At 4 and 12 months (data not shown), the <italic>average </italic>and <italic>NVCB </italic>were the best methods in terms of bias. At 36 months, none of the procedures provided a sufficiently accurate estimate and the bias was greater than -0.04. The number of procedures applicable at 48 months was reduced with the regression based on baseline characteristics showing the smallest bias (b = -0.004).</p>", "<p>Figure ##FIG##1##2## shows the best <italic>PV </italic>value for the 24 month data occurred with the hotdecking methods, which was perhaps expected since these methods impute using random selection from the immediate-responders. Hotdecking with stratification was the best of the two (PV = 0.979). The 'after' methods of <italic>post </italic>and <italic>NVCB </italic>had slightly lower <italic>PV</italic>, just under 0.8. The three <italic>regression </italic>procedures were very poor at preserving the variance. Since the same value is imputed for all missing values using the 'mean' methods, there was no variation in the imputed values, which would have a big impact on any subsequent tests and <italic>p</italic>-values.</p>", "<p>At other time points (data not shown), where applicable, <italic>NVCB </italic>showed reasonable <italic>PV</italic>. The regression methods were consistently poor at preserving the variance. The <italic>hotdeck </italic>with stratification procedure was reasonably good at maintaining the variance at all time points (<italic>PV </italic>ranged from 0.87 to 1.27). By nature of the hotdecking procedure it is expected that the variance of the imputed values would be the same as that of the true values. Although the observed PV was not equal to one, the 95% CI did include the desired value of one, suggesting that the sample being imputed was similar to that from which values are being selected.</p>", "<p>In general, for the RECORD trial methods involving QoL scores surrounding (and in particular those after) the point of imputation were the most accurate in terms of bias and at preserving the variance.</p>" ]
[ "<title>Discussion</title>", "<p>Identification of the correct mode of missingness and most appropriate method of imputation can make a large impact on the analysis of clinical trials. The sensitivity of different analyses depends on the proportion of missing assessments and the strength of the underlying causes for missing data [##REF##9549815##17##]. The undesirable effect of missingness on bias and power increases with the severity of non-randomness as well as the proportion of missingness [##REF##9549820##18##].</p>", "<p>Little's test [##UREF##3##10##] for MCAR showed evidence against MCAR in favour of MAR between responders and non-responders and also between the immediate- and reminder-responders. The logistic regression approach showed on the whole, at each of 12, 24 and 36 months, after adjusting for the required covariates, both the previous and current QoL scores were significant predictors of missing assessment (response by reminder). This implied there was evidence of MNAR data at 12, 24 and 36 months. It is possible that the \"reminder-responders\" may differ from the persistent non-responders, but the analyses found no evidence of this in terms of previous QoL scores. This approach using data collected through reminders has provided an indication of MNAR, with the rationale that reminder-responders were more likely to be similar to the non-responders than the immediate-responders.</p>", "<p>It should be noted that data collected through reminders has been assumed to be equivalent to that collected immediately. However, data collected via reminder are actually reflecting a time two (or four) weeks later than the original assessment time. This may bias the recovered data, but for the purposes of this investigation we assumed it to be comparable to data collected without the need for reminder.</p>", "<p>The missingness mechanism was identified as potentially MNAR, but was simple imputation adequate? The results suggested that for the RECORD study the missing QoL scores could be imputed using assessments close to the point of imputation. In many QoL studies the assessments are taken at frequent intervals and the correlations between successive measurements may be high. Those imputation methods that focus on within-patient assessments close in time to the missing values are likely to be most effective. The population based methods assume the data are either MAR or MCAR. Since the data in this study were most likely MNAR, it is not surprising that these imputation methods were less accurate.</p>", "<p>Data that are MNAR may depend on current and future observations, thus methods that utilise this data are intuitively going to be more accurate than those based on previous measures. <italic>NVCB </italic>and <italic>post-average </italic>showed the smallest bias. Although, the methods involving previous scores are useful, they can never be entirely accurate in the presence of MNAR. The methods of <italic>NVCB </italic>and <italic>post-average </italic>may not be practical as they are dependent on future QoL scores being available, which will only happen when missingness is intermittent. Often, in trials, the final assessment is the main focus and no future data are available to inform the imputation. Only methods using 'before' data are available, and these methods have shown to provide greater bias, suggesting that simple imputation is inadequate in the presence of MNAR data.</p>", "<p>Limitations of this study are that the data are from a single trial, involving older people, and the studied disease is perhaps not typical of studies involving QoL assessments. However, our results agree with Engels and Diehr [##REF##14568628##6##], despite being from a different disease, different country and for different QoL outcomes. We infer from this that the results may perhaps be generalisable.</p>", "<p>If imputation procedures are to be employed, researchers need to be confident of their accuracy. One apparent advantage of imputation is that, once missing values have been filled in, standard methods of analysis can be undertaken on this augmented dataset comprising the observed and the imputed values. However, imputed values cannot be regarded as the same as if the full data has been observed. Although some summary statistics such as means and medians may not be distorted, the corresponding standard deviations may be shrunk and this will have consequences for the subsequent calculation of the confidence intervals [##UREF##7##15##]. This consequence of simple imputation is present whatever the missingness mechanism and provides a major disadvantage against the use of simple imputation procedures, even if one can assume the unlikely scenario of MCAR data.</p>", "<p>Although the imputation may overestimate the true values in the reminder group, it may still bring the overall scores closer. What matters most is minimising the bias in treatment comparisons. An investigation into the effect of the different methods of imputation on the treatment effects forms the basis of future work.</p>", "<p>During RECORD the issuing of reminders substantially increased the number of included patients, with corresponding gains in statistical power and the assurance of reducing the bias by avoiding the need for imputation. The reminder system entails extra resources. However, in any study having as much data as possible for analysis is very important and if the use of reminders can generate a significant proportion of extra data then it is a useful procedure. The reminder process is a viable approach not only for use with postal questionnaires, but also in computer based testing and integrated voice response methods. It should be noted that the best way to prevent the problems of missing data is to simply avoid it, by employing good data collection techniques and making an effort to chase up missing information. When the proportion of missing data becomes too large, no statistical technique will provide the solution.</p>" ]
[ "<title>Conclusion</title>", "<p>The first step in the analysis of incomplete data should involve quantifying the extent of missingness, identifying which individuals have missing data and at which assessments. In usual situations none of the missing QoL data are retrieved, and thus it is not possible to test formally a hypothesis that missingness is MAR as opposed to MNAR. Our study provided an example in which it was possible to carry out a formal test, confirming that data were MNAR and that simple imputation was unsatisfactory in this situation.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Objective</title>", "<p>QoL data were routinely collected in a randomised controlled trial (RCT), which employed a reminder system, retrieving about 50% of data originally missing. The objective was to use this unique feature to evaluate possible missingness mechanisms and to assess the accuracy of simple imputation methods.</p>", "<title>Methods</title>", "<p>Those patients responding after reminder were regarded as providing missing responses. A hypothesis test and a logistic regression approach were used to evaluate the missingness mechanism. Simple imputation procedures were carried out on these missing scores and the results compared to the actual observed scores.</p>", "<title>Results</title>", "<p>The hypothesis test and logistic regression approaches suggested the reminder data were missing not at random (MNAR). Reminder-response data showed that simple imputation procedures utilising information collected close to the point of imputation (last value carried forward, next value carried backward and last-and-next), were the best methods in this setting. However, although these methods were the best of the simple imputation procedures considered, they were not sufficiently accurate to be confident of obtaining unbiased results under imputation.</p>", "<title>Conclusion</title>", "<p>The use of the reminder data enabled the conclusion of possible MNAR data. Evaluating this mechanism was important in determining if imputation was useful. Simple imputation was shown to be inadequate if MNAR are likely and alternative strategies should be considered.</p>" ]
[ "<title>Abbreviations</title>", "<p>CIs: confidence intervals; DF: degrees of freedom; HSRU: Health Service Research Unit; LaN: last-and-next; LVCF: last value carried forwards; MAR: missing at random; MCAR: missing completely at random; MCS: mental component score; MNAR: missing not at random; NVCB: next value carried backwards; PCS: physical component score; PV: proportionate variance; QoL: quality of life; RCT: randomised controlled trial.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>SF analysed and interpreted the data, drafted the manuscript and gave final approval to the submitted manuscript. PMF conceived the idea, assisted in interpretation of the results, commented on drafts and gave final approval to the submitted manuscript. AM and GM were involved in the design and running of the RECORD trial including data collection, commented on drafts and gave final approval to the submitted manuscript. MKC was involved in the design and running of the RECORD trial, commented on drafts and gave final approval to the submitted manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We thank the patients who took part in the RECORD study, without whose help this study would not have been possible. The MRC funded the central organisation of RECORD, and Shire Pharmaceuticals Group plc funded the drugs, which were manufactured by Nycomed Ltd. The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31). The views expressed are, however, not necessarily those of the funding body.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Bias results of EQ5D imputation at the 24 month follow up.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><italic>PV </italic>results of EQ5D imputation at the 24 month follow up.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Patient characteristic of study population (N = 5292)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td/><td/><td align=\"center\" colspan=\"2\"><bold>Percentage with score available at 4 m</bold></td><td align=\"center\" colspan=\"3\"><bold>Percentage without score available at 4 m</bold></td></tr><tr><td/><td/><td align=\"left\"><bold>All Patients Number (%)</bold></td><td align=\"center\"><bold>No reminder</bold></td><td align=\"center\"><bold>After reminder</bold></td><td align=\"center\"><bold>Not returned</bold></td><td align=\"center\"><bold>Absent or withdrawn</bold></td><td align=\"center\"><bold>Dead</bold></td></tr></thead><tbody><tr><td align=\"left\">Age group</td><td align=\"left\">70–74</td><td align=\"left\">1917 (36)</td><td align=\"center\">40</td><td align=\"center\">37</td><td align=\"center\">29</td><td align=\"center\">29</td><td align=\"center\">12</td></tr><tr><td/><td align=\"left\">75–79</td><td align=\"left\">1665 (32)</td><td align=\"center\">33</td><td align=\"center\">31</td><td align=\"center\">31</td><td align=\"center\">30</td><td align=\"center\">18</td></tr><tr><td/><td align=\"left\">80–84</td><td align=\"left\">1030 (19)</td><td align=\"center\">17</td><td align=\"center\">19</td><td align=\"center\">23</td><td align=\"center\">24</td><td align=\"center\">33</td></tr><tr><td/><td align=\"left\">85+</td><td align=\"left\">680 (13)</td><td align=\"center\">10</td><td align=\"center\">13</td><td align=\"center\">17</td><td align=\"center\">17</td><td align=\"center\">36</td></tr><tr><td align=\"left\">Sex</td><td align=\"left\">Male</td><td align=\"left\">811 (15)</td><td align=\"center\">16</td><td align=\"center\">13</td><td align=\"center\">15</td><td align=\"center\">13</td><td align=\"center\">31</td></tr><tr><td/><td align=\"left\">Female</td><td align=\"left\">4480 (85)</td><td align=\"center\">84</td><td align=\"center\">87</td><td align=\"center\">84</td><td align=\"center\">87</td><td align=\"center\">69</td></tr><tr><td align=\"left\">Type of recruiting fracture</td><td align=\"left\">Proximal femur</td><td align=\"left\">904 (17)</td><td align=\"center\">16</td><td align=\"center\">17</td><td align=\"center\">20</td><td align=\"center\">18</td><td align=\"center\">47</td></tr><tr><td/><td align=\"left\">Other leg and pelvic</td><td align=\"left\">1130 (21)</td><td align=\"center\">22</td><td align=\"center\">20</td><td align=\"center\">21</td><td align=\"center\">20</td><td align=\"center\">17</td></tr><tr><td/><td align=\"left\">Distal arm</td><td align=\"left\">1846 (35)</td><td align=\"center\">36</td><td align=\"center\">35</td><td align=\"center\">31</td><td align=\"center\">36</td><td align=\"center\">20</td></tr><tr><td/><td align=\"left\">Other arm</td><td align=\"left\">1403 (27)</td><td align=\"center\">26</td><td align=\"center\">28</td><td align=\"center\">28</td><td align=\"center\">25</td><td align=\"center\">17</td></tr><tr><td/><td align=\"left\">Other</td><td align=\"left\">9 (&lt;1)</td><td align=\"center\">0</td><td align=\"center\">0</td><td align=\"center\">0</td><td align=\"center\">0</td><td align=\"center\">0</td></tr><tr><td align=\"left\">Locomotor ability (Walk unaccompanied)</td><td align=\"left\">Yes</td><td align=\"left\">4979 (94)</td><td align=\"center\">95</td><td align=\"center\">93</td><td align=\"center\">93</td><td align=\"center\">93</td><td align=\"center\">85</td></tr><tr><td/><td align=\"left\">No</td><td align=\"left\">300 (6)</td><td align=\"center\">5</td><td align=\"center\">7</td><td align=\"center\">7</td><td align=\"center\">7</td><td align=\"center\">15</td></tr><tr><td align=\"left\">Time since recruiting fracture</td><td align=\"left\">≤ 90 days</td><td align=\"left\">4331 (82)</td><td align=\"center\">81</td><td align=\"center\">84</td><td align=\"center\">82</td><td align=\"center\">86</td><td align=\"center\">73</td></tr><tr><td/><td align=\"left\">&gt; 90 days</td><td align=\"left\">961 (18)</td><td align=\"center\">19</td><td align=\"center\">16</td><td align=\"center\">18</td><td align=\"center\">14</td><td align=\"center\">27</td></tr><tr><td align=\"left\">Residence type <italic>prior </italic>to recruiting fracture</td><td align=\"left\">Own home</td><td align=\"left\">4628 (88)</td><td align=\"center\">89</td><td align=\"center\">86</td><td align=\"center\">84</td><td align=\"center\">87</td><td align=\"center\">78</td></tr><tr><td/><td align=\"left\">Sheltered housing</td><td align=\"left\">538 (10)</td><td align=\"center\">9</td><td align=\"center\">12</td><td align=\"center\">13</td><td align=\"center\">11</td><td align=\"center\">11</td></tr><tr><td/><td align=\"left\">Other</td><td align=\"left\">126 (2)</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">11</td></tr><tr><td align=\"left\">Residence type <italic>after </italic>recruiting fracture</td><td align=\"left\">Own home</td><td align=\"left\">4555 (86)</td><td align=\"center\">88</td><td align=\"center\">85</td><td align=\"center\">82</td><td align=\"center\">85</td><td align=\"center\">74</td></tr><tr><td/><td align=\"left\">Sheltered housing</td><td align=\"left\">531 (10)</td><td align=\"center\">9</td><td align=\"center\">11</td><td align=\"center\">12</td><td align=\"center\">10</td><td align=\"center\">11</td></tr><tr><td/><td align=\"left\">Other</td><td align=\"left\">206 (4)</td><td align=\"center\">3</td><td align=\"center\">4</td><td align=\"center\">6</td><td align=\"center\">5</td><td align=\"center\">15</td></tr><tr><td align=\"left\">Marital status</td><td align=\"left\">Single</td><td align=\"left\">348 (7)</td><td align=\"center\">7</td><td align=\"center\">6</td><td align=\"center\">7</td><td align=\"center\">6</td><td align=\"center\">7</td></tr><tr><td/><td align=\"left\">Married</td><td align=\"left\">2069 (40)</td><td align=\"center\">42</td><td align=\"center\">36</td><td align=\"center\">32</td><td align=\"center\">39</td><td align=\"center\">25</td></tr><tr><td/><td align=\"left\">Divorced</td><td align=\"left\">222 (4)</td><td align=\"center\">4</td><td align=\"center\">5</td><td align=\"center\">5</td><td align=\"center\">2</td><td align=\"center\">2</td></tr><tr><td/><td align=\"left\">Widow(er)</td><td align=\"left\">2634 (50)</td><td align=\"center\">47</td><td align=\"center\">52</td><td align=\"center\">56</td><td align=\"center\">52</td><td align=\"center\">65</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Number (%) of EQ5D scores at each follow up point</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"5\"><bold>Month of assessment</bold></td></tr><tr><td/><td align=\"center\"><bold>4</bold></td><td align=\"center\"><bold>12</bold></td><td align=\"center\"><bold>24</bold></td><td align=\"center\"><bold>36</bold></td><td align=\"center\"><bold>48</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>EQ5D score (no reminder)</bold></td><td align=\"center\">2908 (59)</td><td align=\"center\">2648 (62)</td><td align=\"center\">2511 (67)</td><td align=\"center\">1670 (69)</td><td align=\"center\">661 (69)</td></tr><tr><td align=\"left\"><bold>EQ5D score (after reminder)</bold></td><td align=\"center\">999 (20)</td><td align=\"center\">840 (20)</td><td align=\"center\">693 (18)</td><td align=\"center\">406 (17)</td><td align=\"center\">162 (17)</td></tr><tr><td align=\"left\"><bold>Not returned</bold></td><td align=\"center\">1042 (21)</td><td align=\"center\">763 (18)</td><td align=\"center\">561 (15)</td><td align=\"center\">338 (14)</td><td align=\"center\">138 (14)</td></tr><tr><td align=\"left\"><bold>Total sent</bold></td><td align=\"center\">4949 (100)</td><td align=\"center\">4251 (100)</td><td align=\"center\">3765 (100)</td><td align=\"center\">2414 (100)</td><td align=\"center\">961 (100)</td></tr><tr><td align=\"left\"><bold>Total available for follow up</bold></td><td align=\"center\">5292</td><td align=\"center\">5292</td><td align=\"center\">5292</td><td align=\"center\">3663</td><td align=\"center\">1629</td></tr><tr><td align=\"left\"><bold>Not Sent</bold></td><td align=\"center\">343 (6)</td><td align=\"center\">1041 (20)</td><td align=\"center\">1527 (29)</td><td align=\"center\">1249 (34)</td><td align=\"center\">668 (41)</td></tr><tr><td align=\"left\"><bold>Proportion of responders who did so by reminder</bold></td><td align=\"center\">26%</td><td align=\"center\">24%</td><td align=\"center\">22%</td><td align=\"center\">20%</td><td align=\"center\">20%</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Log-likelihood's for models 1–4</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"center\" colspan=\"4\"><bold>Month of assessment</bold></td></tr><tr><td/><td align=\"center\"><bold>12</bold></td><td align=\"center\"><bold>24</bold></td><td align=\"center\"><bold>36</bold></td><td align=\"center\"><bold>48</bold></td></tr></thead><tbody><tr><td align=\"left\"><bold>Log-Likelihood</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>L<sub>1</sub>: </bold>MCAR fixed covariates</td><td align=\"center\">-1680.3</td><td align=\"center\">-1411.6</td><td align=\"center\">-846.3</td><td align=\"center\">-350.8</td></tr><tr><td align=\"left\"> <bold>L<sub>2</sub>: </bold>MAR fixed covariates + previous QoL</td><td align=\"center\">-1673.9</td><td align=\"center\">-1409.5</td><td align=\"center\">-845.7</td><td align=\"center\">-350.8</td></tr><tr><td align=\"left\"> <bold>L<sub>3</sub>: </bold>MNAR fixed covariates + current QoL</td><td align=\"center\">-1669.4</td><td align=\"center\">-1406.1</td><td align=\"center\">-843.2</td><td align=\"center\">-350.7</td></tr><tr><td align=\"left\"> <bold>L<sub>4</sub>: </bold>MAR fixed covariates + previous QoL + current QoL</td><td align=\"center\">-1669</td><td align=\"center\">-1406.1</td><td align=\"center\">-843</td><td align=\"center\">-350.7</td></tr><tr><td colspan=\"5\"><hr/></td></tr><tr><td align=\"left\"><bold>Change in log-likelihood</bold></td><td/><td/><td/><td/></tr><tr><td align=\"left\"> <bold>-2*(L<sub>1 </sub>– L<sub>2</sub>) – test of MAR</bold></td><td align=\"center\"><italic>12.8</italic>*</td><td align=\"center\"><italic>4.2</italic>*</td><td align=\"center\">1.2</td><td align=\"center\">0</td></tr><tr><td align=\"left\"> <bold>-2*(L<sub>1 </sub>– L<sub>3</sub>) – test of MNAR</bold></td><td align=\"center\"><italic>21.8</italic>*</td><td align=\"center\"><italic>11.0</italic>*</td><td align=\"center\"><italic>6.2</italic>*</td><td align=\"center\">0.2</td></tr><tr><td align=\"left\"> <bold>-2*(L<sub>2 </sub>– L<sub>4</sub>) – test of MNAR</bold></td><td align=\"center\"><italic>9.8</italic>*</td><td align=\"center\"><italic>6.8</italic>*</td><td align=\"center\"><italic>5.4</italic>*</td><td align=\"center\">0.2</td></tr></tbody></table></table-wrap>" ]
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[]
[]
[]
[]
[]
[ "<table-wrap-foot><p>* significant change, p &lt; 0.05</p></table-wrap-foot>" ]
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[]
[{"surname": ["Rubin"], "given-names": ["DB"], "article-title": ["Inference and missing data"], "source": ["Biometrika"], "year": ["1976"], "volume": ["72"], "fpage": ["359"], "lpage": ["364"]}, {"surname": ["Myers"], "given-names": ["WR"], "article-title": ["Handling missing data in clinical trials: An overview"], "source": ["Drug Inf J"], "year": ["2000"], "volume": ["34"], "fpage": ["525"], "lpage": ["533"]}, {"surname": ["Ware", "Snow", "Kosinski", "Gandek"], "given-names": ["JR", "KK", "M", "B"], "article-title": ["SF-36 health survey manual and interpretation guide"], "year": ["1993"]}, {"surname": ["Little"], "given-names": ["RJA"], "article-title": ["A test of missing completely at random for multivariate data with missing values"], "source": ["Journal of American Statistical Association"], "year": ["1988"], "volume": ["83"], "fpage": ["1198"], "lpage": ["1202"], "pub-id": ["10.2307/2290157"]}, {"surname": ["Diggle"], "given-names": ["PJ"], "article-title": ["Testing for random dropouts in repeated measurements data"], "source": ["Biometrics"], "year": ["1989"], "volume": ["45"], "fpage": ["1255"], "lpage": ["1258"], "pub-id": ["10.2307/2531777"]}, {"surname": ["Listing", "Schlittgen"], "given-names": ["J", "R"], "article-title": ["Tests if dropouts are missed at random"], "source": ["Biometrical Journal"], "year": ["1998"], "volume": ["40"], "fpage": ["929"], "lpage": ["935"], "pub-id": ["10.1002/(SICI)1521-4036(199812)40:8<929::AID-BIMJ929>3.0.CO;2-X"]}, {"surname": ["Fairclough"], "given-names": ["DL"], "source": ["Design and Analysis of Quality of Life Studies in Clinical Trials"], "year": ["2002"], "publisher-name": ["Chapman and Hall"]}, {"surname": ["Fayers", "Machin"], "given-names": ["PM", "D"], "source": ["Quality of Life: Assessment, Analysis and Interpretation Wiley"], "year": ["2001"]}, {"surname": ["Efron", "Tibshirani"], "given-names": ["B", "RJ"], "source": ["An Introduction to the Bootstrap"], "year": ["1993"], "publisher-name": ["London: Chapman and Hall"]}]
{ "acronym": [], "definition": [] }
18
CC BY
no
2022-01-12 14:47:28
Health Qual Life Outcomes. 2008 Aug 4; 6:57
oa_package/7c/a6/PMC2531086.tar.gz
PMC2531087
18706109
[ "<title>Background</title>", "<p>Neuropathic pain results from a nerve lesion or nerve damage and may be experienced as burning, electric shock-like, sharp stabbing pains that come and go, deep aches that make sleep or normal activities difficult, or very sensitive skin that reacts to even a slight touch [##REF##11698022##1##,##REF##11888241##2##]. These sensations not only affect the sensory system, but also translate into a wider impact on patients' health related quality of life in terms of alterations in sleep patterns, concentration and mood. Neuropathic pain has been defined by the International Association for the Study of Pain as pain initiated or caused by a primary lesion or dysfunction of the nervous system [##UREF##0##3##]. Due to the fact that some researchers find this definition overly broad, neuropathic pain has also been characterized as pain caused by lesions of the peripheral or central nervous system (or both) that manifest sensory symptoms or signs [##REF##12933403##4##]. The assessment of neuropathic pain is often complex, given that it is associated with a wide variety of chronic diseases or conditions such as diabetes, carpal or ulnar nerve entrapments, sciatica, spinal cord injury and neuralgia [##REF##15009162##5##].</p>", "<p>Neuropathic pain is a subjective experience and the use of patient-reported outcomes (PROs) in measuring symptoms and their manifestation into the patient's life is important. There are several evaluative instruments dealing with neuropathic pain [##REF##9040716##6##, ####REF##12966256##7##, ##REF##15030944##8##, ##REF##16199253##9####16199253##9##]. Selecting appropriate measures for the complex assessment of neuropathic pain is challenging. Regulatory agencies have developed guidelines that direct researchers on the development and validation of PRO measures [##UREF##1##10##,##UREF##2##11##]. In order for an instrument to be considered well developed, the new guidelines have specified several key points. The development of the instrument must include patient involvement to assist in developing the concepts to be measured or, as the guidelines infer, the question generation process would be incomplete. A wide range of patients should be included in the development of a questionnaire to ensure a representative sample and variations in population characteristics. Following the development of the questions, it is important to review these questions with patients to ensure their clarity and relevance. A questionnaire is not considered valid until the statistical properties have been tested.</p>", "<p>The new guidelines direct researchers on the validation steps to ensure the measurement properties are adequate for use in clinical trials. Regulatory agencies want to be sure the questionnaire reliably measures the concepts it was designed to measure. It should be noted, however, that the statistical testing of the questionnaire should guide the development and not dictate which items remain in the questionnaire. Relevance to the patient and clinical importance should always be considered. Most questionnaires were developed solely based on clinical expert opinions regarding which symptoms subjects experience and not the patients' perspective on treatment outcomes – an important scientific standard in questionnaire development [##UREF##1##10##]. In addition, perceptions and descriptions of neuropathic pain might possibly differ between cultures. Thus, to ensure that the questionnaire is suitable for use in worldwide clinical trials, it should not reflect cultural bias.</p>", "<p>This study evaluates the face and content validity of the Neuropathic Pain Symptom Inventory (NPSI) [##REF##15030944##8##]. The NPSI was developed to assess more specifically the different components of neuropathic pain syndromes (i.e. spontaneous ongoing and paroxysmal pain, evoked pain, paresthesia/dysesthesia). This self-questionnaire includes ten items related to different pain descriptors (e.g. burning, squeezing, electric-shock, stabbing, tingling) allowing the assessment of the different dimensions of neuropathic pain and two items on frequency and duration of pain. Each of the items has a recall of the past 24 hours and items are rated on an 11-point numeric rating scale anchored by 0: No (symptom) and 10: Worst (symptom) imaginable. We employed qualitative research methods to identify symptoms deemed most important to the subjects affected by neuropathic pain and the manner in which the subjects describe those symptoms. Because the NPSI may be used to study several forms of neuropathic pain, it is important to establish a core set of neuropathic pain symptoms. Therefore, this assessment focuses on a core set of symptoms commonly described as symptoms in neuropathic pain that also span multiple cultures.</p>", "<p>The objective of this study was to determine if the NPSI adequately assesses neuropathic pain symptoms, and is acceptable and relevant to patients with diabetic peripheral neuropathy (DPN), post-herpetic neuralgia (PHN), trigeminal neuralgia (TN), and sciatica across multiple, diverse cultural norms.</p>" ]
[ "<title>Methods</title>", "<title>Recruitment</title>", "<p>Focus groups in six countries (U.S. [English], Brazil [Portuguese], Japan [Japanese], China [Mandarin], Finland [Finnish], Spain [Spanish]) were designed to elicit concepts that were most important and relevant to patients with neuropathic pain. Subjects were recruited through pain specialists via recruitment agencies. The recruitment agencies initiated contact with pain specialists who invited subjects to participate in the study. Subjects received an informational letter outlining the purpose of the study and the extent of their involvement, and physicians obtained informed consent prior to study. Both subjects and their physicians were required to complete a case report form (CRF) that included clinician and subject contact information and ensured the eligibility of the subject through a list of inclusion and exclusion criteria. Subjects were informed that the focus group session would last approximately two hours. The CRFs were reviewed for completeness and patient eligibility prior to beginning the focus group sessions.</p>", "<p>Six to ten subjects were recruited for each focus group. An attempt was made to recruit subjects of differing age, gender, and ethnicity (the latter only in the U.S.). Subjects with mild to severe neuropathic pain were included to capture the full spectrum of patient pain.</p>", "<title>Inclusion/exclusion criteria</title>", "<p>Study inclusion criteria included: 18 years of age or older; diagnosed with DPN, PHN, TN, or sciatica; able to discern his/her neuropathic pain from any concomitant pain (e.g., joint pain) as determined by their physician; and ability to participate in a two-hour focus group discussion. In addition, subjects met at least three of the following inclusion criteria (abstracted from the ID Pain [##REF##16870080##12##]) to verify the presence of neuropathic pain: described his/her pain as feeling like pins and needles; described his/her pain as feeling like hot/burning; described his/her pain as feeling numb; has described his/her pain as feeling like electrical shocks; and/or reported that his/her pain is worsened by the touch of clothing or bed sheets. Exclusion criteria included: serious mental health or cognition condition(s), including cognitive impairment, severe mental retardation, schizophrenia, and/or physician-assessed clinical depression.</p>", "<p>Prior to the initiation of the focus groups, subjects completed forms for informed consent and background demographics, as well as pre-focus group questionnaires. These questionnaires asked subjects to list five terms that describe their nerve pain in conjunction with the five most-bothersome symptoms (i.e., \"<italic>People feel pain in many ways and people might describe pain using many different terms. We are interested in how you would describe your nerve pain. Please list below five words that you would use to describe your nerve pain</italic>;\" and \"<italic>Please list below the three most bothersome sensations you feel related to your nerve pain</italic>.\"). Collecting this information spontaneously prior to discussing the topic with other subjects via questionnaire avoids the potential introduction of error through \"yeah-saying\" in the focus groups.</p>", "<title>Concept elicitation and content validation</title>", "<p>Trained moderators conducted the focus group sessions using a semi-structured discussion guide. Prior to the start of the focus group, the moderators explained the purpose of the study, reassured the subjects of the confidentiality of their responses, encouraged the subjects to take their time with their responses, and allowed all subjects an opportunity to share their views with the group. The moderator informed the participants that the focus group sessions would be audio- and/or video-recorded as stated in the consent form that each participant had signed prior to the focus group. The focus group guide consisted of: 1) a concept elicitation phase, and 2) face and content validation phase. During the concept elicitation phase, subjects received open-ended questions about their neuropathic pain experiences, focusing on symptoms they experienced due to their neuropathic pain. Subjects identified and described such sensations in detail. Initially, subjects responded spontaneously to these questions. If sensations previously described in the questionnaire were not mentioned spontaneously, the moderator probed the subjects to determine the accurateness of the sensations. These questions were asked prior to the content validation phase of the interview to ensure that the subjects were not unduly biased by the sensations covered in the NPSI. This allowed for a pure assessment of symptoms prior to the face and content validation of the questionnaire and a more guided assessment of symptoms during the second phase of the focus group.</p>", "<p>During the concept elicitation phase, the importance of each neuropathic pain symptom was ranked by patients detailing the \"most bothersome\" sensation they experience. During the face and content validation phase of the focus groups, the subjects completed the NPSI and commented on the extent to which the questionnaire captured key symptoms associated with neuropathic pain. The purpose of this phase of the focus groups was to ensure: 1) the relevance of the concepts covered by the questionnaire, 2) the questionnaire's comprehensiveness and ease of understanding, and 3) the applicability/acceptability of the items.</p>", "<title>Transcription/translation</title>", "<p>Transcriptions were produced from the audiotapes of the sessions, and verbatim subject comments were analyzed. Recordings in Japanese, Spanish, Portuguese, and Chinese were transcribed into the respective native language prior to English translation. The English transcripts of the other countries' focus group data were then analyzed. The Finnish tapes were transcribed into Finnish and then analyzed in the native language. Subject quotes were grouped together by symptom and compared to the symptoms included in the NPSI.</p>", "<p>Coding schemes were developed to translate descriptions of patient characteristics into thematic trends for data analysis. The thematic coding scheme underwent iterations as the research team coded the preliminary data. Initial coded material was aggregated into broader core categories and analyzed using grounded theory methods [##UREF##3##13##]. For the concept elicitation sections of the focus groups, each subject comment was assigned a \"classification\" and \"domain\" and incorporated into a domain mapping grid. The classifications and domains identified, along with examples of subject quotes, were used as a basis for determining whether all relevant symptoms were included in the NPSI.</p>" ]
[ "<title>Results and discussion</title>", "<p>One hundred and thirty-two subjects from six countries were interviewed (Table ##TAB##0##1##), Background demographics, including age and gender are summarized in Table ##TAB##1##2##. The type of neuropathic pain and clinician-rated severity of pain are included in Table ##TAB##2##3##.</p>", "<p>In the U.S., the majority of the subjects (72%) were Caucasian. The remaining participants were African American (8%), Hispanic/Latino (11%), and from other ethnic groups (3%). As illustrated in Table ##TAB##1##2##, there was some variability by country in both educational level and marital status. Ethnicity was not collected in the other countries due to the ethnic homogeneity for each country. It should be noted that with the exception of the U.S., focus groups were conducted in or around major cities – Sao Paulo, Beijing and Shanghai, Seinajoki (smaller city in western Finland), Madrid and Tokyo.</p>", "<title>Pre-focus group findings</title>", "<p>Table ##TAB##3##4## summarizes the spontaneous, independent report of symptoms by subjects on the pre-focus group questionnaire, as described in Methods. The most frequently listed words to describe neuropathic pain were \"burning,\" \"electric shock,\" \"numbness,\" and \"tingling\"; however, not all of the subjects listed sensations.</p>", "<p>\"Squeezing\" and \"pressure\" were the least likely sensations on the NPSI to be elicited spontaneously on the pre-focus group questionnaire. \"Pressure\" was reported in every country except Brazil and \"squeezing\" was only mentioned in Finland.</p>", "<p>All sensations covered in the NPSI were mentioned spontaneously as being most bothersome on the pre-focus group questionnaire except for squeezing. The most frequent notations of bothersome were burning, tingling, and electric shocks.</p>", "<title>Focus group findings</title>", "<title>Phase 1</title>", "<p>During the focus groups, the most common spontaneous descriptions were burning, electric shocks, numbness, and pins and needles. Subjects often used terms interchangeably; for example, in the U.S., \"tingling\" and \"numbness\" were described as \"pins and needles.\"</p>", "<p>In Brazil, all symptoms on the NPSI were spontaneously mentioned in the focus group except \"squeezing\" and \"tingling.\" After probing, subjects also reported experiencing \"squeezing.\" \"Tingling\" was the only sensation not mentioned by the subjects in Brazil. \"Cramps\" were described as \"similar to twinging\" and \"coming after the burning pain.\" After a discussion with a professional translator, it was discovered that \"twinging\" might be the English translation of the Brazilian word for \"tingling.\" One patient described \"twinging\" as \"stabbing by needles.\"</p>", "<p>In China, subjects also used the terms \"heart stabbing,\" \"needle through heart,\" \"tremble,\" and \"bursting\" to describe their pain. Interviewers in China noted that these terms should not be interpreted literally. \"Bursting\" implies a \"sudden, strong, and unbearable\" feeling of pain. The two terms referring to the heart do not mean that the heart is in pain. When speaking about pain, the Chinese are more likely to relate extreme pain with the heart because they believe the heart is the most critical and sensitive part of the body.</p>", "<p>In Spain, the two sensations of \"pins and needles\" and \"stabbing\" were combined into one term as \"stabbing pins on fire\" (n = 8). One subject defined it as if \"hundreds, thousands of pins on fire (are) stuck into my body.\"</p>", "<p>Table ##TAB##4##5## summarizes the pain sensations experienced by the focus group members that they spontaneously described. The symptoms of the NPSI were consistently reported within the focus groups with the exception of \"squeezing.\" Although \"squeezing\" was reported in the U.S., Finland and Japan, few subjects stated this as a spontaneous expression of their pain. \"Squeezing\" was only spontaneously mentioned by one subject and four subjects mentioned \"squeezing\" while describing other neuropathic pain sensations.</p>", "<p>All of the sensations of neuropathic pain included in the NPSI (e.g., burning, squeezing, pressure, electric shocks, stabbing, pins and needles, and tingling) were spontaneously mentioned by subjects during the focus groups. Of the sensations included in the NPSI, \"burning,\" \"pins and needles,\" and \"electric shocks\" were most frequently mentioned by subjects in the focus groups. Subjects in China did not spontaneously mention three of the seven items (e.g., squeezing, pressure, and stabbing).</p>", "<p>In addition to symptoms included on the NPSI, subjects also frequently mentioned \"numbness\" and \"sharp\" as sensations they experienced, although \"sharp\" was only mentioned in the U.S.</p>", "<p>Patients in each country consistently described their pain with a single statement. Subjects in the U.S. used \"burning,\" \"electric shocks,\" and \"sharp\" while those in Spain used \"electric shocks\" or \"sharp\" only. Finnish and Japanese subjects also described their pain as \"electric shocks,\" In addition, Japanese subjects used the term, \"pins and needles.\"</p>", "<p>The two most bothersome sensations in the U.S. were burning and electric shocks while the two most bothersome sensations in Brazil were cramps and pins and needles. The most bothersome sensations for Spanish subjects were either electric shocks or \"stabbing pins on fire.\" Interestingly, subjects in China defined their worst pain by the emotions they felt or their inability to sleep in addition to the type and duration of the pain episode.</p>", "<title>Review of the Neuropathic Pain Symptom Inventory questionnaire</title>", "<p>The majority of the subjects did not raise any concerns with the NPSI: only three subjects mentioned that the recall period was too short, one subject felt that the questionnaire was confusing and another thought it did not capture all of their symptoms. Subjects responded positively when asked if the questionnaire was easy to understand, though one person reported that they did not know what was meant by \"squeezing\" pain. Next, subjects were asked which words (or kanji characters) they thought were above a sixth-grade reading level. The majority of subjects in other countries stated no concerns; however, words that some U.S. subjects thought were above a six-grade reading level included imaginable, neuropathic, provoke, severity, spontaneous, stimulation, and sensation. Although not thought to be above a six-grade reading level, Japanese subjects suggested that \"pressure\" was a concept that may be difficult to understand. However, no changes to the NPSI were consistently suggested by focus group subjects.</p>", "<p>In China, four subjects felt that the questionnaire did not adequately reflect Chinese and/or Asian culture, and they suggested using a simplified NPSI. Because pain is not judged on a numerical scale, patients did not define their pain in such detail. Instead, subjects in China typically used descriptive terms (\"mild,\" \"moderate,\" or \"severe\") rather than numbers to quantify pain. However, five individuals felt that the NPSI was an acceptable tool, even if it incorporated a scale to measure pain.</p>" ]
[ "<title>Results and discussion</title>", "<p>One hundred and thirty-two subjects from six countries were interviewed (Table ##TAB##0##1##), Background demographics, including age and gender are summarized in Table ##TAB##1##2##. The type of neuropathic pain and clinician-rated severity of pain are included in Table ##TAB##2##3##.</p>", "<p>In the U.S., the majority of the subjects (72%) were Caucasian. The remaining participants were African American (8%), Hispanic/Latino (11%), and from other ethnic groups (3%). As illustrated in Table ##TAB##1##2##, there was some variability by country in both educational level and marital status. Ethnicity was not collected in the other countries due to the ethnic homogeneity for each country. It should be noted that with the exception of the U.S., focus groups were conducted in or around major cities – Sao Paulo, Beijing and Shanghai, Seinajoki (smaller city in western Finland), Madrid and Tokyo.</p>", "<title>Pre-focus group findings</title>", "<p>Table ##TAB##3##4## summarizes the spontaneous, independent report of symptoms by subjects on the pre-focus group questionnaire, as described in Methods. The most frequently listed words to describe neuropathic pain were \"burning,\" \"electric shock,\" \"numbness,\" and \"tingling\"; however, not all of the subjects listed sensations.</p>", "<p>\"Squeezing\" and \"pressure\" were the least likely sensations on the NPSI to be elicited spontaneously on the pre-focus group questionnaire. \"Pressure\" was reported in every country except Brazil and \"squeezing\" was only mentioned in Finland.</p>", "<p>All sensations covered in the NPSI were mentioned spontaneously as being most bothersome on the pre-focus group questionnaire except for squeezing. The most frequent notations of bothersome were burning, tingling, and electric shocks.</p>", "<title>Focus group findings</title>", "<title>Phase 1</title>", "<p>During the focus groups, the most common spontaneous descriptions were burning, electric shocks, numbness, and pins and needles. Subjects often used terms interchangeably; for example, in the U.S., \"tingling\" and \"numbness\" were described as \"pins and needles.\"</p>", "<p>In Brazil, all symptoms on the NPSI were spontaneously mentioned in the focus group except \"squeezing\" and \"tingling.\" After probing, subjects also reported experiencing \"squeezing.\" \"Tingling\" was the only sensation not mentioned by the subjects in Brazil. \"Cramps\" were described as \"similar to twinging\" and \"coming after the burning pain.\" After a discussion with a professional translator, it was discovered that \"twinging\" might be the English translation of the Brazilian word for \"tingling.\" One patient described \"twinging\" as \"stabbing by needles.\"</p>", "<p>In China, subjects also used the terms \"heart stabbing,\" \"needle through heart,\" \"tremble,\" and \"bursting\" to describe their pain. Interviewers in China noted that these terms should not be interpreted literally. \"Bursting\" implies a \"sudden, strong, and unbearable\" feeling of pain. The two terms referring to the heart do not mean that the heart is in pain. When speaking about pain, the Chinese are more likely to relate extreme pain with the heart because they believe the heart is the most critical and sensitive part of the body.</p>", "<p>In Spain, the two sensations of \"pins and needles\" and \"stabbing\" were combined into one term as \"stabbing pins on fire\" (n = 8). One subject defined it as if \"hundreds, thousands of pins on fire (are) stuck into my body.\"</p>", "<p>Table ##TAB##4##5## summarizes the pain sensations experienced by the focus group members that they spontaneously described. The symptoms of the NPSI were consistently reported within the focus groups with the exception of \"squeezing.\" Although \"squeezing\" was reported in the U.S., Finland and Japan, few subjects stated this as a spontaneous expression of their pain. \"Squeezing\" was only spontaneously mentioned by one subject and four subjects mentioned \"squeezing\" while describing other neuropathic pain sensations.</p>", "<p>All of the sensations of neuropathic pain included in the NPSI (e.g., burning, squeezing, pressure, electric shocks, stabbing, pins and needles, and tingling) were spontaneously mentioned by subjects during the focus groups. Of the sensations included in the NPSI, \"burning,\" \"pins and needles,\" and \"electric shocks\" were most frequently mentioned by subjects in the focus groups. Subjects in China did not spontaneously mention three of the seven items (e.g., squeezing, pressure, and stabbing).</p>", "<p>In addition to symptoms included on the NPSI, subjects also frequently mentioned \"numbness\" and \"sharp\" as sensations they experienced, although \"sharp\" was only mentioned in the U.S.</p>", "<p>Patients in each country consistently described their pain with a single statement. Subjects in the U.S. used \"burning,\" \"electric shocks,\" and \"sharp\" while those in Spain used \"electric shocks\" or \"sharp\" only. Finnish and Japanese subjects also described their pain as \"electric shocks,\" In addition, Japanese subjects used the term, \"pins and needles.\"</p>", "<p>The two most bothersome sensations in the U.S. were burning and electric shocks while the two most bothersome sensations in Brazil were cramps and pins and needles. The most bothersome sensations for Spanish subjects were either electric shocks or \"stabbing pins on fire.\" Interestingly, subjects in China defined their worst pain by the emotions they felt or their inability to sleep in addition to the type and duration of the pain episode.</p>", "<title>Review of the Neuropathic Pain Symptom Inventory questionnaire</title>", "<p>The majority of the subjects did not raise any concerns with the NPSI: only three subjects mentioned that the recall period was too short, one subject felt that the questionnaire was confusing and another thought it did not capture all of their symptoms. Subjects responded positively when asked if the questionnaire was easy to understand, though one person reported that they did not know what was meant by \"squeezing\" pain. Next, subjects were asked which words (or kanji characters) they thought were above a sixth-grade reading level. The majority of subjects in other countries stated no concerns; however, words that some U.S. subjects thought were above a six-grade reading level included imaginable, neuropathic, provoke, severity, spontaneous, stimulation, and sensation. Although not thought to be above a six-grade reading level, Japanese subjects suggested that \"pressure\" was a concept that may be difficult to understand. However, no changes to the NPSI were consistently suggested by focus group subjects.</p>", "<p>In China, four subjects felt that the questionnaire did not adequately reflect Chinese and/or Asian culture, and they suggested using a simplified NPSI. Because pain is not judged on a numerical scale, patients did not define their pain in such detail. Instead, subjects in China typically used descriptive terms (\"mild,\" \"moderate,\" or \"severe\") rather than numbers to quantify pain. However, five individuals felt that the NPSI was an acceptable tool, even if it incorporated a scale to measure pain.</p>" ]
[ "<title>Conclusion</title>", "<p>The focus groups and interviews consisted of 2 phases: 1) concept elicitation, and 2) face and content validation. The information gathered from the focus groups in other countries (e.g., Japan, Brazil, China, Finland, and Spain) was consistent with that from group in the U.S. Descriptive terms for sensations of neuropathic pain were similar in all countries studied. Burning, electric shocks, and pins and needles were among the most-common sensations. Based on feedback from focus group subjects during the concept elicitation phase, all sensations included in the NPSI are indeed experienced by people with neuropathic pain. During the focus groups or individual interviews, subjects used the terms burning, electric shocks, and pins and needles.</p>", "<p>Numbness was also consistently mentioned. Although \"numbness\" is not a true pain descriptor but is related to non-painful paresthesia/dysesthesia, the occurrence of numbness as a frequently reported sensation reflects the number of DPN subjects in the focus groups, as this sensation is typically experienced in DPN. Subjects also used the words numbness and pins and needles interchangeably to describing pain symptoms. Because pins and needles are already included in the NPSI, adding numbness should be considered when a DPN-specific questionnaire is required. Because numbness would not be a component of the validated scoring algorithm, this issue would be considered separately. Similarly, \"itchiness\" is not a true pain descriptor. In the validation of the NPSI [##REF##15030944##8##], \"itchiness\" was found to be an unreliable item and therefore was removed. This study, unfortunately, was not designed to evaluate the \"global\" reliability of responses and therefore, we cannot recommend its inclusion at this time. The descriptor of \"squeezing\" was not consistently reported across cultures; however, \"pressure\" was reported more consistently. These two descriptors have been found to belong to the same pain dimension [##REF##15030944##8##] – spontaneous ongoing pain, with similar factor loadings (0.88 and 0.87, respectively). It is therefore thought that these two descriptors will complementarily assess the spontaneous ongoing pain symptoms.</p>", "<p>This study was not able to evaluate the differing etiology of pain in the analysis due to the separation of the participant's personal health information from the focus group transcripts. It is likely that subjects across different etiologies describe their pain slightly differently. It would have also been interesting to investigate the terminology utilized by subjects across cultures with the same etiology. As the objective of this study was to evaluate the adequacy of the NPSI for use in different neuropathic pain etiologies in different countries, the results support the broad objective. This is the first study to the knowledge of the authors to confirm such a \"universality\" of core neuropathic pain descriptors across etiologies and cultures. This study suggests that the small impact of culture on neuropathic pain expression may be related to its specific pathophysiologic mechanism; confirming the notion that neuropathic pain is a specific category of chronic pain that deserves special attention.</p>", "<p>In conclusion, the information collected during the focus groups and their analyses demonstrate that the NPSI is an acceptable instrument for assessing neuropathic pain worldwide. Country-specific terms might further enhance its applicability.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Neuropathic pain results from a nerve lesion or nerve damage. Because it is a subjective experience, patient-reported outcomes may measure both the symptoms and impact on the patient's life. The purpose of this study was to determine whether the Neuropathic Pain Symptom Inventory (NPSI) adequately assesses neuropathic pain symptoms in patients with diabetic peripheral neuropathy, post-herpetic neuralgia, trigeminal neuralgia, and sciatica across multiple cultures.</p>", "<title>Methods</title>", "<p>From data collected from 132 subjects in 6 countries, qualitative research methods identified their most important symptoms (and verbal descriptions) associated with neuropathic pain. A core set of commonly described symptoms spanning multiple cultures was also described. Moderators using a semi-structured discussion guide conducted focus groups consisting of patients in the U.S., Brazil, Japan, China, Finland, and Spain to elicit concepts that were most important and relevant (concept elicitation phase). Study subjects ranked the importance of each neuropathic pain symptom, completed the NPSI, and commented on its ability to capture key symptoms (face and content validation phase).</p>", "<title>Results</title>", "<p>Descriptive terms for sensations of neuropathic pain were similar in all countries; burning, electric shocks, and pins and needles were among the most-common sensations. Individuals with neuropathic pain experienced all sensations that were included in the NPSI. They also tended to describe pins and needles and numbness interchangeably, perhaps reflecting the relative number of DPN subjects on study.</p>", "<title>Conclusion</title>", "<p>Based on data from these focus groups, the NPSI is an acceptable instrument for assessing neuropathic pain.</p>" ]
[ "<title>Abbreviations</title>", "<p>CRF: Case Report Form; DPN: Diabetic Peripheral Neuropathy; NPSI: Neuropathic Pain Symptom Inventory; PHN: Post-herpetic Neuralgia; PROs: Patient-reported Outcomes; TN: Trigeminal Neuralgia.</p>", "<title>Competing interests</title>", "<p>BC and AW are employees of Mapi Values, an outcomes research consulting firm. ED is an employee of Pfizer Inc. DB has received funding for research and speaking engagements from numerous pharmaceutical companies. There are no other competing interests.</p>", "<title>Authors' contributions</title>", "<p>BC and ED were responsible for the design and execution of this study. AW was the primary analyst. DB assisted in the interpretation of the results. All co-authors assisted in drafting the manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>This research study was funded by Pfizer, Inc., New York, New York. The authors would like to thank Crystal Tellefsen, Jonathan Stokes and Kristina Fitzgerald for their assistance in the data collection and analysis process.</p>" ]
[]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Focus Group Populations</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Country</bold></td><td align=\"center\"><bold>Number of focus groups</bold></td><td align=\"center\"><bold>Total number of subjects</bold></td></tr></thead><tbody><tr><td align=\"left\">United States</td><td align=\"center\">6</td><td align=\"center\">50</td></tr><tr><td align=\"left\">Brazil</td><td align=\"center\">1 (plus 10 individual in-depth<break/> interviews)<sup>a</sup></td><td align=\"center\">16</td></tr><tr><td align=\"left\">China</td><td align=\"center\">2</td><td align=\"center\">18</td></tr><tr><td align=\"left\">Finland</td><td align=\"center\">2</td><td align=\"center\">17</td></tr><tr><td align=\"left\">Spain</td><td align=\"center\">2</td><td align=\"center\">16</td></tr><tr><td align=\"left\">Japan</td><td align=\"center\">2</td><td align=\"center\">13</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Focus Group Demographics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Demographic Information</bold></td><td align=\"center\"><bold>U.S.</bold><break/><bold> (N = 50)</bold></td><td align=\"center\"><bold>Brazil</bold><break/><bold> (N = 16)</bold></td><td align=\"center\"><bold>China</bold><break/><bold> (N = 18)</bold></td><td align=\"center\"><bold>Finland</bold><break/><bold> (N = 17)</bold></td><td align=\"center\"><bold>Spain</bold><break/><bold> (N = 16)</bold></td><td align=\"center\"><bold>Japan</bold><break/><bold> (N = 13)</bold></td></tr></thead><tbody><tr><td/><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td></tr><tr><td align=\"left\"><bold>Gender</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">- Female</td><td align=\"center\">24 (48)</td><td align=\"center\">9 (56)</td><td align=\"center\">8 (44)</td><td align=\"center\">11 (65)</td><td align=\"center\">13 (81)</td><td align=\"center\">5 (38)</td></tr><tr><td align=\"left\">- Male</td><td align=\"center\">26 (52)</td><td align=\"center\">6 (38)</td><td align=\"center\">9 (50)</td><td align=\"center\">6 (35)</td><td align=\"center\">3 (19)</td><td align=\"center\">8 (62)</td></tr><tr><td align=\"left\">- Missing Data</td><td align=\"center\">0 (0)</td><td align=\"center\">1 (6)</td><td align=\"center\">1 (6)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td></tr><tr><td align=\"left\"><bold>Age</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">- Range</td><td align=\"center\">19–81 years</td><td align=\"center\">50–76 years</td><td align=\"center\">28–61 years</td><td align=\"center\">43–90 years</td><td align=\"center\">23–78 years</td><td align=\"center\">54–80 years</td></tr><tr><td align=\"left\">- Mean</td><td align=\"center\">52 years</td><td align=\"center\">62 years</td><td align=\"center\">47 years</td><td align=\"center\">70 years</td><td align=\"center\">66 years</td><td align=\"center\">66 years</td></tr><tr><td align=\"left\">- Median</td><td align=\"center\">51 years</td><td align=\"center\">61 years</td><td align=\"center\">66 years</td><td align=\"center\">61 years</td><td align=\"center\">72 years</td><td align=\"center\">64 years</td></tr><tr><td align=\"left\"><bold>Education*</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>- </bold>Less than high school</td><td align=\"center\">4 (8)</td><td align=\"center\">9 (56)</td><td align=\"center\">9 (50)</td><td align=\"center\">15 (88)</td><td align=\"center\">10 (63)</td><td align=\"center\">3 (23)</td></tr><tr><td align=\"left\"><bold>- </bold>High school diploma/Some college</td><td align=\"center\">28 (56)</td><td align=\"center\">7 (44)</td><td align=\"center\">3 (17)</td><td align=\"center\">--</td><td align=\"center\">4 (25)</td><td align=\"center\">5 (38)</td></tr><tr><td align=\"left\"><bold>- </bold>College or university degree (2 or 4 year)</td><td align=\"center\">16 (32)</td><td align=\"center\">--</td><td align=\"center\">6 (33)</td><td align=\"center\">--</td><td align=\"center\">2 (12)</td><td align=\"center\">4 (31)</td></tr><tr><td align=\"left\"><bold>Marital Status**</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\"><bold>- </bold>Married</td><td align=\"center\">31 (62)</td><td align=\"center\">10 (63)</td><td align=\"center\">18 (100)</td><td align=\"center\">10 (59)</td><td align=\"center\">9 (56)</td><td align=\"center\">12 (92)</td></tr><tr><td align=\"left\"><bold>- </bold>Not married</td><td align=\"center\">19 (38)</td><td align=\"center\">6 (37)</td><td align=\"center\">--</td><td align=\"center\">7 (41)</td><td align=\"center\">7 (44)</td><td align=\"center\">--</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p>Focus Group Health Information</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Health </bold><break/><bold>Information</bold></td><td align=\"center\"><bold>U.S.</bold><break/><bold> (N = 50)</bold></td><td align=\"center\"><bold>Brazil</bold><break/><bold> (N = 16)</bold></td><td align=\"center\"><bold>China</bold><break/><bold> (N = 18)</bold></td><td align=\"center\"><bold>Finland</bold><break/><bold> (N = 17)</bold></td><td align=\"center\"><bold>Spain</bold><break/><bold> (N = 16)</bold></td><td align=\"center\"><bold>Japan</bold><break/><bold> (N = 13)</bold></td></tr></thead><tbody><tr><td/><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td><td align=\"center\">n (%)</td></tr><tr><td align=\"left\"><bold>Type of Neuropathic Pain</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">- Diabetic Peripheral Neuropathy</td><td align=\"center\">18 (36)</td><td align=\"center\">14 (88)</td><td align=\"center\">5 (28)</td><td align=\"center\">0 (0)</td><td align=\"center\">1 (6)</td><td align=\"center\">5 (38)</td></tr><tr><td align=\"left\">- Post-Herpetic Neuralgia</td><td align=\"center\">10 (20)</td><td align=\"center\">1 (6)</td><td align=\"center\">6 (33)</td><td align=\"center\">5 (29)</td><td align=\"center\">10 (63)</td><td align=\"center\">8 (62)</td></tr><tr><td align=\"left\">- Trigeminal Neuralgia</td><td align=\"center\">8 (16)</td><td align=\"center\">1 (6)</td><td align=\"center\">7 (39)</td><td align=\"center\">12 (71)</td><td align=\"center\">4 (25)</td><td align=\"center\">0 (0)</td></tr><tr><td align=\"left\">- Sciatica</td><td align=\"center\">14 (28)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td></tr><tr><td align=\"left\">- Missing data</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">1 (6)</td><td align=\"center\">0 (0)</td></tr><tr><td align=\"left\"><bold>Clinician-rated pain level</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">- Mild</td><td align=\"center\">15 (30)</td><td align=\"center\">3 (19)</td><td align=\"center\">1 (6)</td><td align=\"center\">0 (0)</td><td align=\"center\">0 (0)</td><td align=\"center\">3 (23)</td></tr><tr><td align=\"left\">- Moderate</td><td align=\"center\">26 (52)</td><td align=\"center\">12 (75)</td><td align=\"center\">13 (72)</td><td align=\"center\">1 (6)</td><td align=\"center\">9 (56)</td><td align=\"center\">6 (46)</td></tr><tr><td align=\"left\">- Severe</td><td align=\"center\">9 (18)</td><td align=\"center\">1 (6)</td><td align=\"center\">4 (22)</td><td align=\"center\">16 (94)</td><td align=\"center\">7 (44)</td><td align=\"center\">4 (31)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4</label><caption><p>Sensations of Neuropathic Pain Included in the NPSI Compared to Sensations Reported on the Pre-Focus Group Questionnaire</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Neuropathic Pain</bold><break/><bold>Sensations of </bold><break/><bold> Included in the NPSI</bold></td><td align=\"center\" colspan=\"6\"><bold>Sensations of Neuropathic Pain Reported on Pre-Focus Group</bold><break/><bold> Questionnaire</bold></td></tr><tr><td/><td align=\"center\"><bold>U.S.</bold><break/><bold> (n = 50)</bold></td><td align=\"center\"><bold>Brazil</bold><break/><bold> (n = 16)</bold></td><td align=\"center\"><bold>China</bold><break/><bold> (n = 18)</bold></td><td align=\"center\"><bold>Finland</bold><break/><bold> (n = 17)</bold></td><td align=\"center\"><bold>Spain</bold><break/><bold> (n = 16)</bold></td><td align=\"center\"><bold>Japan</bold><break/><bold> (n = 13)</bold></td></tr></thead><tbody><tr><td align=\"left\">Burning</td><td align=\"center\">12</td><td align=\"center\">5</td><td align=\"center\">7</td><td align=\"center\">1</td><td align=\"center\">5</td><td align=\"center\">1</td></tr><tr><td align=\"left\">Squeezing</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">3</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Pressure</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">3</td><td align=\"center\">1</td><td align=\"center\">2</td></tr><tr><td align=\"left\">Electric Shocks</td><td align=\"center\">5</td><td align=\"center\">3</td><td align=\"center\">5</td><td align=\"center\">7</td><td align=\"center\">4</td><td align=\"center\">5</td></tr><tr><td align=\"left\">Stabbing</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">7</td><td align=\"center\">--</td><td align=\"center\">5</td><td align=\"center\">1</td></tr><tr><td align=\"left\">Pins and Needles</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">6</td></tr><tr><td align=\"left\">Tingling</td><td align=\"center\">6</td><td align=\"center\">5</td><td align=\"center\">11</td><td align=\"center\">2</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\"><bold>Non-NPSI Sensations</bold></td><td/><td/><td/><td/><td/><td/></tr><tr><td align=\"left\">Numbness</td><td align=\"center\">9</td><td align=\"center\">2</td><td align=\"center\">7</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">3</td></tr><tr><td align=\"left\">Prickling</td><td align=\"center\">2</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">2</td></tr><tr><td align=\"left\">Itchiness</td><td align=\"center\">7</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">8</td><td align=\"center\">4</td></tr><tr><td align=\"left\">Sharp</td><td align=\"center\">9</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Shooting</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">3</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Throbbing</td><td align=\"center\">2</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Stinging</td><td align=\"center\">4</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Piercing</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Cramps</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Cutting</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">3</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Hot</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Pulsating</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Drilling</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T5\"><label>Table 5</label><caption><p>Sensations Reported in the Focus Groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Sensations of</bold><break/><bold>Neuropathic Pain</bold><break/><bold> Included in the NPSI</bold></td><td align=\"center\" colspan=\"6\"><bold>Sensations of Neuropathic Pain Spontaneously Mentioned by Subjects</bold><break/><bold> During the Focus Groups</bold></td></tr><tr><td/><td align=\"center\"><bold>U.S.</bold><break/><bold> (n = 50)</bold></td><td align=\"center\"><bold>Brazil</bold><break/><bold> (n = 16)</bold></td><td align=\"center\"><bold>China</bold><break/><bold> (n = 18)</bold></td><td align=\"center\"><bold>Finland</bold><break/><bold> (n = 17)</bold></td><td align=\"center\"><bold>Spain</bold><break/><bold> (n = 16)</bold></td><td align=\"center\"><bold>Japan</bold><break/><bold> (n = 13)</bold></td></tr></thead><tbody><tr><td align=\"left\">Burning</td><td align=\"center\">14</td><td align=\"center\">10</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">11</td><td align=\"center\">--</td></tr><tr><td align=\"left\">Squeezing</td><td align=\"center\">1</td><td align=\"center\">--</td><td align=\"center\">--</td><td align=\"center\">5</td><td align=\"center\">--</td><td align=\"center\">1</td></tr><tr><td align=\"left\">Pressure</td><td align=\"center\">6</td><td align=\"center\">3</td><td align=\"center\">--</td><td align=\"center\">5</td><td align=\"center\">5</td><td align=\"center\">2</td></tr><tr><td align=\"left\">Electric Shocks</td><td align=\"center\">10</td><td align=\"center\">3</td><td align=\"center\">2</td><td align=\"center\">10</td><td align=\"center\">15</td><td align=\"center\">6</td></tr><tr><td align=\"left\">Stabbing</td><td align=\"center\">6</td><td align=\"center\">2</td><td align=\"center\">--</td><td align=\"center\">1</td><td align=\"center\">8</td><td align=\"center\">3</td></tr><tr><td align=\"left\">Pins and Needles</td><td align=\"center\">8</td><td align=\"center\">7</td><td align=\"center\">6</td><td align=\"center\">1</td><td align=\"center\">5</td><td align=\"center\">4</td></tr><tr><td align=\"left\">Tingling</td><td align=\"center\">6</td><td align=\"center\">2</td><td align=\"center\">2</td><td align=\"center\">3</td><td align=\"center\">1</td><td align=\"center\">2</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p><sup>a </sup>Conducted in place of a focus group due to scheduling conflicts.</p></table-wrap-foot>", "<table-wrap-foot><p>* Note: Two patients from the U.S. did not respond; two patients from Finland did not respond; one patient from Japan did not respond.</p><p>**Note: One patient from Japan did not respond.</p></table-wrap-foot>" ]
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[{"surname": ["Merskey", "Bogduk"], "given-names": ["H", "N"], "source": ["Classification of Chronic Pain: Descriptions of Chronic Pain Syndromes and Definitions of Pain Terms"], "year": ["1994"], "edition": ["2"], "publisher-name": ["Seattle: IASP Press"]}, {"collab": ["US Department of Health and Human Services"], "article-title": ["Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims"], "source": ["Food and Drug Administration"], "year": ["2006"]}, {"collab": ["EMEA"], "article-title": ["Reflection Paper On The Regulatory Guidance For The Use Of Health Related Quality Of Life (HRQL): Measures In The Evaluation Of Medicinal Products"], "year": ["2005"]}, {"surname": ["Charmaz"], "given-names": ["K"], "source": ["Constructing Grounded Theory: A Practical Guide through Qualitative Analysis"], "year": ["2006"], "publisher-name": ["Thousand Oaks, CA: SAGE Pu"]}]
{ "acronym": [], "definition": [] }
13
CC BY
no
2022-01-12 14:47:28
Health Qual Life Outcomes. 2008 Aug 18; 6:62
oa_package/63/d0/PMC2531087.tar.gz
PMC2531088
18710554
[ "<title>Background</title>", "<p>Cysticercosis is the commonest parasitic infestation of the central nervous system worldwide. It is caused by the ingestion of the eggs or larvae of the tapeworm <italic>Taenia solium</italic>, found in faecally contaminated water and undercooked pork, affecting the gut initially and spreading haematogenously [##REF##12467692##1##]. Sufferers often experience a long asymptomatic period, and can present with a variety of neurological manifestations, including focal neurological deficits, migraines, visual hallucinations and seizures [##REF##17668846##2##]. The diagnosis of cysticercosis is often only made coincidentally on post-mortem examination. Extra-neurological manifestations include ocular deposition and skeletal muscle nodules. As such, careful fundoscopy and plain film radiology are mandatory for all in whom the diagnosis is suspected. A single short course of the vermicidal albendazole is usually sufficient to clear an infestation. The patient will need repeat imaging after several months to ensure complete eradication. Even having achieved this, long-term neurological sequelae are not uncommon.</p>" ]
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[ "<title>Discussion</title>", "<p>In our case, the seizures were most likely due to cerebral oedema rather than the parasite itself, as evidenced by their settling with dexamethasone. Although not a feature of this case, cysticercosis antibodies and antigens can be detected in the CSF when it is the cause of meningitis. In the developing world the vascular complications of neurocysticercosis are an important cause of haemorrhagic and ischaemic stroke.</p>", "<p>The 'classic' CT appearance of neurocysticercosis is a single enhancing ring lesion, with or without scolex. This is not always seen however, particularly in Asian patients [##REF##15269463##3##]. Indeed, as this case highlights, a less well-defined marginally enhancing subcortical lesion could be mistaken for an area of ischaemia.</p>", "<p>This case highlights that, with ever increasing worldwide migration, the diagnosis of neurocysticercosis is likely to become more common [##REF##10644776##4##], and should be considered in patients presenting with seizures in whom social history is commensurate and initial imaging non-diagnostic.</p>" ]
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>We report the case of a 28 year-old immigrant Asian man from the Punjab region with a first presentation of seizures. This patient had no significant past medical history, but suffered several headaches in the preceding week and was pyrexial on presentation. A CT scan of his head showed a single area of subcortical low attenuation initially suggesting ischaemia. A lumbar puncture and CSF examination was unremarkable. Further investigation revealed discrete calcified gluteal lesions on pelvic X-ray, and serum immunology positive for cysticercosis. The diagnosis of neurocysticercosis was made, and the patient improved on dexamethasone and a short course of vermicide, to be discharged a week later. With increasing global migration, the prevalence of neurological parasitic infections seen in the UK is likely to rise. This case highlights the importance of careful interpretation of non-specific head CTs in the context of first presentation of seizures in a susceptible population.</p>" ]
[ "<title>Case presentation</title>", "<p>The patient, a 28 year-old Asian man, was admitted via ambulance to the Accident &amp; Emergency department of City Hospital, Birmingham UK, following three successive witnessed tonic-clonic seizures. Each seizure lasted approximately 6 minutes, being separated by several minutes of incomplete recovery. The patient had complained to relatives of minor headaches during the preceding week, settling with simple analgesia. He was otherwise fit and well, on no medication, with no significant past medical history of note and no history of head trauma. He moved to the UK from the Punjab region 10 years previously, and has not been abroad since.</p>", "<p>On presentation to the Emergency Department he had a GCS of 6, a temperature of 38.5°C, and subsequently fitted again. His right pupil was comparatively slightly dilated, but fundoscopy appeared normal. He was intubated and ventilated in the Emergency Department for transfer to the CT scanner. Immediately following the head CT scan a lumbar puncture was performed, the results of which were within normal limits with no organisms seen by Gram staining.</p>", "<p>The head CT scan showed a single low attenuation area extending from the right lateral ventricle to the cortex (fig ##FIG##0##1##), but no other indications of raised intracranial pressure. Whilst it was initially thought in keeping with ischaemia, following neuroradiological review the diagnosis of cerebral cysticercosis was made. A plain pelvic X-Ray demonstrated multiple small calcific densities projecting into the soft tissues of the left gluteus (fig ##FIG##0##1##), and a subsequent serum cysticercosis immunoblot was positive. He was treated with albendazole and dexamethasone, his seizures settled and he was discharged from hospital 7 days later.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MB principally authored the manuscript, and undertook the literature review. CS and LD collected primary data and were major contributors to the manuscript. All authors have read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the patient for the publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Head CT scan at presentation and pelvic plain film x-ray</bold>. Left: Head CT showing an area of subcortical reduced attenuation that could be mistaken for ischaemia. Right: Pelvic x-ray demonstrating several small, discrete, calcified lesions in the left gluteal muscles.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-104-1\"/>" ]
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{ "acronym": [], "definition": [] }
4
CC BY
no
2022-01-12 14:47:28
Cases J. 2008 Aug 18; 1:104
oa_package/31/7f/PMC2531088.tar.gz
PMC2531089
18752668
[]
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[ "<title>Discussion</title>", "<p>Portomesenteric ischemia accounts for approximately 5–15% of all cases of mesenteric ischemia and has been associated with mortality rates of 20–50% [##REF##16013196##1##,##REF##11759648##2##]. Recent thrombosis of the portal vein may be asymptomatic or else may be associated with a systemic inflammatory syndrome with or without signs of intestinal ischemia. Old thrombosis of the portal vein is usually only recognizable on imaging by the demonstration of its cavernous transformation. Such a \"portal cavernoma\" refers to venous collateralization around the portal vein which develops in response to occlusion of the extrahepatic portal system and which partially maintains hepatopedal blood flow [##REF##12538094##3##]. It has been previously shown that the interval between obstruction of the portal vein and the cavernous transformation is approximately 5 weeks [##REF##6090259##4##]. These multiple, millimetric veins tends to occur predominantly around the suprapancreatic part of the common bile duct and may result in cholestasis due to the resulting angulation and even stenosis of the duct [##REF##12774008##5##]. The main complication however of chronic portal vein thrombosis is gastrointestinal bleeding due to rupture of esophageal varices or portal hypertensive gastropathy. Although less frequent, intestinal necrosis may occur due to thrombotic extension that can result in obstruction of the superior mesenteric vein. The cause of thrombosis may be either a general prothrombotic state (e.g. myeloproliferative syndrome, antiphospholipid syndrome, antithrombin deficiency, protein C or S deficiencies, or factor gene mutations) or intraabdominal inflammation (including pancreatitis and inflammatory bowel disease). Furthermore, portal vein occlusion has been reported to occur after abdominal surgery (in particular splenectomy)[##REF##15650628##6##].</p>", "<p>Although surgery may be required when venous gangrene of the intestine occurs, early diagnosis may allow successful conservative management with anticoagulation. Although thrombolysis has been recently proposed [##REF##14681650##7##], heparinization remains the first-line treatment. For this, unfractionated heparin infusion is preferable to fractionated subtypes because of its shorter half-life and ease of reversibility. Upper gastrointestinal bleeding risk can be prevented by beta-adrenergic blockade, endoscopic ligation, or endoscopic sclerosis of varices. Because the risk of disease progression persists early after initiation of therapy, a low threshold for operative exploration is required during conservative management. In the long-term, permanent anticoagulant treatment is recommended when a permanent prothrombotic state exists, even in patients who have a history of gastrointestinal bleeding.</p>" ]
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Although uncommon and often asymptomatic, portal venous thrombosis can have catastrophic consequences for the individual it afflicts, particularly when the process propagates to involve the superior mesenteric vein. Familiarity with the condition's pathogenesis and presentation however permits early diagnosis and allows aggressive conservative management to achieve a successful outcome. Here we describe the successful outcome of such management for a 42-year-old male patient who developed this condition spontaneously.</p>" ]
[ "<title>Case Presentation</title>", "<p>A 42-year-old male Caucasian lawyer presented as an emergency with severe generalized abdominal pain of sudden onset that radiated straight through to his back without marked abdominal tenderness on examination. He also reported several episodes of vomiting but no particular aggravating or relieving factors. He was an ex-smoker of three years standing and admitted moderate alcohol consumption (25 U/week). His weight was 115 kg while his height was 195 cm (BMI = 30.2 kg/m<sup>2</sup>). Both his father and mother had suffered myocardial infarcts at an early age (respectively at 40 and 50 years of age). Hematological and biochemical profiling revealed a mild neutrophilia but normal amylase and troponin levels. A computerized tomogram of his abdomen demonstrated hypoperfusion of the right side of his liver (see Figure ##FIG##0##1[a]##) with cavernous replacement of the portal vein (consistent with thrombotic occlusion of this vessel, see Figure ##FIG##0##1[b]##) and varices around the gallbladder (see Figure ##FIG##0##1[c]##). In addition the scan showed a thickened loop of ileum suggestive of incipient venous gangrene secondary to concomitant thrombosis of the superior mesenteric vein (see Figure ##FIG##0##1[d]##). An MRI was also performed to further visualize these findings (see Figure ##FIG##1##2##) and to investigate the patency of the superior mesenteric vein (occluded also). The patient was immediately commenced on full therapeutic anticoagulation (intravenous unfractionated heparin) and was closely observed for signs of peritonitis. He gradually made a full recovery over the next five days. Although his thrombotic screen failed to determine the presence of any specific, inherent procoagulant tendency, he was empirically commenced on oral coumarin treatment. He remains well on follow-up after a period of six months.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>MM and RAC analyzed and interpreted the patient's clinical data. JS provided the radiological expertise in interpreting the images. All authors contributed to the writing of the manuscript. All authors read and approved the final manuscript.</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>Computerized tomographic imaging of patient's abdomen at presentation</bold>. A computerized tomogram of the patient's abdomen performed soon after admission demonstrating (a) hypoperfusion of the right side of his liver (demarcation line indicated by blue arrow); (b) cavernous replacement of the portal vein (arrowed) consistent with thrombotic occlusion of this vessel and (c) varices around the gallbladder (arrowed). In addition the scan showed (d) a thickened loop of ileum (arrowed) suggestive of incipient venous gangrene secondary to concomitant thrombosis of the superior mesenteric vein.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Magnetic resonance imaging of patient's abdomen shortly after admission</bold>. Saggital Magnetic Resonance Image showing varices around the gallbladder as well as marked splenomegaly.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-128-1\"/>", "<graphic xlink:href=\"1757-1626-1-128-2\"/>" ]
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{ "acronym": [], "definition": [] }
7
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 27; 1:128
oa_package/51/c4/PMC2531089.tar.gz
PMC2531090
18727818
[ "<title>Introduction</title>", "<p>Mediastinal cysts comprise 10–18% of radiologically detected masses in the mediastinum [##UREF##0##1##,##REF##12853514##2##]. Foregut cysts including bronchogenic cysts are the most common and comprise 50% of all mediastinal cysts [##REF##12853514##2##,##REF##11832637##3##]. Complications of mediastinal cysts include infection (in about 30–36%), local pressure effects and malignant transformation [##REF##12853514##2##, ####REF##11832637##3##, ##REF##2069465##4####2069465##4##]. The frequency of symptoms due to mediastinal cysts ranges from 35–90% and these include chest pain, dyspnoea, wheeze, cough, fever and hoarsenes of voice [##REF##12853514##2##,##REF##2069465##4##,##REF##10836240##5##]. There is a lack of consensus on the best approach to managing those patients without symptoms. We present a case of a simple mediastinal cyst that was initially managed conservatively.</p>" ]
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[ "<title>Discussion</title>", "<p>Our patient first presented to us when she already had symptoms. It is not clear how long she had been with the cyst. We can only assume that it could have been congenital. We can not explain the subsequent development of the pleural effusions, nor the lack of microbial growth in the cystic contents after resection. We however postulate that the pleural effusions could have been reactive and the lack of any microbial growth could be due to the pre-operative antibiotics she received. We acknowledge that we did not aspirate fluid from the pleural effusions for analysis.</p>", "<p>Initially we chose to manage her by observation. At the time of first presentation, she had symptoms of cough, wheeze and thoracic back pain which can be attributed to compression by the cyst on the tracheobronchial tree. This should have been the time to refer her to the thoracic surgeons for further management. Our conservative approach did not work and the patient susbsequently developed signs of an infected cyst that eventually required radical treatment. The current available treatment options for bronchogenic cysts, whether asymptomatic or symptomatic, include observation, fine needle aspiration, mediastinoscopic aspiration and biopsy, thoracoscopy, and mediastinoscopy or thoracotomy for resection [##REF##1596146##6##]. Some authors advocate a conservative approach for small classic asymptomatic cysts [##REF##1596146##6##, ####REF##12853491##7##, ##UREF##1##8####1##8##]. Conservative management is defined by observation or minimal invasive procedures rather than open thoracotomy. The argument given is the lack of long term follow-up data on asymptomatic patients to know the natural history of the cysts, a very low risk of malignant transformation, the improved diagnostic radiological methods (CT scanning and MRI) and better diagnostic tools (thoracoscopy or transbroncial needle aspiration or mediastinoscopic aspiration) now available that can help avoid unnecessary surgical thoracotomies [##REF##1596146##6##,##REF##12853491##7##]. We are not sure whether such aspiration would have been the appropriate course for our patient at initial presentation to relieve her symptoms.</p>", "<p>Other authors however advocate a radical approach by complete resection of the cyst using an open thoracotomy [##REF##12853514##2##,##REF##2069465##4##,##UREF##2##9##,##REF##8020324##10##]. The reasons forwarded for radical resection are threefold. Firstly, complete resection prevents future complications. Although the natural history of bronchogenic cysts is not known, majority of the cysts will eventually cause symptoms. In the study by St Georges et al, 43% of the patients eventually became symptomatic although they had been known to have the mediastinal cysts for periods ranging from 6 months to several years [##REF##2069465##4##]. In the series by Patel et al, 3 patients followed up for periods between 1.5 to 10 years eventually required resection due to development of symptoms [##REF##8020324##10##]. Secondly, despite the low risk of malignant transformation of the cyst, resection helps to definitely exclude malignancy. Resection also provides a definite diagnosis as to the nature of the lesion. Although CT scanning can outline the lesion &amp; define the contents (low hounsefield units), the density of the cyst may vary and make precise diagnosis by imaging difficult [##REF##1596146##6##,##UREF##2##9##]. CT scanning only correctly defined the benign cystic nature of 5 lesions out of 8 in one series (62.5%) [##REF##8020324##10##]. St Georges and colleagues have also discouraged aspiration of the cystic contents as a diagnostic or therapeutic procedure because the aspirate does not provide specific data on the cyst epithelium, the aspirate may be insufficient to exclude malignancy, and needling the cyst may predispose it to being infected. Thirdly, complete resection of symptomatic mediastinal cysts seems to be associated with greater intra- or post-operative complications than performing surgery in asymptomatic patients. In the series by Patel et al, there was a trend towards increased post-operative complications in those operated at time of symptom presentation compared to asymptomatic patients (27% vs 14%) [##REF##8020324##10##]. It is also noted that about 44% of patients with mediastinal bronchogenic cysts in the study by St Georges et al had major operative difficulties or intra-operative complications and all of them were symptomatic [##REF##2069465##4##].</p>", "<p>Despite the lack of consensus on how to manage asymptomatic patients, there seems to be a general agreement that when the bronchogenic cyst increases in size or causes symptoms, intervention is warranted. Patients diagnosed with bronchogenic cysts should be referred to thoracic surgeons early so that the treatment options are explained to them to enable them make an informed choice on what intervention is appropriate at the time. However, the type of intervention will ultimately depend on the operating surgeon.</p>" ]
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[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Mediastinal cysts have an unpredictable course but can cause complications such as infection or local pressure effects. Persons with mediastinal cysts can be asymptomatic for many years or can develop symptoms as a result of complications of the cyst. There is a lack of consensus on the best approach to managing those patients without symptoms. In this case report, a 56 year old woman with an indolent mediastinal cyst initially managed conservatively suddenly developed symptoms suggestive of an infected mediastinal cyst requiring surgical resection.</p>" ]
[ "<title>Case</title>", "<p>A 56 year old previously healthy Caucasian woman and non-smoker, presented to the outpatient chest department with symptoms of episodic bouts of dry cough associated with an occasional wheeze for 12 months and upper thoracic back pain for 3 months. She had no history of chest trauma. On examination, she was not breathless or wheezy and she had a normal temperature. The chest radiograph revealed an area of gas-filled tissue in the upper right mediastinum (fig. ##FIG##0##1##) and a Computer Tomography (CT) scan confirmed a loculated air-filled collection predominantly anterior to the trachea and extending below the carina with no evidence of fluid within the locules. There was no air tracking into the neck or the abdomen. (fig. ##FIG##1##2##). The patient had no recollection of having the cyst diagnosed in the past. The patient was stable and a wait and watch approach was taken, and the patient was to be reviewed in 3 months.</p>", "<p>She was readmitted 2 months later with severe chest pain. On examination she was breathless with an expiratory wheeze and was febrile (37.5°C). The patient was not in shock. A repeat chest radiograph (fig ##FIG##2##3##) and CT scan (figs. ##FIG##3##4## and ##FIG##4##5##) demonstrated a 9 × 5 cm loculated mass containing fluid and gas, encasing the lower trachea and the main proximal bronchi and extending from the innominate vein to the left atrium consistent with a mediastinal abscess. Bilateral pleural effusions were also present. The lungs were normal. The patient was treated with broad spectrum antibiotics and then had an open thoracotomy three days later with complete resection of the mass. The air-fluid level seen on the CT scans would suggest a tracheobronchial communication but no such communication was found during the operation. Histopathological examination of the mass revealed a collapsed thick walled cyst about 55 mm in diameter. The cyst wall consisted of fibrous and granulation tissue with heavy, chronic active inflammation. There were fragmented seromucinous glands on the inner surface of the cyst wall. These findings were consistent with an infected cyst likely to be bronchogenic in origin. Culture results of the cystic contents were however negative. The patient recovered from the surgical operation uneventfully and had no recurrence of her previous symptoms when reviewed 3 months later</p>", "<title>Consent section</title>", "<p>Written informed consent was obtained from the patient for the publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>ABN coordinated and wrote the manuscript. MP reported, prepared and labelled the radiology images. RDP contributed to the surgical aspects of the case. GFB and DM suggested the writing up of this case and reviewed the manuscript. All authors read and approved the manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>We are grateful to Dr Ghislaine Sayer at Gwynedd Hospital for assisting in the selection of the radiology images.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Chest radiograph showing the air-filled mediastinal mass.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>Contrast enhanced axial CT-scan of the thorax showing the air-filled cystic mass both anterior and posterior to the carina (lung window setting).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Chest radiograph showing the cystic mass, now with an air-fluid level (compare Fig 1).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p>Contrast enhanced axial CT-scan of the thorax showing the cystic mass, now containing fluid, at the level of the aortic arch (mediastinal window setting).</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Contrast enhanced axial CT-scan of the thorax showing the cystic mass at the level of the carina, now with air-fluid levels, compare fig 2</bold>. Also shows bilateral pleural effusions worse on right side (lung window setting).</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-126-1\"/>", "<graphic xlink:href=\"1757-1626-1-126-2\"/>", "<graphic xlink:href=\"1757-1626-1-126-3\"/>", "<graphic xlink:href=\"1757-1626-1-126-4\"/>", "<graphic xlink:href=\"1757-1626-1-126-5\"/>" ]
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[{"surname": ["Wick"], "given-names": ["RM"], "article-title": ["Cystic lesions of the mediastinum"], "source": ["Seminar in Diagnostic Pathology"], "year": ["2006"], "volume": ["22"], "fpage": ["241"], "lpage": ["253"], "pub-id": ["10.1053/j.semdp.2006.02.008"]}, {"surname": ["Ginsberg", "Kirby", "Grillo HC, Austin WG, Wilkins EW Jr, Mathisen DJ, Vlahakes GJ"], "given-names": ["RJ", "TJ"], "article-title": ["Bronchogenic cysts"], "source": ["Current therapy in cardiothoracic surgery"], "year": ["1989"], "publisher-name": ["Toronto: B.C. decker"], "fpage": ["84"]}, {"surname": ["Cartmill", "Hughes"], "given-names": ["JA", "CF"], "article-title": ["Bronchogenic cysts: a persistent dilemma"], "source": ["Aust N J Surg"], "year": ["1989"], "volume": ["59"], "fpage": ["253"], "lpage": ["56"], "pub-id": ["10.1111/j.1445-2197.1989.tb01510.x"]}]
{ "acronym": [], "definition": [] }
10
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 26; 1:126
oa_package/be/a4/PMC2531090.tar.gz
PMC2531091
18710564
[ "<title>Introduction</title>", "<p>Human herpesvirus 6 (HHV-6) was first isolated from patients with the acquired immunodeficiency syndrome or lymphoproliferative diseases and was named human B lymphotropic virus [##REF##2876520##1##]. HHV-6 has been identified as the etiologic agent of exanthema subitum in infants [##REF##2896909##2##] and an acute febrile illness in young children [##REF##1315416##3##]. Most people are seropositive for HHV-6 by the age of three years [##REF##2896904##4##]. HHV-6 also produces latent or chronic infections [##REF##1646280##5##] and is occasionally reactivated in immunocompromised hosts [##REF##2876520##1##,##REF##1652312##6##]. Furthermore, HHV-6 has been implicated in several diseases in immunocompetent adults, including Kikuchi's lymphadenitis [##UREF##0##7##] and an infectious mononucleosis-like syndrome that is negative for Epstein-Barr virus and cytomegalovirus [##REF##2902266##8##]. We describe the immunopathological and clinical features of a severe acute hepatitis in a 18-year-old woman that was probably caused by a primary infection with HHV-6.</p>" ]
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[ "<title>Discussion</title>", "<p>Our data indicate that HHV-6 was the cause of our patient's acute illness. Serologic studies excluded the possibility of active infection by HCV, HBV or other human herpesviruses such CMV and EBV. The presence of HHV-6 IgM antibodies shortly after the onset of liver disease and positive ANA titres suggest that HHV6 or an autoimmune disease may also be involved in the pathogenesis. HHV-6 is a CD4 lymphotropic virus isolated from T-cells cultures derived from the blood of subjects HIV+ [##REF##2876520##1##]. Infection by HHV6 is rapidly controlled by the host immune response, and the virus established a state of latency. Primary infection occurs mostly in early childhood and only rarely in adults, in whom the prevalence of anti-HHV6 IgG is more than 90% [##REF##1315416##3##]. Symptomatic infection is characterized by fever, skin rash (exanthema subitum), sometimes associated with mild respiratory illness, leukopenia and atypical lymphocytosis [##REF##1315416##3##,##REF##16148562##10##]. Recovery is usually rapid and benign, although a more severe course with meningitis, encephalitis, myocarditis or hepatitis, variable from mild hepatitis to fulminant liver failure, has been described [##REF##1315416##3##]. HHV6 has also been associated with interstitial pneumonia and encephalitis in immunocompromised patients. [##REF##12116000##11##] We assume that HHV-6 caused the initial clinical manifestations but an humoral virus-triggered autoimmune reaction, indicated by the positive ANA titre, responding to immunosuppression induced hepatic damage. Manifestations of autoimmune hepatitis have been described repeatedly after infection with hepatitis A, B and C as well as with herpes viruses, namely HSV1, EBV and HHV6 [##REF##1849614##12##,##REF##8843841##13##].</p>" ]
[ "<title>Conclusion</title>", "<p>Autoantibodies may be triggered by a virus specific mechanism to evade immune responses called 'molecular mimicry', when domains on viral proteins closely resembling human self-epitopes are generated [##REF##7499793##14##,##REF##10432280##15##]. Thus, we believe that in addition to causing exanthem subitum in infants and a febrile illness in children, HHV-6 type B can cause an acute and potentially fulminant hepatitis in adults with an autoimmune pathogenetic mechanism.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>HHV-6 has been identified as the aetiologic agent of exanthem subitum in infants and an acute febrile illness in young children. HHV-6 probably remains latent in the body after the primary infection and it reactivates upon host immunosuppression in a manner similar to other human herpes viruses. Primary HHV-6 infection in adults is very rare and it is not clear whether disease manifestations are similar to those observed in children.</p>", "<p>We report the case of acute hepatitis in a 18-year-old immunocompetent woman presenting with sever jaundice and liver dysfunction. Serum immunoglobulin levels were elevated (3.8 gr/dl) with a titre of anti nucleus antibody of 1:640. Serological data demonstrated the presence of IgM antibodies against human herpesvirus-6 in the serum and of viral DNA on liver biopsy by real time quantitative polymerase chain reaction, with a viral load of 280 genomes/10<sup>6</sup> of cellular genomes. No other etiologic agents were found to induce hepatitis and the patient was diagnosed as having HHV-6 triggered autoimmune acute hepatitis.</p>" ]
[ "<title>Case presentation</title>", "<p>A 18-year-old woman was admitted to S.Caterina Novella Hospital on October 10, 2006, with a fifteenday history of flu-like syndrome. She had been healthy and had a history of self-limiting viral infections including measles and rubella in childhood. Physical examination revealed left cervical lymphadenopathy, splenomegaly and sever jaundice. Abnormal laboratory findings included a white blood cell count of 4.9 × 10<sup>9</sup>/L (3% atypical lymphocytes) with large granular cells and anisocytosy in peripheral smear.</p>", "<p>Liver dysfunction was seen, with an increase in the levels of aspartate aminotransferase (1515 IU/l), alanine aminotransferase (1658 IU/l), lactate dehydrogenase (1080 IU/l) and total bilirubin (18.6 mg/dl). Prothrombin time was 28%. Serum immunoglobulin levels were elevated (3.8 gr/dl) with a titre of anti nucleus antibody (ANA) of 1:640. No antibodies against human immunodeficiency virus (HIV), hepatitis C virus (HCV), Hepatitis B virus (HBV), Cytomegalovirus (CMV), Epstein Barr Virus (EBV) were detected. However anti-HHV-6 antibody (IgG and IgM) were detected with IgM index of 3.2 (cut off for positive control &gt; 1.1). A diagnosis of hepatic failure was made, and liver biopsy was performed during the acute stage. Histologic examination showed moderate infiltration of atypical lymphoid cells and diffuse focal vacuolar degeneration of hepatocytes. The infiltrating lymphocytes were positive for CD3, CD4, and CD8, but negative for CD20. The presence of HHV-6 DNA was shown in liver tissue by polymerase chain reaction (PCR) with a viral load of 280 genomes/10<sup>6 </sup>of cellular genomes, suggesting active viral replication in the hepatocytes. Methylprednisolone was administered for three weeks beginning on the seventh day of hospitalization with dosage of 25 mg every twelve hours. The jaundice, lymphadenopathy and splenomegaly gradually disappeared and patient was sent home on the 35th hospital day with a normal hepatic function and no clinical sequelae. At 2 months HHV6 IgM antibodies decreased and disappeared after 3 months.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>PGjr acquisited data and made analysis and interpretation of data, PC assessed ultrasonographic examination, RC assessed liver biopsy, PG analized tissue molecular test and helped to draft the manuscript. All authors read and approved the final manuscript. Informed written consent was received from the patient for publication of the manuscript.</p>" ]
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[{"surname": ["Eizuru", "Minematsu", "Minamishima", "Kikuchi", "Yamanishi", "Takahashi", "Kurata"], "given-names": ["Y", "T", "Y", "M", "K", "M", "T"], "article-title": ["Human herpesvirus 6 in lymph nodes"], "source": ["Lancet"], "year": ["1989"], "volume": ["1"], "fpage": ["40"], "lpage": ["40"], "pub-id": ["10.1016/S0140-6736(89)91690-5"]}]
{ "acronym": [], "definition": [] }
15
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 18; 1:110
oa_package/94/f5/PMC2531091.tar.gz
PMC2531092
18710555
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[]
[ "<title>Discussion</title>", "<p>Phendimetrazine is a medication currently being used for weight loss, with potential for illicit use. It has a similar chemical composition of amphetamines, which is thought to account for its clinical actions [##REF##16130373##1##]. Amphetamines are well recognized as an etiology of cardiac ischemia, however phendimetrazine is more rarely described in the literature as causing cardiac events. [##REF##16645436##2##,##REF##8579044##3##]. Acute effects include hyperpyrexia, mydriasis, chest pain, arrhytmias, delirium, and, rhabdomylosis, among others [##REF##16645436##2##]. Long term use has been associated with dilated cardiomyopathies, some of which have resolved with discontinuation of the medication [##REF##8579044##3##]. In this particular case, it appears she may have developed a demand ischemia from the medication. It is not known how much of the drug she was taking. Initially, she was resistant to accepting that phendimetrazine could induce side effects, and there was suspicion that she could have been taking more of the drug that recommended. In addition, she was not prescribed the medication and would not admit to where she obtained it. As the public seems to have more focus on using medications to induce weight loss, this may be a more recognized complication and heart conditions should likely be monitored prior to starting amphetamine based weight loss pills.</p>" ]
[ "<title>Conclusion</title>", "<p>Due to potentially detrimental effects of this medication, phendimetrazine should be used cautiously in many situations. As it shares its chemical structure with amphetamines, it also shares many of the side effects and the potential for abuse/addiction. There have been other reports in literature describing adverse outcomes from phendimetrazine as well as other weight loss medications. Therefore, cautious use is warranted.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Introduction</title>", "<p>Phendimetrazine is a medication currently being used to help patients with weight loss. It shares a chemical structure with amphetamines. As such, it shares some of the same toxicities, which can include cardiac toxicity. This case highlights this principle.</p>", "<title>Case presentation</title>", "<p>a 54 year old Caucasian female presented to our urgent care facility with complaints of chest pains and other symptoms suggestive of acute coronary syndrome. Ultimately, she was transferred to the emergency room. After evaluation there, it appeared she was having demand ischemia from prescription diet pills</p>", "<title>Conclusion</title>", "<p>This case report demonstrates the potential dangers of amphetamine based diet pills. There have been other cases of cardiomyopathies related to phendimetrazine, but it is something that is rarely recognized in an outpatient setting. A case such as this demonstrates the importance of obtaining a careful medication history in all patients and in recognizing diet pills with an amphetamine base can cause cardiac toxicity.</p>" ]
[ "<title>Case presentation</title>", "<p>A 54 year-old Caucasian female presented to our urgent care facility complaining of nausea and vomiting, sense of impending doom and vague chest pain radiating toward her left side for about five hours. She never had similar symptoms in the past. She also denied anything that could have precipitated these symptoms. Her only past medical history was significant for spina bifida. Her medications included occasional Fiorinal (unknown dose), Xanax 0.5 mg as needed, and Phendimetrazine (unclear dose). Her social history was significant for smoking 1/2 pack per day cigarette use. She denied alcohol use. Family history was non contributory. She worked from home. Her physical exam showed a tachycardia of around 100 beats per minute, respiratory rate of 16, temperature of 98.1, and O2 saturation of 100% on room air. She was approximately 5'7\" and 145 pounds. In general, she was an anxious appearing, diaphoretic woman in moderate distress, she had no elevated JVD at 30 degrees, her heart was tachycardic, but otherwise without murmur, gallops, or rubs, her lungs were clear, abdomen soft, and she had no peripheral edema. An EKG was checked which appears below (figure ##FIG##0##1##). After examination, there was concern for acute coronary syndrome (ACS). She was given nitroglycerin with relief of her chest discomfort. She was also given aspirin to chew. EMS was called and she was transferred to a local emergency room. She was hospitalized there for three days and after her discharge, we got permission from her to request records. While hospitalized, she was ruled out for ACS with negative troponins. She was also given beta blockade which resolved her tachycardia and her T wave changes on EKG. The next morning, she had an adenosine stress test which revealed normal uptake with no areas of ischemia and an ejection fraction of 55%. She was monitored for one more day and then discharged with instructions to discontinue her diet pills.</p>", "<title>Abbreviations</title>", "<p>ACS: Acute Coronary Syndrome.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>DL, JJ, GG have all been involved in and approve of the writing of this case presentation.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the patient for publication purposes. A copy can be obtained if requested by the Editor in Chief of this journal.</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>An EKG taken from the patient while they were having chest pain.</bold> It demonstrates T wave depression in lateral leads.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-105-1\"/>" ]
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{ "acronym": [], "definition": [] }
3
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 18; 1:105
oa_package/47/ce/PMC2531092.tar.gz
PMC2531093
18702807
[ "<title>Introduction</title>", "<p>The first case of gas-forming renal infection was reported in 1898 by Kelly and MacCallum [##REF##6366247##1##]. Since then many names have been used to describe emphysematous pyelonephritis (EPN) such as renal emphysema, pyelonephritis emphysematousa and pneumonephritis [##REF##13909504##2##]. In 1962 Schultz and Klorfein proposed emphysematous pyelonephritis as the preferred designation name, because it stresses the relationship between acute renal infection and gas formation [##UREF##0##3##].</p>", "<p>Emphysematous pyelonephritis is a severe, potentially fatal, necrotizing pyelonephritis with a variable clinical picture ranging from mild abdominal pain to septic shock. The majority of cases occur in diabetics with poor glycemic control while a small percentage may be due to urinary tract obstruction [##REF##10737279##4##,##REF##9123695##5##]. Previous researchers have postulated that vigorous resuscitation and appropriate medical treatment should be followed by immediate nephrectomy [##REF##9123695##5##,##REF##4057396##6##]. However current advances in treatment, allow patients to be treated with percutaneous drainage in combination with broad spectrum antibiotics [##REF##10737279##4##,##REF##17469032##7##,##REF##16129204##8##].</p>", "<p>We present a case of emphysematous pyelonephritis in a patient with no prior medical history of diabetes or urinary obstruction that was successfully treated with antibiotics and open drainage.</p>" ]
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[ "<title>Discussion</title>", "<p>Emphysematous pyelonephritis has been defined as a necrotizing infection of the renal parenchyma and its surrounding areas that results in the presence of gas in the renal parenchyma, collecting system or perinephric tissue [##REF##10737279##4##]. More than 90% of cases occur in diabetics with poor glycemic control. Other predisposing factors include urinary tract obstruction, polycystic kidneys, end stage renal disease and immunosupression [##REF##10737279##4##,##REF##9123695##5##].</p>", "<p>The pathogenesis of EPN remains unclear however four factors have been implicated, including gas-forming bacteria, high tissue glucose level (favoring rapid bacterial growth), impaired tissue perfusion (diabetic nephropathy leads to further compromise regional oxygen delivery in the kidney resulting in tissue ischemia and necrosis; nitrogen released during tissue necrosis) and a defective immune response due to impaired vascular supply. Intrarenal thrombi and renal infarctions have been claimed to be predisposing factors in non-diabetic patients [##REF##10737279##4##,##REF##9123695##5##].</p>", "<p>The main bacteria causing emphysematous pyelonephritis are the classical germs of urinary tract infection. The most common is Escherichia coli. Other bacteria include Klebsiella pneumoniae, Proteus mirabilis and Pseudomonas aeruginosa [##REF##10737279##4##, ####REF##9123695##5##, ##REF##4057396##6##, ##REF##17469032##7####17469032##7##]. Anaerobic infection is extremely uncommon [##REF##8220960##9##].</p>", "<p>The mean patient age is 55 years old. Women outnumbered men probably due to their increased susceptibility to urinary tract infections. The left kidney was more frequently involved than the right one [##REF##10737279##4##].</p>", "<p>The clinical manifestations of EPN appear to be similar to those encountered in classical cases of upper urinary tract infections. According to Huang and Tseng [##REF##10737279##4##] fever was encountered in 79% of the patients, abdominal or back pain in 71%, nausea and vomiting in 17%, lethargy and confusion in 19%, dyspnea in 13% and shock in 29%. Laboratory testing revealed elevated glycosylated hemoglobin in 72%, leukocytosis in 67%, thrombocytopenia in 46% and pyuria in 79%. This data comes to agreement with those generally reported in the literature [##REF##9123695##5##, ####REF##4057396##6##, ##REF##17469032##7##, ##REF##16129204##8##, ##REF##8220960##9##, ##REF##12394626##10##, ##REF##15769576##11##, ##REF##16984853##12####16984853##12##].</p>", "<p>Various imaging techniques can be used to detect gas within the genitourinary system. Ultrasound is insensitive for the diagnosis of renal gas, but useful in diagnosing urinary tract obstruction. It is also a readily available, non-invasive method that is quite useful in the hands of experienced practitioners [##REF##15769576##11##]. Non-contrast CT scan remains the diagnostic method of choice. In addition to showing the presence of gas, it defines the extent of the infection and can diagnose any obstruction [##REF##10737279##4##,##REF##9123695##5##].</p>", "<p>Two staging systems, based on CT findings, have been proposed for prognostic and therapeutic reasons. Wan et al [##REF##8596845##13##] described two types. Type I included patients showing parenchymal destruction with streaky or mottled gas but with no fluid collection. These patients had a mortality rate of 69%. Type II patients had renal or perirenal fluid collections that contained bubbly or loculated gas or gas within the collecting system. The mortality rate in this group was 18%. Huang and Tseng et al defined four classes. In class1, gas was limited in the collecting system. In class2, gas was in the renal parenchyma without extension to the extrarenal space. In class 3A, gas extended to the perinephric space, in class 3B, to the pararenal space. Class 4, was referred to bilateral emphysematous pyelonephritis or a solitary kidney with emphysematous pyelonephritis [##REF##10737279##4##,##REF##16129204##8##].</p>" ]
[ "<title>Conclusion</title>", "<p>The treatment of EPN remains controversial. According to some investigators [##REF##9123695##5##,##REF##4057396##6##] vigorous resuscitation, administration of antimicrobial agents and control of blood glucose and electrolytes should be followed by immediate nephrectomy. Huang and Tseng et al [##REF##10737279##4##,##REF##16129204##8##] proposed certain therapeutic modalities based upon their radiological classification system. Localized emphysematous pyelonephritis (class 1 and 2) is confronted by antibiotic treatment, combined with CT-guided percutaneous drainage. For extensive EPN (classes 3 and 4) without signs of organ dysfunction antibiotic therapy combined with percutaneous catheter placement should be attempted. However nephrectomy should be promptly attempted in patients with extensive EPN and signs of organ dysfunction.</p>", "<p>Risk factors indicating poor prognosis include thrombocytopenia, acute renal failure, disturbance of consciousness and shock [##REF##10737279##4##,##REF##9649241##14##]. However Falagas et al [##REF##17631348##15##] suggested that increased serum creatinine level, disturbance of consciousness and hypotension may need further research to confirm their potential use as risk factors for fatal outcome. Furthermore their meta-analysis suggest that conservative treatment alone is a risk factor for adverse outcome, although one must take into consideration the different scheme, used by the authors of the studies included, when defining terms such as conservative treatment.</p>", "<p>In summary, in high risk groups, such as diabetics, presenting with persistent upper urinary tract infection semiology that does not resolve with proper antibiotic treatment, the presence of a severe renal infection such as EPN should be considered. CT-guided percutaneous drainage or open drainage, along with antibiotic treatment, may be a reasonable alternative to nephrectomy. However surgical intervention should not be delayed in patients with extensive disease or in those who do not substantially improve after appropriate medical treatment and drainage.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Introduction</title>", "<p>Emphysematous pyelonephritis is a gas-producing necrotizing bacterial infection that involves the renal parenchyma and perirenal tissue.</p>", "<title>Case presentation</title>", "<p>We report on a case of a 55 year old Caucasian male with no prior medical history presented with left flank pain and malaise. He was diagnosed with emphysematous pyelonephritis, and was successfully treated in our department. The case is presented along with a literature review.</p>", "<title>Conclusion</title>", "<p>Prompt diagnosis and early treatment is crucial because of the high rate of mortality. Therapeutic modalities and prognostic factors regarding emphysematous pyelonephritis remain controversial.</p>" ]
[ "<title>Case presentation</title>", "<p>A 55 year old Caucasian male with no prior medical history, non-smoker, presented to the emergency department due to left flank pain that was located abruptly, 2 days ago, with progressive aggravation and malaise.</p>", "<p>Initial vital signs showed a temperature of 40°C, heart rate of 88 beats per minute, blood pressure of 120/80 mmHg and a respiratory rate of 20 breaths per minute. Physical examination on admission revealed an ill-appearing man, with left-sided costovertebral angle tenderness; he appeared confused and slightly agitated. He had been anuric for 12 hours prior to admission, providing 500 ml of urine after catheterization of his urinary bladder.</p>", "<p>Laboratory tests revealed a white blood cell count (WBC) count of 12,100/mm<sup>3 </sup>with 76% granulocytes, hemoglobin of 15.7 g/dl, platelet count of 173,010/mm<sup>3</sup>, creatinine level of 1.3 mg/dl and urea of 118 mg/dl. Urine analysis demonstrated numerous WBC and gram negative bacilli.</p>", "<p>Ultrasound (US) examination of the abdomen revealed distention of the major calyces and the ureteric pelvis of the left kidney without evidence of urolithiasis. In the following hours the patient slowly deteriorated and became hemodynamically unstable. An abdominal computed tomography (CT) scan took place and the patient was carried to the Intensive Care Unit. Free gas was detected in the intra-peritoneal, as well as, in the extra-peritoneal space (see Figure ##FIG##0##1##). The extra-peritoneal collected gas was located mostly in the left side of the posterior peritoneal cavity. Moreover, gaseous extension was imaged in the collecting system of the left kidney, without obvious obstruction (see Figure ##FIG##1##2##).</p>", "<p>At laparotomy an extra-peritoneal abscess, located in the left perinephric area, was found and treated with drainage. The patient was treated with intravenous ticarcillin – clavulanic acid (5<sup>a</sup>+0.2<sup>b</sup>) g/vial (TIMENTIN/Smith Kline Beecham, Athens, Greece), 4 times per day, for 12 days. Cultures from the blood and urine sample showed the offensive microorganism to be Escherichia coli. The patient had an uneventful postoperative course and during his hospital stay his symptoms resolved completely.</p>", "<title>Abbreviations</title>", "<p>EPN: emphysematous pyelonephritis; WBC: white blood cell count; US: ultrasound; CT: computed tomography.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>AS contributed to data collection, interpretation of data and literature search of the manuscript. AM contributed to final approval, revision and drafting of the manuscript. EEL contributed to data collection, interpretation of data and literature search of the manuscript. AK contributed to literacy search and drafting of the manuscript. AP contributed to data collection, interpretation of data and literature search of the manuscript. IC contributed to final approval revision and drafting of the manuscript. EM contributed to literacy search and drafting of the manuscript. All authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>\"Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.\"</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Abdominal CT; intraperitoneal air in the upper abdomen sets the diagnosis of acute abdomen necessitating surgical exploration.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>CT scan of the abdomen revealed extensive collections of gaseous fluid mainly in the left retroperitoneum extending along the paracolic gutter and to the middle. The air in the dilated left renal calyces gives the only clue to the diagnosis (arrow).</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-91-1\"/>", "<graphic xlink:href=\"1757-1626-1-91-2\"/>" ]
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[{"surname": ["Huang Kelly", "MacCallum"], "given-names": ["HA", "WG"], "article-title": ["Pneumaturia"], "source": ["JAMA"], "year": ["1898"], "volume": ["31"], "fpage": ["375"], "lpage": ["381"]}]
{ "acronym": [], "definition": [] }
15
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 14; 1:91
oa_package/06/2b/PMC2531093.tar.gz
PMC2531094
18702819
[ "<title>Background</title>", "<p>Acquired constriction ring syndrome (ACRS) is more commonly known as hair tourniquet syndrome or hair thread tourniquet syndrome [##REF##16707638##1##]. It is clinical condition characterized by circumferential constriction of an appendage or genitalia by human hair, synthetic fibre or a thread [##REF##17322445##2##]. It usually affects infants and has infrequently involved adolescent and cognitively impaired adults [##UREF##0##3##,##REF##15343084##4##]. Congenital, [##REF##16707638##1##,##REF##11079205##5##] accidental as well as non-accidental [##REF##15179509##6##, ####REF##10742338##7##, ##REF##10379786##8####10379786##8##] aetiologies have been proposed by different authors. Lesser toes remain the most common site affected<sup>1 </sup>followed by fingers and genitalia [##REF##10379786##8##, ####REF##17413434##9##, ##REF##6851641##10##, ##UREF##1##11####1##11##].</p>" ]
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[ "<title>Discussion</title>", "<p>Hair tourniquet syndrome is characterized by circumferential strangulation of an appendages or genitalia by human hairs or fibres. The condition was first described in 1832 [##UREF##2##12##] and the term hair tourniquet syndrome was first coined by Quinn in 1971[##REF##5561867##13##]. Since then various authors have used eponyms hair thread tourniquet syndrome and acquired constriction ring syndrome to describe the same entity. The exact incidence of the condition is not known but mostly considered to be rare [##REF##17322445##2##]. Whereas there are reports in paediatric, emergency and orthopaedic subspecialty periodicals, the condition failed to attract the attention of general orthropods [##REF##17322445##2##]. The condition primarily involves infants with over 80% of the cases being reported in a population younger than 2 months [##REF##16707638##1##]. However, there are occasional reports of adolescents and adults with cognitive impairment being involved [##UREF##0##3##,##REF##15343084##4##]. Hair remains the most common causative agent with a reported incidence of 79% in one study [##REF##12612260##14##]. Hair has unique physical characteristics which make it an ideal tourniquet. It is thin, elastic and expansible when wet while constricts as it dries off without losing its tensile strength [##REF##16707638##1##].</p>", "<p>Etiology of ACRS remains a matter of debate with more then one mechanisms playing role including accidental, [##REF##16707638##1##,##REF##17322445##2##] non-accidental [##REF##15179509##6##, ####REF##10742338##7##, ##REF##10379786##8####10379786##8##] and congenital [##REF##16707638##1##,##REF##11079205##5##]. There are some predisposing conditions described in literature indicating the accidental nature. Baby cloths such as mittens and single piece jumpsuits may cause accumulation of hairs and may pose increase risk of ACRS [##REF##16707638##1##,##REF##17322445##2##]. Postpartum excessive hair loss (telogen-effluvium) has been known to predispose risk of ACRS [##REF##12612260##14##,##UREF##3##15##]. In spite of the name acquired constriction, a group of authors believe ACRS to be of congenital origin [##REF##16707638##1##,##REF##11079205##5##] and the high incidence (&gt; 80%) of cases in early infantile life has been attributed to its congenital origin. The other group, however strongly advocate ARCS to be non-accidental in nature until proven otherwise [##REF##15179509##6##, ####REF##10742338##7##, ##REF##10379786##8####10379786##8##]. Authors suggested that non-accidental injury (NAI) to be considered where there is no reasonable explanation for presence of meticulously wrapped constriction ring or presence of well formed knots [##REF##11079205##5##]. ACRS involving limb are mostly accidental and that involving genitals are thought to be associated with NAI [##UREF##3##15##]. The absence of well formed knots and an unremarkable paediatric assessment effectively ruled out NAI in our case. Contrary to digital variety the genital cases are usually seen in relatively older children ranging from 4–11 years. This age corresponds to the Sigmund Freud's phallic (or clitoral) stage of psychosexual development when kids start toying with their genitalia out of curiosity. The application of a tourniquet around genitalia may be a possible reflection of this psychosexual tendency. Some of the cultural practices also warrant a mention where an intentional tourniquet applied to ward off evil spirits or treat urinary incontinence and nocturnal emissions led to ACRS [##REF##10379786##8##]. The pathophysiology of ACRS can be compared with compartment syndrome. The constriction ring interferes with the distal venous and lymphatic drainage at first, leading to venous engorgement. This progressive oedema further exacerbates the constriction setting a vicious circle ultimately ushering the ischemia and gangrene if not relieved in time [##REF##15179509##6##].</p>", "<p>Different measures have been suggested to prevent the ACRS. Counselling of postpartum mother is imperative and crucial. It is important to educate her to perform through regular checks of limbs to ensure that no hairs are entangled around fingers and toes of the baby. Mittens, single piece jumpsuits and clothing covering fingers and toes should be regularly checked for presence of loose hairs and should be washed inside out [##REF##12612260##14##].</p>", "<p>Early recognition with an urgent decompression remains the mainstay of treatment in an established case. Apart from the affected toe, a thorough examination should include all unaffected toes, fingers and genitals in order to rule out simultaneous involvement else where. Decompression may be carried out with fine scissors or using a depilatory cream in initial stages of ACRS when skin is intact and swelling is minimal [##REF##16707638##1##,##REF##17322445##2##]. In advanced stages when swelling is profuse or skin has breached, then it is advisable to carry out urgent complete decompression in theatre preferably under general anaesthetic [##REF##17322445##2##]. Adequate light and magnification will further aid the search for constricting agent which may be lying buried deeply inside the subcutaneous tissues. The skin may re-epithelialize over the buried hair making the exploration further difficult [##REF##17322445##2##]. Once the constricting agent is completely removed the soft tissue constriction ring itself should be decompressed by making a small vertical bone deep incision on dorsal surface avoiding extensor tendons [##REF##17322445##2##]. It is vital to examine the tourniquet for presence of a well formed knot as it would strongly suggest presence of NAI. Decompression in casualty may be performed in early stages of ACRS but must be avoided if skin has breached or there is profound swelling making adequate visualization of constricting agent impossible. Inadequate visualization in fully established ACRS may lead to incomplete release and adverse clinical consequences.</p>" ]
[ "<title>Conclusion</title>", "<p>Parental education is of immense value in reducing the incidence of ACRS. For clinicians, it is crucial to treat ACRS as an appendage threatening emergency and perform urgent complete decompression in theatre. Incomplete decompression may lead to further ischemia and auto-amputation. Inconsolable cry and refusal to feed are two most common but equally no-specific symptoms in infants and young children. Clinicians working in acute paediatric settings should be aware of the entity of acquired constriction ring syndrome as a possible cause, after excluding the common ones. NAI should be kept in mind when dealing with ACRS and through examination for presence of knots at time of decompression is important as it may give precious clue to etiology. ARCS, a potentially reversible cause of neurovascular compromise, should be kept in differential diagnosis of acute swelling in an appendage or genitalia of an obscure etiology.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Acute constriction ring syndrome (ACRS) is a rare clinical condition characterized by formation of a circumferential constriction ring around an appendage or genitalia. Cases are mostly reported in infants and young children. Early recognition and a definitive treatment are of paramount importance in order to avoid irreversible ischemia and possible auto-amputation. We describe a case of a 14-month-old child presented to casualty with a history of refusal to feed and inconsolable cry. Parents noticed a recent swelling of left third toe. On careful examination the child was found to have an acquired constriction ring secondary to a tightly wrapped hair around left third toe. An urgent surgical decompression was done by the orthopaedic team with complete resolution of symptoms. We summarized the pathophysiology of ACRS underlining the need of awareness in treating physicians. The possible medico legal implications should be kept in mind bearing a suggested link with non-accidental injury.</p>" ]
[ "<title>Case report</title>", "<p>A 14-months-old baby girl was brought to casualty by mother with an inconsolable cry and refusal to feed in our institute just after Christmas in 2007. Mother noticed a swelling and redness of left third toe while changing her socks in the morning. There was no history of trauma or any congenital deformity. On examination child was afebrile and systemically well. The digit was tender on touch with profound redness and swelling involving the distal phalange. The initial naked-eye examination done in emergency department failed to reveal a definitive cause. The child was referred to orthopaedic team for further opinion. The extensive swelling in an already distressed child was restricting the extent of assessment. Appropriate analgesics were given after establishing an intravenous access. A lens and loupe examination performed in emergency treatment room under light sedation, revealed the presence of a constriction ring at distal inter-phalangeal joint (Fig ##FIG##0##1##, ##FIG##1##2##). The skin was breached on dorsal surface of toe with volar surface showing a deeply buried ring of hair. Delayed capillary refill indicated an imminent danger of gangrene secondary to ACRS. The examination of other digits and genitalia was unremarkable. A non-accidental cause was ruled out with the help of paediatricians.</p>", "<p>Child was urgently taken to theatre to avoid further neurovascular compromise. Constriction ring of hair was removed with a pair of fine scissors under general anaesthetic (Fig ##FIG##2##3##). Hair was examined for the presence of well formed knots. Constriction ring was also decompressed by a small dorsal slit-incision down to the bone avoiding extensor tendons and the wound was left open. The child was comfortable in the postoperative period with a significant reduction of swelling by the next day. Prophylactic oral antibiotics were given for a week to avoid secondary infection. The kid was discharged from clinic in a week's time after complete resolution of symptoms.</p>", "<title>Abbreviations</title>", "<p>ACRS: Acquired constriction ring syndrome</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>VS: Operating surgeon, collected clinical details including photographs, summarised the case history and prepared first draft. PS:Conducted a literature search, design and formatting of final manuscript, including grammar. Verified the authenticity of scientific content. AS: Helped in conducting the literature search and extracting the papers from library and internet. He also contribute in preparation of electronic images and electronic formatting of manuscript. JS: Treating consultant and performed final editing of the manuscript.</p>", "<title>Consent</title>", "<p>A fully informed written consent was obtained from the patient for the publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]
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[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>The constriction ring of third toe can be seen clearly at the level of distal inter-phalangeal joint with redness and oedema.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p>The constriction ring is visible in another image with resultant distal oedema and swelling.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p>Intraoperative photograph taken after the release of constriction ring. Note the releasing incision at the dorsal surface.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1757-1626-1-92-1\"/>", "<graphic xlink:href=\"1757-1626-1-92-2\"/>", "<graphic xlink:href=\"1757-1626-1-92-3\"/>" ]
[]
[{"surname": ["Bacon", "Burgis"], "given-names": ["JL", "JT"], "article-title": ["Hair tourniquet syndrome in adolescents: a presentation and review of literature"], "source": ["J Paediatr Adolesc Gynecol"], "year": ["2005"], "volume": ["18"], "fpage": ["155"], "lpage": ["6"], "pub-id": ["10.1016/j.jpag.2005.03.010"]}, {"surname": ["Haddad"], "given-names": ["FS"], "article-title": ["Penile strangulation by human hair. Report of three cases and review of literature"], "source": ["Uorl Int"], "year": ["1982"], "volume": ["37"], "fpage": ["375"], "lpage": ["388"]}, {"surname": ["Dr"], "given-names": ["G"], "article-title": ["Ligature of the penis"], "source": ["Lancet"], "year": ["1832"], "volume": ["2"], "fpage": ["136"]}, {"surname": ["Lohana", "Vashishta", "Price"], "given-names": ["P", "GN", "N"], "article-title": ["Toe-tourniquet syndrome: a diagnostic dilemma!"], "source": ["Ann R Coll Surg Engl"], "year": ["2006"], "volume": ["88"], "fpage": ["6"], "lpage": ["8"], "pub-id": ["10.1308/147870806X95276"]}]
{ "acronym": [], "definition": [] }
15
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 14; 1:92
oa_package/40/1c/PMC2531094.tar.gz
PMC2531095
18706094
[ "<title>Background</title>", "<p>Postoperative stroke is a serious adverse event after coronary artery bypass surgery (CABG) and may be increased in patients with multiple risk factors for cerebral ischemia [##REF##10800793##1##]. The off-pump procedure can reduce neurological complications avoiding the use of cardiopulmonary bypass and aortic manipulation [##REF##12458242##2##,##REF##16863772##3##]. However, this can cause hemodynamic instability related to a low cardiac output, low vascular resistance, preload variation and a physical obstruction of the venous return, with subsequent hypotension [##REF##14970136##4##].</p>", "<p>Reduced cerebral perfusion can be further aggravated in patients with significant carotid stenosis [##REF##3820401##5##].</p>", "<p>Intraoperative neurophysiological assistance provides information on the brain's functional reserve allowing the anesthesiologist and the surgeon to perform a neuroprotective strategy [##REF##16305918##6##,##REF##15030797##7##].</p>" ]
[]
[ "<title>Results</title>", "<p>The SEPs and the hemodynamic parameters did not change until the end of the first graft.</p>", "<p>During the heart displacement to perform the second coronary anastomosis, the cardiac index (CI) markedly decreased (from 2.9 to 1.8 l/min/m<sup>2</sup>), without arterial pressure variation and the right SEP disappeared (Table ##TAB##0##1##), (Figure ##FIG##0##1##). The left SEP amplitude was reduced by 30%. No variation were noted on the Erb's recording.</p>", "<p>After 10 minutes, with establishment of a neuroprotection strategy, the right SEP reappeared (Table ##TAB##0##1##) (Figure ##FIG##0##1##). The systolic blood pressure was increased to 173 mmHg using norepinephrine intravenous boluses of 15 mcg. The inspiratory fraction of oxygen was set at 100% until the end of last anastomosis. The volemia was also increased by administering 500 ml of Hydroxyethylstarch and two packed red cells. Furthermore, the brain metabolism was reduced by increased the end tidal minimal alveolar concentration (MAC) of isoflurane until 1. The CI didn't change during this time.</p>", "<p>A norepinephrine infusion of 0.08 mcg/Kg/h was then started and maintained until the end of the surgery (Table ##TAB##0##1##). When the heart was replaced in the pericardium the CI went up to 2.4 l/min/m<sup>2</sup>.</p>", "<p>At end surgery the cortical SEP amplitude in the right and left hemispheres was respectively 13% and 33% lower from baseline values which are still in the normal range [##REF##15030797##7##]. No significative variation on the SEP latency were noted during the case.</p>", "<p>The patient was estubated after 5 hours without any neurologic impairment.</p>" ]
[ "<title>Discussion</title>", "<p>Cerebral ischemia in off-pump cardiac surgery occurs due to brain hypoperfusion induced by heart dislocation and possible macroembolic events during partial clamping of the aorta. In our patient, the SEP amplitude disappeared after heart enucleation because of reduced brain oxygen delivery. This variation allowed us to increase Cerebral Blood Flow and arterial concentration of oxygen by enhancing the haemoglobin concentration and the inspiratory fraction of oxygen. A bolus of norepinephrine was administered to increase cerebral perfusion pressure. Brain vascular resistances were reduced by increasing the MAC of isoflurane that reduced also the brain oxygen consumption. The increasing dose of volatile agent did not influence the ability to use SEP to monitor the effect of treatment but produced an attenuation of wave amplitude recorded until the end of surgery like few authors have reported [##REF##8023658##8##].</p>", "<p>In literature there is no papers about the use of SEPs for monitoring the brain function in high risk patients for cerebral ischemia submitted to off pump cardiac surgery while there is one paper about the use of electroencephalogram in this setting [##REF##16305918##6##].</p>", "<p>The SEPs are a reliable method to monitor brain function during surgery and they present some advantages in respect to the Electroencephalogram, because they are particularly resistant to the anaesthesia, moderate hypothermia and enviromental electrical interference because of averaging [##REF##15030797##7##].</p>", "<p>We chose SEP because the brain generators of the cortical SEP (N20/P25) are situated within the middle cerebral artery territory which covers 60% of the brain. Thus, continuous monitoring of the cortical SEP not only provides information on the integrity of the Central Nervous System, but also indirectly on the level of cerebral flow necessary to maintain minimal cortical function.</p>", "<p>A 50% reduction of the N20 amplitude and a 20% increase in its latency is considered a clear sign of brain ischemia, in absence of ischemic arm, global hypoxia and bolus of anesthetic drugs [##REF##15030797##7##].</p>" ]
[ "<title>Conclusion</title>", "<p>The low cardiac index produced by the heart enucleation during the CABG off-pump, increases the risk of cerebral hypoperfusion. Intraoperative SEP monitoring seems to be a reliable method to perform a neuroprotection strategy and prevent cortical damage also in off pump coronary artery bypass grafting. Further studies are necessary to confirm this hypothesis.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Early postoperative stroke is an adverse syndrome after coronary bypass surgery. This report focuses on overcoming of cerebral ischemia as a result of haemodynamic instability during heart enucleation in off-pump procedure.</p>", "<title>Case presentation</title>", "<p>A 67 year old male patient, Caucasian race, with a body mass index of 28, had a recent non-Q posterolateral myocardial infarction one month before and recurrent instable angina. His past history includes an uncontrolled hypertension, dyslipidemia, insulin dependent diabetes mellitus, epiaortic vessel stenosis. The patient was scheduled for an off-pump procedure and monitored with bilateral somatosensory evoked potentials, whose alteration signalled the decrement of the cardiac index during operation.</p>", "<p>The somatosensory evoked potentials appeared when the blood pressure was increased with a pharmacological treatment.</p>", "<title>Conclusion</title>", "<p>During the off-pump coronary bypass surgery, a lower cardiac index, predisposes patients, with multiple stroke risk factors, to a reduction of the cerebral blood flow. Intraoperative somatosensory evoked potentials monitoring provides informations about the functional status of somatosensory cortex to reverse effects of brain ischemia.</p>" ]
[ "<title>Case presentation</title>", "<p>A 67 year old male patient, Caucasian race, body mass index of 28, with two-vessel disease not amenable to angioplasty, was scheduled for an off-pump procedure, consisting in a left internal mammary artery graft on anterior descending coronary artery and venous grafts on the obtuse marginal. His medical history included one month before, a non-Q posterolateral myocardial infarction and recurrent instable angina; complete occlusion of the right internal carotid and left vertebral artery and 50% stenosis of the left internal carotid artery; uncontrolled hypertension; dyslipidemia, and insulin dependent diabetes mellitus complicated with lower limb sensory neuropathy. Seven years ago, the patient had suffered a stroke because of closing right carotid artery, without clinical effects.</p>", "<p>Brain Magnetic Resonance Imaging Scan diagnosed a suffering circle of Willis. The preoperative echocardiography revealed a mild posterolateral hypokinetic wall movement with normal ejection fraction. The chest X-ray showed moderate aortosclerosis of the ascending aorta. The neurologic examination was negative with the exception of altered tactile sensibility of the legs bilaterally.</p>", "<p>During operation we used the Pressure Invasive Continuos Cardiac Output technology to monitor in continuous, the cardiac output. Epicardial echocardiography was obtained to exclude any atheromatous plaques in the ascending aorta.</p>", "<p>SEPs (somatosensory evoked potentials) from median nerve by electrical stimulation were recorded in continuous, after general anesthesia induction. The median nerve were bilaterally stimulated with subdermal needle electrods at the wrist. The recording electrods were placed at the homolateral Erb's point and at the C3'/C4' at opposite side the stimulation site. The stimulation rate was 3.7 Hz. After a baseline obtained with a 300 stimulus average, the ongoing average was obtained with 30 stimulus.</p>", "<title>Abbreviations</title>", "<p>CABG: Coronary artery bypass surgery; SEPs: Somatosensory evoked potentials; CI: Cardiac index; MAC: Minimum alveolar concentration; N20/P25: Cortical SEP.</p>", "<title>Competing interests</title>", "<p>The authors declare that they have no competing interests.</p>", "<title>Authors' contributions</title>", "<p>PZ conceived the work, collected and analyzed the data and write the article. EB analyzed the data. PDP analyzed the data. AN helped to write the article. VC conceived the work and analized the data. SC analized the data. All authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.</p>" ]
[]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>SEP (N20/P25) amplitude variation during the intraoperative steps</bold>. The right SEP disappeared when the heart is enucleated. Both SEPs have a non significant wave amplitude attenuation at the end of surgery. N20/P25 complex is the most important scalp-recorded cortical component that has a negative peak about 20 msec followed by a positive peak at about 25 msec.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>SEP, hemodynamic and respiratory variable recordings and the neuroprotection strategy during the operative steps.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td/><td align=\"left\" colspan=\"2\"><bold>Post anaesthesia</bold><break/><bold> induction</bold></td><td align=\"left\" colspan=\"2\"><bold>Heart enucleation</bold></td><td align=\"left\" colspan=\"2\"><bold>\"Anestesiologist </bold><break/><bold>reaction\"</bold></td><td align=\"left\" colspan=\"2\"><bold>End surgery</bold></td></tr><tr><td/><td colspan=\"8\"><hr/></td></tr><tr><td/><td align=\"left\">Right</td><td align=\"left\">Left</td><td align=\"left\">Right</td><td align=\"left\">Left</td><td align=\"left\">Right</td><td align=\"left\">Left</td><td align=\"left\">Right</td><td align=\"left\">Left</td></tr></thead><tbody><tr><td align=\"left\">N20/P25 latency (msec)</td><td align=\"left\">22.67</td><td align=\"left\">23.83</td><td align=\"left\">-</td><td align=\"left\">25.54 ± 0.08</td><td align=\"left\">23.86 ± 0.13</td><td align=\"left\">25.52 ± 0.08</td><td align=\"left\">25.15</td><td align=\"left\">23.35</td></tr><tr><td align=\"left\">N20/P25 amplitude (uV)</td><td align=\"left\">1.922 ± 0.09</td><td align=\"left\">2.51 ± 0.13</td><td align=\"left\">0.02</td><td align=\"left\">1.83 ± 0.007</td><td align=\"left\">1.03 ± 0.3</td><td align=\"left\">2 ± 0.04</td><td align=\"left\">1.67 ± 0.03</td><td align=\"left\">1.68 ± 0.01</td></tr><tr><td align=\"left\">CF (pulse/min)</td><td align=\"left\">80</td><td/><td align=\"left\">60</td><td/><td align=\"left\">65</td><td/><td align=\"left\">65</td><td/></tr><tr><td align=\"left\">SAP (mmHg)</td><td align=\"left\">135</td><td/><td align=\"left\">130</td><td/><td align=\"left\"><bold>173</bold></td><td/><td align=\"left\">146</td><td/></tr><tr><td align=\"left\">MAP (mmHg)</td><td align=\"left\">84</td><td/><td align=\"left\">91</td><td/><td align=\"left\"><bold>114</bold></td><td/><td align=\"left\">95</td><td/></tr><tr><td align=\"left\">DAP (mmHg)</td><td align=\"left\">58</td><td/><td align=\"left\">70</td><td/><td align=\"left\"><bold>76</bold></td><td/><td align=\"left\">67</td><td/></tr><tr><td align=\"left\">CVP (mmHg)</td><td align=\"left\">18</td><td/><td align=\"left\">24</td><td/><td align=\"left\">27</td><td/><td align=\"left\">18</td><td/></tr><tr><td align=\"left\">CI (l/min/m<sup>2</sup>)</td><td align=\"left\">2.9</td><td/><td align=\"left\"><bold>1.8</bold></td><td/><td align=\"left\"><bold>1.8</bold></td><td/><td align=\"left\">2.6</td><td/></tr><tr><td align=\"left\">SVRI (dyn.sec.m<sup>2</sup>/cm<sup>5</sup>)</td><td align=\"left\">1820</td><td/><td align=\"left\">2977</td><td/><td align=\"left\"><bold>3866</bold></td><td/><td align=\"left\">2369</td><td/></tr><tr><td align=\"left\">PaCO<sub>2 </sub>(mmHg)</td><td align=\"left\">38</td><td/><td align=\"left\">39</td><td/><td align=\"left\">40</td><td/><td align=\"left\">40.5</td><td/></tr><tr><td align=\"left\">SaO<sub>2 </sub>(mmHg)</td><td align=\"left\">100</td><td/><td align=\"left\">100</td><td/><td align=\"left\">100</td><td/><td align=\"left\">100</td><td/></tr><tr><td align=\"left\">T (°C)</td><td align=\"left\">36.2</td><td/><td align=\"left\">35</td><td/><td align=\"left\">34.8</td><td/><td align=\"left\">35</td><td/></tr><tr><td colspan=\"9\"><hr/></td></tr><tr><td align=\"left\"><bold>neuro-protection</bold></td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td colspan=\"9\"><hr/></td></tr><tr><td align=\"left\">Norephi (mcg/Kg/h)</td><td align=\"left\">-</td><td/><td align=\"left\">-</td><td/><td align=\"left\"><bold>0.08</bold></td><td/><td align=\"left\">-</td><td/></tr><tr><td align=\"left\">MAC (Et isoflurane)</td><td align=\"left\">0.5</td><td/><td align=\"left\">0.5</td><td/><td align=\"left\"><bold>1</bold></td><td/><td align=\"left\">1</td><td/></tr><tr><td align=\"left\">FiO2 (%)</td><td align=\"left\">50</td><td/><td align=\"left\">50</td><td/><td align=\"left\"><bold>100</bold></td><td/><td align=\"left\">50</td><td/></tr><tr><td align=\"left\">Hb (g/dl)</td><td align=\"left\">10</td><td/><td align=\"left\">10</td><td/><td align=\"left\"><bold>12</bold></td><td/><td align=\"left\">12</td><td/></tr><tr><td align=\"left\">Volume load (liter)</td><td/><td/><td/><td/><td align=\"left\"><bold>0.5 HES.+ 0.5 blood</bold></td><td/><td/><td/></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p>N20/P25 = cortical SEP, CF = cardiac frequency, SAP = systolic arterial pressure, MAP = mean arterial pressure, DAP = diastolic arterial pressure, CVP = central venous pressure, CI = cardiac index, SVRI = systemic vascular resistence index, PaCO<sup>2 </sup>= arterial pressure of CO<sup>2</sup>, SaO<sup>2 </sup>= oxygen saturation, Norephi = norephinefrine, MAC = minimum alveolar concentration, FiO<sup>2 </sup>= inspiratory fraction of oxygen, Hb = haemoglobin, T = temperature, HES = Hydroxyethylstarch</p></table-wrap-foot>" ]
[ "<graphic xlink:href=\"1757-1626-1-94-1\"/>" ]
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[]
{ "acronym": [], "definition": [] }
8
CC BY
no
2022-01-12 14:47:29
Cases J. 2008 Aug 15; 1:94
oa_package/60/96/PMC2531095.tar.gz
PMC2531096
18718013
[ "<title>Background</title>", "<p>Wegener's Granulomatosis (WG) is a multi-system disease, characterised by the triad of necrotising granulomata affecting the upper and lower respiratory tracts, disseminated vasculitis and glomerulonephritis. WG is included in the ANCA-associated small-vessel vasculitis group (including also microscopic polyangiitis, Churg-Strauss syndrome and renal-limited vasculitis). Oral lesions are associated with up to 50% of cases, although are rare as a presenting feature. The most common oral lesions associated with WG are ulceration and strawberry gingivitis. We present a case of lingual infarction, an extremely rare oral lesion associated with WG, in a severe, rapidly progressive and ultimately fatal form of the disease.</p>" ]
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[ "<title>Discussion</title>", "<p>WG was first described by Freidrich Wegener in 1936 and 1939 [##UREF##0##1##,##UREF##1##2##]. In 1954, Godman and Churg further delineated the clinical and pathological features by describing the classical triad of necrotising granulomata affecting the upper and lower respiratory tracts, disseminated vasculitis and glomerulonephritis [##UREF##2##3##]. The aetiology of WG remains unknown.</p>", "<p>In Europe the prevalence of WG is 5 cases per 100,000 population. The incidence is greater in Northern Europe. WG can occur in all racial groups, but predominantly affects Caucasians. Both sexes are affected equally. WG affects a wide age range (8–99 years) with a mean age at diagnosis of 40 years [##REF##14567060##4##]. Historically WG, if untreated, had a poor prognosis with a mean survival time of 5 months [##REF##13560836##5##]. In recent decades prognosis has improved with the introduction of immunosuppressive therapy using glucocorticosteroids and cyclophosphamide [##REF##6336643##6##].</p>", "<p>WG can be rapidly progressive or mild and indolent. Generalised symptoms such as fever, weight loss, fatigue and malaise may be present. Specifically, pulmonary manifestations such as cough, haemoptysis and pleuritis are the most common presenting symptoms. Renal disease can be the presenting feature in up to 18% of patients [##REF##6336643##6##]. Renal involvement is characterised by abnormal renal function with red cell casts in urinalysis, and glomerulonephritis on renal biopsy. Renal involvement and a late age at onset are associated with an increased risk of mortality [##REF##10809800##7##]. 80–90% of patients will have renal and/or pulmonary involvement at some time in their disease process.</p>", "<p>WG can also affect the eyes, skin, joints, nervous system, ear, nose and throat [##REF##994555##8##]. The most common anatomical site for presenting lesions of WG is the upper airway. Up to 30% of patients may present with nasal problems including nasal obstruction, ulceration, septal perforation, mucus discharge, or epistaxis [##REF##8592744##9##].</p>", "<p>Diagnosis of WG is based on a combination of clinical, histological, biochemical and immunological features. Inconsistency of histopathological findings can make diagnosis based on this method in isolation difficult. In 1985 diagnosis was aided by the description of anti-neutrophil cytoplasmic antibodies (ANCA) associated with WG [##REF##2857806##10##]. Two forms exist: cytoplasmic (cANCA) of which the principal target is protease-3 (PR3) and perinuclear (pANCA) which is directed against myeloperoxidase (MPO). These targets are antigens stored in the azurophilic granules of neutrophils and monocytes.</p>", "<p>Lesions of the oral mucosa occur in 6–50% of patients with WG [##REF##8496414##11##,##REF##8024273##12##]. Duna et al reported oral lesions in 6–13% of patients but as the presenting feature in only 2% [##REF##8592744##9##]. Oral and oropharyngeal ulcers resembling large aphthous ulcers are the most common oral lesion. Indeed at autopsy nearly all patients were reported to have oropharyngeal ulceration [##UREF##0##1##,##UREF##3##13##]. Hyperplastic gingivitis, dark-red to purple in colour with a granular surface, resembling an over-ripe strawberry, either generalised or affecting a single dental papilla, can be considered specific to WG [##REF##9503448##14##, ####REF##8408694##15##, ##REF##1995819##16##, ##REF##1569367##17##, ##REF##2335672##18##, ##REF##7299989##19##, ##REF##3928853##20##, ##REF##4565579##21####4565579##21##]. In fact, the combination of 'strawberry gingivitis' exhibiting pseudoepitheliomatous hyperplasia, microabscesses and multi-nucleate giant cells upon biopsy with severe systemic upset can be considered diagnostic for WG [##REF##8408694##15##]. Oro-antral fistulae [##REF##277882##22##], palatal osteonecrosis and labial mucosal nodules have also been reported [##REF##16487139##23##]. WG can also affect the salivary glands with reported cases affecting the parotid, submandibular and sublingual salivary glands [##REF##11202329##24##, ####UREF##4##25##, ##REF##11936391##26####11936391##26##].</p>", "<p>Lingual necrosis is a rare oral manifestation with only two previously reported cases. Bachmeyer et al reported a case of necrotic lingual ulceration which resolved with immunosuppressive therapy [##REF##16487139##23##]. Rodgers et al reported a case of bilateral infarction of the tongue associated with a severe and rapidly progressive form of WG in 1992 [##REF##1290739##27##]. The patient died 39 days after onset of symptoms (18 days after presentation). At post mortem examination the anterior two thirds of the tongue were infarcted. Our case was also fatal, exhibiting a similar clinical course with a similar anatomical distribution of lingual infarction. WG affects the small arteries of the lung and kidney causing pulmonary and renal infarction [##REF##3928853##20##,##REF##1290739##27##]. In our case the lingual arteries may have been similarly affected but unfortunately post mortem examination was not performed, therefore we cannot confirm that the lingual infarction was solely a result of lingual end-arteritis.</p>", "<p>Lingual infarction can occur secondary to embolism, radiotherapy [##REF##2199141##28##], tumour infiltration, radical neck dissection [##REF##3220775##29##], transient ischaemic attack [##REF##10710456##30##], and cardiac arrest [##REF##11887150##31##]. Lingual infarction has also been reported in cranial arteritis, giant cell arteritis [##REF##7917830##32##, ####REF##9031536##33##, ##REF##15547815##34##, ##UREF##5##35####5##35##], and microscopic polyangiitis (MPA) [##REF##12464895##36##]. WG and MPA share many similar clinical and histological features. Oral, upper airway, pulmonary and renal vasculitis are present in both conditions, however the vasculitis associated with WG is granulomatous whereas MPA exhibits non-granulomatous vasculitis [##REF##12464895##36##].</p>", "<p>Most patients with Wegener's granulomatosis exhibit cANCA with PR3 specificity and 25% exhibit pANCA with MPO specificity. In contrast microscopic polyangiitis exhibits cANCA with PR3 specificity in approximately 30% of patients and pANCA with MPO specificity in 60% of patients [##REF##12464895##36##,##REF##10191771##37##]. A positive ANCA result suggests a systemic vasculitis but in the absence of a tissue biopsy, cANCA/pANCA distribution may suggest either WG or MPA but cannot definitively differentiate between the two diagnoses. In our case a tissue biopsy could not be performed safely due to persistent thrombocytopenia and a post mortem examination was not performed. Therefore the presence of granulomatous vasculitis was not confirmed histologically but the histological differential diagnosis can be complicated in that not all biopsy material associated with WG exhibits the classical pathological triad of granulomatous infiltration, necrosis and vasculitis [##REF##2337204##38##]. The clinical, biochemical and immunological features of our case were suggestive of a WG diagnosis.</p>", "<p>Early diagnosis is important, expediting aggressive immunosuppressive therapy with glucocorticosteroids and cyclophosphamide, which can potentially limit a more severe systemic disease progression. Other treatment options include the use of trimethoprim and sulfamethoxazole either as a stand alone treatment or in combination with glucocorticosteroids and cyclophosphamide [##REF##8592744##9##]. More recent treatment options include Cyclosporin, intravenous pooled immunoglobulin, anti-CD20 monoclonal antibodies (Rituximab) and anti-tumour necrosis factor alpha with the latter two options restricted to refractory and relapsed disease [##REF##8296660##39##,##REF##8296659##40##]. Regular review and maintenance therapy are also important to identify and prevent relapse. A multidisciplinary approach must be undertaken involving oral and maxillofacial surgeons, oral physicians, otorhinolaryngologists, rheumatologists, renal and respiratory physicians, ophthalmologists, and ITU supportive care if required. WG should be considered in the presence of oral lesions associated with a systemic illness. An oral biopsy and blood investigations assessing full blood count, renal and hepatic function, inflammatory markers and autoimmune status, specifically cANCA and pANCA, should be performed.</p>", "<p>Oral lesions are associated with the onset of active systemic disease [##REF##8024273##12##]. Therefore isolated oral lesions may herald the onset of further systemic involvement. Mahr et al reported that oral, ear, nose or throat involvement was not associated with survival in their multivariate analysis. Mahr et al did indicate that granulomatous WG probably has a more benign course than vasculitic WG [##REF##11371656##41##]. Our case and the previously reported case of lingual infarction were associated with a severe, rapidly progressive and ultimately fatal form of WG. Thus lingual infarction as a result of vasculitis may indicate more aggressive disease. The severity of oral mucosal lesions, even in the absence of systemic signs, may therefore reflect or predict the severity of the generalised systemic disease and indicate a more aggressive vasculitis.</p>" ]
[]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>Wegener's granulomatosis (WG) is a multi-system disease, characterised by the triad of necrotising granulomata affecting the upper and lower respiratory tracts, disseminated vasculitis and glomerulonephritis. Oral lesions are associated with up to 50% of cases, although are rare as a presenting feature. The most common oral lesions associated with WG are ulceration and strawberry gingivitis. We review the literature regarding oral manifestations of WG and present a case of lingual infarction, an extremely rare oral lesion associated with WG, in a severe, rapidly progressive and ultimately fatal form of the disease.</p>" ]
[ "<title>Case Report</title>", "<p>A 56 year old female presented with headache, sinus pain, shortness of breath, cough productive of green sputum and haemoptysis. She had a history of bronchiectasis (diagnosed at age 20), hypertension, and a three month history of sinus problems with associated bilateral hearing loss. Chest radiograph revealed bilateral pleural effusions with apical opacities. Blood investigations revealed a C-reactive protein (CRP) of 454 mg/L and a creatinine of 76 umol/L. An initial diagnosis of lower respiratory tract infection was made and treatment with intravenous amoxicillin and erythromycin started. Pseudomonas was cultured from sputum after seven days, at which point intravenous gentamicin was started. She then developed pulmonary oedema and the haemoptysis worsened. Renal impairment also developed (urine protein/creatinine index 14395, urine protein 5.47 g/L, urine creatinine 3.8 mmol/L) and subsequently the gentamicin therapy was stopped. Her respiratory and renal function continued to deteriorate and she developed anterior t-wave inversion on ECG. Pulmonary haemorrhage secondary to systemic vasculitis was suspected. Blood investigations revealed haemoglobin (Hb) 9.0 g/dL, white cell count (WCC) 22.15 10<sup>9</sup>/L, platelets (PLT) 522 10<sup>9</sup>/L, CRP 227 mg/L, creatinine 159 umol/L, and positive cytoplasmic pattern anti-neutrophil cytoplasmic antibodies (cANCA) (ANCA protease-3 (PR3) 18 u/ml and ANCA myeloperoxidase (MPO) 1 u/ml). A diagnosis of Wegener's granulomatosis was considered most likely and intravenous methylprednisolone commenced. No upper airway lesions were identified on nasal endoscopy so no tissue biopsy could be taken. As a result of worsening respiratory and renal function she was intubated, ventilated and inotropic support with noradrenaline started. Nasogastric feeding was also commenced and plasmaphoresis undertaken. A renal biopsy was planned but she became progressively anaemic Hb 5.7 g/dL, thrombocytopenic PLT 35 10<sup>9</sup>/L, and her liver function deteriorated with a prothombin time of 31 s, making renal biopsy unsafe in the presence of coagulopathy. Gastrointestinal haemorrhage was suspected as the cause of anaemia. Oesophago-gastro-duodenoscopy (OGD) was performed which revealed oesophagitis, gastritis and duodenitis consistent with vasculitis, see figure ##FIG##0##1##. These lesions were injected with epinephrine and intravenous omeprazole was commenced. At this point, 19 days after initial presentation, sloughing of her lingual mucosa was noted, see figure ##FIG##1##2##. The mucosal sloughing involved the entire anterior two thirds of her tongue bilaterally. After the addition of intravenous cyclophosphamide her respiratory and renal function stabilised. The lingual sloughing persisted, and over the next 14 days progressed to an area of well demarcated necrosis of the anterior two thirds of the tongue bilaterally, see figure ##FIG##2##3##. The necrotic area began to separate but unfortunately she developed further pulmonary haemorrhage and her renal and cardiac function continued to deteriorate despite plasmaphoresis and inotropic support. Her condition was deemed irretrievable and supportive care was withdrawn. She died 48 days after initial presentation with the cause of death reported as multi-organ dysfunction syndrome (MODS) secondary to Wegener's granulomatosis. No post mortem examination was performed.</p>", "<title>Competing interests</title>", "<p>The authors have no financial and personal relationships with other people, or organisations, that could inappropriately influence (bias) their work, all within 3 years of beginning the work submitted.</p>", "<title>Authors' contributions</title>", "<p>LMC and EB prepared the case report, discussion and manuscript. Both authors read and approved the final manuscript.</p>", "<title>Consent</title>", "<p>Written consent for publication of the clinical images could not be obtained because the patient died before written consent could be recorded.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The authors would like to thank Mr John L. Russell, Consultant Maxillofacial Surgeon, Leeds Dental Institute for allowing access to his patient for this case report and for advice in preparation of the manuscript.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p>Endoscopic views of gastrointestinal lesions consistent with vasculitis: A oesophagus, B gastric mucosa, C duodenal mucosa.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Sloughing of the lingual mucosa</bold>.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Necrosis of anterior two thirds of tongue</bold>.</p></caption></fig>" ]
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[ "<graphic xlink:href=\"1746-160X-4-19-1\"/>", "<graphic xlink:href=\"1746-160X-4-19-2\"/>", "<graphic xlink:href=\"1746-160X-4-19-3\"/>" ]
[]
[{"surname": ["Wegener"], "given-names": ["F"], "article-title": ["Uber generalisierte, septische Gefaesserkrankungen"], "source": ["Verh Dtsch Ges Pathol"], "year": ["1936"], "volume": ["29"], "fpage": ["202"], "lpage": ["227"]}, {"surname": ["Wegener"], "given-names": ["F"], "article-title": ["Uber eine eigenartige Rhinogene Granulomatose mit besondere Beteilgung des Arteriensystems and der Nieren"], "source": ["Beitrage Pathologie Anatomie"], "year": ["1939"], "volume": ["102"], "fpage": ["36"], "lpage": ["51"]}, {"surname": ["Godman", "Churg"], "given-names": ["GC", "J"], "article-title": ["Wegener's granulomatosis: pathology and review of the literature"], "source": ["Arch Pathol Lab Med"], "year": ["1954"], "volume": ["6"], "fpage": ["533"], "lpage": ["553"]}, {"surname": ["Wegener", "Staemmler M"], "given-names": ["F"], "article-title": ["Die pneumogene allgemeine Granulomatose (PG) - sog. Wegnersche Granulomatose."], "source": ["Lehrbuch der speziellen pathologischen Anatomie, Ergaenzungsbans, I/1"], "year": ["1967"], "publisher-name": ["Berlin, De Gruyter"], "fpage": ["225"], "lpage": ["299"]}, {"surname": ["Lustmann", "Segal", "Markitziu"], "given-names": ["J", "N", "A"], "article-title": ["Salivary gland involvement in Wegener's granulomatosis. A case report and review of the literature"], "source": ["Oral Surg Oral Med Oral Pathol Oral Radiol Endod"], "year": ["1994"], "volume": ["77"], "fpage": ["254"], "lpage": ["259"]}, {"surname": ["Ciantar", "Adlam"], "given-names": ["M", "DM"], "article-title": ["Glossodynia and necrosis of the tongue caused by giant cell arteritis"], "source": ["Br J Oral Maxillofac Surg"], "year": ["2007"], "volume": ["doi:10.1016/j.bjoms.2007.03.014"]}]
{ "acronym": [], "definition": [] }
41
CC BY
no
2022-01-12 14:47:29
Head Face Med. 2008 Aug 21; 4:19
oa_package/74/83/PMC2531096.tar.gz
PMC2531097
18687115
[]
[]
[]
[]
[ "<title>Conclusion</title>", "<p>(a) The evolutionary mechanisms that maintain the non-virulent state of a pathogen are of high interest. Since there are a large number of mild pathogens in nature and in spite of the selective advantage that a virulent mutant would temporarily have, the 'gentle' pathogen state does arise and does persist. A proposed mechanism for this, not involving group or kin selection, has been made Koch [##REF##17996103##86##]. This new model is that the pathogen protects itself (and incidentally its host) against other similar or identical pathogens arriving subsequently and that this generates and maintains the 'gentle' state.</p>", "<p>(b) It is argued that pathogens that depend on humans as a resource faced a much different problem during the hunter/gatherer Stone Age than now, and at this early time the spectrum of viruses could be expected to have been largely STDs because of the sparse availability of hosts. Almost of necessity many pathogens must have been 'gentle' because the interactions between social groups were infrequent. Before moving to humans from the primates, HIV must have been gentle to its nonhuman primate host, and possibly it has not had time to readjust to become gentle its new host.</p>", "<p>(c) Being a 'gentle' pathogen requires elaborate controls to self-limit growth of its host. Persistence over long times of a sexually transmitted disease depends, therefore, on growth inhibitory mechanisms in part coded by the virus, but frequently dependent on host function. The host immune system limits the viremia and viral encoded mechanisms and may act to modulate the immune response and act in other ways to control and limit viral growth. It can be assumed that the lentiviruses of nonhuman primates, such as SIV are adapted to a low rate of vertical transmission because of the devastating action of many viruses on neonates due to the latter's underdeveloped immune system. These may be avoided by slow growth. HIV is transmitted to offspring <italic>in utero</italic>, perinatally, or via breast-feeding, but the transmission is less efficient than for some other viral diseases. This suggests that host and viral mechanisms restrict vertical transmission or its effects for the case of HIV for the fetus and the neonate.</p>", "<p>(d) The HIV retrovirus strategy depends on selectively infecting a restricted class of cells, mainly the CD4+ or CD8+ T helper cells. These and other T and B cells happen to be nearly the only suitable cells, <italic>a priori</italic>, in an mammal; these uniquely continue to grow, replicate, and divide to form progeny that remaining inside the host. Such a type of cell is the necessary condition for the strategy of retroviral growth. Thus these host cells are different than other cells of the body that only divide rarely or of cells that are shed from the animal such as skin and intestinal epithelium.</p>", "<p>(e) It is proposed that the response of the mucosal part of the host immune system is not only the key factor to the prevention of many viral infections, but also it is to prevent or reduce the infection of the offspring of an infected female. This is because the selection for an ability to elicit an effective mucosal immune response by the products generated by the virus that would block secondary infection and is in the best interests of the virus, therefore. But it also makes the resident pathogen less destructive to its host and its host's progeny. I suggest that the role of mucosal immunological response and other defensive responses is critical for the biology of the STD retroviruses.</p>", "<p>(f) The important link in the STD lifestyle as typified by the HIV/human interaction is that the virus must work against itself under conditions in which the hosts are at a premium and must somehow protect the fetus and neonate. It is the fact that such protection is manifest even with HIV infection of humans. About 85% of the offspring of HIV-infected mother are not HIV infected and in simian viruses in wild monkeys the number may be closer to 100%. Compared with some other viral diseases, this suggests that specific immune protection of the fetus perinatally does occur.</p>", "<p>(g) The change from the forebears of HIV, presumed to be a 'gentle' virus of its non-human primates, to the devastating human virus of AIDS is mostly because the new host of the virus lives much longer than its previous host. Added to this are the factors due to the human host's social behavior, most notably the extensive movements of humans from place to place. Also HIV appears very ungentle now because of its spread to a greatly expanded habitat, and because of the sociology due to the homosexual involvement and intravenous drug use.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<p>The great advantage of being a sexually transmitted disease is the ability to survive and specialize solely on a host species that is present in low numbers and widely distributed so that contact between infected and uninfected organisms by chance is rare.</p>", "<p>Pathogens of a sparse, but widely distributed host species, must either: i) have an alternative host; ii) be able to survive in a dormant state; or iii) be non-destructive to their host. For the pathogens of a diploid there is a particularly effective strategy, that of being sexually transmitted. Then the hosts' themselves transfer the pathogen.</p>" ]
[ "<title>Sexually Transmitted Diseases</title>", "<p>The advantages of this mode of pathogenic transmission are clear, but the great disadvantage is that evolution has now produced many very sophisticated mechanisms in the host to block pathogens of various origins and specialties from entering and growing effectively in the sexual apparatus of the female mammal.</p>", "<p>Thus, the biological problems that STD mechanisms face are because of the countermeasures against the pathogens that are due to evolution of the host. However, the mechanisms that have developed that protect the fetus from the host's immune system do help the STP.</p>", "<title>Problems with the STD approach from its point of view</title>", "<p>There are two major problems with the STD strategy: First, a sexually transmitted disease must be (at least relatively), a 'gentle pathogen' or a 'prudent predator' and remain as such (i.e., it may only evolve towards greater virulence slowly, or not at all). Secondly, such a pathogen must, to a large extent, be able to avoid destroying its mammalian host's offspring before the child grows to a sexual adult stage. Both factors would seemingly be countermanded by the immediate selective advantage of being more virulent and producing more propagules.</p>", "<title>The Vertebrate's Female Reproductive Tract (FRT)</title>", "<p>The immune system of the mammalian sexual system has many wonderful properties because of the many problems that the Female Reproductive Tract (FRT) faces. The immune system of the FRT is nonpareil for its diverse abilities to solve these problems. Possible pathogens include a large range of viruses, bacteria, and protozoa. Mostly these are destroyed or eliminated by the FRT. Additionally, when there is an embryo: this propagule is foreign because half its genes are not derived from the mother. The mechanisms of the FRT must not operate against it. Although, it is a foreign object like all the pathogens of the host, this particular entity is to be protected.</p>", "<title>The Fetus is Special</title>", "<p>Consider a Sexually Transmitted Disease (STD) from the pathogen's point of view: it must successfully pass into the female sexual tract, possibly into her body, and into the next adult generation of hosts. So the STD faces a quite different fate than other kinds of pathogens.</p>", "<title>Advantages of Vertical Transmission</title>", "<p>To reiterate the virtue from the pathogens' point of view, the STD strategy is favorable because the pathogen is propagated to new hosts without entering the environment. Thus, the infection of a new host is expedited by the old host's behavior in searching for mates, and thus many mechanisms used by other pathogens for transmission are not needed.</p>", "<title>Complications of the Many Roles in the Female Sexual Tract</title>", "<p>The FTR is a complex of organs: ovary, fallopian tubes, uterus, cervix, and vagina. They have many physiological aspects that are quite different from each other and each is essential for the propagation of the host species. They, of course, all do act in a way favorable for reproduction, but unfavorable, at various degrees, to pathogens. But the STD's are treated quite differently.</p>", "<title>Consequence of the Absence of an Immune System in the Fetus and Newborn</title>", "<p>The lack of an effective immune system in the fetus (even in the presence of maternal passive immunity) and in the newborn (even given mother's milk with colostrum) implies that the STD pathogens might propagate <italic>in utero </italic>even though the mucosal immune system of the female sexual tract may be able to reduce the danger of infection and disease to many disease agents to the offspring.</p>", "<title>Importance of Protecting the Fetus, the Neonate, and the Pre-pubescence Child</title>", "<p>In the long run, with many ins-and-outs, the current situation is favorable for the STD pathogens because the long term interest of the host species is that the young members of the host population grow to become sexually mature and this provides hosts to become infected and propagate the pathogen.</p>", "<title>Stone age life</title>", "<p>When populations of humans arose and expanded and emigrated in the early Stone Age, the problem that pathogens would have of how to propagate and surviving in this sparse host population became critical. Cave-dwelling primitive humans, 30 thousand years ago that lived in small well-separated tribal populations would have had quite different diseases than humans have today. Workable disease possibilities are few but would include, sexual transmitted diseases. However, this required that the pathogens were 'gentle' to their host. Other possibilities for these pathogens are either: acquiring and using an ability to remain dormant for long periods of time or of being able to infect a ubiquitously occurring alternative species. These latter two possibilities will not be examined further here, but they do occur in nature. The option of being 'gentle' to the fetus is the main topic here with respect to STDs, but a further aspect of this will also be presented; this is that an STD of a mammal will face the special problems of preventing the destruction of the fetuses and neonates of its host because these are needed for its own propagation.</p>", "<title>The Advantage of being an STD in a Sparsely Populated World</title>", "<p>There is an essential feature needed for a successful infectious disease of social animals that are distributed in dispersed state in individual groups with small numbers. This is to have a way to spread from social group to social group. In sundry diseases [##UREF##0##1##, ####UREF##1##2##, ##UREF##2##3##, ##UREF##3##4####3##4##], this is done by forming long-lived spores, or passing through intermediate hosts or vectors, or being carried by various animal and insect vectors over significant distances. Another very effective and quite successful way, however, is to use a sexual mode of transmission, which depends on the host actively interacting with spatially remote populations of hosts. In general, many STD pathogens are not long lived within the environment outside the host's body. In addition, they are usually highly specific so that they do not have an alternate host (except only very rarely); consequently before the invention of the hypodermic needle, these STD diseases usually had only a sexual mode of transmission within the individuals of a species [##UREF##4##5##, ####UREF##5##6##, ##UREF##6##7##, ##UREF##7##8##, ##UREF##8##9####8##9##]. STDs must be extremely common since there are many viruses that are quite mild (Hoeprich <italic>et al</italic>. [##UREF##0##1##]) and I assume that there are many more such agents that are so mild that they have not been detected.</p>", "<p>Depending on the degree of interaction between social groups, this strategy would be unsuccessful if the pathogen was highly lethal and the host populations widely distributed, since they would be eliminated from a local population by destruction of subpopulations and usually not be successful in reaching neighboring communities. This is the way, I presume, that ebola was in rural Africa in the past: it arose from some animal into a human social group, destroying that group, then the viruses were eliminated, ending that particular episode. Consequently, any successful STD disease would need to be as 'gentle' as possible while still retaining its infectiousness, if it is to be effectively transmitted to other hosts during rare encounters between groups when population levels are low and well separated.</p>", "<p>A distinction needs to be made from a mechanism that differentiates survival of a 'gentle pathogen' from the evolution of the pathogen as the results of group selection. Group selection occurs, for instance, if there is the possibility that some pathogen may contain or acquire a gene that confers some advantage or level of resistance to the pathogen. This gene and its population would then be selected because pathogens with that gene would prosper and become dominant.</p>", "<p>The long-term persistence of a disease among sparsely distributed social animals would depend on the particulars of the social interaction between groups. Passage of a STD from group to group is aided by the social behavior of the hosts, which may institutionalize transfer. For example, the social groups of many kinds of mammals' centers around a single dominate male or a female of a bonded social group [##UREF##8##9##, ####UREF##9##10##, ##UREF##10##11##, ##UREF##11##12##, ##UREF##12##13####12##13##]. Consequently, other (usually younger) males or females of a variety of species are ejected from their natal small groups. Sometimes these may become the dominant in other groups. However, they may incidentally carry STDs with them.</p>", "<p>In chimpanzee troops and other primates living in groups there is often the exchange of young females between groups as the females reach sexual maturity. In many cases, these females are forced to leave their original group. Possibly this same strategy was practiced by early human groups. After the female chimpanzees emigrate, they are taken into neighboring colonies, spreading STDs. Not only is this mixing of populations known from direct observation of various non-human primate species, but it also can be deduced by the smaller degree of polymorphism for genetic markers exhibited in the male population of a clan of chimpanzees relative to the females in the same group.</p>", "<p>This process of exchange may serve the primate species very well by limiting the effects of inbreeding. The explanation of these behaviors by geneticists and sociobiologists is that consanguinity is bad for any species (except obligatorily selfing-organisms, like Mendel's garden peas that do practice incest habitually). Whatever the validity of this explanation and how the custom arose, sexual mixing occurs between social groups as an institutionalized process even in the absence of prostitution, rape, and war. Even if these violent transmission events between host groups may be fairly infrequent they, and the less violent, custom-justified, mixing between social groups are essential to the STD's way of life.</p>", "<title>Diseases in primitive humans</title>", "<p>A sexually transmitted disease gains most of the advantages of vertical transmission in not needing to be transmitted through the environment. Moreover and very importantly, it can survive in sparse populations because of its host's sexual proclivities. In these two sentences the essential elements for a parasite (especially a virus) to survive by infecting humans as its only host in the Stone Age have been spelled out. In such circumstances it was necessary for a pathogen to be able to compensate for the low population density and sporadic distribution of its host. During the tail end of the most recent Ice Age, the human hosts survived in small, mostly isolated, groups that figuratively chased mastodons and other big game as a group effort.</p>", "<p>Those pathogens that depend on humans as a resource faced a much different problem after domestication of plants and animals than during the hunter/gatherer Stone Age culture. Tuberculosis did occur in people in the ancient Egyptian empire, but by then the population was locally quite dense and transmission through the air from person to person in large populations became efficient. The problem of pathogens became still quite different during the Industrial Revolution and in the medically sophisticated world of today. Possibly, at the time of Christ the world population was a thousand-fold larger than during the Ice Age, and probably, the world population of humans has increased more than a thousand-fold since. Today, with the world human population increased, with transportation easier, with higher local population densities, with more rapid migration, and with mixing of humanity taking place at an unprecedented rate the situation is entirely altered. These changes must result in a great increase and alteration of the spectrum of diseases, and particularly of communicable diseases that are transmitted effectively between people that are crowded closely together. For this reason, it can be argued that the major diseases of Stone Age humans were largely STDs (or with those pathogens capable of remaining dormant or propagating in other hosts) and the typical major infectious epidemic diseases experienced in the early Christian era and afterwards were not. The aftermath of hypodermic needles, jet planes, and other modern inventions are that the spectrum of diseases has become much different still.</p>", "<p>The two keys of the matter are: first, that many of today's STDs have had a long association with primates, including humans, and have had an opportunity to modify their hosts; and in return, the pathogens have been modified by their host's biology. Second, the same group-group interactions, as in the hunter/gatherer cultures described above, apply to other primate populations living in the wild today. So we can assume that STDs specializing in particular species have been around for a very long time in human and other primates populations, and that they should generally be 'well-tuned' to their host species' particular sociality. Furthermore it can be assumed, that only occasionally will such adapted STDs be dangerous or lethal to their specific host species, or at least to a fraction of the individuals of that population. To the degree that HIV has recently entered the human population or more specifically into the population of a some times medically treated, modern, industrialized, jet age men and women, it is now in an unfamiliar milieu and is especially dangerous (Gilbert [##UREF##1##2##]).</p>", "<title>The strategy of cave man's diseases and those of modern primates</title>", "<p>All STD's of cave men (I presume) and today's wild primates face the same general problems. But here we will be more specific and consider the problems of the retroviral STDs of monkeys and man [##UREF##9##10##, ####UREF##10##11##, ##UREF##11##12##, ##UREF##12##13##, ##UREF##13##14####13##14##]. Now let us match the retroviruses of primates to the design criteria for a STD pathogen of sparse populations. Point by point these criteria are met by viruses abundant in non-human primate populations in Africa today, such as SIV (Simian Immunodeficiency Virus) that is resident in the African Green Monkey. Of course, SIV may be more virulent when transferred to different monkeys; e.g., to the geographically distant Asian ones, than the species from which it was initially isolated. Of course, SIV would be virulent in animals that happen to have a defective immune system. The general point is that the small deviation from its behavior that has been optimized for a retroviruses' life in its native host now may lead to catastrophic problems in the variant host, whether it is the human, the Asian monkey, or the compromised host.</p>", "<p>It is assumed that HIV emigrated to humans relatively recently; see [##UREF##14##15##, ####UREF##15##16##, ##REF##18489743##17##, ##UREF##16##18##, ##REF##2152590##19##, ##REF##8820538##20##, ##UREF##17##21####17##21##] (and it is thought that 1930 was in time of transmission to man from primate in the Belgian Congo [##REF##8820538##20##]. This virus and its new human hosts have not had a chance to adapt genetically to each other though there are some indications that there have been developments in this direction (Ewald [##UREF##17##21##]). One of the mismatches between the disease and its human host that most affects disease virulence is that the immune system after HIV infection of the human being deteriorates in 5 to 15 years; this same period would be unimportant to non-human primate populations in the wild because they have shorter mean life-spans and maturation periods and may generally die of other causes before they lose their effective immune system. On this basis, it may be that the changes needed to re-establish the 'gentle' parasite mode in the new human hosts of SIV are minimal: For example, just a shortening of the human life span to that of the turn of the nineteenth century level would do. A disturbing, but realistic, suggestion of a change that would certainly make HIV infection relatively more 'gentle' is a general re-emergence of life-shortening infectious diseases, such as tuberculosis. There are many other diseases that may erupt as the antibiotic era closes, and with the loss of efficacy of antibiotics, the human life span may decrease dramatically and the immune system may survive to the end of the life-span and outlasts the shortened length of life-span of its host as the result of deaths from other causes than just the effects of the AIDS. Then there would be a lower proportion of individuals with signs of ARC or AIDS. Changes of the other kinds will be suggested below that might increase the longevity of the immune system in an AIDS-infected individual in a long-lived population, but other possible changes might decrease it.</p>", "<title>The mucosal immune system</title>", "<p>Of the many kinds of immunological responses, the ones that function at mucosal surfaces are most relevant to STDs during their transmission from individual to individual [##UREF##18##22##, ####REF##1360702##23##, ##REF##7865335##24##, ##REF##7865284##25##, ##UREF##19##26##, ##REF##7966240##27##, ##REF##7892606##28##, ##REF##8527070##29##, ##REF##8827215##30##, ##REF##8834460##31##, ##UREF##20##32##, ##UREF##21##33##, ##REF##8648204##34##, ##UREF##22##35##, ##UREF##23##36##, ##REF##15812489##37##, ##UREF##24##38##, ##UREF##25##39##, ##REF##16048557##40####16048557##40##] The feature relevant to STDs epidemiology is that the female sexual tract is the right place for ways to prevent new infections.</p>", "<title>Immunology of the female reproductive tract</title>", "<p>In order to be propagated, a sexually transmitted disease needs to grow and be transmitted through both the female and male reproductive tracts to new hosts. On the other hand, particularly the female productive tract has evolved mechanisms to overcome pathogens that enter it. However, an important additional component for the disease is that the STD's somehow needs to ensure that the embryo, fetus, and child of its host do not die, but grow to sexual adults so that they too can serve as habitats for STD to grow. \"Ensure\" is too strong a word as long as the pathogen is only successful in attacking a small portion of the host population.</p>", "<p>Evidently, there are mechanisms contributed from both the human and the virus to protect the juvenile against destruction by the HIV, beginning at conception and continuing on to near adulthood. This is because otherwise the fetus and neonate would be destroyed before leading to an adult that can receive and propagate the virus.</p>", "<p>The complex immunological processes within the pregnant female are indeed very sophisticated. This has been well studied because the interest in this question is high since this may be a possible basis for the prevention of the spread of AIDS. It was, and is hoped, that immunological ways could be developed. However, here the interest is to try to understand the biology to the host-parasite interaction from the point of view of the disease process. There must be some blockade to transmission extent now because even though HIV infection through sexual contact of adult-to-adult is efficient, there are some fetuses and children that are not infected or killed by the infection, but grow up to become adults to be reservoirs for growth and propagation of the virus.</p>", "<p>A strong indicator that HIV is usually prevented from infecting children perinatally is that while the majority of children born to HIV-positive mothers are not infected, they almost all carry maternal antibodies against HIV 41 (McWhinney, Pagano, and Thomas. [##REF##16048557##40##]). Presumably they had them when <italic>in utero</italic>, but either had not become infected or had overcome it.</p>", "<p>Going even farther out on a limb, one can imagine that the mucosal immune response could protect the baby during the birthing process [##REF##8410670##41##]. This might be accomplished by a strong IgA or T-cell response, or by an IgG response that is delivered through the placenta [##REF##11406408##42##]. If this were possible in terms of the host's immunological repertoire modulated by some stimulatory action of the pathogen, this would increase the long-term fitness of the STD. Such a situation would lead to continued selection for such genetic variants of the host and/or in the STD. Then of course, through successive generations of virus and host in an environment with high levels of other pathogens, these developments would 'tune' the growth of the particular retrovirus to a particular primate species. These changes may lead to effective prevention of infection due to secondary pathogens or due to superinfection of the resident sexual disease by other copies of itself, and would have a long term (and incidental effect of maintaining the host population). Such selection would lead to corresponding changes so that the pathogen becomes milder to the host as a consequence of induction of greater immunogenicity against the resident pathogen and would be beneficial to the host because the first pathogen would prevent other, possibly more dangerous pathogens, from entering and destroying it.</p>", "<p>The converse possibility is that the pathogen causes deleterious effects due to stimulation of the immune system. In many cases these effects are significant, but generally not lethal.</p>", "<title>Human antimicrobial factors</title>", "<title>Defensins</title>", "<p>There are a number antimicrobial factors made by the mammal that protect it against pathogens. These include many defensins. Of these, human β-definsin-1 is especially to be considered because of its location in urogenital tissues (Valora <italic>et al</italic>., [##REF##9541493##43##]).</p>", "<p>Retrocyclins that are θ-defensins and their variations have important effect against anthrax [##REF##16790431##44##]. Also, some forms of θ-defensin and α-defensin have protective effect against HIV-1 [##REF##17506607##45##,##REF##17331027##46##]. But the latter paper did not find an effect on the mother-to-child transmission. Quinones-Mateu <italic>et al</italic>. [##REF##14571200##47##] show that human epithelial β-defensins 2 and 3 inhibit HIV-1 replication. Aono <italic>et al</italic>. [##REF##8410670##41##](2006) find that in bovines the bovine β-defensin-1 acting against <italic>E. coli </italic>and is present in teat mucosa, vagina, ovary, and oviduct. These references, and also MasCasullo <italic>et al</italic>. [##UREF##26##48##], allows the possibility that I have not seen explicitly suggested that these substances in the (female reproductive tract) FRT act to prevent the embryo and fetus from infection by a virus that has infected the mother. Furthermore, that they, except for the α-defensins, have a role in the partial protection of the newborn from infection by way of the mother's milk.</p>", "<title>Secretory leukocyte protease inhibitor</title>", "<p>The secretory leukocyte protease inhibitor (SLPI) may also be a factor that acts in prevention of HIV infection. It is known that salivary gland tissue may have a role in suppressing transmission by the oral route. Wahl <italic>et al</italic>. [##REF##9094984##49##] were able to account for the rarity of oral transmission even though there is HIV in oral secretions. Once inside the oral cavity, HIV is exposed to antiviral levels of SLPI, and their data suggest that this may greatly impede infection. An extrapolation, for which there is no data, could be that HIV might stimulate the synthesis of SLPI. A further extension from known facts is that it may also takes place in the genital tract and prevents secondary infection of HIV viruses, or variants, or a broader class of STDs [##REF##18268354##50##]. These suggestions could be tested.</p>", "<title>Variable stimulation of the immune system of primates under different conditions</title>", "<p>The long-term survival of the retrovirus pathogen in vertebrate hosts depends on proper balance of the pathogen's activity versus the hosts' immune systems. A possibly critical factor of difference between modern humans, the Stone Age man, and the modern non-human primate needs to be recognized here. Some of the possible differences would make the response greater or weaker, and consequently could support or conflict with the hypothesis proposed here. This balance could depend on the variety and variable extent of immune stimulation in general and is, in addition, to the responses of these host species to their particular varieties of retroviruses. The stimulation of the immune system, variety of stimulations, and the age dependency of individuals exposed to other antigens before they are challenged by viral antigen do vary (see for example, Esquerré M <italic>et al</italic>. [##REF##18268354##50##]; Walker <italic>et al</italic>., [##REF##7865295##51##]; Chirmule <italic>et al</italic>., [##REF##8573377##52##]; Rabin <italic>et al</italic>., [##UREF##27##53##]; Miedema and Klein, [##REF##8614796##54##]; Wolensky <italic>et al</italic>., [##REF##8614801##55##]). There must be marked differences in the sensitivity to antigenic stimulation of the members of primate societies in the wild, of the humans in developing countries, and of the humans in developed countries. Either too much, too little, or the wrong kind of stimulation may mean that the immune system will be either over or under active, because of the effect on the mucosal part of the system and, this is significant for the theme of this paper. A slight variation may eliminate the pathogen or fail to prevent new pathogens from entering and supplanting the original one. The immune system may be secondarily modified by destruction of certain specialized T-cells in which HIV or SIV pathogen are present. As a speculative example, young female chimpanzees engage in some sexual activity for some time before they can become pregnant. If the female has been infected with SIV for several years before she becomes fecund it would not be surprising if she had slowly developed a range of mucosal immune responses that would have a protective effect on her and/or the developing fetus. This effect might not occur if she had been infected more immediately before pregnancy. Some aspects of the AIDS disease process in humans are certainly similar to autoimmune diseases and the destruction of cells of the immune system bearing viral or similar receptors is well known. All that is being suggested here is that such phenomena need be only slightly altered to give virulent disease instead of an innocuous one in the not quite well adapted AIDS-human pair from those of well-adapted long-term retrovirus-primate pairs. In the current worldwide AIDS epidemic these could affect the timing of symptom onset and the severity of debilitating disease.</p>", "<p>At an earlier time when our blood supplies were contaminated, hemophiliacs were very likely to receive the AIDS virus and become HIV seropositive. A significant point is that their time to conversion to ARC and to fulminating AIDS was 90% longer than other non-hemophiliac seropositive persons at that time Darby <italic>et al</italic>. [##REF##7659168##56##] in 1995. One possible reason is that the medically treated hemophiliacs were being continuously challenged and immunized against a large variety of other substances that were present in the various blood transfusions that they regularly received. An additional factor concerning the AIDS disease at the beginning of the world epidemic is that when hemophilia patients were initially infected by transfusion, the disease would have been started with a much larger number of viruses than that transmitted either by usual or unusual sexual practices or by a drug addict's re-used needle. Consequently, their immune system was stimulated in a way that the immune systems of normal people infected by sexual contact are not, simply because the intensity of the immune challenge was greater. These facts raise the possibilities that African green monkeys are immunologically equivalent to human hemophiliacs receiving blood transfusions. As a result, an African green monkey from which SIV can be isolated might naturally have an especially effective immune system and be able to continuously destroy a much larger proportion of retroviruses and thus limit the viremia. This could mean that the animal would live for a long time before it might become immunologically deficient. In any case, it will be interesting to see the disease progression in fresh hemophiliac patients that were infected by sexual transmission and not by transfusion either while they are receiving recombinant clotting factor without these multiple sources of diverse immunogenic stimulation or with them.</p>", "<p>Immune reactivity can depend on the presence of other pathogens: Infection with mycoplasma, Herpes, Epstein-Barr, and several other viruses may affect how HIV infection leads to an HIV-immune deficiency state. With co-infection of such viruses, it is likely that the time at which AIDS erupts may be sped or slowed in either the human or monkey. This raises the possibility that a different spectrum of viruses and other possible diseases might influence the course of a retroviral disease. A sometimes-symptomless retrovirus that suppresses the development of debilitating immune deficiency might do so differently when infecting a different host species. On the contrary, other pathogens may trigger the emergence of the AIDS virus from the host's chromosomes. These assorted immunological events could greatly affect the life history of a retrovirus in any new primate host. Consequently, it can be argued that the AIDS virus may well have been adapted to be 'gentle' and un-obstructive in its old host, however, at present, those strategies do not work as well in humans because the human pathogens are different than the non-human primate. Because of our social organization, our life expectancy, and our antigenic environment, the outcome of this parasitism might well be quite different.</p>", "<p>A very striking observation was made fourteen years ago [##REF##7816094##57##,##REF##7529365##58##](Ho <italic>et al</italic>., 1995; Wei <italic>et al</italic>., 1995; but see Pang S, [##REF##11069994##59##] and see Wain-Hobson, [##REF##7816085##60##] and Miller, Antia and Levin, [##UREF##28##61##]. The major conclusion is that the HIV viruses grow very rapidly and are very rapidly destroyed by the apparently healthy, but HIV positive individuals. It leaves unanswered how much of this destruction is due directly to the action of the host immune system versus the action of the host when modified by the virus or responding in other ways.</p>", "<p>The possibility is that there are protective mechanisms implemented by the resident HIV. A finding that is relevant concerns an individual who became infected with only one strain even though two different varieties of HIV were transfused simultaneously into him [##REF##8870851##62##].</p>", "<title>Epidemiology of AIDS and the 'rapid' change of HIV</title>", "<p>By comparing the epidemiology of HIV-I and HIV-II in their respective parts of Africa, Ewald [##UREF##17##21##] has made the case that the diseases vary in the virulence/gentleness scale in a way that is correlated with the number of sexual partners and the group's societal norms. This would be predicted by a game theory type calculation on the bald assumption that the virus was omniscient without providing any suggestion of how it got to be so. Accepting his ideas and facts as true, we can only be amazed by the speed with which the virus can apparently change its strategy and optimize the course of infection to the new conditions present in a new host. I think that this means something more significant. As an alternative to <italic>de novo </italic>evolution, I suggest that this apparent rapid adaptation is consistent with the hypothesis that STD retroviruses have been exposed over long evolutionary times (in terms of millions of years) to fluctuations in the behaviors of a series of hosts (or a host under a series of very different conditions) and have developed and retain a genetic repertoire to be able to achieve response to variations in the host. From the molecular point of view this could allow them to track and respond to the behavior of their current host and conditions. Although the genetic sequences are now known, the roles of the gene products are not fully evident and some of the regulatory genes may have functions over a long time period of years and centuries.</p>", "<title>Why mammalian STD pathogens must protect their new generation of pathogens from their own lethal action</title>", "<p>A careful strategy must be maintained for long term persistence between the STD virus spreading from adult-to-adult and in injuring or killing the children of an infected host. The biology of pediatric AIDS is reviewed in Pizzo and Wilfert [##UREF##29##63##], Remington [##UREF##30##64##], Roizman [##UREF##2##3##], Sweet and Gibbs [##UREF##31##65##], and Kaschula [##UREF##32##66##]. Teleologically, the virus must spare the host's young in order that they can be used as a resource in the future, but that necessary-truth provides no mechanistic way to implement such a situation. HTLV-I (see below) seems to have achieved this balance by infecting most offspring of infected mothers but having the virus grows so slowly that a child's life before puberty is virtually unaffected. Such slow viral growth permits propagation of the virus to virus-free individuals within a society in which sexual contact is frequent and avoids the usual destructive effects of vertical transmission from mother to child. The AIDS viruses, HIV-I and HIV-II, do not seem able to avoid injuring the children after infection occurs. But still a large proportion of the young of infected mothers do not become infected – more than 60% and in some estimates as much as 85% for HIV-1. Some infants, born infected, clear the HIV virus (Clerici <italic>et al</italic>. [##REF##8280407##67##]; Roques <italic>et al</italic>. [##REF##8605047##68##]).</p>", "<p>An infectious disease spreading to the next generation by vertical transmission has advantages over pathogens disseminated in other ways. The main advantage is that the disease organism never has to survive in the environment outside of the host. Survival of a pathogen outside of its host in air or water takes special mechanisms. Although the fetus is a convenient and a built-in susceptible host, the vertical process in vertebrates has a special complexity beyond that present in the cases when the host grows by binary fission as bacteria do. This is because the animal host gives birth to an immunologically ill-equipped neonate that may not be able to survive due to damage caused by the pathogen. This is a critical problem for an STD's survival strategy because they generally have no alternative propagation strategy such as survival in alternative hosts or persisting in the environment.</p>", "<p>Because the newborn offspring do not have a full immunity system the result is often catastrophic. This is what happens when some pathogens, such as Herpes Type II, Rubella, or Cytomegalovirus infect an embryo, a fetus, or a newborn child. In these cases, without a fully protective immune mechanism, the infection may be destructive or lethal to the offspring and is much more severe than if the pathogen infects an adult that has a responsive immune system with an immune repertoire already in place.</p>", "<p>Therefore, in many cases a would-be pathogen cannot effectively persist via pure vertical transmission simply because the pathogen cannot depends on its host immune response system to bridle its growth. Viruses like Herpes, Rubella, and Cytomegalovirus that go across uterine, vaginal, and placental tissue are frequently dangerous to the fetus, but of course these viruses survive because their primary means of spreading through the population is from an adult individual to an adult individual and are not dependent on vertical transmission. Destroying a few children apparently does not upset the growth success of these viruses. However, from the viewpoint of a disease that has elected to only use a non-virulent STD strategy, going directly from mother to baby could be expedient, although destructive. This must somehow be avoided, and it apparently is often curbed by successful pathogens.</p>", "<p>There may be a number of factors involved in the AIDS case. One is that the AIDS virus is just not very infectious or long-lived. Some suggestion of this comes from the fact that AIDS is not transmitted by way of bloodsucking mosquitoes or efficiently through punctures of health care workers with contaminated needles. Presumably, in the mosquito example, this is because the virus does not last long enough between successive blood meals of the female mosquito. Additionally, it may not be transmitted because infection requires large inoculums. Similarly for the second example, the fact that HIV is quite seldom transmitted through needle sticks to health care workers, is possibly because most particles are inactive and many viable particles are needed to cause infection of a healthy health care provider.</p>", "<p>While these could be trivial or based on molecular biological necessity, I suggest that the virus has been selected by evolution to extend these characters. Evidently, this is the characteristic of particular viruses, but it also may be due a human or primate characteristic. This can be if mankind has habitually, over the eons, been exposed to retrovirus type pathogens that are not AIDS. For both virus and host, their fitness is increased in not permitting infection of the still immunological-incompetent neonates. It may be that the low infectivity of neonates is because of some molecular biological or biochemical limitation or necessity, but in the retrovirus case it has been especially extended. This can be argued since there are diseases that are transmitted very efficiently by mosquito bites and by limited blood-to-blood contact (such as Hepatitis B). So I suggest that the HIV retrovirus is poorly infective due to innate biological mechanisms evolved to favor the long-term goals of its survival as an STD. This is a prediction of the model proposed here that would have aspects that could be experimentally pursued.</p>", "<p>Although I will discuss below how the retrovirus human T-cell lymphotrophic virus I (HTLV-1) avoids this difficulty by known mechanisms, just how HIV perinatally infects only a portion (15 – 30%) of children (Scott [##UREF##33##69##]; Cotton [##UREF##34##70##]) and not more is not clear. Women that acquire HIV after delivery have a higher transmission rate to their child by breast-feeding than do women previously infected. We have also a hint because reducing the viremia by zidovudine (AZT) treatment before and during the delivery process has been shown to reduce the transmission to the child (Connor <italic>et al</italic>., [##REF##7935654##71##]; Spector <italic>et al</italic>. [##REF##7935655##72##]: Rouse <italic>et al</italic>., [##UREF##35##73##]; Brossard <italic>et al</italic>., [##REF##7794540##74##]) and now is routine. However, there are reports of several children that have been initially infected and apparently cleared themselves of the infection (Bryson [##REF##8605058##75##]); this makes it difficult to compare different studies.</p>", "<title>The coping strategy of HIV, HTLV-I, and HTLV-II with their hosts</title>", "<p>HTLV-I and HTLV-II are both retroviruses of the subclass oncornaviruses that propagate primarily in human T lymphocytes. The former parasitizes the CD4-bearing helper T cells and the latter the CD8-bearing cytotoxic T cells (See Anderson [##UREF##36##76##]; Höllsberg and Hafler [##UREF##37##77##]; Wiktor and Blattner [##UREF##38##78##]; Blattner [##UREF##39##79##]; White and Fenner 1994 [##UREF##4##5##]). HIV is a retrovirus of the subclass, lentivirus, and both it and the oncornaviruses incorporate their reversely transcribed double-stranded DNA into a chromosome of a human cell as the heart of their survival strategy. Both classes of virus infect only a particular human cell-type and only a cell type that continues to divide. Nerve cells and kidney cells are not appropriate host cells because in the adult they divide only very occasionally and therefore the equivalent of the prophage state would never be adequately created or propagated. Also an inappropriate cell type would be a relatively rapidly growing epithelial cell because while the stem cell remains, the sister cell is sloughed from the skin or into the intestine and does not remain and be propagated within the body.</p>", "<p>The oncornaviruses, but not the lentiviruses, have a growth pattern that allows both effective vertical transmission and horizontal transmission to new hosts. The viruses are passed vertically to the neonate, although not with high efficiency and not in a way that causes childhood death. They are also passed as a STD between sexually active individuals. In addition, in the modern world these viruses can also be passed via needles. Before extensive movements of peoples of the world population, both HTLV viruses were geographically restricted in distribution. Although HTLV-I is now found world wide, it was and is highly abundant in southern Japan and the Caribbean. It is also present in South America, west and central Africa, India, Melanesia, and Iran. There is evidence that similar viruses inhabit non-human primates. HTLV-II is now highly prevalent in intravenous drug users, but is (and presumably was) abundant in the Guaymi tribe in Panama and, more generally, in Amer-Indians.</p>", "<p>The strategy of both HTLV viruses in the absence of IV drug usage and rapid movements of peoples was very close to the 'gentle' pathogen persuasion. Both viruses persist with little damage to their host by replicating very slowly while being passed vertically or between adults as an STD. Being poorly transmitted from cell-to-cell and individual-to-individual favors the 'gentle' character, but another feature is that they have mechanisms to cause the hosts T-cells to replicate. Thus a syndrome, similar to that of AIDS does not occur, resulting from depletion of T-cells. This allows more opportunities for the virus to be transmitted between humans. They are poorly transmissible and transmission from male to female is rare and the transmission from female to male is very slow (at least for HTLV-I). Though it is passed presumably in the same three ways that HIV is passed to the neonate (i.e., in utero, perinatally by blood-to-blood transfer, and (predominately) postnatally via mother's milk). There is little damage to the child because the virus grows so slowly. Thus the immune system has time to develop and respond to these viruses and is even aided by the increased growth of T-cells. Both viruses are almost perfect 'gentle' pathogens. However, the current longevity of the human host is sufficient to cause problems (see White and Fenner, [##UREF##4##5##]). For example, there is the occasional generation by HTLV-I of an adult T-cell leukemia (ATL). Less frequently T-cell chronic lymphocytic leukemia, non-Hodgkin's lymphoma, mycosis fungoides, and Sezary syndrome arise. HTLV-II has not been adequately studied, but appears to be even more 'gentle', and only very rarely causes certain diseases: i.e., glomerular nephritis and HTLV-associated myelopathy.</p>", "<p>Molecular biological information is mainly available for HTLV-I, but the studies of the <italic>rex </italic>and <italic>tax </italic>genes are relevant to the trick that this virus has of alternately stimulating T-cell growth and then subsequently switching to the HTLV-I replication mode (Yoshida <italic>et al</italic>. [##UREF##40##80##]; Hinrichs <italic>et al</italic>. [##UREF##41##81##]). Of particular importance to the major thesis of this speculative paper is that the <italic>rex </italic>gene of HTLV-I interacts with the <italic>rev </italic>gene of HIV and this may suggest that the human virus may act to protect its host against some other pathogen.</p>", "<title>How HIV is 'Gentle' to its host</title>", "<p>Some of the methods that HIV uses to be lysogenic and spread from host to host are obvious from its generally known biology (Daniel <italic>et al</italic>. [##REF##1470917##82##]). Most obvious is that a retrovirus, in principle, would only be able to grow as the provirus in concert with a reproducing cell. HIV for example, can enter a quiescent cell and give rise to DNA products, but the DNA is only integrated after activation of the cell and replication of its chromosome (Zack <italic>et al</italic>., [##REF##2331748##83##]; Stevenson <italic>et al</italic>., [##REF##2184033##84##]; Haase <italic>et al</italic>., [##REF##16200081##85##]). But the sole use of CD4 as a target receptor limits its opportunity to grow and reproduce. Maintenance of the latent state is another essential factor that keeps the virus from rapidly destroying the immune system. This is in addition to the host's immune system acting as an essential safeguard for viral long-term propagation within the host and transmission into other hosts.</p>" ]
[ "<title>Acknowledgements</title>", "<p>This is a paper I have been writing since 1985. Very many people and papers have modified my point of view. But most basically, it was the foundation in the biology of bacteriophages that I received during my PhD studies sixty years ago. My education was not only from the phage group in Chicago, but the larger phage group that started molecular biology in the 40's and 50's.</p>" ]
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{ "acronym": [], "definition": [] }
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CC BY
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2022-01-12 14:47:29
Virol J. 2008 Aug 7; 5:93
oa_package/28/8f/PMC2531097.tar.gz
PMC2531098
18700011
[ "<title>Background</title>", "<p>Yersiniosis or enteric red mouth disease (ERM) is a serious systemic bacterial infection of fishes which causes significant economic losses in salmonid aquaculture worldwide [##REF##18237828##1##]. Although infection with this agent has been reported in other fish species, salmonids especially rainbow trout <italic>Oncorhrynchus mykiss</italic>, are highly susceptible to ERM [##UREF##0##2##,##UREF##1##3##]. The disease was first described in the rainbow trout in the United State in 1958, from Hagerman Valley, Idaho by Rucker [##REF##5963322##4##], and later the causative organism named <italic>Yersinia ruckeri </italic>[##UREF##2##5##]. The disease is endemic in North America [##UREF##1##3##] and widespread elsewhere. It was also described in 1981 in France, Germany and United Kingdom and has now been reported in most of Europe, Australia [##UREF##3##6##,##UREF##4##7##] and South Africa [##REF##1881658##8##].</p>", "<p>The causative agent, <italic>Yersinia ruckeri</italic>, is a gram-negative, non-spore-forming rod-shaped bacterium with rounded ends and like the other members of the Enterobacteriaceae family is glucose-fermentative, oxidase-negative and nitrate-reductive [##REF##6007992##9##,##UREF##5##10##]. ERM outbreaks usually begin with low mortality, and then escalate to result in high losses. Characteristic symptoms of ERM are haemorrhages of the mouth and gills, though these are rarely seen in acute infections but may be present in chronic infections, diffuse haemorrhages within the swim bladder, petechial haemorrhage of the pyloric caecae, bilateral exophthalmia, abdominal distension as a result of fluid accumulation, general septicaemia with inflammation of the gut, the spleen is often enlarged and can be almost black in colour [##REF##5963322##4##]. Transmission occurs by direct contact with carrier fish, other aquatic invertebrates and birds [##REF##5963322##4##,##UREF##6##11##]. The ability of <italic>Y. ruckeri </italic>to survive and remain infective in the aquatic environment is considered to be a major factor in spread of the disease. Furthermore, <italic>Y. ruckeri </italic>is able to form biofilms and grow on surfaces and solid supports in fish tanks, like many bacteria in aquatic environments, which lead to recurrent infections in rainbow trout farms [##UREF##7##12##]. Although vaccination has for a decade been very successful in the control of infections caused by <italic>Y. ruckeri </italic>in trout farms [##REF##9270840##13##], cases of yersiniosis have been reported in trout farms where vaccination didn't provide enough protection against the infection [##REF##12747420##14##] and due to carrier state [##REF##9270840##13##]. Different diagnostic methods have been developed for detection of <italic>Y. ruckeri </italic>including culturing, serological and molecular techniques. Isolation and identification using agar media and the organism's biochemical characteristics are considered the gold standard for <italic>Y. ruckeri </italic>diagnosis. Serological methods for detection of <italic>Y. ruckeri </italic>have also been developed and these include ELISA, agglutination, and the immunofluorescence antibody technique (IFAT) [##UREF##8##15##]. Molecular techniques are able to detect low levels of the bacterium and facilitate detection of asymptomatic carriers, which is very important for prevention of ERM transmission and spread [##REF##17501736##16##]. Restriction fragmentation-length polymorphism [##REF##9871314##17##] and PCR assays [##REF##9872807##18##, ####REF##11253871##19##, ##UREF##9##20####9##20##] are widely used for detection of low levels of <italic>Y. ruckeri </italic>in infected trout tissues and blood and also for detection of asymptomatic carriers. Although PCR has been shown to be a powerful and sensitive tool in detection of <italic>Y. ruckeri</italic>, its requirements for expensive equipments, a precision thermocycler and laboratory training limit its use in the field as a routine diagnostic tool.</p>", "<p>Alternate isothermal nucleic acid amplification methods, which require only a simple heating device, have been developed to offer feasible platforms for rapid and sensitive detection of a target nucleic acid. These include nucleic acid-based amplification (NASBA), loop-mediated isothermal amplification (LAMP) and ramification amplification [##REF##1706072##21##, ####UREF##10##22##, ##REF##11468700##23####11468700##23##]. LAMP is a nucleic acid amplification method that synthesises large amounts of DNA in a short period of time with high specificity [##UREF##10##22##,##REF##15163526##24##]. The strand displacement activity of <italic>Bst </italic>DNA polymerase impels auto-cyclic DNA synthesis with loop-forming primers to yield long-stem loop products under isothermal conditions: 60–65°C for about 60 min [##UREF##10##22##,##REF##12144774##25##]. The LAMP reaction requires four or six primers that target six or eight separate DNA sequences on the target and give the assay very high specificity [##UREF##10##22##,##REF##12144774##25##]. LAMP amplification products can be detected by gel electrophoresis, by real time monitoring of turbidity with a turbidimeter [##REF##15163526##24##,##REF##11708792##26##] or with the naked-eye. Visual detection can be accomplished using different methods such as detection of a white precipitate (magnesium pyrophosphate), use of an intercalating DNA dye such as SYBR Green I gel stain [##REF##16216123##27##], use of florescent detection reagent, FDR, [##REF##17245722##28##], or use of oligonucleotide probes labelled with different fluorescent dyes and low molecular weight cationic polymers such as polyethylenimine, PEI [##REF##16401354##29##].</p>", "<p>LAMP-based assays have been developed for numerous aquaculture animal pathogens, including white spot syndrome virus [##REF##14656461##30##], yellow head virus [##REF##16597466##31##], <italic>Edwardsiella tarda </italic>[##REF##14711699##32##] and <italic>Nocardia seriolae </italic>[##REF##16696687##33##], <italic>Tetracapsuloides bryosalmonae</italic>, <italic>Myxobolus cerebralis</italic>, <italic>Thelohania contejeani </italic>[##REF##15895254##34##, ####REF##16266328##35##, ##REF##16724564##36####16724564##36##], Koi herpes virus (CyHV-3) and viral hemorrhagic septicaemia (VHS) [##REF##16216123##27##,##REF##16384659##37##]. The objective of this study was to develop and evaluate LAMP, as a simple, rapid and sensitive diagnostic tool for ERM disease.</p>" ]
[ "<title>Methods</title>", "<title>Bacteria</title>", "<p>The bacterial strains used in this study were listed in (table ##TAB##0##1##). <italic>Y. ruckeri </italic>strains were cultured on trypticase-soy-agar [##UREF##1##3##]. The purity of the cultures was tested with Gram stain and confirmed biochemically with the API 20E rapid identification system.</p>", "<p>Each strain from other bacterial strains was propagated on its specific medium and then tested by Gram stain and biochemically.</p>", "<title>DNA extraction</title>", "<p>DNA was extracted from bacterial cultures using QIAamp<sup>® </sup>DNA mini kit (QIAGEN, Hilden, Germany). Bacterial cells were harvested in a microcentrifuge tube by centrifugation at 5000 × <italic>g </italic>for 10 min. Cell pellets were re-suspended in 180 μl lysis buffer (20 mg/ml lysozym; 20 mM Tris-HCl, pH 8.0; 2 mM EDETA; 1.2% Triton) and incubated at 37°C for 30 min. Proteinase K and Buffer AL were then added and mixed by vortexing. After 30 min incubation at 56°C, ethanol was added and thoroughly mixed to yield a homogenous solution. DNA was then extracted as per manufacturer's instructions. DNA was extracted from tissue samples (liver, kidney, spleen) by QIAamp<sup>® </sup>DNA mini kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions following the animal tissues protocol.</p>", "<title>Oligonucleotide primers</title>", "<p>ERM-LAMP primers were designed according to the published sequence of <italic>yruI/yruR </italic>(GenBank accession number <ext-link ext-link-type=\"gen\" xlink:href=\"AF274748\">AF274748</ext-link>, [##UREF##9##20##]) using Primer Explorer version 4 (Net Laboratory, Tokyo, Japan). Five primers were constructed; two outer primers F3 and B3, two inner primers: forward inner primer (FIP) backward inner primer (BIP) and loop forward primer (LF). FIP comprised the F1c sequence complementary to F1, a TTTT linker, and F2 sequence. BIP consisted of the B1c sequence complementary to B1, a TTTT Linker and B2 sequence. After modification of the 3' end with Rox, the loop forward primer LF was used as an Oligo DNA Probe (ODP). PCR specific primers IF-2 and IR-2 were used to amplify 1000 bp of <italic>yruI/yruR </italic>genes of <italic>Y. ruckeri </italic>[##UREF##9##20##]. Details of the LAMP and PCR primers are given in (Table ##TAB##1##2##).</p>", "<title>Optimization of ERM- LAMP condition</title>", "<p>ERM-LAMP reactions were carried out in a Loopamp real-time turbidimeter (LA-200, Teramecs Co., Ltd., Kyoto, Japan) at 60, 63 and 65°C, for 30, 45 and 60 min, followed by 80°C for 2 min to terminate the reaction. The reaction mixture contained 40 pmol each of inner primers FIP and BIP, 5 pmol each of outer primers F3 and B3, 20 pmol of LF (forward loop primer), 1.4 mM of dNTP mix, 1.6 M betaine (Sigma-Aldrich, GmbH, Schnelldorf, Germany), 4.5 mM MgSO<sub>4</sub>, 8 U of <italic>Bst </italic>DNA polymerase (New England Biolabs GmbH, Frankfurt, Germany), 1× of the supplied Thermopol buffer, and a specified amount of template DNA in a final volume of 25 μl. Reaction mix without DNA template was included as a negative control.</p>", "<title>PCR amplification</title>", "<p>Amplification was performed in a 50 μl reaction volume with 2× ready mix PCR Master mix (Thermo Scientific, Hamburg, Germany) which contained (75 mM Tris-HCl (pH 8.8), 20 mM (NH<sub>4</sub>)<sub>2 </sub>SO<sub>4</sub>, 1.5 mM MgCl<sub>2</sub>, 0.01% Tween-20, 0.2 mM each nucleotide triphosphate, 1.25 U thermoprime plus DNA polymerase, and red dye for electrophoresis), 1.5 μl of DNA template and 20 pmol each of forward and reverse primers. The amplification was carried out in Mastercycler Gradient thermocycler, Eppendorf, with the following cycling profile: 94°C for 2 min, then 40 PCR cycles of 92°C for 1 min (DNA denaturation), 65°C for 1 min (primer annealing) and 72°C for 1.5 min (DNA extension), with a terminal extension step of 72°C for 5 min.</p>", "<title>Detection of the amplification products</title>", "<p>Three detection methods were used: real-time turbidity detection, agarose gel analysis and visual detection. Changes in absorbance at 650 nm were measured for real-time turbidity detection with a Loopamp real-time turbidimeter (LA-200). A cut off value was determined based on the mean of the negative detection control optical density. Specimens with an optical density of less than 0.1 were determined to be negative for <italic>Y. ruckeri </italic>bacterial DNA. LAMP and PCR amplification products were analysed by gel electrophoresis stained with GelRed™ Nucleic Acid Gel Stain, 10,000× in water (BIOTREND Chemikalien GmbH, Köln, Germany) and then visualised under UV light. A TrackIt™ 100 bp DNA ladder (Invitrogen GmbH, Karlsruhe, Germany) was used as molecular weight marker. Visual detection of the LAMP products was carried out either by using 1 μl of Fluorescent Detection Reagent, FDR, (Eiken Chemical Co., Ltd) added before incubation of the reaction mixture at 63°C, or by addition of 1 μl of 1:10 diluted SYBR Green I nucleic acid gel stain 10,000 × concentration in DMSO (Cambrex BioSceince, Rockland, Inc., ME, USA) to the LAMP product after termination of the reaction. Any colour changes of the reaction mixture were noted. For detection with Rox- labelled probe, 0.2 μmol of low molecular weight PEI (Wako chemical GmbH, Neuss, Germany) was added to the LAMP product after centrifugation for 10 s at 6000 rpm to form insoluble PEI-amplicon complex, containing the Rox- labelled probe, which was precipitated by additional centrifugation at 6000 rpm for 10 s. Reaction tubes were then visualised under a conventional UV illuminator or by fluorescence microscopy.</p>", "<title>Restriction analysis digestion of the ERM- LAMP products</title>", "<p>To confirm the structure of the LAMP amplicons, it was purified using a High pure PCR purification kit (Roche Molecular Biochemicals, Mannheim, Germany) and then subjected to digestion with restriction enzyme <italic>Hph</italic>I (New England BioLabs GmbH, Frankfurt, Germany). Fragment sizes were analyzed by 2% agarose gels electrophoresis stained with GelRed™ Nucleic Acid Gel Stain, 10,000× in water (BIOTREND Chemikalien GmbH, Köln, Germany) and then visualised under UV light.</p>", "<title>ERM- LAMP assay specificity</title>", "<p>DNAs from <italic>Y. ruckeri strains </italic>and from other bacterial strains (<italic>Y. aldovae, Y. enterocolitica, Y. frederiksenii, Y. intermedia, Y. kristensenii, Aeromonas salmonicida, Aeromonas sorbia, Pseudomonas aeruginosa, Renibacterium salmoninarum and Flavobacterium columnare</italic>) were tested by ERM-LAMP assay to assess the specificity of the constructed primers. DNA from non-infected fish tissues and a negative LAMP reaction control were used to detect any non-specific amplification.</p>", "<title>Sensitivity of the ERM-LAMP assay</title>", "<p>One microgram genomic <italic>Y. ruckeri </italic>DNA was 10-fold serially diluted to assess the lower detection limit of the LAMP assay compared with conventional PCR. The products were analysed visually and by 2% agarose gel electrophoresis.</p>", "<title>Feasibility of the ERM- LAMP assay</title>", "<p>The use of the ERM-LAMP assay to detect <italic>Y. ruckeri </italic>DNA in clinical specimens was evaluated by testing 15 rainbow trout samples infected with ERM submitted to our clinic and 4 control fish samples. These fish were suffering from diffuse haemorrhages in the swim bladder and enlarged black spleen. The samples were tested by both ERM-LAMP assay and PCR assay.</p>" ]
[ "<title>Results</title>", "<p>Optimal amplification of the <italic>Y. ruckeri yruI/yruR </italic>gene by ERM-LAMP assay was obtained at 63°C for 60 min, as shown by both agarose gel electrophoresis and real time turbidity measurements. Amplified products exhibited a ladder-like pattern on the gel (Fig. ##FIG##0##1##). Specificity of the amplification was confirmed by digestion of the LAMP products using <italic>Hph</italic>I restriction enzyme (Fig. ##FIG##0##1##), the sizes of the resultant digestion products were as predicted (87 bp and 108 bp). Results obtained with the visual detection methods correlated with agarose gel electrophoresis results. When FDR used, a strong green fluorescence was emitted by LAMP positive reactions (F ig. 2, Tube No.3) when exposed to UV light and no fluorescence was evident for a negative reaction (Fig. ##FIG##1##2##, Tube No. 4). Likewise, after addition of SYBR Green I dye, the ERM-LAMP products appeared green (Fig. ##FIG##1##2##, Tube No. 5), while in the negative control tube the original orange colour of SYBR Green I did not change (Fig. ##FIG##1##2##, Tube No. 6). With Rox-labelled probe, a pellet formed emitted a red fluorescence for a positive reaction (Fig. ##FIG##1##2##, Tube No. 2), but there was neither pellet nor fluorescence observed in the negative control tube (Fig. ##FIG##1##2##, Tube No. 1).</p>", "<p>The specificity of ERM-LAMP primers was confirmed by amplification of <italic>yruI/yruR </italic>gene from all <italic>Y. ruckeri </italic>tested strains while there are no amplification products detected from the other bacterial species, non-infected fish tissues or negative (no template) LAMP reaction control (Fig. ##FIG##2##3##). Both agarose gel electrophoresis and visual detection methods showed that, the lower detection limit of the ERM- LAMP method is 10<sup>-6 </sup>dilution, which equal to 1 pg of the <italic>Y. ruckeri </italic>genomic DNA (Fig. ##FIG##3##4##), while PCR showed no amplification after a dilution of 10<sup>-5 </sup>which equal to 10 pg <italic>Y. ruckeri </italic>genomic DNA (Fig. ##FIG##4##5##). The LAMP assay detected <italic>Y. ruckeri </italic>DNA from 15 infected fish samples, which were also positive by PCR (Fig. ##FIG##5##6## &amp;##FIG##6##7##). Samples from all 4 control fish were negative in both assays.</p>" ]
[ "<title>Discussion</title>", "<p>Efficient, rapid and timely diagnosis is critical for successful management of diseases in aquaculture. For field diagnosis, the optimal detection system should be economical, quick, and easy to operate, moreover should meet the requirements of specificity and sensitivity [##REF##17868915##38##]. ERM disease is a serious infection that causes sever economic losses in salmonid aquaculture. It usually occurs as an acute condition with high morbidity and mortality rates, which necessitates rapid and accurate methods for detection of its causative agent, <italic>Y. ruckeri </italic>[##REF##9872807##18##]. A traditional microbiological approach for isolation and identification usually takes 2 to 3 days, and given that different numerical profiles for <italic>Y. ruckeri </italic>can be obtained with commercial multi-substrate identification systems, particularly the API 20E system, they must be interpreted with caution [##UREF##1##3##]. Although PCR assays are more accurate, specific, and faster than the microbiological approach [##REF##9872807##18##, ####REF##11253871##19##, ##UREF##9##20####9##20##], they require precision equipments which are beyond the capacity of most diagnostic sites to purchase, maintain and operate, and the complexity of the assay procedures obviates the possibility of point-of-care use.</p>", "<p>In this study, a rapid and sensitive diagnostic system based on LAMP technology was developed to detect <italic>Y. ruckeri</italic>. The ERM-LAMP assay requires only a simple water bath or heating block to incubate the reaction mixture at 63°C for 1 hr before the reaction products are visualised. The assay utilizes a single DNA polymerase that is active at relatively high isothermal amplification temperatures, which diminishes the probability of non-specific priming [##REF##17392443##39##]. The <italic>yruI/yruR </italic>quorum sensing system encoding gene of <italic>Y. ruckeri </italic>was chosen as a suitable target, as it controls virulence gene expression through cell to cell communication and has great potential for rapid and specific identification of this fish pathogen [##UREF##9##20##]. Although there is a serotypic diversity among <italic>Y. ruckeri </italic>strains [##REF##1771752##40##,##REF##2024435##41##], <italic>yruI/yruR </italic>gene was amplified from all <italic>Y. ruckeri </italic>tested strains by PCR and produced one RFLP pattern which demonstrate a high degree of genotypic homogeneity among <italic>Y. ruckeri </italic>strains regarding this gene [##UREF##9##20##].</p>", "<p>A LAMP assay requires at least 4 highly specific primers to distinguish six distinct regions on the target DNA [##REF##12958269##42##]. In developing the ERM-LAMP assay, several primer sets were appraised, with the most effective set presented here. The assay was optimized to amplify <italic>Y. ruckeri </italic>at 63°C using a set of 4 or 5 primers. In initial trials of the assay, a characteristic ladder-like pattern of LAMP amplification is demonstrated upon gel electrophoresis [##UREF##11##43##] and confirmed the identity of the product by <italic>Hph</italic>I digestion. The ERM-LAMP assay was able to amplify the target <italic>yruI/yruR </italic>gene from all <italic>Y. ruckeri </italic>tested strains while it did not show any cross-reactivity with a panel of DNAs from other Yersinia species or from other related bacterial species, which confirm its specificity. Due to the isothermal nature of the LAMP assay, there is no time lost in temperature cycling, which leads to extremely high efficiency compared with regular PCR [##UREF##10##22##,##REF##11514425##44##]. Another advantage of LAMP is that real-time monitoring of the reaction is possible [##REF##15163526##24##] and this decreases the time needed to get results and reduces the risk of carry-over contamination in the post-PCR process [##REF##17218021##45##]. Alternatively, LAMP reaction products can be visualized using SYBR Green I nucleic gel stain which has high binding affinity to double stranded DNA and hence turns from orange to green as the LAMP amplicons are produced [##REF##8576305##46##,##REF##12791888##47##]. LAMP product can also be monitored by placing a reaction tube directly on a UV transilluminator; when the FDR added into the reaction mixture. The calcein in FDR is initially combined with manganese ions and is quenched, but as amplification generates by-product pyrophosphate ions, these bind to and remove manganese from the calcein, resulting in fluorescence which is intensified further as calcein combines with magnesium ions [##REF##17245722##28##,##REF##17218021##45##]. On the other hand, if low molecular weight PEI is used, this forms an insoluble complex with high molecular weight DNAs, like LAMP products, which then captures the hybridized Rox-labelled probe into a pellet which fluoresces red under UV light [##REF##16401354##29##]. All of our data confirmed that visual detection of assay results was compatible with the real-time turbidity measurement and agarose gel electrophoresis. Hence simple visual detection facilitates use of the assay in basic laboratories and in fish farms.</p>", "<p>Compared with biochemical, microbial culture methods and PCR assay (24–48 hrs, 3 hrs respectively); the ERM-LAMP is convenient, rapid, and sensitive. The ERM-LAMP assay is 10-fold more sensitive than regular PCR as it detected a very low concentration of <italic>Y. ruckeri </italic>genomic DNA (1 pg), while the PCR can detect only till 10 pg <italic>Y. ruckeri </italic>genomic DNA. The assay successfully detected <italic>Y. ruckeri </italic>DNA in infected fish samples and hence appears suitable for use with clinical specimens.</p>" ]
[ "<title>Conclusion</title>", "<p>Loop mediated isothermal amplification assay as a new diagnostic tool for diagnosis of ERM disease in salmonids was developed and evaluated. The ERM-LAMP assay is rapid, as its result appeared after one hour, and sensitive than the conventional diagnostic method of ERM disease. The ERM-LAMP assay requires only a regular laboratory water bath and is hence suitable as a routine diagnostic tool in private clinics and field applications where equipment such as thermal cycling machines and electrophoresis apparatus are not available.</p>" ]
[ "<p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/2.0\"/>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>", "<title>Background</title>", "<p>Enteric Redmouth (ERM) disease also known as Yersiniosis is a contagious disease affecting salmonids, mainly rainbow trout. The causative agent is the gram-negative bacterium <italic>Yersinia ruckeri</italic>. The disease can be diagnosed by isolation and identification of the causative agent, or detection of the <italic>Pathogen </italic>using fluorescent antibody tests, ELISA and PCR assays. These diagnostic methods are laborious, time consuming and need well trained personnel.</p>", "<title>Results</title>", "<p>A loop-mediated isothermal amplification (LAMP) assay was developed and evaluated for detection of <italic>Y. ruckeri </italic>the etiological agent of enteric red mouth (ERM) disease in salmonids. The assay was optimised to amplify the <italic>yruI/yruR </italic>gene, which encodes <italic>Y. ruckeri </italic>quorum sensing system, in the presence of a specific primer set and <italic>Bst </italic>DNA polymerase at an isothermal temperature of 63°C for one hour. Amplification products were detected by visual inspection, agarose gel electrophoresis and by real-time monitoring of turbidity resulted by formation of LAMP amplicons. Digestion with <italic>Hph</italic>I restriction enzyme demonstrated that the amplified product was unique. The specificity of the assay was verified by the absence of amplification products when tested against related bacteria. The assay had 10-fold higher sensitivity compared with conventional PCR and successfully detected <italic>Y. ruckeri </italic>not only in pure bacterial culture but also in tissue homogenates of infected fish.</p>", "<title>Conclusion</title>", "<p>The ERM-LAMP assay represents a practical alternative to the microbiological approach for rapid, sensitive and specific detection of <italic>Y. ruckeri </italic>in fish farms. The assay is carried out in one hour and needs only a heating block or water bath as laboratory furniture. The advantages of the ERM-LAMP assay make it a promising tool for molecular detection of enteric red mouth disease in fish farms.</p>" ]
[ "<title>Authors' contributions</title>", "<p>MS carried out all the experimental work, data acquisition and drafted the manuscript. HS participated in the design of the study, analysis and interpretation of the data and helped to draft the manuscript. ME–M conceived and supervised the study, and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.</p>" ]
[ "<title>Acknowledgements</title>", "<p>The Authors are grateful for Dr. Joachim Nils, Fischgesundheitsdienst im Staatlichen Untersuchungsamt, Veterinäruntersuchungsamt Mittelhessen, Giessen, Germany, for providing <italic>Yersinia ruckeri </italic>strain used in this endeavour. We would like also to thank Dr. Sieghard Frischmann, Mast Diagnostica Laboratoriumspräparate GmbH, Reinfeld, Germany, for providing us the real-time turbidimeter LA-200.</p>" ]
[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p><bold>ERM-LAMP</bold>. <italic>Yersinia ruckeri </italic>loop-mediated isothermal amplification (ERM-LAMP) products and restriction analysis of ERM- LAMP product with <italic>Hph</italic>I enzyme. Lane Mar = 100-base-pair DNA ladder, lane Y. ruc = Amplified <italic>Y. ruckeri </italic>LAMP product shows a ladder-like pattern, lane Y. ruc dig = Digested <italic>Y. ruckeri </italic>LAMP product with <italic>Hph</italic>I with production of 87 bp and 108 bp bands, lane – veco = Negative (No template) control.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p><bold>Visual detection of ERM-LAMP product</bold>. Using different naked eye detection methods: 1 = Negative control of ERM-LAMP reaction using Rox- labelled probe, there is neither pellet nor red fluorescence; 2 = Positive ERM-LAMP reaction using Rox- labelled probe, the pellet emitted red fluorescence; 3 = positive sample by using FDR, emitted strong green fluorescence when exposed to UV light; 4 = negative sample by using FDR, did not emitted strong green fluorescence under UV light; 5 = positive sample with green colour by using SYBR green I stain; 6 = negative sample with orange colour by using SYBR green I stain.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p><bold>Specificity of ERM-LAMP primers for detection of <italic>Y. ruckeri </italic>DNA</bold>. Lane Mar = 100-base-pair DNA ladder, lane Y. ald = DNA from <italic>Yersinia aldovae</italic>, lane Y. ent = DNA from <italic>Yersinia enterocolitica</italic>, lane Y. fre = DNA from <italic>Yersinia frederiksenii</italic>, lane Y. int = DNA from <italic>Yersinia intermedia</italic>, lane Y. kri = DNA from <italic>Yersinia kristensenii</italic>, lane A. sal = DNA from <italic>Aeromonas salmonicida</italic>, lane A. sor = DNA from <italic>Aeromonas sorbia</italic>, lane P. aer = DNA from <italic>Pseudomonas aeruginosa</italic>, lane R. sal = DNA from <italic>Renibacterium salmoninarum</italic>, lane F. col = DNA from <italic>Flavobacterium columnare</italic>, lane NF = DNA from non-infected Fish tissues, lane Y. ruc = DNA from <italic>Yersinia ruckeri</italic>, lane – veco = Negative control.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p><bold>Sensitivity of ERM-LAMP assay</bold>. Lower detection limit of the <italic>Yersinia ruckeri </italic>DNA by LAMP assay. Lane Mar = 100-base-pair DNA ladder, lane 1–10 = 10-fold serial dilution of 1 μg <italic>Yersinia ruckeri </italic>DNA from 10<sup>-1</sup>-10<sup>-10</sup>; lane – veco = No template control.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p><bold>Sensitivity of ERM-PCR assay</bold>. Lower detection limit of (1000 bp fragment) <italic>Yersinia ruckeri </italic>DNA by PCR. Lane Mar = 100-base-pair DNA ladder, lane 1–9 = 10-fold serial dilution of 1 μg <italic>Yersinia ruckeri </italic>DNA from 10<sup>-1</sup>-10<sup>-9</sup>; lane – veco = No template control.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p><bold>Feasibility of ERM-LAMP assay</bold>. Detection of <italic>Yersinia ruckeri </italic>DNA from 15 infected kidney samples by ERM-LAMP while there is no amplifications appeared with the non-infected kidney samples. Lane Mar = 100-base-pair DNA ladder, lanes 1–15 = DNA from infected kidney samples, lanes 16–19 = DNA from non-infected kidney samples.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p><bold>Feasibility of ERM-PCR assay</bold>. Detection of <italic>Yersinia ruckeri </italic>DNA from 15 infected kidney samples by ERM-PCR while there is no amplifications appeared with the non – infected kidney samples. Lane Mar = 100-base-pair DNA ladder, lanes 1–15 = DNA from infected kidney samples, lanes 16–19 = DNA from non-infected kidney samples.</p></caption></fig>" ]
[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p>Bacterial species assayed in ERM-LAMP experiments</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Bacterial Strains</bold></td><td align=\"left\"><bold>Source</bold></td></tr></thead><tbody><tr><td align=\"left\"><italic>Y. ruckeri</italic></td><td align=\"left\">DSMZ<sup>1 </sup>18506 (ATCC 29473)</td></tr><tr><td align=\"left\"><italic>Y. ruckeri</italic></td><td align=\"left\">CECT<sup>2 </sup>955</td></tr><tr><td align=\"left\"><italic>Y. ruckeri</italic></td><td align=\"left\">CECT 956</td></tr><tr><td align=\"left\"><italic>Y. ruckeri</italic></td><td align=\"left\">Dr. Joachim Nils<sup>3</sup></td></tr><tr><td align=\"left\"><italic>Y. aldovae</italic></td><td align=\"left\">DSMZ 18303 (ATCC 35236)</td></tr><tr><td align=\"left\"><italic>Y. enterocolitica</italic></td><td align=\"left\">DSMZ 4780 (ATCC 9610)</td></tr><tr><td align=\"left\"><italic>Y. frederiksenii</italic></td><td align=\"left\">DSMZ 18490 (ATCC 33641)</td></tr><tr><td align=\"left\"><italic>Y. intermedia</italic></td><td align=\"left\">DSMZ 18517 (ATCC 29909)</td></tr><tr><td align=\"left\"><italic>Y. kristensenii</italic></td><td align=\"left\">DSMZ 18543(ATCC 33638)</td></tr><tr><td align=\"left\"><italic>Aeromonas salmonicida</italic></td><td align=\"left\">Clinic for Fish and Reptiles</td></tr><tr><td align=\"left\"><italic>Aeromonas sorbia</italic></td><td align=\"left\">Clinic for Fish and Reptiles</td></tr><tr><td align=\"left\"><italic>Renibacterium salmoninarum</italic></td><td align=\"left\">Clinic for Fish and Reptiles</td></tr><tr><td align=\"left\"><italic>Flavobacterium columnare</italic></td><td align=\"left\">Clinic for Fish and Reptiles</td></tr><tr><td align=\"left\"><italic>Pseudomonas aeroginosa</italic></td><td align=\"left\">Clinic for Fish and Reptiles</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p>Details of oligonucleotide primers used for ERM-LAMP assay and PCR assay.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\"><bold>Primer name</bold></td><td align=\"left\"><bold>Length</bold></td><td align=\"left\"><bold>Sequence (5'-3')</bold></td></tr></thead><tbody><tr><td align=\"left\">F3</td><td align=\"left\">20-mer</td><td align=\"left\">TCGATATAGTTACCTTCCGG</td></tr><tr><td align=\"left\">B3</td><td align=\"left\">18-mer</td><td align=\"left\">ATGGGCAGTGAACTGTAG</td></tr><tr><td align=\"left\">FIP</td><td align=\"left\">46-mer</td><td align=\"left\">TGTTCGTTTATTGAACTTCACCGATTTTCGTCGAACTGAGCGTTAA</td></tr><tr><td align=\"left\">BIP</td><td align=\"left\">50-mer</td><td align=\"left\">AAGCTGATTTCCATAAATTCCGAGTTTTTAATGACATGGAGTTTGATGAG</td></tr><tr><td align=\"left\">Loop Forward(LF)</td><td align=\"left\">25-mer</td><td align=\"left\">AGGTATCGTGTGTTAGGATTATCGT</td></tr><tr><td align=\"left\">ODP</td><td align=\"left\">25-mer</td><td align=\"left\">AGGTATCGTGTGTTAGGATTATCGT-Rox</td></tr><tr><td align=\"left\">IF-2</td><td align=\"left\">24-mer</td><td align=\"left\">GAGCGCTACGACAGTCCCAGATAT</td></tr><tr><td align=\"left\">IR-2</td><td align=\"left\">24-mer</td><td align=\"left\">CATACCTTTAACGCTCAGTTCGAC</td></tr></tbody></table></table-wrap>" ]
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[ "<table-wrap-foot><p><sup>1) </sup>DSMZ: Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (German Collection of Micro-organisms and Cell Cultures) Braunschweig, Germany.</p><p><sup>2) </sup>CECT: Colección Española de Cultivos Tipo (Spanish Type Culture Collection) Valencia, Spain.</p><p><sup>3) </sup>Fischgesundheitsdienst im Staatlichen Untersuchungsamt, Veterinäruntersuchungsamt Mittelhessen, Giessen, Germany.</p></table-wrap-foot>" ]
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{ "acronym": [], "definition": [] }
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2022-01-12 14:47:29
BMC Vet Res. 2008 Aug 12; 4:31
oa_package/5a/5c/PMC2531098.tar.gz